Nonlinear Model Predictive Control of a Cable-Robot-Based Motion Simulator
DEFF Research Database (Denmark)
Katliar, Mikhail; Fischer, Joerg; Frison, Gianluca
2017-01-01
In this paper we present the implementation of a model-predictive controller (MPC) for real-time control of a cable-robot-based motion simulator. The controller computes control inputs such that a desired acceleration and angular velocity at a defined point in simulator's cabin are tracked while...... satisfying constraints imposed by working space and allowed cable forces of the robot. In order to fully use the simulator capabilities, we propose an approach that includes the motion platform actuation in the MPC model. The tracking performance and computation time of the algorithm are investigated...
Maljaars, E.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J. M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A. A.; Vu, N. M. T.; The EUROfusion MST1-team; The TCV-team
2017-12-01
The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety factor profile (q-profile) and kinetic plasma parameters such as the plasma beta. This demands to establish reliable profile control routines in presently operational tokamaks. We present a model predictive profile controller that controls the q-profile and plasma beta using power requests to two clusters of gyrotrons and the plasma current request. The performance of the controller is analyzed in both simulation and TCV L-mode discharges where successful tracking of the estimated inverse q-profile as well as plasma beta is demonstrated under uncertain plasma conditions and the presence of disturbances. The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.
Simulation research on multivariable fuzzy model predictive control of nuclear power plant
International Nuclear Information System (INIS)
Su Jie
2012-01-01
To improve the dynamic control capabilities of the nuclear power plant, the algorithm of the multivariable nonlinear predictive control based on the fuzzy model was applied in the main parameters control of the nuclear power plant, including control structure and the design of controller in the base of expounding the math model of the turbine and the once-through steam generator. The simulation results show that the respond of the change of the gas turbine speed and the steam pressure under the algorithm of multivariable fuzzy model predictive control is faster than that under the PID control algorithm, and the output value of the gas turbine speed and the steam pressure under the PID control algorithm is 3%-5% more than that under the algorithm of multi-variable fuzzy model predictive control. So it shows that the algorithm of multi-variable fuzzy model predictive control can control the output of the main parameters of the nuclear power plant well and get better control effect. (author)
Dynamic Simulation of Human Gait Model With Predictive Capability.
Sun, Jinming; Wu, Shaoli; Voglewede, Philip A
2018-03-01
In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS. The plant model is a seven-segment, six-joint model that has nine degrees-of-freedom (DOF). The plant model is validated using data collected from able-bodied human subjects. The proposed controller utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize the control input so that the predicted error is minimal. To decrease the complexity of the model, two joints are controlled using a proportional-derivative (PD) controller. The developed predictive human gait model is validated by simulating able-bodied human gait. The simulation results show that the developed model is able to simulate the kinematic output close to experimental data.
Maljaars, E.; Felici, F.; Blanken, T.C.; Galperti, C.; Sauter, O.; de Baar, M.R.; Carpanese, F.; Goodman, T.P.; Kim, D.; Kim, S.H.; Kong, M.G.; Mavkov, B.; Merle, A.; Moret, J.M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A.A.; Vu, N.M.T.
2017-01-01
The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety
Maljaars, B.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J.; Nouailletas, R.; Scheffer, M.; Teplukhina, A.; Vu, T.
2017-01-01
The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety
Bonne, F.; Alamir, M.; Bonnay, P.
2017-02-01
This paper deals with multivariable constrained model predictive control for Warm Compression Stations (WCS). WCSs are subject to numerous constraints (limits on pressures, actuators) that need to be satisfied using appropriate algorithms. The strategy is to replace all the PID loops controlling the WCS with an optimally designed model-based multivariable loop. This new strategy leads to high stability and fast disturbance rejection such as those induced by a turbine or a compressor stop, a key-aspect in the case of large scale cryogenic refrigeration. The proposed control scheme can be used to achieve precise control of pressures in normal operation or to avoid reaching stopping criteria (such as excessive pressures) under high disturbances (such as a pulsed heat load expected to take place in future fusion reactors, expected in the cryogenic cooling systems of the International Thermonuclear Experimental Reactor ITER or the Japan Torus-60 Super Advanced fusion experiment JT-60SA). The paper details the simulator used to validate this new control scheme and the associated simulation results on the SBTs WCS. This work is partially supported through the French National Research Agency (ANR), task agreement ANR-13-SEED-0005.
Model Predictive Control for Smart Energy Systems
DEFF Research Database (Denmark)
Halvgaard, Rasmus
pumps, heat tanks, electrical vehicle battery charging/discharging, wind farms, power plants). 2.Embed forecasting methodologies for the weather (e.g. temperature, solar radiation), the electricity consumption, and the electricity price in a predictive control system. 3.Develop optimization algorithms....... Chapter 3 introduces Model Predictive Control (MPC) including state estimation, filtering and prediction for linear models. Chapter 4 simulates the models from Chapter 2 with the certainty equivalent MPC from Chapter 3. An economic MPC minimizes the costs of consumption based on real electricity prices...... that determined the flexibility of the units. A predictive control system easily handles constraints, e.g. limitations in power consumption, and predicts the future behavior of a unit by integrating predictions of electricity prices, consumption, and weather variables. The simulations demonstrate the expected...
Energy Technology Data Exchange (ETDEWEB)
Sparn, Bethany F [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ruth, Mark F [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnamurthy, Dheepak [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Pratt, Annabelle [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Lunacek, Monte S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jones, Wesley B [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Wu, Hongyu [Kansas State University; Mittal, Saurabh [Mitre Corporation; Marks, Jesse [University of Missouri
2017-08-01
Many have proposed that responsive load provided by distributed energy resources (DERs) and demand response (DR) are an option to provide flexibility to the grid and especially to distribution feeders. However, because responsive load involves a complex interplay between tariffs and DER and DR technologies, it is challenging to test and evaluate options without negatively impacting customers. This paper describes a hardware-in-the-loop (HIL) simulation system that has been developed to reduce the cost of evaluating the impact of advanced controllers (e.g., model predictive controllers) and technologies (e.g., responsive appliances). The HIL simulation system combines large-scale software simulation with a small set of representative building equipment hardware. It is used to perform HIL simulation of a distribution feeder and the loads on it under various tariff structures. In the reported HIL simulation, loads include many simulated air conditioners and one physical air conditioner. Independent model predictive controllers manage operations of all air conditioners under a time-of-use tariff. Results from this HIL simulation and a discussion of future development work of the system are presented.
Model predictive control of a wind turbine modelled in Simpack
International Nuclear Information System (INIS)
Jassmann, U; Matzke, D; Reiter, M; Abel, D; Berroth, J; Schelenz, R; Jacobs, G
2014-01-01
Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine
Model predictive control of a wind turbine modelled in Simpack
Jassmann, U.; Berroth, J.; Matzke, D.; Schelenz, R.; Reiter, M.; Jacobs, G.; Abel, D.
2014-06-01
Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine to
Model Predictive Control based on Finite Impulse Response Models
DEFF Research Database (Denmark)
Prasath, Guru; Jørgensen, John Bagterp
2008-01-01
We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...... and related to the uncertainty of the impulse response coefficients. The simulations can be used to benchmark l2 MPC against FIR based robust MPC as well as to estimate the maximum performance improvements by robust MPC....
Dynamics and control of quadcopter using linear model predictive control approach
Islam, M.; Okasha, M.; Idres, M. M.
2017-12-01
This paper investigates the dynamics and control of a quadcopter using the Model Predictive Control (MPC) approach. The dynamic model is of high fidelity and nonlinear, with six degrees of freedom that include disturbances and model uncertainties. The control approach is developed based on MPC to track different reference trajectories ranging from simple ones such as circular to complex helical trajectories. In this control technique, a linearized model is derived and the receding horizon method is applied to generate the optimal control sequence. Although MPC is computer expensive, it is highly effective to deal with the different types of nonlinearities and constraints such as actuators’ saturation and model uncertainties. The MPC parameters (control and prediction horizons) are selected by trial-and-error approach. Several simulation scenarios are performed to examine and evaluate the performance of the proposed control approach using MATLAB and Simulink environment. Simulation results show that this control approach is highly effective to track a given reference trajectory.
Fast core prediction simulator for load follow control
International Nuclear Information System (INIS)
Yim, Man Sung; Lee, Sang Hoon; Lee, Un Chul
1990-01-01
An operator-assisting system for the reactor core control under power changing operating condition was developed. The system is consisted of core simulator routine and Xenon and Iodine initial condition generation routine. The initial condition generation routine, without exactly knowing the core status, is capable of providing accurate number densities and axial offset conditions of Xenon and Iodine after several hours of predictor- corrector calculations using the plant instrumentation signals of power level and power axial offset. The core simulator routine, even with the two node core model, gives equivalently accurate results as the one-dimensional model for the core behaviour simulation under power changing condition and can provide proper control strategies for load follow operation. The core simulator can also be used by the operator to develop remedial actions to restore the distorted power distribution by using its prediction capability
Model predictive controller design of hydrocracker reactors
GÖKÇE, Dila
2011-01-01
This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...
Model predictive control for Z-source power converter
DEFF Research Database (Denmark)
Mo, W.; Loh, P.C.; Blaabjerg, Frede
2011-01-01
This paper presents Model Predictive Control (MPC) of impedance-source (commonly known as Z-source) power converter. Output voltage control and current control for Z-source inverter are analyzed and simulated. With MPC's ability of multi- system variables regulation, load current and voltage...
Energy Technology Data Exchange (ETDEWEB)
Goertler, Gregor [Fachhochschulstudiengaenge Burgenland GesmbH, Pinkafeld (Austria). Kernkompetenzbereich Energie- und Umweltmanagement; Beigelboeck, Barbara
2010-12-15
Due to the use of MPC (Model Predictive Control) for room heating applications users and constructors expect nameable energy savings. By usage of a simulation for a special case the energy saving potential of predictive control algorithm for room temperature control in connection with an ideal weather forecast, in comparison to established algorithms (PI-control, two level controller) is estimated. The controlled system with the control variable room temperature is a room with floor heating which was modelled in TRNSYS. A linear state space model of the controlled system was derived with suitable identification methods. This model was used by the predictive control algorithm, which was programmed in MATLAB. The weather data was taken from the TRNSYS library and has been made available also for the control algorithm, so that an ideal weather forecast was established. For the example considered in this paper, the amount of energy saving was 10 % per year with the MPC-controller compared to a PI-controller. (Copyright copyright 2010 Ernst and Sohn Verlag fuer Architektur und technische Wissenschaften GmbH and Co. KG, Berlin)
Model Predictive Control for Connected Hybrid Electric Vehicles
Directory of Open Access Journals (Sweden)
Kaijiang Yu
2015-01-01
Full Text Available This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV for Pbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electric vehicle is developed using the maps of the engine efficiency characteristics. Fourth, simulations and analysis (under different parameters, i.e., road conditions, vehicle state of charge, etc. are conducted to verify the effectiveness of the method to achieve higher fuel efficiency. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results reveal improvements in fuel economy using the proposed control method.
Generalized Predictive Control and Neural Generalized Predictive Control
Directory of Open Access Journals (Sweden)
Sadhana CHIDRAWAR
2008-12-01
Full Text Available As Model Predictive Control (MPC relies on the predictive Control using a multilayer feed forward network as the plants linear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. This paper presents a detailed derivation of the Generalized Predictive Control and Neural Generalized Predictive Control with Newton-Raphson as minimization algorithm. Taking three separate systems, performances of the system has been tested. Simulation results show the effect of neural network on Generalized Predictive Control. The performance comparison of this three system configurations has been given in terms of ISE and IAE.
REALIGNED MODEL PREDICTIVE CONTROL OF A PROPYLENE DISTILLATION COLUMN
Directory of Open Access Journals (Sweden)
A. I. Hinojosa
Full Text Available Abstract In the process industry, advanced controllers usually aim at an economic objective, which usually requires closed-loop stability and constraints satisfaction. In this paper, the application of a MPC in the optimization structure of an industrial Propylene/Propane (PP splitter is tested with a controller based on a state space model, which is suitable for heavily disturbed environments. The simulation platform is based on the integration of the commercial dynamic simulator Dynsim® and the rigorous steady-state optimizer ROMeo® with the real-time facilities of Matlab. The predictive controller is the Infinite Horizon Model Predictive Control (IHMPC, based on a state-space model that that does not require the use of a state observer because the non-minimum state is built with the past inputs and outputs. The controller considers the existence of zone control of the outputs and optimizing targets for the inputs. We verify that the controller is efficient to control the propylene distillation system in a disturbed scenario when compared with a conventional controller based on a state observer. The simulation results show a good performance in terms of stability of the controller and rejection of large disturbances in the composition of the feed of the propylene distillation column.
Rate-Based Model Predictive Control of Turbofan Engine Clearance
DeCastro, Jonathan A.
2006-01-01
An innovative model predictive control strategy is developed for control of nonlinear aircraft propulsion systems and sub-systems. At the heart of the controller is a rate-based linear parameter-varying model that propagates the state derivatives across the prediction horizon, extending prediction fidelity to transient regimes where conventional models begin to lose validity. The new control law is applied to a demanding active clearance control application, where the objectives are to tightly regulate blade tip clearances and also anticipate and avoid detrimental blade-shroud rub occurrences by optimally maintaining a predefined minimum clearance. Simulation results verify that the rate-based controller is capable of satisfying the objectives during realistic flight scenarios where both a conventional Jacobian-based model predictive control law and an unconstrained linear-quadratic optimal controller are incapable of doing so. The controller is evaluated using a variety of different actuators, illustrating the efficacy and versatility of the control approach. It is concluded that the new strategy has promise for this and other nonlinear aerospace applications that place high importance on the attainment of control objectives during transient regimes.
Integrated predictive modelling simulations of burning plasma experiment designs
International Nuclear Information System (INIS)
Bateman, Glenn; Onjun, Thawatchai; Kritz, Arnold H
2003-01-01
Models for the height of the pedestal at the edge of H-mode plasmas (Onjun T et al 2002 Phys. Plasmas 9 5018) are used together with the Multi-Mode core transport model (Bateman G et al 1998 Phys. Plasmas 5 1793) in the BALDUR integrated predictive modelling code to predict the performance of the ITER (Aymar A et al 2002 Plasma Phys. Control. Fusion 44 519), FIRE (Meade D M et al 2001 Fusion Technol. 39 336), and IGNITOR (Coppi B et al 2001 Nucl. Fusion 41 1253) fusion reactor designs. The simulation protocol used in this paper is tested by comparing predicted temperature and density profiles against experimental data from 33 H-mode discharges in the JET (Rebut P H et al 1985 Nucl. Fusion 25 1011) and DIII-D (Luxon J L et al 1985 Fusion Technol. 8 441) tokamaks. The sensitivities of the predictions are evaluated for the burning plasma experimental designs by using variations of the pedestal temperature model that are one standard deviation above and below the standard model. Simulations of the fusion reactor designs are carried out for scans in which the plasma density and auxiliary heating power are varied
High level model predictive control for plug-and-play process control with stability guaranty
DEFF Research Database (Denmark)
Michelsen, Axel Gottlieb; Stoustrup, Jakob
2010-01-01
In this paper a method for designing a stabilizing high level model predictive controller for a hierarchical plug- and-play process is presented. This is achieved by abstracting the lower layers of the controller structure as low order models with uncertainty and by using a robust model predictive...... controller for generating the references for these. A simulation example, in which the actuators in a process control system are changed, is reported to show the potential of this approach for plug and play process control....
NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES
Directory of Open Access Journals (Sweden)
SILVA R. G.
1999-01-01
Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.
Energy Technology Data Exchange (ETDEWEB)
Kassem, Ahmed M. [Beni-Suef University, Electrical Dept., Beni Suef (Egypt)
2012-09-15
This paper investigates the application of the model predictive control (MPC) approach to control the voltage and frequency of a stand alone wind generation system. This scheme consists of a wind turbine which drives an induction generator feeding an isolated load. A static VAR compensator is connected at the induction generator terminals to regulate the load voltage. The rotor speed, and thereby the load frequency are controlled via adjusting the mechanical power input using the blade pitch-angle. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed. Digital simulations have been carried out in order to validate the effectiveness of the proposed scheme. The proposed controller has been tested through step changes in the wind speed and the load impedance. Simulation results show that adequate performance of the proposed wind energy scheme has been achieved. Moreover, this scheme is robust against the parameters variation and eliminates the influence of modeling and measurement errors. (orig.)
Nonlinear Model Predictive Control with Constraint Satisfactions for a Quadcopter
Wang, Ye; Ramirez-Jaime, Andres; Xu, Feng; Puig, Vicenç
2017-01-01
This paper presents a nonlinear model predictive control (NMPC) strategy combined with constraint satisfactions for a quadcopter. The full dynamics of the quadcopter describing the attitude and position are nonlinear, which are quite sensitive to changes of inputs and disturbances. By means of constraint satisfactions, partial nonlinearities and modeling errors of the control-oriented model of full dynamics can be transformed into the inequality constraints. Subsequently, the quadcopter can be controlled by an NMPC controller with the updated constraints generated by constraint satisfactions. Finally, the simulation results applied to a quadcopter simulator are provided to show the effectiveness of the proposed strategy.
Advanced feeder control using fast simulation models
Verheijen, O.S.; Op den Camp, O.M.G.C.; Beerkens, R.G.C.; Backx, A.C.P.M.; Huisman, L.; Drummond, C.H.
2005-01-01
For the automatic control of glass quality in glass production, the relation between process variable and product or glass quality and process conditions/process input parameters must be known in detail. So far, detailed 3-D glass melting simulation models were used to predict the effect of process
Real-Time Optimization for Economic Model Predictive Control
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Edlund, Kristian; Frison, Gianluca
2012-01-01
In this paper, we develop an efficient homogeneous and self-dual interior-point method for the linear programs arising in economic model predictive control. To exploit structure in the optimization problems, the algorithm employs a highly specialized Riccati iteration procedure. Simulations show...
Data Driven Economic Model Predictive Control
Directory of Open Access Journals (Sweden)
Masoud Kheradmandi
2018-04-01
Full Text Available This manuscript addresses the problem of data driven model based economic model predictive control (MPC design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example.
Hybrid Model Predictive Control as a LFC solution in Hydropower Plants
Directory of Open Access Journals (Sweden)
Donaisky Emerson
2015-01-01
Full Text Available For Electric Power System safety and stable operation, planning and analysis by using simulation environments are necessary. An important point for frequency stability analysis is, on one hand, an adequate representation of Load-Frequency Control (LFC loops and, on the other hand, the design of advanced control strategies to deal with the power system dynamic complexity. Therefore, in this paper we propose to represent the group turbine/penstock, found in hydropower plants, in a Piecewise Affine (PWA modelling structure. Based on such modelling, we also propose the use of a Hybrid Model Predictive algorithm to be use as a control law in LFC loops. Among the advantages of this PWA representation is the use of this model in the controller algorithm, thereby improving the Load-Frequency Control performance. Simulation results, on a 200 MW hydropower plant compares the performance of predictive control strategy presented with the classical PID control strategy in an isolated condition.
Model predictive control based on reduced order models applied to belt conveyor system.
Chen, Wei; Li, Xin
2016-11-01
In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Model Predictive Control of Z-source Neutral Point Clamped Inverter
DEFF Research Database (Denmark)
Mo, Wei; Loh, Poh Chiang; Blaabjerg, Frede
2011-01-01
This paper presents Model Predictive Control (MPC) of Z-source Neutral Point Clamped (NPC) inverter. For illustration, current control of Z-source NPC grid-connected inverter is analyzed and simulated. With MPC’s advantage of easily including system constraints, load current, impedance network...... response are obtained at the same time with a formulated Z-source NPC inverter network model. Operation steady state and transient state simulation results of MPC are going to be presented, which shows good reference tracking ability of this method. It provides new control method for Z-source NPC inverter...
Thermal Storage Power Balancing with Model Predictive Control
DEFF Research Database (Denmark)
Halvgaard, Rasmus; Poulsen, Niels Kjølstad; Madsen, Henrik
2013-01-01
The method described in this paper balances power production and consumption with a large number of thermal loads. Linear controllers are used for the loads to track a temperature set point, while Model Predictive Control (MPC) and model estimation of the load behavior are used for coordination....... The total power consumption of all loads is controlled indirectly through a real-time price. The MPC incorporates forecasts of the power production and disturbances that influence the loads, e.g. time-varying weather forecasts, in order to react ahead of time. A simulation scenario demonstrates...
Domestic appliances energy optimization with model predictive control
International Nuclear Information System (INIS)
Rodrigues, E.M.G.; Godina, R.; Pouresmaeil, E.; Ferreira, J.R.; Catalão, J.P.S.
2017-01-01
Highlights: • An alternative power management control for home appliances that require thermal regulation is presented. • A Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat. • Problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. • A modulation scheme of a two-level Model Predictive Control signal as an interface block is presented. • The implementation costs in home appliances with thermal regulation requirements are reduced. - Abstract: A vital element in making a sustainable world is correctly managing the energy in the domestic sector. Thus, this sector evidently stands as a key one for to be addressed in terms of climate change goals. Increasingly, people are aware of electricity savings by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Much of the reduction was due to technological improvements, however with the advancing of the years new types of control arise. Domestic appliances with the purpose of heating and cooling rely on thermostatic regulation technique. The study in this paper is focused on the subject of an alternative power management control for home appliances that require thermal regulation. In this paper a Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat with the aim of minimizing the cooling energy consumption through the minimization of the energy cost while satisfying the adequate temperature range for the human comfort. In addition, the Model Predictive Control problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. For this purpose, the typical consumption of a 24 h period of a summer day was simulated a three-level tariff scheme was used. The new
Health-aware Model Predictive Control of Pasteurization Plant
Karimi Pour, Fatemeh; Puig, Vicenç; Ocampo-Martinez, Carlos
2017-01-01
In order to optimize the trade-off between components life and energy consumption, the integration of a system health management and control modules is required. This paper proposes the integration of model predictive control (MPC) with a fatigue estimation approach that minimizes the damage of the components of a pasteurization plant. The fatigue estimation is assessed with the rainflow counting algorithm. Using data from this algorithm, a simplified model that characterizes the health of the system is developed and integrated with MPC. The MPC controller objective is modified by adding an extra criterion that takes into account the accumulated damage. But, a steady-state offset is created by adding this extra criterion. Finally, by including an integral action in the MPC controller, the steady-state error for regulation purpose is eliminated. The proposed control scheme is validated in simulation using a simulator of a utility-scale pasteurization plant.
Modeling and Model Predictive Power and Rate Control of Wireless Communication Networks
Directory of Open Access Journals (Sweden)
Cunwu Han
2014-01-01
Full Text Available A novel power and rate control system model for wireless communication networks is presented, which includes uncertainties, input constraints, and time-varying delays in both state and control input. A robust delay-dependent model predictive power and rate control method is proposed, and the state feedback control law is obtained by solving an optimization problem that is derived by using linear matrix inequality (LMI techniques. Simulation results are given to illustrate the effectiveness of the proposed method.
Model Predictive Control of Integrated Gasification Combined Cycle Power Plants
Energy Technology Data Exchange (ETDEWEB)
B. Wayne Bequette; Priyadarshi Mahapatra
2010-08-31
The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.
Robust multi-model predictive control of multi-zone thermal plate system
Directory of Open Access Journals (Sweden)
Poom Jatunitanon
2018-02-01
Full Text Available A modern controller was designed by using the mathematical model of a multi–zone thermal plate system. An important requirement for this type of controller is that it must be able to keep the temperature set-point of each thermal zone. The mathematical model used in the design was determined through a system identification process. The results showed that when the operating condition is changed, the performance of the controller may be reduced as a result of the system parameter uncertainties. This paper proposes a weighting technique of combining the robust model predictive controller for each operating condition into a single robust multi-model predictive control. Simulation and experimental results showed that the proposed method performed better than the conventional multi-model predictive control in rise time of transient response, when used in a system designed to work over a wide range of operating conditions.
Model Predictive Control of Mineral Column Flotation Process
Directory of Open Access Journals (Sweden)
Yahui Tian
2018-06-01
Full Text Available Column flotation is an efficient method commonly used in the mineral industry to separate useful minerals from ores of low grade and complex mineral composition. Its main purpose is to achieve maximum recovery while ensuring desired product grade. This work addresses a model predictive control design for a mineral column flotation process modeled by a set of nonlinear coupled heterodirectional hyperbolic partial differential equations (PDEs and ordinary differential equations (ODEs, which accounts for the interconnection of well-stirred regions represented by continuous stirred tank reactors (CSTRs and transport systems given by heterodirectional hyperbolic PDEs, with these two regions combined through the PDEs’ boundaries. The model predictive control considers both optimality of the process operations and naturally present input and state/output constraints. For the discrete controller design, spatially varying steady-state profiles are obtained by linearizing the coupled ODE–PDE model, and then the discrete system is obtained by using the Cayley–Tustin time discretization transformation without any spatial discretization and/or without model reduction. The model predictive controller is designed by solving an optimization problem with input and state/output constraints as well as input disturbance to minimize the objective function, which leads to an online-solvable finite constrained quadratic regulator problem. Finally, the controller performance to keep the output at the steady state within the constraint range is demonstrated by simulation studies, and it is concluded that the optimal control scheme presented in this work makes this flotation process more efficient.
Economic Optimization of Spray Dryer Operation using Nonlinear Model Predictive Control
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2014-01-01
In this paper we investigate an economically optimizing Nonlinear Model Predictive Control (E-NMPC) for a spray drying process. By simulation we evaluate the economic potential of this E-NMPC compared to a conventional PID based control strategy. Spray drying is the preferred process to reduce...... the water content for many liquid foodstuffs and produces a free flowing powder. The main challenge in controlling the spray drying process is to meet the residual moisture specifications and avoid that the powder sticks to the chamber walls of the spray dryer. We present a model for a spray dryer that has...... been validated on experimental data from a pilot plant. We use this model for simulation as well as for prediction in the E-NMPC. The E-NMPC is designed with hard input constraints and soft output constraints. The open-loop optimal control problem in the E-NMPC is solved using the single...
Model Predictive Control of the Hybrid Ventilation for Livestock
DEFF Research Database (Denmark)
Wu, Zhuang; Stoustrup, Jakob; Trangbæk, Klaus
2006-01-01
In this paper, design and simulation results of Model Predictive Control (MPC) strategy for livestock hybrid ventilation systems and associated indoor climate through variable valve openings and exhaust fans are presented. The design is based on thermal comfort parameters for poultry in barns...
A Combined Cooperative Braking Model with a Predictive Control Strategy in an Electric Vehicle
Directory of Open Access Journals (Sweden)
Hongqiang Guo
2013-12-01
Full Text Available Cooperative braking with regenerative braking and mechanical braking plays an important role in electric vehicles for energy-saving control. Based on the parallel and the series cooperative braking models, a combined model with a predictive control strategy to get a better cooperative braking performance is presented. The balance problem between the maximum regenerative energy recovery efficiency and the optimum braking stability is solved through an off-line process optimization stream with the collaborative optimization algorithm (CO. To carry out the process optimization stream, the optimal Latin hypercube design (Opt LHD is presented to discrete the continuous design space. To solve the poor real-time problem of the optimization, a high-precision predictive model based on the off-line optimization data of the combined model is built, and a predictive control strategy is proposed and verified through simulation. The simulation results demonstrate that the predictive control strategy and the combined model are reasonable and effective.
Enhanced pid vs model predictive control applied to bldc motor
Gaya, M. S.; Muhammad, Auwal; Aliyu Abdulkadir, Rabiu; Salim, S. N. S.; Madugu, I. S.; Tijjani, Aminu; Aminu Yusuf, Lukman; Dauda Umar, Ibrahim; Khairi, M. T. M.
2018-01-01
BrushLess Direct Current (BLDC) motor is a multivariable and highly complex nonlinear system. Variation of internal parameter values with environment or reference signal increases the difficulty in controlling the BLDC effectively. Advanced control strategies (like model predictive control) often have to be integrated to satisfy the control desires. Enhancing or proper tuning of a conventional algorithm results in achieving the desired performance. This paper presents a performance comparison of Enhanced PID and Model Predictive Control (MPC) applied to brushless direct current motor. The simulation results demonstrated that the PSO-PID is slightly better than the PID and MPC in tracking the trajectory of the reference signal. The proposed scheme could be useful algorithms for the system.
Directory of Open Access Journals (Sweden)
Fitri Yakub
2016-01-01
Full Text Available We present a comparative study of model predictive control approaches of two-wheel steering, four-wheel steering, and a combination of two-wheel steering with direct yaw moment control manoeuvres for path-following control in autonomous car vehicle dynamics systems. Single-track mode, based on a linearized vehicle and tire model, is used. Based on a given trajectory, we drove the vehicle at low and high forward speeds and on low and high road friction surfaces for a double-lane change scenario in order to follow the desired trajectory as close as possible while rejecting the effects of wind gusts. We compared the controller based on both simple and complex bicycle models without and with the roll vehicle dynamics for different types of model predictive control manoeuvres. The simulation result showed that the model predictive control gave a better performance in terms of robustness for both forward speeds and road surface variation in autonomous path-following control. It also demonstrated that model predictive control is useful to maintain vehicle stability along the desired path and has an ability to eliminate the crosswind effect.
System Identification, Prediction, Simulation and Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1997-01-01
a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...
Analysis of explicit model predictive control for path-following control.
Lee, Junho; Chang, Hyuk-Jun
2018-01-01
In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration.
Analysis of explicit model predictive control for path-following control
2018-01-01
In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration. PMID:29534080
Modelling and simulation of process control systems for WWER
Energy Technology Data Exchange (ETDEWEB)
Pangelov, N [Energoproekt, Sofia (Bulgaria)
1996-12-31
A dynamic modelling method for simulation of process control system is developed (method for identification). It is based on the least squares method and highly efficient linear uninterrupted differential equations. The method has the following advantages: there are no significant limitations in the type of input/output signals and in the length of data time series; identification at none zero initial condition is possible; on-line identification is possible; a high accuracy is observed in case of noise. On the basis of real experiments and data time series simulated with known computer codes it is possible to construct highly efficient models of different systems for solving the following problems: real time simulation with high accuracy for training purposes; estimation of immeasurable parameters important to safety; malfunction diagnostics based on plant dynamics; prediction of dynamic behaviour; control vector estimation in regime adviser. Two real applications of this method are described: in dynamic behaviour modelling for steam generator level, and in creating of a Process Control System Simulator (PCSS) based on KASKAD-2 for WWER-1000 units of the Kozloduy NPP. 6 refs., 8 figs.
Modeling and dynamic control simulation of unitary gas engine heat pump
International Nuclear Information System (INIS)
Zhao Yang; Haibo Zhao; Zheng Fang
2007-01-01
Based on the dynamic model of the gas engine heat pump (GEHP) system, an intelligent control simulation is presented to research the dynamic characteristics of the system in the heating operation. The GEHP system simulation model consists of eight models for its components including a natural gas engine, a compressor, a condenser, an expansion valve, an evaporator, a cylinder jacket heat exchanger, an exhaust gas heat exchanger and an auxiliary heater. The intelligent control model is composed of the prediction controller model and the combined controller model. The Runge-Kutta Fehlberg fourth-fifth order algorithms are used to solve the differential equations. The results show that the model is very effective in analyzing the effects of the control system, and the steady state accuracy of the intelligent control scheme is higher than that of the fuzzy controller
Recent Advances in Explicit Multiparametric Nonlinear Model Predictive Control
Domínguez, Luis F.
2011-01-19
In this paper we present recent advances in multiparametric nonlinear programming (mp-NLP) algorithms for explicit nonlinear model predictive control (mp-NMPC). Three mp-NLP algorithms for NMPC are discussed, based on which novel mp-NMPC controllers are derived. The performance of the explicit controllers are then tested and compared in a simulation example involving the operation of a continuous stirred-tank reactor (CSTR). © 2010 American Chemical Society.
International Nuclear Information System (INIS)
Li, Liang; Zhang, Yuanbo; Yang, Chao; Yan, Bingjie; Marina Martinez, C.
2016-01-01
Highlights: • A 7-degree-of-freedom model of hybrid electric vehicle with regenerative braking system is built. • A modified nonlinear model predictive control strategy is developed. • The particle swarm optimization algorithm is employed to solve the optimization problem. • The proposed control strategy is verified by simulation and hardware-in-loop tests. • Test results verify the effectiveness of the proposed control strategy. - Abstract: As one of the main working modes, the energy recovered with regenerative braking system provides an effective approach so as to greatly improve fuel economy of hybrid electric bus. However, it is still a challenging issue to ensure braking stability while maximizing braking energy recovery. To solve this problem, an efficient energy recovery control strategy is proposed based on the modified nonlinear model predictive control method. Firstly, combined with the characteristics of the compound braking process of single-shaft parallel hybrid electric bus, a 7 degrees of freedom model of the vehicle longitudinal dynamics is built. Secondly, considering nonlinear characteristic of the vehicle model and the efficiency of regenerative braking system, the particle swarm optimization algorithm within the modified nonlinear model predictive control is adopted to optimize the torque distribution between regenerative braking system and pneumatic braking system at the wheels. So as to reduce the computational time of modified nonlinear model predictive control, a nearest point method is employed during the braking process. Finally, the simulation and hardware-in-loop test are carried out on road conditions with different tire–road adhesion coefficients, and the proposed control strategy is verified by comparing it with the conventional control method employed in the baseline vehicle controller. The simulation and hardware-in-loop test results show that the proposed strategy can ensure vehicle safety during emergency braking
Directory of Open Access Journals (Sweden)
Hongxiao Yu
2015-05-01
Full Text Available Trajectory tracking and state estimation are significant in the motion planning and intelligent vehicle control. This article focuses on the model predictive control approach for the trajectory tracking of the intelligent vehicles and state estimation of the nonlinear vehicle system. The constraints of the system states are considered when applying the model predictive control method to the practical problem, while 4-degree-of-freedom vehicle model and unscented Kalman filter are proposed to estimate the vehicle states. The estimated states of the vehicle are used to provide model predictive control with real-time control and judge vehicle stability. Furthermore, in order to decrease the cost of solving the nonlinear optimization, the linear time-varying model predictive control is used at each time step. The effectiveness of the proposed vehicle state estimation and model predictive control method is tested by driving simulator. The results of simulations and experiments show that great and robust performance is achieved for trajectory tracking and state estimation in different scenarios.
Characteristic Model-Based Robust Model Predictive Control for Hypersonic Vehicles with Constraints
Directory of Open Access Journals (Sweden)
Jun Zhang
2017-06-01
Full Text Available Designing robust control for hypersonic vehicles in reentry is difficult, due to the features of the vehicles including strong coupling, non-linearity, and multiple constraints. This paper proposed a characteristic model-based robust model predictive control (MPC for hypersonic vehicles with reentry constraints. First, the hypersonic vehicle is modeled by a characteristic model composed of a linear time-varying system and a lumped disturbance. Then, the identification data are regenerated by the accumulative sum idea in the gray theory, which weakens effects of the random noises and strengthens regularity of the identification data. Based on the regenerated data, the time-varying parameters and the disturbance are online estimated according to the gray identification. At last, the mixed H2/H∞ robust predictive control law is proposed based on linear matrix inequalities (LMIs and receding horizon optimization techniques. Using active tackling system constraints of MPC, the input and state constraints are satisfied in the closed-loop control system. The validity of the proposed control is verified theoretically according to Lyapunov theory and illustrated by simulation results.
Analytic Model Predictive Control of Uncertain Nonlinear Systems: A Fuzzy Adaptive Approach
Directory of Open Access Journals (Sweden)
Xiuyan Peng
2015-01-01
Full Text Available A fuzzy adaptive analytic model predictive control method is proposed in this paper for a class of uncertain nonlinear systems. Specifically, invoking the standard results from the Moore-Penrose inverse of matrix, the unmatched problem which exists commonly in input and output dimensions of systems is firstly solved. Then, recurring to analytic model predictive control law, combined with fuzzy adaptive approach, the fuzzy adaptive predictive controller synthesis for the underlying systems is developed. To further reduce the impact of fuzzy approximation error on the system and improve the robustness of the system, the robust compensation term is introduced. It is shown that by applying the fuzzy adaptive analytic model predictive controller the rudder roll stabilization system is ultimately uniformly bounded stabilized in the H-infinity sense. Finally, simulation results demonstrate the effectiveness of the proposed method.
ECONOMIC MODELING STOCKS CONTROL SYSTEM: SIMULATION MODEL
Климак, М.С.; Войтко, С.В.
2016-01-01
Considered theoretical and applied aspects of the development of simulation models to predictthe optimal development and production systems that create tangible products andservices. It isproved that theprocessof inventory control needs of economicandmathematical modeling in viewof thecomplexity of theoretical studies. A simulation model of stocks control that allows make managementdecisions with production logistics
Pohjoranta, Antti; Halinen, Matias; Pennanen, Jari; Kiviaho, Jari
2015-03-01
Generalized predictive control (GPC) is applied to control the maximum temperature in a solid oxide fuel cell (SOFC) stack and the temperature difference over the stack. GPC is a model predictive control method and the models utilized in this work are ARX-type (autoregressive with extra input), multiple input-multiple output, polynomial models that were identified from experimental data obtained from experiments with a complete SOFC system. The proposed control is evaluated by simulation with various input-output combinations, with and without constraints. A comparison with conventional proportional-integral-derivative (PID) control is also made. It is shown that if only the stack maximum temperature is controlled, a standard PID controller can be used to obtain output performance comparable to that obtained with the significantly more complex model predictive controller. However, in order to control the temperature difference over the stack, both the stack minimum and the maximum temperature need to be controlled and this cannot be done with a single PID controller. In such a case the model predictive controller provides a feasible and effective solution.
Unreachable Setpoints in Model Predictive Control
DEFF Research Database (Denmark)
Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp
2008-01-01
In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optimal...... steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...
Model Predictive Flight Control System with Full State Observer using H∞ Method
Sanwale, Jitu; Singh, Dhan Jeet
2018-03-01
This paper presents the application of the model predictive approach to design a flight control system (FCS) for longitudinal dynamics of a fixed wing aircraft. Longitudinal dynamics is derived for a conventional aircraft. Open loop aircraft response analysis is carried out. Simulation studies are illustrated to prove the efficacy of the proposed model predictive controller using H ∞ state observer. The estimation criterion used in the {H}_{∞} observer design is to minimize the worst possible effects of the modelling errors and additive noise on the parameter estimation.
Model predictive and reallocation problem for CubeSat fault recovery and attitude control
Franchi, Loris; Feruglio, Lorenzo; Mozzillo, Raffaele; Corpino, Sabrina
2018-01-01
In recent years, thanks to the increase of the know-how on machine-learning techniques and the advance of the computational capabilities of on-board processing, expensive computing algorithms, such as Model Predictive Control, have begun to spread in space applications even on small on-board processor. The paper presents an algorithm for an optimal fault recovery of a 3U CubeSat, developed in MathWorks Matlab & Simulink environment. This algorithm involves optimization techniques aiming at obtaining the optimal recovery solution, and involves a Model Predictive Control approach for the attitude control. The simulated system is a CubeSat in Low Earth Orbit: the attitude control is performed with three magnetic torquers and a single reaction wheel. The simulation neglects the errors in the attitude determination of the satellite, and focuses on the recovery approach and control method. The optimal recovery approach takes advantage of the properties of magnetic actuation, which gives the possibility of the redistribution of the control action when a fault occurs on a single magnetic torquer, even in absence of redundant actuators. In addition, the paper presents the results of the implementation of Model Predictive approach to control the attitude of the satellite.
Model predictive Controller for Mobile Robot
Alireza Rezaee
2017-01-01
This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is...
Introducing Model Predictive Control for Improving Power Plant Portfolio Performance
DEFF Research Database (Denmark)
Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon
2008-01-01
This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...... reference tracking and disturbance rejection in an economically optimal way. The performance function is chosen as a mixture of the `1-norm and a linear weighting to model the economics of the system. Simulations show a significant improvement of the performance of the MPC compared to the current...
Data-Based Predictive Control with Multirate Prediction Step
Barlow, Jonathan S.
2010-01-01
Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.
Model predictive control for a thermostatic controlled system
DEFF Research Database (Denmark)
Shafiei, Seyed Ehsan; Rasmussen, Henrik; Stoustrup, Jakob
2013-01-01
This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff temperat......This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff...
Downey, Brandon; Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey
2017-11-01
As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.
Directory of Open Access Journals (Sweden)
César Hernández-Hernández
2017-06-01
Full Text Available Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.
Study on Noise Prediction Model and Control Schemes for Substation
Gao, Yang; Liu, Songtao
2014-01-01
With the government's emphasis on environmental issues of power transmission and transformation project, noise pollution has become a prominent problem now. The noise from the working transformer, reactor, and other electrical equipment in the substation will bring negative effect to the ambient environment. This paper focuses on using acoustic software for the simulation and calculation method to control substation noise. According to the characteristics of the substation noise and the techniques of noise reduction, a substation's acoustic field model was established with the SoundPLAN software to predict the scope of substation noise. On this basis, 4 reasonable noise control schemes were advanced to provide some helpful references for noise control during the new substation's design and construction process. And the feasibility and application effect of these control schemes can be verified by using the method of simulation modeling. The simulation results show that the substation always has the problem of excessive noise at boundary under the conventional measures. The excess noise can be efficiently reduced by taking the corresponding noise reduction methods. PMID:24672356
Piloted Simulation of a Model-Predictive Automated Recovery System
Liu, James (Yuan); Litt, Jonathan; Sowers, T. Shane; Owens, A. Karl; Guo, Ten-Huei
2014-01-01
This presentation describes a model-predictive automatic recovery system for aircraft on the verge of a loss-of-control situation. The system determines when it must intervene to prevent an imminent accident, resulting from a poor approach. It estimates the altitude loss that would result from a go-around maneuver at the current flight condition. If the loss is projected to violate a minimum altitude threshold, the maneuver is automatically triggered. The system deactivates to allow landing once several criteria are met. Piloted flight simulator evaluation showed the system to provide effective envelope protection during extremely unsafe landing attempts. The results demonstrate how flight and propulsion control can be integrated to recover control of the vehicle automatically and prevent a potential catastrophe.
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2015-01-01
In this paper, we compare the performance of an economically optimizing Nonlinear Model Predictive Controller (E-NMPC) to a linear tracking Model Predictive Controller (MPC) for a spray drying plant. We find in this simulation study, that the economic performance of the two controllers are almost...... equal. We evaluate the economic performance with an industrially recorded disturbance scenario, where unmeasured disturbances and model mismatch are present. The state of the spray dryer, used in the E-NMPC and MPC, is estimated using Kalman Filters with noise covariances estimated by a maximum...
Litt, Jonathan S.; Liu, Yuan; Sowers, Thomas S.; Owen, A. Karl; Guo, Ten-Huei
2014-01-01
This paper describes a model-predictive automatic recovery system for aircraft on the verge of a loss-of-control situation. The system determines when it must intervene to prevent an imminent accident, resulting from a poor approach. It estimates the altitude loss that would result from a go-around maneuver at the current flight condition. If the loss is projected to violate a minimum altitude threshold, the maneuver is automatically triggered. The system deactivates to allow landing once several criteria are met. Piloted flight simulator evaluation showed the system to provide effective envelope protection during extremely unsafe landing attempts. The results demonstrate how flight and propulsion control can be integrated to recover control of the vehicle automatically and prevent a potential catastrophe.
International Nuclear Information System (INIS)
Hindi, H.; Prabhakar, S.; Fox, J.; Teytelman, D.
1997-12-01
The authors present a technique for the design and verification of efficient bunch-by-bunch controllers for damping longitudinal multibunch instabilities. The controllers attempt to optimize the use of available feedback amplifier power--one of the most expensive components of a feedback system--and define the limits of closed loop system performance. The design technique alternates between analytic computation of single bunch optimal controllers and verification on a multibunch numerical simulator. The simulator identifies unstable coupled bunch modes and predicts their growth and damping rates. The results from the simulator are shown to be in reasonable agreement with analytical calculations based on the single bunch model. The technique is then used to evaluate the performance of a variety of controllers proposed for PEP-II
Model predictive control classical, robust and stochastic
Kouvaritakis, Basil
2016-01-01
For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...
Energy Technology Data Exchange (ETDEWEB)
Wang, Guo Xu; Wu, Jie; Zeng, Bifan; Wu, Wangqiang; Ma, Xiao Qian [School of Electric Power, South China University of Technology, Guangzhou (China); Xu, Zhibin [Electric Power Research Institute of Guangdong Power Grid Corporation, Guangzhou (China)
2017-02-15
A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP). The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.
Modeling and Control of CSTR using Model based Neural Network Predictive Control
Shrivastava, Piyush
2012-01-01
This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some commen...
Li, Guang
2017-01-01
This paper presents a fast constrained optimization approach, which is tailored for nonlinear model predictive control of wave energy converters (WEC). The advantage of this approach relies on its exploitation of the differential flatness of the WEC model. This can reduce the dimension of the resulting nonlinear programming problem (NLP) derived from the continuous constrained optimal control of WEC using pseudospectral method. The alleviation of computational burden using this approach helps to promote an economic implementation of nonlinear model predictive control strategy for WEC control problems. The method is applicable to nonlinear WEC models, nonconvex objective functions and nonlinear constraints, which are commonly encountered in WEC control problems. Numerical simulations demonstrate the efficacy of this approach.
International Nuclear Information System (INIS)
Huang Gongsheng; Wang Shengwei; Xu Xinhua
2009-01-01
This paper presents a robust model predictive control strategy for improving the supply air temperature control of air-handling units by dealing with the associated uncertainties and constraints directly. This strategy uses a first-order plus time-delay model with uncertain time-delay and system gain to describe air-conditioning process of an air-handling unit usually operating at various weather conditions. The uncertainties of the time-delay and system gain, which imply the nonlinearities and the variable dynamic characteristics, are formulated using an uncertainty polytope. Based on this uncertainty formulation, an offline LMI-based robust model predictive control algorithm is employed to design a robust controller for air-handling units which can guarantee a good robustness subject to uncertainties and constraints. The proposed robust strategy is evaluated in a dynamic simulation environment of a variable air volume air-conditioning system in various operation conditions by comparing with a conventional PI control strategy. The robustness analysis of both strategies under different weather conditions is also presented.
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Jørgensen, John Bagterp; Rawlings, James B.
2015-01-01
In this paper, we develop an economically optimizing Nonlinear Model Predictive Controller (E-NMPC) for a complete spray drying plant with multiple stages. In the E-NMPC the initial state is estimated by an extended Kalman Filter (EKF) with noise covariances estimated by an autocovariance least...... squares method (ALS). We present a model for the spray drying plant and use this model for simulation as well as for prediction in the E-NMPC. The open-loop optimal control problem in the E-NMPC is solved using the single-shooting method combined with a quasi-Newton Sequential Quadratic programming (SQP......) algorithm and the adjoint method for computation of gradients. We evaluate the economic performance when unmeasured disturbances are present. By simulation, we demonstrate that the E-NMPC improves the profit of spray drying by 17% compared to conventional PI control....
DEFF Research Database (Denmark)
Flores-Alsina, Xavier; Rodriguez-Roda, Ignasi; Sin, Gürkan
2009-01-01
The objective of this paper is to perform an uncertainty and sensitivity analysis of the predictions of the Benchmark Simulation Model (BSM) No. 1, when comparing four activated sludge control strategies. The Monte Carlo simulation technique is used to evaluate the uncertainty in the BSM1 predict...
Model predictive control using fuzzy decision functions
Kaymak, U.; Costa Sousa, da J.M.
2001-01-01
Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the
Electric vehicle charge planning using Economic Model Predictive Control
DEFF Research Database (Denmark)
Halvgaard, Rasmus; Poulsen, Niels K.; Madsen, Henrik
2012-01-01
Economic Model Predictive Control (MPC) is very well suited for controlling smart energy systems since electricity price and demand forecasts are easily integrated in the controller. Electric vehicles (EVs) are expected to play a large role in the future Smart Grid. They are expected to provide...... grid services, both for peak reduction and for ancillary services, by absorbing short term variations in the electricity production. In this paper the Economic MPC minimizes the cost of electricity consumption for a single EV. Simulations show savings of 50–60% of the electricity costs compared...... to uncontrolled charging from load shifting based on driving pattern predictions. The future energy system in Denmark will most likely be based on renewable energy sources e.g. wind and solar power. These green energy sources introduce stochastic fluctuations in the electricity production. Therefore, energy...
Convergence Guaranteed Nonlinear Constraint Model Predictive Control via I/O Linearization
Directory of Open Access Journals (Sweden)
Xiaobing Kong
2013-01-01
Full Text Available Constituting reliable optimal solution is a key issue for the nonlinear constrained model predictive control. Input-output feedback linearization is a popular method in nonlinear control. By using an input-output feedback linearizing controller, the original linear input constraints will change to nonlinear constraints and sometimes the constraints are state dependent. This paper presents an iterative quadratic program (IQP routine on the continuous-time system. To guarantee its convergence, another iterative approach is incorporated. The proposed algorithm can reach a feasible solution over the entire prediction horizon. Simulation results on both a numerical example and the continuous stirred tank reactors (CSTR demonstrate the effectiveness of the proposed method.
Dinucleotide controlled null models for comparative RNA gene prediction.
Gesell, Tanja; Washietl, Stefan
2008-05-27
Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. SISSIz
Dinucleotide controlled null models for comparative RNA gene prediction
Directory of Open Access Journals (Sweden)
Gesell Tanja
2008-05-01
Full Text Available Abstract Background Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. Results We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. Conclusion SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require
Wind turbine control and model predictive control for uncertain systems
DEFF Research Database (Denmark)
Thomsen, Sven Creutz
as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...
Modeling, robust and distributed model predictive control for freeway networks
Liu, S.
2016-01-01
In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of
Output-Feedback Model Predictive Control of a Pasteurization Pilot Plant based on an LPV model
Karimi Pour, Fatemeh; Ocampo-Martinez, Carlos; Puig, Vicenç
2017-01-01
This paper presents a model predictive control (MPC) of a pasteurization pilot plant based on an LPV model. Since not all the states are measured, an observer is also designed, which allows implementing an output-feedback MPC scheme. However, the model of the plant is not completely observable when augmented with the disturbance models. In order to solve this problem, the following strategies are used: (i) the whole system is decoupled into two subsystems, (ii) an inner state-feedback controller is implemented into the MPC control scheme. A real-time example based on the pasteurization pilot plant is simulated as a case study for testing the behavior of the approaches.
Steering Angle Control of Car for Dubins Path-tracking Using Model Predictive Control
Kusuma Rahma Putri, Dian; Subchan; Asfihani, Tahiyatul
2018-03-01
Car as one of transportation is inseparable from technological developments. About ten years, there are a lot of research and development on lane keeping system(LKS) which is a system that automaticaly controls the steering to keep the vehicle especially car always on track. This system can be developed for unmanned cars. Unmanned system car requires navigation, guidance and control which is able to direct the vehicle to move toward the desired path. The guidance system is represented by using Dubins-Path that will be controlled by using Model Predictive Control. The control objective is to keep the car’s movement that represented by dinamic lateral motion model so car can move according to the path appropriately. The simulation control on the four types of trajectories that generate the value for steering angle and steering angle changes are at the specified interval.
Predictive simulations of radio frequency heated plasmas of Tore Supra using the Multi-Mode model
International Nuclear Information System (INIS)
Voitsekhovitch, Irina; Bateman, Glenn; Kritz, Arnold H.; Pankin, Alexei
2002-01-01
Multichannel integrated predictive simulations using the Multi-Mode transport model are carried out for radio frequency heated Tore Supra tokamak discharges in which helium is the primary ion component. Lower hybrid heated discharges in which the total current is driven noninductively [X. Litaudon et al., Plasma Phys. Controlled Fusion 43, 677 (2001)] and a discharge with ion cyclotron radio frequency heating of the hydrogen minority ions [G. T. Hoang et al., Nucl. Fusion 38, 117 (1998)] are simulated. The simulations of these discharges represent the first test of the Multi-Mode model in helium plasmas with dominant electron heating. Also for the first time, the particle transport in Tore Supra discharges is computed and the density profiles are predicted self-consistently with other transport channels. It is found in these simulations that the anomalous transport driven by trapped electron mode turbulence is dominant compared to the transport driven by the ion temperature gradient turbulence. The feature of the Multi-Mode model to calculate the impurity transport self-consistently with other transport channels is used in this study to predict the influence of carbon impurity influx on the discharge evolution
Nonlinear Fuzzy Model Predictive Control for a PWR Nuclear Power Plant
Directory of Open Access Journals (Sweden)
Xiangjie Liu
2014-01-01
Full Text Available Reliable power and temperature control in pressurized water reactor (PWR nuclear power plant is necessary to guarantee high efficiency and plant safety. Since the nuclear plants are quite nonlinear, the paper presents nonlinear fuzzy model predictive control (MPC, by incorporating the realistic constraints, to realize the plant optimization. T-S fuzzy modeling on nuclear power plant is utilized to approximate the nonlinear plant, based on which the nonlinear MPC controller is devised via parallel distributed compensation (PDC scheme in order to solve the nonlinear constraint optimization problem. Improved performance compared to the traditional PID controller for a TMI-type PWR is obtained in the simulation.
International Nuclear Information System (INIS)
Bonoli, P.T.; Barbato, E.; Imbeaux, F.
2003-01-01
This paper reviews the status of lower hybrid current drive (LHCD) simulation and modeling. We first discuss modules used for wave propagation, absorption, and current drive with particular emphasis placed on comparing exact numerical solutions of the Fokker Planck equation in 2-dimension with solution methods that employ 1-dimensional and adjoint approaches. We also survey model predictions for LHCD in past and present experiments showing detailed comparisons between simulated and observed current drive efficiencies and hard X-ray profiles. Finally we discuss several model predictions for lower hybrid current profile control in proposed next step reactor options. (authors)
Predicting landscape vegetation dynamics using state-and-transition simulation models
Colin J. Daniel; Leonardo. Frid
2012-01-01
This paper outlines how state-and-transition simulation models (STSMs) can be used to project changes in vegetation over time across a landscape. STSMs are stochastic, empirical simulation models that use an adapted Markov chain approach to predict how vegetation will transition between states over time, typically in response to interactions between succession,...
Offset-Free Model Predictive Control of Open Water Channel Based on Moving Horizon Estimation
Ekin Aydin, Boran; Rutten, Martine
2016-04-01
Model predictive control (MPC) is a powerful control option which is increasingly used by operational water managers for managing water systems. The explicit consideration of constraints and multi-objective management are important features of MPC. However, due to the water loss in open water systems by seepage, leakage and evaporation a mismatch between the model and the real system will be created. These mismatch affects the performance of MPC and creates an offset from the reference set point of the water level. We present model predictive control based on moving horizon estimation (MHE-MPC) to achieve offset free control of water level for open water canals. MHE-MPC uses the past predictions of the model and the past measurements of the system to estimate unknown disturbances and the offset in the controlled water level is systematically removed. We numerically tested MHE-MPC on an accurate hydro-dynamic model of the laboratory canal UPC-PAC located in Barcelona. In addition, we also used well known disturbance modeling offset free control scheme for the same test case. Simulation experiments on a single canal reach show that MHE-MPC outperforms disturbance modeling offset free control scheme.
Model predictive control of room temperature with disturbance compensation
Kurilla, Jozef; Hubinský, Peter
2017-08-01
This paper deals with temperature control of multivariable system of office building. The system is simplified to several single input-single output systems by decoupling their mutual linkages, which are separately controlled by regulator based on generalized model predictive control. Main part of this paper focuses on the accuracy of the office temperature with respect to occupancy profile and effect of disturbance. Shifting of desired temperature and changing of weighting coefficients are used to achieve the desired accuracy of regulation. The final structure of regulation joins advantages of distributed computing power and possibility to use network communication between individual controllers to consider the constraints. The advantage of using decoupled MPC controllers compared to conventional PID regulators is demonstrated in a simulation study.
Model-predictive control based on Takagi-Sugeno fuzzy model for electrical vehicles delayed model
DEFF Research Database (Denmark)
Khooban, Mohammad-Hassan; Vafamand, Navid; Niknam, Taher
2017-01-01
Electric vehicles (EVs) play a significant role in different applications, such as commuter vehicles and short distance transport applications. This study presents a new structure of model-predictive control based on the Takagi-Sugeno fuzzy model, linear matrix inequalities, and a non......-quadratic Lyapunov function for the speed control of EVs including time-delay states and parameter uncertainty. Experimental data, using the Federal Test Procedure (FTP-75), is applied to test the performance and robustness of the suggested controller in the presence of time-varying parameters. Besides, a comparison...... is made between the results of the suggested robust strategy and those obtained from some of the most recent studies on the same topic, to assess the efficiency of the suggested controller. Finally, the experimental results based on a TMS320F28335 DSP are performed on a direct current motor. Simulation...
Directory of Open Access Journals (Sweden)
W. H. Kwong
2000-06-01
Full Text Available The development of a new simplified model predictive control algorithm has been proposed in this work. The algorithm is developed within the framework of internal model control, and it is easy to understanding and implement. Simulation results for a continuous fermenter, which show that the proposed control algorithm is robust for moderate variations in plant parameters, are presented. The algorithm shows a good performance for setpoint tracking.
Explicit Nonlinear Model Predictive Control for a Saucer-Shaped Unmanned Aerial Vehicle
Directory of Open Access Journals (Sweden)
Zhihui Xing
2013-01-01
Full Text Available A lifting body unmanned aerial vehicle (UAV generates lift by its body and shows many significant advantages due to the particular shape, such as huge loading space, small wetted area, high-strength fuselage structure, and large lifting area. However, designing the control law for a lifting body UAV is quite challenging because it has strong nonlinearity and coupling, and usually lacks it rudders. In this paper, an explicit nonlinear model predictive control (ENMPC strategy is employed to design a control law for a saucer-shaped UAV which can be adequately modeled with a rigid 6-degrees-of-freedom (DOF representation. In the ENMPC, control signal is calculated by approximation of the tracking error in the receding horizon by its Taylor-series expansion to any specified order. It enhances the advantages of the nonlinear model predictive control and eliminates the time-consuming online optimization. The simulation results show that ENMPC is a propriety strategy for controlling lifting body UAVs and can compensate the insufficient control surface area.
Zakaria, M. A.; Majeed, A. P. P. A.; Taha, Z.; Alim, M. M.; Baarath, K.
2018-03-01
The movement of a lower limb exoskeleton requires a reasonably accurate control method to allow for an effective gait therapy session to transpire. Trajectory tracking is a nontrivial means of passive rehabilitation technique to correct the motion of the patients’ impaired limb. This paper proposes an inverse predictive model that is coupled together with the forward kinematics of the exoskeleton to estimate the behaviour of the system. A conventional PID control system is used to converge the required joint angles based on the desired input from the inverse predictive model. It was demonstrated through the present study, that the inverse predictive model is capable of meeting the trajectory demand with acceptable error tolerance. The findings further suggest the ability of the predictive model of the exoskeleton to predict a correct joint angle command to the system.
Data-Reconciliation Based Fault-Tolerant Model Predictive Control for a Biomass Boiler
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Palash Sarkar
2017-02-01
Full Text Available This paper presents a novel, effective method to handle critical sensor faults affecting a control system devised to operate a biomass boiler. In particular, the proposed method consists of integrating a data reconciliation algorithm in a model predictive control loop, so as to annihilate the effects of faults occurring in the sensor of the flue gas oxygen concentration, by feeding the controller with the reconciled measurements. Indeed, the oxygen content in flue gas is a key variable in control of biomass boilers due its close connections with both combustion efficiency and polluting emissions. The main benefit of including the data reconciliation algorithm in the loop, as a fault tolerant component, with respect to applying standard fault tolerant methods, is that controller reconfiguration is not required anymore, since the original controller operates on the restored, reliable data. The integrated data reconciliation–model predictive control (MPC strategy has been validated by running simulations on a specific type of biomass boiler—the KPA Unicon BioGrate boiler.
DEFF Research Database (Denmark)
Hovgaard, Tobias Gybel; Larsen, Lars F. S.; Edlund, Kristian
2012-01-01
. In this paper we describe a novel economic-optimizing Model Predictive Control (MPC) scheme that reduces operating costs by utilizing the thermal storage capabilities. A nonlinear optimization tool to handle a non-convex cost function is utilized for simulations with validated scenarios. In this way we...... explicitly address advantages from daily variations in outdoor temperature and electricity prices. Secondly, we formulate a new cost function that enables the refrigeration system to contribute with ancillary services to the balancing power market. This involvement can be economically beneficial...... of the system models allows us to describe and handle model as well as prediction uncertainties in this framework. This means we can demonstrate means for robustifying the performance of the controller....
Shi, Ke; Yuan, Xiaofang; Liu, Liang
2018-01-01
Distributed drive electric vehicle(DDEV) has been widely researched recently, its longitudinal stability is a very important research topic. Conventional wheel slip ratio control strategies are usually designed for one special operating mode and the optimal performance cannot be obtained as DDEV works under various operating modes. In this paper, a novel model predictive controller-based multi-model control system (MPC-MMCS) is proposed to solve the longitudinal stability problem of DDEV. Firstly, the operation state of DDEV is summarized as three kinds of typical operating modes. A submodel set is established to accurately represent the state value of the corresponding operating mode. Secondly, the matching degree between the state of actual DDEV and each submodel is analyzed. The matching degree is expressed as the weight coefficient and calculated by a modified recursive Bayes theorem. Thirdly, a nonlinear MPC is designed to achieve the optimal wheel slip ratio for each submodel. The optimal design of MPC is realized by parallel chaos optimization algorithm(PCOA)with computational accuracy and efficiency. Finally, the control output of MPC-MMCS is computed by the weighted output of each MPC to achieve smooth switching between operating modes. The proposed MPC-MMCS is evaluated on eight degrees of freedom(8DOF)DDEV model simulation platform and simulation results of different condition show the benefits of the proposed control system. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Model Prediction Control For Water Management Using Adaptive Prediction Accuracy
Tian, X.; Negenborn, R.R.; Van Overloop, P.J.A.T.M.; Mostert, E.
2014-01-01
In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for
Economic Model Predictive Control for Building Climate Control in a Smart Grid
DEFF Research Database (Denmark)
Halvgaard, Rasmus; Poulsen, Niels Kjølstad; Madsen, Henrik
2012-01-01
Model Predictive Control (MPC) can be used to control a system of energy producers and consumers in a Smart Grid. In this paper, we use heat pumps for heating residential buildings with a floor heating system. We use the thermal capacity of the building to shift the electricity consumptions...... to periods with low energy prices. In this way the heating system of the house becomes a flexible power consumer in the Smart Grid. This scenario is relevant for systems with a significant share of stochastic energy producers, e.g. wind turbines, where the ability to shift power consumption according...... and electricity price. Simulation studies demonstrate the capabilities of the proposed model and algorithm. Compared to traditional operation of heat pumps with constant electricity prices, the optimized operating strategy saves 25-33% of the electricity cost....
Comparison of strategies for model predictive control for home heating in future energy systems
DEFF Research Database (Denmark)
Vogler-Finck, Pierre Jacques Camille; Popovski, Petar; Wisniewski, Rafal
2017-01-01
Model predictive control is seen as one of the key future enabler in increasing energy efficiency in buildings. This paper presents a comparison of the performance of the control for different formulations of the objective function. This comparison is made in a simulation study on a single buildi...
Multiple Model Predictive Hybrid Feedforward Control of Fuel Cell Power Generation System
Directory of Open Access Journals (Sweden)
Long Wu
2018-02-01
Full Text Available Solid oxide fuel cell (SOFC is widely considered as an alternative solution among the family of the sustainable distributed generation. Its load flexibility enables it adjusting the power output to meet the requirements from power grid balance. Although promising, its control is challenging when faced with load changes, during which the output voltage is required to be maintained as constant and fuel utilization rate kept within a safe range. Moreover, it makes the control even more intractable because of the multivariable coupling and strong nonlinearity within the wide-range operating conditions. To this end, this paper developed a multiple model predictive control strategy for reliable SOFC operation. The resistance load is regarded as a measurable disturbance, which is an input to the model predictive control as feedforward compensation. The coupling is accommodated by the receding horizon optimization. The nonlinearity is mitigated by the multiple linear models, the weighted sum of which serves as the final control execution. The merits of the proposed control structure are demonstrated by the simulation results.
Fuzzy predictive filtering in nonlinear economic model predictive control for demand response
DEFF Research Database (Denmark)
Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.
2016-01-01
problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...
Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control
Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.
1997-01-01
One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.
Walter, Jonathan P; Pandy, Marcus G
2017-10-01
The aim of this study was to perform multi-body, muscle-driven, forward-dynamics simulations of human gait using a 6-degree-of-freedom (6-DOF) model of the knee in tandem with a surrogate model of articular contact and force control. A forward-dynamics simulation incorporating position, velocity and contact force-feedback control (FFC) was used to track full-body motion capture data recorded for multiple trials of level walking and stair descent performed by two individuals with instrumented knee implants. Tibiofemoral contact force errors for FFC were compared against those obtained from a standard computed muscle control algorithm (CMC) with a 6-DOF knee contact model (CMC6); CMC with a 1-DOF translating hinge-knee model (CMC1); and static optimization with a 1-DOF translating hinge-knee model (SO). Tibiofemoral joint loads predicted by FFC and CMC6 were comparable for level walking, however FFC produced more accurate results for stair descent. SO yielded reasonable predictions of joint contact loading for level walking but significant differences between model and experiment were observed for stair descent. CMC1 produced the least accurate predictions of tibiofemoral contact loads for both tasks. Our findings suggest that reliable estimates of knee-joint loading may be obtained by incorporating position, velocity and force-feedback control with a multi-DOF model of joint contact in a forward-dynamics simulation of gait. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.
Two stage neural network modelling for robust model predictive control.
Patan, Krzysztof
2018-01-01
The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Nonconvex model predictive control for commercial refrigeration
Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John
2013-08-01
We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.
Directory of Open Access Journals (Sweden)
Jianzhong Zhou
2017-12-01
Full Text Available Model simulation and control of pumped storage unit (PSU are essential to improve the dynamic quality of power station. Only under the premise of the PSU models reflecting the actual transient process, the novel control method can be properly applied in the engineering. The contributions of this paper are that (1 a real-time accurate equivalent circuit model (RAECM of PSU via error compensation is proposed to reconcile the conflict between real-time online simulation and accuracy under various operating conditions, and (2 an adaptive predicted fuzzy PID controller (APFPID based on RAECM is put forward to overcome the instability of conventional control under no-load conditions with low water head. Respectively, all hydraulic factors in pipeline system are fully considered based on equivalent lumped-circuits theorem. The pretreatment, which consists of improved Suter-transformation and BP neural network, and online simulation method featured by two iterative loops are synthetically proposed to improve the solving accuracy of the pump-turbine. Moreover, the modified formulas for compensating error are derived with variable-spatial discretization to improve the accuracy of the real-time simulation further. The implicit RadauIIA method is verified to be more suitable for PSUGS owing to wider stable domain. Then, APFPID controller is constructed based on the integration of fuzzy PID and the model predictive control. Rolling prediction by RAECM is proposed to replace rolling optimization with its computational speed guaranteed. Finally, the simulation and on-site measurements are compared to prove trustworthy of RAECM under various running conditions. Comparative experiments also indicate that APFPID controller outperforms other controllers in most cases, especially low water head conditions. Satisfying results of RAECM have been achieved in engineering and it provides a novel model reference for PSUGS.
Composite control for raymond mill based on model predictive control and disturbance observer
Directory of Open Access Journals (Sweden)
Dan Niu
2016-03-01
Full Text Available In the raymond mill grinding process, precise control of operating load is vital for the high product quality. However, strong external disturbances, such as variations of ore size and ore hardness, usually cause great performance degradation. It is not easy to control the current of raymond mill constant. Several control strategies have been proposed. However, most of them (such as proportional–integral–derivative and model predictive control reject disturbances just through feedback regulation, which may lead to poor control performance in the presence of strong disturbances. For improving disturbance rejection, a control method based on model predictive control and disturbance observer is put forward in this article. The scheme employs disturbance observer as feedforward compensation and model predictive control controller as feedback regulation. The test results illustrate that compared with model predictive control method, the proposed disturbance observer–model predictive control method can obtain significant superiority in disturbance rejection, such as shorter settling time and smaller peak overshoot under strong disturbances.
Design and analysis of a model predictive controller for active queue management.
Wang, Ping; Chen, Hong; Yang, Xiaoping; Ma, Yan
2012-01-01
Model predictive (MP) control as a novel active queue management (AQM) algorithm in dynamic computer networks is proposed. According to the predicted future queue length in the data buffer, early packets at the router are dropped reasonably by the MPAQM controller so that the queue length reaches the desired value with minimal tracking error. The drop probability is obtained by optimizing the network performance. Further, randomized algorithms are applied to analyze the robustness of MPAQM successfully, and also to provide the stability domain of systems with uncertain network parameters. The performances of MPAQM are evaluated through a series of simulations in NS2. The simulation results show that the MPAQM algorithm outperforms RED, PI, and REM algorithms in terms of stability, disturbance rejection, and robustness. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.
Huang, Darong; Bai, Xing-Rong
Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.
Directory of Open Access Journals (Sweden)
Ronghui Zhang
2017-05-01
Full Text Available Focusing on safety, comfort and with an overall aim of the comprehensive improvement of a vision-based intelligent vehicle, a novel Advanced Emergency Braking System (AEBS is proposed based on Nonlinear Model Predictive Algorithm. Considering the nonlinearities of vehicle dynamics, a vision-based longitudinal vehicle dynamics model is established. On account of the nonlinear coupling characteristics of the driver, surroundings, and vehicle itself, a hierarchical control structure is proposed to decouple and coordinate the system. To avoid or reduce the collision risk between the intelligent vehicle and collision objects, a coordinated cost function of tracking safety, comfort, and fuel economy is formulated. Based on the terminal constraints of stable tracking, a multi-objective optimization controller is proposed using the theory of non-linear model predictive control. To quickly and precisely track control target in a finite time, an electronic brake controller for AEBS is designed based on the Nonsingular Fast Terminal Sliding Mode (NFTSM control theory. To validate the performance and advantages of the proposed algorithm, simulations are implemented. According to the simulation results, the proposed algorithm has better integrated performance in reducing the collision risk and improving the driving comfort and fuel economy of the smart car compared with the existing single AEBS.
Predictive Capability Maturity Model for computational modeling and simulation.
Energy Technology Data Exchange (ETDEWEB)
Oberkampf, William Louis; Trucano, Timothy Guy; Pilch, Martin M.
2007-10-01
The Predictive Capability Maturity Model (PCMM) is a new model that can be used to assess the level of maturity of computational modeling and simulation (M&S) efforts. The development of the model is based on both the authors experience and their analysis of similar investigations in the past. The perspective taken in this report is one of judging the usefulness of a predictive capability that relies on the numerical solution to partial differential equations to better inform and improve decision making. The review of past investigations, such as the Software Engineering Institute's Capability Maturity Model Integration and the National Aeronautics and Space Administration and Department of Defense Technology Readiness Levels, indicates that a more restricted, more interpretable method is needed to assess the maturity of an M&S effort. The PCMM addresses six contributing elements to M&S: (1) representation and geometric fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) model validation, and (6) uncertainty quantification and sensitivity analysis. For each of these elements, attributes are identified that characterize four increasing levels of maturity. Importantly, the PCMM is a structured method for assessing the maturity of an M&S effort that is directed toward an engineering application of interest. The PCMM does not assess whether the M&S effort, the accuracy of the predictions, or the performance of the engineering system satisfies or does not satisfy specified application requirements.
Mathematical Modelling, Simulation, and Optimal Control of the 2014 Ebola Outbreak in West Africa
Directory of Open Access Journals (Sweden)
Amira Rachah
2015-01-01
it is crucial to modelize the virus and simulate it. In this paper, we begin by studying a simple mathematical model that describes the 2014 Ebola outbreak in Liberia. Then, we use numerical simulations and available data provided by the World Health Organization to validate the obtained mathematical model. Moreover, we develop a new mathematical model including vaccination of individuals. We discuss different cases of vaccination in order to predict the effect of vaccination on the infected individuals over time. Finally, we apply optimal control to study the impact of vaccination on the spread of the Ebola virus. The optimal control problem is solved numerically by using a direct multiple shooting method.
Intelligent Predictive Control of Nonlienar Processes Using
DEFF Research Database (Denmark)
Nørgård, Peter Magnus; Sørensen, Paul Haase; Poulsen, Niels Kjølstad
1996-01-01
This paper presents a novel approach to design of generalized predictive controllers (GPC) for nonlinear processes. A neural network is used for modelling the process and a gain-scheduling type of GPC is subsequently designed. The combination of neural network models and predictive control has...... frequently been discussed in the neural network community. This paper proposes an approximate scheme, the approximate predictive control (APC), which facilitates the implementation and gives a substantial reduction in the required amount of computations. The method is based on a technique for extracting...... linear models from a nonlinear neural network and using them in designing the control system. The performance of the controller is demonstrated in a simulation study of a pneumatic servo system...
Sun, Xiaoqiang; Yuan, Chaochun; Cai, Yingfeng; Wang, Shaohua; Chen, Long
2017-09-01
This paper presents the hybrid modeling and the model predictive control of an air suspension system with damping multi-mode switching damper. Unlike traditional damper with continuously adjustable damping, in this study, a new damper with four discrete damping modes is applied to vehicle semi-active air suspension. The new damper can achieve different damping modes by just controlling the on-off statuses of two solenoid valves, which makes its damping adjustment more efficient and more reliable. However, since the damping mode switching induces different modes of operation, the air suspension system with the new damper poses challenging hybrid control problem. To model both the continuous/discrete dynamics and the switching between different damping modes, the framework of mixed logical dynamical (MLD) systems is used to establish the system hybrid model. Based on the resulting hybrid dynamical model, the system control problem is recast as a model predictive control (MPC) problem, which allows us to optimize the switching sequences of the damping modes by taking into account the suspension performance requirements. Numerical simulations results demonstrate the efficacy of the proposed control method finally.
2016-03-31
AFRL-AFOSR-VA-TR-2016-0309 Predictive simulation of material failure using peridynamics- advanced constitutive modeling, verification , and validation... Self -explanatory. 8. PERFORMING ORGANIZATION REPORT NUMBER. Enter all unique alphanumeric report numbers assigned by the performing organization, e.g...for public release. Predictive simulation of material failure using peridynamics-advanced constitutive modeling, verification , and validation John T
Multi-model predictive control method for nuclear steam generator water level
International Nuclear Information System (INIS)
Hu Ke; Yuan Jingqi
2008-01-01
The dynamics of a nuclear steam generator (SG) is very different according to the power levels and changes as time goes on. Therefore, it is an intractable as well as challenging task to improve the water level control system of the SG. In this paper, a robust model predictive control (RMPC) method is developed for the level control problem. Based on a multi-model framework, a combination of a local nominal model with a polytopic uncertain linear parameter varying (LPV) model is built to approximate the system's non-linear behavior. The optimization problem solved here is based on a receding horizon scheme involving the linear matrix inequality (LMI) technique. Closed loop stability and constraints satisfaction in the entire operating range are guaranteed by the feasibility of the optimization problem. Finally, simulation results show the effectiveness and the good performance of the proposed method
Gstat: a program for geostatistical modelling, prediction and simulation
Pebesma, Edzer J.; Wesseling, Cees G.
1998-01-01
Gstat is a computer program for variogram modelling, and geostatistical prediction and simulation. It provides a generic implementation of the multivariable linear model with trends modelled as a linear function of coordinate polynomials or of user-defined base functions, and independent or dependent, geostatistically modelled, residuals. Simulation in gstat comprises conditional or unconditional (multi-) Gaussian sequential simulation of point values or block averages, or (multi-) indicator sequential simulation. Besides many of the popular options found in other geostatistical software packages, gstat offers the unique combination of (i) an interactive user interface for modelling variograms and generalized covariances (residual variograms), that uses the device-independent plotting program gnuplot for graphical display, (ii) support for several ascii and binary data and map file formats for input and output, (iii) a concise, intuitive and flexible command language, (iv) user customization of program defaults, (v) no built-in limits, and (vi) free, portable ANSI-C source code. This paper describes the class of problems gstat can solve, and addresses aspects of efficiency and implementation, managing geostatistical projects, and relevant technical details.
A model predictive speed tracking control approach for autonomous ground vehicles
Zhu, Min; Chen, Huiyan; Xiong, Guangming
2017-03-01
This paper presents a novel speed tracking control approach based on a model predictive control (MPC) framework for autonomous ground vehicles. A switching algorithm without calibration is proposed to determine the drive or brake control. Combined with a simple inverse longitudinal vehicle model and adaptive regulation of MPC, this algorithm can make use of the engine brake torque for various driving conditions and avoid high frequency oscillations automatically. A simplified quadratic program (QP) solving algorithm is used to reduce the computational time, and the approach has been applied in a 16-bit microcontroller. The performance of the proposed approach is evaluated via simulations and vehicle tests, which were carried out in a range of speed-profile tracking tasks. With a well-designed system structure, high-precision speed control is achieved. The system can robustly model uncertainty and external disturbances, and yields a faster response with less overshoot than a PI controller.
Model complexity control for hydrologic prediction
Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.
2008-01-01
A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore
Directory of Open Access Journals (Sweden)
Mariana Santos Matos Cavalca
2012-01-01
Full Text Available One of the main advantages of predictive control approaches is the capability of dealing explicitly with constraints on the manipulated and output variables. However, if the predictive control formulation does not consider model uncertainties, then the constraint satisfaction may be compromised. A solution for this inconvenience is to use robust model predictive control (RMPC strategies based on linear matrix inequalities (LMIs. However, LMI-based RMPC formulations typically consider only symmetric constraints. This paper proposes a method based on pseudoreferences to treat asymmetric output constraints in integrating SISO systems. Such technique guarantees robust constraint satisfaction and convergence of the state to the desired equilibrium point. A case study using numerical simulation indicates that satisfactory results can be achieved.
Takács, Gergely
2012-01-01
Real-time model predictive controller (MPC) implementation in active vibration control (AVC) is often rendered difficult by fast sampling speeds and extensive actuator-deformation asymmetry. If the control of lightly damped mechanical structures is assumed, the region of attraction containing the set of allowable initial conditions requires a large prediction horizon, making the already computationally demanding on-line process even more complex. Model Predictive Vibration Control provides insight into the predictive control of lightly damped vibrating structures by exploring computationally efficient algorithms which are capable of low frequency vibration control with guaranteed stability and constraint feasibility. In addition to a theoretical primer on active vibration damping and model predictive control, Model Predictive Vibration Control provides a guide through the necessary steps in understanding the founding ideas of predictive control applied in AVC such as: · the implementation of ...
Flores-Alsina, Xavier; Rodriguez-Roda, Ignasi; Sin, Gürkan; Gernaey, Krist V
2009-01-01
The objective of this paper is to perform an uncertainty and sensitivity analysis of the predictions of the Benchmark Simulation Model (BSM) No. 1, when comparing four activated sludge control strategies. The Monte Carlo simulation technique is used to evaluate the uncertainty in the BSM1 predictions, considering the ASM1 bio-kinetic parameters and influent fractions as input uncertainties while the Effluent Quality Index (EQI) and the Operating Cost Index (OCI) are focused on as model outputs. The resulting Monte Carlo simulations are presented using descriptive statistics indicating the degree of uncertainty in the predicted EQI and OCI. Next, the Standard Regression Coefficients (SRC) method is used for sensitivity analysis to identify which input parameters influence the uncertainty in the EQI predictions the most. The results show that control strategies including an ammonium (S(NH)) controller reduce uncertainty in both overall pollution removal and effluent total Kjeldahl nitrogen. Also, control strategies with an external carbon source reduce the effluent nitrate (S(NO)) uncertainty increasing both their economical cost and variability as a trade-off. Finally, the maximum specific autotrophic growth rate (micro(A)) causes most of the variance in the effluent for all the evaluated control strategies. The influence of denitrification related parameters, e.g. eta(g) (anoxic growth rate correction factor) and eta(h) (anoxic hydrolysis rate correction factor), becomes less important when a S(NO) controller manipulating an external carbon source addition is implemented.
Robust Distributed Model Predictive Load Frequency Control of Interconnected Power System
Directory of Open Access Journals (Sweden)
Xiangjie Liu
2013-01-01
Full Text Available Considering the load frequency control (LFC of large-scale power system, a robust distributed model predictive control (RDMPC is presented. The system uncertainty according to power system parameter variation alone with the generation rate constraints (GRC is included in the synthesis procedure. The entire power system is composed of several control areas, and the problem is formulated as convex optimization problem with linear matrix inequalities (LMI that can be solved efficiently. It minimizes an upper bound on a robust performance objective for each subsystem. Simulation results show good dynamic response and robustness in the presence of power system dynamic uncertainties.
Model Predictive Control of a Wave Energy Converter
DEFF Research Database (Denmark)
Andersen, Palle; Pedersen, Tom Søndergård; Nielsen, Kirsten Mølgaard
2015-01-01
In this paper reactive control and Model Predictive Control (MPC) for a Wave Energy Converter (WEC) are compared. The analysis is based on a WEC from Wave Star A/S designed as a point absorber. The model predictive controller uses wave models based on the dominating sea states combined with a model...... connecting undisturbed wave sequences to sequences of torque. Losses in the conversion from mechanical to electrical power are taken into account in two ways. Conventional reactive controllers are tuned for each sea state with the assumption that the converter has the same efficiency back and forth. MPC...
Explicit simulation of ice particle habits in a Numerical Weather Prediction Model
Hashino, Tempei
2007-05-01
This study developed a scheme for explicit simulation of ice particle habits in Numerical Weather Prediction (NWP) Models. The scheme is called Spectral Ice Habit Prediction System (SHIPS), and the goal is to retain growth history of ice particles in the Eulerian dynamics framework. It diagnoses characteristics of ice particles based on a series of particle property variables (PPVs) that reflect history of microphysieal processes and the transport between mass bins and air parcels in space. Therefore, categorization of ice particles typically used in bulk microphysical parameterization and traditional bin models is not necessary, so that errors that stem from the categorization can be avoided. SHIPS predicts polycrystals as well as hexagonal monocrystals based on empirically derived habit frequency and growth rate, and simulates the habit-dependent aggregation and riming processes by use of the stochastic collection equation with predicted PPVs. Idealized two dimensional simulations were performed with SHIPS in a NWP model. The predicted spatial distribution of ice particle habits and types, and evolution of particle size distributions showed good quantitative agreement with observation This comprehensive model of ice particle properties, distributions, and evolution in clouds can be used to better understand problems facing wide range of research disciplines, including microphysics processes, radiative transfer in a cloudy atmosphere, data assimilation, and weather modification.
Catalytic cracking models developed for predictive control purposes
Directory of Open Access Journals (Sweden)
Dag Ljungqvist
1993-04-01
Full Text Available The paper deals with state-space modeling issues in the context of model-predictive control, with application to catalytic cracking. Emphasis is placed on model establishment, verification and online adjustment. Both the Fluid Catalytic Cracking (FCC and the Residual Catalytic Cracking (RCC units are discussed. Catalytic cracking units involve complex interactive processes which are difficult to operate and control in an economically optimal way. The strong nonlinearities of the FCC process mean that the control calculation should be based on a nonlinear model with the relevant constraints included. However, the model can be simple compared to the complexity of the catalytic cracking plant. Model validity is ensured by a robust online model adjustment strategy. Model-predictive control schemes based on linear convolution models have been successfully applied to the supervisory dynamic control of catalytic cracking units, and the control can be further improved by the SSPC scheme.
Model Predictive Control of the Exit Part Temperature for an Austenitization Furnace
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Hari S. Ganesh
2016-12-01
Full Text Available Quench hardening is the process of strengthening and hardening ferrous metals and alloys by heating the material to a specific temperature to form austenite (austenitization, followed by rapid cooling (quenching in water, brine or oil to introduce a hardened phase called martensite. The material is then often tempered to increase toughness, as it may decrease from the quench hardening process. The austenitization process is highly energy-intensive and many of the industrial austenitization furnaces were built and equipped prior to the advent of advanced control strategies and thus use large, sub-optimal amounts of energy. The model computes the energy usage of the furnace and the part temperature profile as a function of time and position within the furnace under temperature feedback control. In this paper, the aforementioned model is used to simulate the furnace for a batch of forty parts under heuristic temperature set points suggested by the operators of the plant. A model predictive control (MPC system is then developed and deployed to control the the part temperature at the furnace exit thereby preventing the parts from overheating. An energy efficiency gain of 5.3 % was obtained under model predictive control compared to operation under heuristic temperature set points tracked by a regulatory control layer.
Directory of Open Access Journals (Sweden)
Jing Lian
2017-01-01
Full Text Available Plug-in hybrid electric vehicles (PHEVs can be considered as a hybrid system (HS which includes the continuous state variable, discrete event, and operation constraint. Thus, a model predictive control (MPC strategy for PHEVs based on the mixed logical dynamical (MLD model and short-term vehicle speed prediction is proposed in this paper. Firstly, the mathematical model of the controlled PHEV is set-up to evaluate the energy consumption using the linearized models of core power components. Then, based on the recognition of driving intention and the past vehicle speed data, a nonlinear auto-regressive (NAR neural network structure is designed to predict the vehicle speed for known driving profiles of city buses and the predicted vehicle speed is used to calculate the total required torque. Next, a MLD model is established with appropriate constraints for six possible driving modes. By solving the objective function with the Mixed Integer Linear Programming (MILP algorithm, the optimal motor torque and the corresponding driving mode sequence within the speed prediction horizon can be obtained. Finally, the proposed energy control strategy shows substantial improvement in fuel economy in the simulation results.
International Nuclear Information System (INIS)
Moon, Jin Woo; Yoon, Younju; Jeon, Young-Hoon; Kim, Sooyoung
2017-01-01
Highlights: • Initial ANN model was developed for predicting the time to the setback temperature. • Initial model was optimized for producing accurate output. • Optimized model proved its prediction accuracy. • ANN-based algorithms were developed and tested their performance. • ANN-based algorithms presented superior thermal comfort or energy efficiency. - Abstract: In this study, a temperature control algorithm was developed to apply a setback temperature predictively for the cooling system of a residential building during occupied periods by residents. An artificial neural network (ANN) model was developed to determine the required time for increasing the current indoor temperature to the setback temperature. This study involved three phases: development of the initial ANN-based prediction model, optimization and testing of the initial model, and development and testing of three control algorithms. The development and performance testing of the model and algorithm were conducted using TRNSYS and MATLAB. Through the development and optimization process, the final ANN model employed indoor temperature and the temperature difference between the current and target setback temperature as two input neurons. The optimal number of hidden layers, number of neurons, learning rate, and moment were determined to be 4, 9, 0.6, and 0.9, respectively. The tangent–sigmoid and pure-linear transfer function was used in the hidden and output neurons, respectively. The ANN model used 100 training data sets with sliding-window method for data management. Levenberg-Marquart training method was employed for model training. The optimized model had a prediction accuracy of 0.9097 root mean square errors when compared with the simulated results. Employing the ANN model, ANN-based algorithms maintained indoor temperatures better within target ranges. Compared to the conventional algorithm, the ANN-based algorithms reduced the duration of time, in which the indoor temperature
Decisions on control of foot-and-mouth disease informed using model predictions
DEFF Research Database (Denmark)
Hisham Beshara Halasa, Tariq; Willeberg, P.; Christiansen, Lasse Engbo
2013-01-01
, epidemic duration, geographical size and costs. The first 14 days spatial spread (FFS) was also included to further support the prediction. The epidemic data was obtained from a Danish version (DTU-DADS) of a pre-existing FMD simulation model (Davis Animal Disease Spread – DADS) adapted to model the spread......The decision on whether or not to change the control strategy, such as introducing emergency vaccination, is perhaps one of the most difficult decisions faced by the veterinary authorities during a foot-and-mouth disease (FMD) epidemic. A simple tool that may predict the epidemic outcome...... and consequences would be useful to assist the veterinary authorities in the decision-making process. A previously proposed simple quantitative tool based on the first 14 days outbreaks (FFO) of FMD was used with results from an FMD simulation exercise. Epidemic outcomes included the number of affected herds...
A Mathematical Model that Simulates Control Options for African Swine Fever Virus (ASFV.
Directory of Open Access Journals (Sweden)
Mike B Barongo
Full Text Available A stochastic model designed to simulate transmission dynamics of African swine fever virus (ASFV in a free-ranging pig population under various intervention scenarios is presented. The model was used to assess the relative impact of the timing of the implementation of different control strategies on disease-related mortality. The implementation of biosecurity measures was simulated through incorporation of a decay function on the transmission rate. The model predicts that biosecurity measures implemented within 14 days of the onset of an epidemic can avert up to 74% of pig deaths due to ASF while hypothetical vaccines that confer 70% immunity when deployed prior to day 14 of the epidemic could avert 65% of pig deaths. When the two control measures are combined, the model predicts that 91% of the pigs that would have otherwise succumbed to the disease if no intervention was implemented would be saved. However, if the combined interventions are delayed (defined as implementation from > 60 days only 30% of ASF-related deaths would be averted. In the absence of vaccines against ASF, we recommend early implementation of enhanced biosecurity measures. Active surveillance and use of pen-side diagnostic assays, preferably linked to rapid dissemination of this data to veterinary authorities through mobile phone technology platforms are essential for rapid detection and confirmation of ASF outbreaks. This prediction, although it may seem intuitive, rationally confirms the importance of early intervention in managing ASF epidemics. The modelling approach is particularly valuable in that it determines an optimal timing for implementation of interventions in controlling ASF outbreaks.
Input-constrained model predictive control via the alternating direction method of multipliers
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Frison, Gianluca; Andersen, Martin S.
2014-01-01
This paper presents an algorithm, based on the alternating direction method of multipliers, for the convex optimal control problem arising in input-constrained model predictive control. We develop an efficient implementation of the algorithm for the extended linear quadratic control problem (LQCP......) with input and input-rate limits. The algorithm alternates between solving an extended LQCP and a highly structured quadratic program. These quadratic programs are solved using a Riccati iteration procedure, and a structure-exploiting interior-point method, respectively. The computational cost per iteration...... is quadratic in the dimensions of the controlled system, and linear in the length of the prediction horizon. Simulations show that the approach proposed in this paper is more than an order of magnitude faster than several state-of-the-art quadratic programming algorithms, and that the difference in computation...
Model Predictive Control Based on Kalman Filter for Constrained Hammerstein-Wiener Systems
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Man Hong
2013-01-01
Full Text Available To precisely track the reactor temperature in the entire working condition, the constrained Hammerstein-Wiener model describing nonlinear chemical processes such as in the continuous stirred tank reactor (CSTR is proposed. A predictive control algorithm based on the Kalman filter for constrained Hammerstein-Wiener systems is designed. An output feedback control law regarding the linear subsystem is derived by state observation. The size of reaction heat produced and its influence on the output are evaluated by the Kalman filter. The observation and evaluation results are calculated by the multistep predictive approach. Actual control variables are computed while considering the constraints of the optimal control problem in a finite horizon through the receding horizon. The simulation example of the CSTR tester shows the effectiveness and feasibility of the proposed algorithm.
Directory of Open Access Journals (Sweden)
Ling Lu
2016-12-01
Full Text Available This paper considers the problem of event-triggered decentralized model predictive control (MPC for constrained large-scale linear systems subject to additive bounded disturbances. The constraint tightening method is utilized to formulate the MPC optimization problem. The local predictive control law for each subsystem is determined aperiodically by relevant triggering rule which allows a considerable reduction of the computational load. And then, the robust feasibility and closed-loop stability are proved and it is shown that every subsystem state will be driven into a robust invariant set. Finally, the effectiveness of the proposed approach is illustrated via numerical simulations.
DEFF Research Database (Denmark)
Sossan, Fabrizio; Kosek, Anna Magdalena; Martinenas, Sergejus
2013-01-01
This paper presents a model predictive control (MPC) strategy for maximizing photo-voltaic (PV) selfconsumption in a household context exploiting the flexible demand of an electric water heater. The predictive controller uses a water heater model and forecast of the hot Water consumption in order...... to predict the future temperature of the water and it manages its state (on and off) according to the forecasted PV production, which are computed starting from forecast of the solar irradiance. Simulations for the proof of concept and for validating the proposed control strategy are proposed. Results...... of the control approach are compared with a traditional thermostatic controller using historical measurements of a 10 kW PV installation. Economic results based on the Italian self consumption tariffs are also reported. The model of the water heater complex is a mixed grey and white box and its parameters have...
Tuning SISO offset-free Model Predictive Control based on ARX models
DEFF Research Database (Denmark)
Huusom, Jakob Kjøbsted; Poulsen, Niels Kjølstad; Jørgensen, Sten Bay
2012-01-01
, the proposed controller is simple to tune as it has only one free tuning parameter. These two features are advantageous in predictive process control as they simplify industrial commissioning of MPC. Disturbance rejection and offset-free control is important in industrial process control. To achieve offset......In this paper, we present a tuning methodology for a simple offset-free SISO Model Predictive Controller (MPC) based on autoregressive models with exogenous inputs (ARX models). ARX models simplify system identification as they can be identified from data using convex optimization. Furthermore......-free control in face of unknown disturbances or model-plant mismatch, integrators must be introduced in either the estimator or the regulator. Traditionally, offset-free control is achieved using Brownian disturbance models in the estimator. In this paper we achieve offset-free control by extending the noise...
Reducing usage of the computational resources by event driven approach to model predictive control
Misik, Stefan; Bradac, Zdenek; Cela, Arben
2017-08-01
This paper deals with a real-time and optimal control of dynamic systems while also considers the constraints which these systems might be subject to. Main objective of this work is to propose a simple modification of the existing Model Predictive Control approach to better suit needs of computational resource-constrained real-time systems. An example using model of a mechanical system is presented and the performance of the proposed method is evaluated in a simulated environment.
Integrated predictive modeling simulations of the Mega-Amp Spherical Tokamak
International Nuclear Information System (INIS)
Nguyen, Canh N.; Bateman, Glenn; Kritz, Arnold H.; Akers, Robert; Byrom, Calum; Sykes, Alan
2002-01-01
Integrated predictive modeling simulations are carried out using the BALDUR transport code [Singer et al., Comput. Phys. Commun. 49, 275 (1982)] for high confinement mode (H-mode) and low confinement mode (L-mode) discharges in the Mega-Amp Spherical Tokamak (MAST) [Sykes et al., Phys. Plasmas 8, 2101 (2001)]. Simulation results, obtained using either the Multi-Mode transport model (MMM95) or, alternatively, the mixed-Bohm/gyro-Bohm transport model, are compared with experimental data. In addition to the anomalous transport, neoclassical transport is included in the simulations and the ion thermal diffusivity in the inner third of the plasma is found to be predominantly neoclassical. The sawtooth oscillations in the simulations radially spread the neutral beam injection heating profiles across a broad sawtooth mixing region. The broad sawtooth oscillations also flatten the central temperature and electron density profiles. Simulation results for the electron temperature and density profiles are compared with experimental data to test the applicability of these models and the BALDUR integrated modeling code in the limit of low aspect ratio toroidal plasmas
Dynamic modeling, simulation and control of energy generation
Vepa, Ranjan
2013-01-01
This book addresses the core issues involved in the dynamic modeling, simulation and control of a selection of energy systems such as gas turbines, wind turbines, fuel cells and batteries. The principles of modeling and control could be applied to other non-convention methods of energy generation such as solar energy and wave energy.A central feature of Dynamic Modeling, Simulation and Control of Energy Generation is that it brings together diverse topics in thermodynamics, fluid mechanics, heat transfer, electro-chemistry, electrical networks and electrical machines and focuses on their appli
Robust entry guidance using linear covariance-based model predictive control
Directory of Open Access Journals (Sweden)
Jianjun Luo
2017-02-01
Full Text Available For atmospheric entry vehicles, guidance design can be accomplished by solving an optimal issue using optimal control theories. However, traditional design methods generally focus on the nominal performance and do not include considerations of the robustness in the design process. This paper proposes a linear covariance-based model predictive control method for robust entry guidance design. Firstly, linear covariance analysis is employed to directly incorporate the robustness into the guidance design. The closed-loop covariance with the feedback updated control command is initially formulated to provide the expected errors of the nominal state variables in the presence of uncertainties. Then, the closed-loop covariance is innovatively used as a component of the cost function to guarantee the robustness to reduce its sensitivity to uncertainties. After that, the models predictive control is used to solve the optimal problem, and the control commands (bank angles are calculated. Finally, a series of simulations for different missions have been completed to demonstrate the high performance in precision and the robustness with respect to initial perturbations as well as uncertainties in the entry process. The 3σ confidence region results in the presence of uncertainties which show that the robustness of the guidance has been improved, and the errors of the state variables are decreased by approximately 35%.
International Nuclear Information System (INIS)
Kim, Jae Hwan; Park, Soon Ho; Na, Man Gyun
2014-01-01
Highlights: • A model predictive controller for load-following operation was developed. • Genetic algorithm optimizes the five nonlinear discrete control rod speeds. • The boron concentration is adjusted with automatic adjustment logic. • The proposed controller reflects the realistic control rod drive mechanism movement. • The performance was confirmed to be satisfactory by simulation from BOC to EOC. - Abstract: Currently, most existing nuclear power plants alter the reactor power by adjusting the boron concentration in the coolant because it has a smaller effect on the reactor power distribution. Frequent control rod movements for load-following operation induce xenon-oscillation. Therefore, a controller that can subdue this phenomenon effectively is needed. At an APR1400 nuclear power plant which is a pressurized water reactor (PWR), the reactor power is controlled automatically using a Reactor Regulating System (RRS) but the power distribution is controlled manually by operators. Therefore, for APR+ nuclear power plants which is an improved version of APR1400 nuclear reactor, a new concept of a reactor controller is needed to control both the reactor power and power distribution automatically. The model predictive control (MPC) method is applicable to multiple-input multiple-output control, and can be applied for complex and nonlinear systems, such as the nuclear power plants. In this study, an MPC controller was developed by applying a genetic algorithm to optimize the discrete control rod speeds and by reflecting the realistic movement of the control rod drive mechanism that moves at only five discrete speeds. The performance of the proposed controller was confirmed to be satisfactory by simulating the load-following operation of an APR+ nuclear power plant through interface with KISPAC-1D code
Jin, N.; Yang, F.; Shang, S. Y.; Tao, T.; Liu, J. S.
2016-08-01
According to the limitations of the LVRT technology of traditional photovoltaic inverter existed, this paper proposes a low voltage ride through (LVRT) control method based on model current predictive control (MCPC). This method can effectively improve the photovoltaic inverter output characteristics and response speed. The MCPC method of photovoltaic grid-connected inverter designed, the sum of the absolute value of the predictive current and the given current error is adopted as the cost function with the model predictive control method. According to the MCPC, the optimal space voltage vector is selected. Photovoltaic inverter has achieved automatically switches of priority active or reactive power control of two control modes according to the different operating states, which effectively improve the inverter capability of LVRT. The simulation and experimental results proves that the proposed method is correct and effective.
Frequency weighted model predictive control of wind turbine
DEFF Research Database (Denmark)
Klauco, Martin; Poulsen, Niels Kjølstad; Mirzaei, Mahmood
2013-01-01
This work is focused on applying frequency weighted model predictive control (FMPC) on three blade horizontal axis wind turbine (HAWT). A wind turbine is a very complex, non-linear system influenced by a stochastic wind speed variation. The reduced dynamics considered in this work are the rotatio...... predictive controller are presented. Statistical comparison between frequency weighted MPC, standard MPC and baseline PI controller is shown as well.......This work is focused on applying frequency weighted model predictive control (FMPC) on three blade horizontal axis wind turbine (HAWT). A wind turbine is a very complex, non-linear system influenced by a stochastic wind speed variation. The reduced dynamics considered in this work...... are the rotational degree of freedom of the rotor and the tower for-aft movement. The MPC design is based on a receding horizon policy and a linearised model of the wind turbine. Due to the change of dynamics according to wind speed, several linearisation points must be considered and the control design adjusted...
International Nuclear Information System (INIS)
Zhang, Fan; Wu, Xiao; Shen, Jiong
2017-01-01
Highlights: • A novel ESOFMPC is proposed based on the combination of ESO and stable MPC. • The improved ESO can overcome unknown disturbances on any channel of MIMO system. • Nonlinearity and disturbance of boiler-turbine unit can be handled simultaneously. - Abstract: The regulation of ultra-supercritical (USC) boiler-turbine unit in large-scale power plants is vulnerable to various unknown disturbances, meanwhile, the internal nonlinearity makes it a challenging task for wide range load tracking. To overcome these two issues simultaneously, an extended state observer based fuzzy model predictive control is proposed for the USC boiler-turbine unit. Firstly, the fuzzy model of a 1000-MW coal-fired USC boiler-turbine unit is established through the nonlinearity analysis. Then a fuzzy stable model predictive controller is devised on the fuzzy model using output cost function for the purpose of wide range load tracking. An improved linear extended state observer, which can estimate plant behavior variations and unknown disturbances regardless of the direct feedthrough characteristic of the system, is synthesized with the predictive controller to enhance its disturbance rejection property. Closed-loop stability of the overall control system is guaranteed. Simulation results on a 1000-MW USC boiler-turbine unit model demonstrate the effectiveness of the proposed approach.
Modeling, Simulation and Position Control of 3DOF Articulated Manipulator
Directory of Open Access Journals (Sweden)
Hossein Sadegh Lafmejani
2014-08-01
Full Text Available In this paper, the modeling, simulation and control of 3 degrees of freedom articulated robotic manipulator have been studied. First, we extracted kinematics and dynamics equations of the mentioned manipulator by using the Lagrange method. In order to validate the analytical model of the manipulator we compared the model simulated in the simulation environment of Matlab with the model was simulated with the SimMechanics toolbox. A sample path has been designed for analyzing the tracking subject. The system has been linearized with feedback linearization and then a PID controller was applied to track a reference trajectory. Finally, the control results have been compared with a nonlinear PID controller.
Fuzzy model predictive control algorithm applied in nuclear power plant
International Nuclear Information System (INIS)
Zuheir, Ahmad
2006-01-01
The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)
Modeling and simulation of Indus-2 RF feedback control system
International Nuclear Information System (INIS)
Sharma, D.; Bagduwal, P.S.; Tiwari, N.; Lad, M.; Hannurkar, P.R.
2012-01-01
Indus-2 synchrotron radiation source has four RF stations along with their feedback control systems. For higher beam energy and current operation amplitude and phase feedback control systems of Indus-2 are being upgraded. To understand the behaviour of amplitude and phase control loop under different operating conditions, modelling and simulation of RF feedback control system is done. RF cavity baseband I/Q model has been created due to its close correspondence with actual implementation and better computational efficiency which makes the simulation faster. Correspondence between cavity baseband and RF model is confirmed by comparing their simulation results. Low Level RF (LLRF) feedback control system simulation is done using the same cavity baseband I/Q model. Error signals are intentionally generated and response of the closed loop system is observed. Simulation will help us in optimizing parameters of upgraded LLRF system for higher beam energy and current operation. (author)
Model predictive control for spacecraft rendezvous in elliptical orbit
Li, Peng; Zhu, Zheng H.
2018-05-01
This paper studies the control of spacecraft rendezvous with attitude stable or spinning targets in an elliptical orbit. The linearized Tschauner-Hempel equation is used to describe the motion of spacecraft and the problem is formulated by model predictive control. The control objective is to maximize control accuracy and smoothness simultaneously to avoid unexpected change or overshoot of trajectory for safe rendezvous. It is achieved by minimizing the weighted summations of control errors and increments. The effects of two sets of horizons (control and predictive horizons) in the model predictive control are examined in terms of fuel consumption, rendezvous time and computational effort. The numerical results show the proposed control strategy is effective.
DEFF Research Database (Denmark)
Petersen, Steffen; Svendsen, Svend
2011-01-01
A method for simulating predictive control of building systems operation in the early stages of building design is presented. The method uses building simulation based on weather forecasts to predict whether there is a future heating or cooling requirement. This information enables the thermal...... control systems of the building to respond proactively to keep the operational temperature within the thermal comfort range with the minimum use of energy. The method is implemented in an existing building simulation tool designed to inform decisions in the early stages of building design through...... parametric analysis. This enables building designers to predict the performance of the method and include it as a part of the solution space. The method furthermore facilitates the task of configuring appropriate building systems control schemes in the tool, and it eliminates time consuming manual...
Directory of Open Access Journals (Sweden)
Guo-Qiang Zeng
2017-11-01
Full Text Available As the penetration level of renewable distributed generations such as wind turbine generator and photovoltaic stations increases, the load frequency control issue of a multi-area interconnected power system becomes more challenging. This paper presents an adaptive model predictive load frequency control method for a multi-area interconnected power system with photovoltaic generation by considering some nonlinear features such as a dead band for governor and generation rate constraint for steam turbine. The dynamic characteristic of this system is formulated as a discrete-time state space model firstly. Then, the predictive dynamic model is obtained by introducing an expanded state vector, and rolling optimization of control signal is implemented based on a cost function by minimizing the weighted sum of square predicted errors and square future control values. The simulation results on a typical two-area power system consisting of photovoltaic and thermal generator have demonstrated the superiority of the proposed model predictive control method to these state-of-the-art control techniques such as firefly algorithm, genetic algorithm, and population extremal optimization-based proportional-integral control methods in cases of normal conditions, load disturbance and parameters uncertainty.
Robust human body model injury prediction in simulated side impact crashes.
Golman, Adam J; Danelson, Kerry A; Stitzel, Joel D
2016-01-01
This study developed a parametric methodology to robustly predict occupant injuries sustained in real-world crashes using a finite element (FE) human body model (HBM). One hundred and twenty near-side impact motor vehicle crashes were simulated over a range of parameters using a Toyota RAV4 (bullet vehicle), Ford Taurus (struck vehicle) FE models and a validated human body model (HBM) Total HUman Model for Safety (THUMS). Three bullet vehicle crash parameters (speed, location and angle) and two occupant parameters (seat position and age) were varied using a Latin hypercube design of Experiments. Four injury metrics (head injury criterion, half deflection, thoracic trauma index and pelvic force) were used to calculate injury risk. Rib fracture prediction and lung strain metrics were also analysed. As hypothesized, bullet speed had the greatest effect on each injury measure. Injury risk was reduced when bullet location was further from the B-pillar or when the bullet angle was more oblique. Age had strong correlation to rib fractures frequency and lung strain severity. The injuries from a real-world crash were predicted using two different methods by (1) subsampling the injury predictors from the 12 best crush profile matching simulations and (2) using regression models. Both injury prediction methods successfully predicted the case occupant's low risk for pelvic injury, high risk for thoracic injury, rib fractures and high lung strains with tight confidence intervals. This parametric methodology was successfully used to explore crash parameter interactions and to robustly predict real-world injuries.
DEFF Research Database (Denmark)
Weerts, Hermanus H. M.; Shafiei, Seyed Ehsan; Stoustrup, Jakob
2014-01-01
A new formulation of model predictive control for supermarket refrigeration systems is proposed to facilitate the regulatory power services as well as energy cost optimization of such systems in the smart grid. Nonlinear dynamics existed in large-scale refrigeration plants challenges the predictive...... control design. It is however shown that taking into account the knowledge of different time scales in the dynamical subsystems makes possible a linear formulation of a centralized predictive controller. A realistic scenario of regulatory power services in the smart grid is considered and formulated...... in the same objective as of cost optimization one. A simulation benchmark validated against real data and including significant dynamics of the system are employed to show the effectiveness of the proposed control scheme....
Plant control using embedded predictive models
International Nuclear Information System (INIS)
Godbole, S.S.; Gabler, W.E.; Eschbach, S.L.
1990-01-01
B and W recently undertook the design of an advanced light water reactor control system. A concept new to nuclear steam system (NSS) control was developed. The concept, which is called the Predictor-Corrector, uses mathematical models of portions of the controlled NSS to calculate, at various levels within the system, demand and control element position signals necessary to satisfy electrical demand. The models give the control system the ability to reduce overcooling and undercooling of the reactor coolant system during transients and upsets. Two types of mathematical models were developed for use in designing and testing the control system. One model was a conventional, comprehensive NSS model that responds to control system outputs and calculates the resultant changes in plant variables that are then used as inputs to the control system. Two other models, embedded in the control system, were less conventional, inverse models. These models accept as inputs plant variables, equipment states, and demand signals and predict plant operating conditions and control element states that will satisfy the demands. This paper reports preliminary results of closed-loop Reactor Coolant (RC) pump trip and normal load reduction testing of the advanced concept. Results of additional transient testing, and of open and closed loop stability analyses will be reported as they are available
Good Models Gone Bad: Quantifying and Predicting Parameter-Induced Climate Model Simulation Failures
Lucas, D. D.; Klein, R.; Tannahill, J.; Brandon, S.; Covey, C. C.; Domyancic, D.; Ivanova, D. P.
2012-12-01
Simulations using IPCC-class climate models are subject to fail or crash for a variety of reasons. Statistical analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation failures of the Parallel Ocean Program (POP2). About 8.5% of our POP2 runs failed for numerical reasons at certain combinations of parameter values. We apply support vector machine (SVM) classification from the fields of pattern recognition and machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. The SVM classifiers readily predict POP2 failures in an independent validation ensemble, and are subsequently used to determine the causes of the failures via a global sensitivity analysis. Four parameters related to ocean mixing and viscosity are identified as the major sources of POP2 failures. Our method can be used to improve the robustness of complex scientific models to parameter perturbations and to better steer UQ ensembles. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was funded by the Uncertainty Quantification Strategic Initiative Laboratory Directed Research and Development Project at LLNL under project tracking code 10-SI-013 (UCRL LLNL-ABS-569112).
Directory of Open Access Journals (Sweden)
Thongchart Kerdphol
2017-05-01
Full Text Available Renewable energy sources (RESs, such as wind and solar generations, equip inverters to connect to the microgrids. These inverters do not have any rotating mass, thus lowering the overall system inertia. This low system inertia issue could affect the microgrid stability and resiliency in the situation of uncertainties. Today’s microgrids will become unstable if the capacity of RESs become larger and larger, leading to the weakening of microgrid stability and resilience. This paper addresses a new concept of a microgrid control incorporating a virtual inertia system based on the model predictive control (MPC to emulate virtual inertia into the microgrid control loop, thus stabilizing microgrid frequency during high penetration of RESs. The additional controller of virtual inertia is applied to the microgrid, employing MPC with virtual inertia response. System modeling and simulations are carried out using MATLAB/Simulink® software. The simulation results confirm the superior robustness and frequency stabilization effect of the proposed MPC-based virtual inertia control in comparison to the fuzzy logic system and conventional virtual inertia control in a system with high integration of RESs. The proposed MPC-based virtual inertia control is able to improve the robustness and frequency stabilization of the microgrid effectively.
Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems
DEFF Research Database (Denmark)
Morstyn, Thomas; Hredzak, Branislav; Aguilera, Ricardo P.
2018-01-01
, and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical......This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear $d$ – $q$ reference frame voltage...... feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real...
International Nuclear Information System (INIS)
Su Jie; Xia Guoqing; Zhang Wei
2007-01-01
For further improving the dynamic control capabilities of the gas turbine of the nuclear power plant, this paper puts forward to apply the algorithm of global predictive control with self-adaptive in the rotate speed control of the gas turbine, including control structure and the design of controller in the base of expounding the math model of the gas turbine of the nuclear power plant. the simulation results show that the respond of the change of the gas turbine speed under the control algorithm of global predictive control with self-adaptive is ten second faster than that under the PID control algorithm, and the output value of the gas turbine speed under the PID control algorithm is 1%-2% higher than that under the control slgorithm of global predictive control with self-adaptive. It shows that the algorithm of global predictive control with self-adaptive can better control the output of the speed of the gas turbine of the nuclear power plant and get the better control effect. (authors)
Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y
2014-05-01
This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Including model uncertainty in the model predictive control with output feedback
Directory of Open Access Journals (Sweden)
Rodrigues M.A.
2002-01-01
Full Text Available This paper addresses the development of an efficient numerical output feedback robust model predictive controller for open-loop stable systems. Stability of the closed loop is guaranteed by using an infinite horizon predictive controller and a stable state observer. The performance and the computational burden of this approach are compared to a robust predictive controller from the literature. The case used for this study is based on an industrial gasoline debutanizer column.
Modelling and controlling hydropower plants
Munoz-Hernandez, German Ardul; Jones, Dewi Ieuan
2013-01-01
Hydroelectric power stations are a major source of electricity around the world; understanding their dynamics is crucial to achieving good performance. Modelling and Controlling Hydropower Plants discusses practical and well-documented cases of modelling and controlling hydropower station modelling and control, focussing on a pumped storage scheme based in Dinorwig, North Wales. Single-input-single-output and multiple-input-multiple-output models, which cover the linear and nonlinear characteristics of pump-storage hydroelectric power stations, are reviewed. The most important dynamic features are discussed, and the verification of these models by hardware in the loop simulation is described. To show how the performance of a pump-storage hydroelectric power station can be improved, classical and modern controllers are applied to simulated models of the Dinorwig power plant. These include PID, fuzzy approximation, feed-forward and model-based predictive control with linear and hybrid prediction models. Mod...
Decisions on foot-and-mouth disease control informed by model prediction
DEFF Research Database (Denmark)
Hisham Beshara Halasa, Tariq; Willeberg, Preben; Christiansen, Lasse Engbo
2013-01-01
of affected herds, epidemic duration, geographical size, and costs. The first fourteen days spatial spread (FFS) was also included to support the prediction. The epidemic data were obtained from a Danish version (DTU-DADS) of the Davis Animal Disease Spread simulation model. The FFI and FFS showed good......The predictive capability of the first fortnight incidence (FFI), which is the number of detected herds within the first 14 days following detection of the disease, of the course of a foot-and-mouth disease (FMD) epidemic and its outcomes were investigated. Epidemic outcomes included the number...... correlations with the epidemic outcomes. The predictive capability of the FFI was high. This indicates that the FFI may take a part in the decision of whether or not to boost FMD control, which might prevent occurrence of a large epidemic in the face of an FMD incursion. The prediction power was improved...
Robust Model Predictive Control of a Wind Turbine
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2012-01-01
In this work the problem of robust model predictive control (robust MPC) of a wind turbine in the full load region is considered. A minimax robust MPC approach is used to tackle the problem. Nonlinear dynamics of the wind turbine are derived by combining blade element momentum (BEM) theory...... of the uncertain system is employed and a norm-bounded uncertainty model is used to formulate a minimax model predictive control. The resulting optimization problem is simplified by semidefinite relaxation and the controller obtained is applied on a full complexity, high fidelity wind turbine model. Finally...... and first principle modeling of the turbine flexible structure. Thereafter the nonlinear model is linearized using Taylor series expansion around system operating points. Operating points are determined by effective wind speed and an extended Kalman filter (EKF) is employed to estimate this. In addition...
Wind turbine control with constraint handling: a model predictive control approach
DEFF Research Database (Denmark)
Henriksen, Lars Christian; Hansen, Morten Hartvig; Poulsen, Niels Kjølstad
2012-01-01
on model predictive control, a control method well suited for constraint handling. The performance of the presented controller during an extreme operating gust is compared to that of a proportional-integral controller with integrator anti-windup. Furthermore, the presented controller-s capability...
Disturbance observer based model predictive control for accurate atmospheric entry of spacecraft
Wu, Chao; Yang, Jun; Li, Shihua; Li, Qi; Guo, Lei
2018-05-01
Facing the complex aerodynamic environment of Mars atmosphere, a composite atmospheric entry trajectory tracking strategy is investigated in this paper. External disturbances, initial states uncertainties and aerodynamic parameters uncertainties are the main problems. The composite strategy is designed to solve these problems and improve the accuracy of Mars atmospheric entry. This strategy includes a model predictive control for optimized trajectory tracking performance, as well as a disturbance observer based feedforward compensation for external disturbances and uncertainties attenuation. 500-run Monte Carlo simulations show that the proposed composite control scheme achieves more precise Mars atmospheric entry (3.8 km parachute deployment point distribution error) than the baseline control scheme (8.4 km) and integral control scheme (5.8 km).
Modeling a multivariable reactor and on-line model predictive control.
Yu, D W; Yu, D L
2005-10-01
A nonlinear first principle model is developed for a laboratory-scaled multivariable chemical reactor rig in this paper and the on-line model predictive control (MPC) is implemented to the rig. The reactor has three variables-temperature, pH, and dissolved oxygen with nonlinear dynamics-and is therefore used as a pilot system for the biochemical industry. A nonlinear discrete-time model is derived for each of the three output variables and their model parameters are estimated from the real data using an adaptive optimization method. The developed model is used in a nonlinear MPC scheme. An accurate multistep-ahead prediction is obtained for MPC, where the extended Kalman filter is used to estimate system unknown states. The on-line control is implemented and a satisfactory tracking performance is achieved. The MPC is compared with three decentralized PID controllers and the advantage of the nonlinear MPC over the PID is clearly shown.
Model Predictive Control with Constraints of a Wind Turbine
DEFF Research Database (Denmark)
Henriksen, Lars Christian; Poulsen, Niels Kjølstad
2007-01-01
Model predictive control of wind turbines offer a more systematic approach of constructing controllers that handle constraints while focusing on the main control objective. In this article several controllers are designed for different wind conditions and appropriate switching conditions ensure a...... an efficient control of the wind turbine over the entire range of wind speeds. Both onshore and floating offshore wind turbines are tested with the controllers.......Model predictive control of wind turbines offer a more systematic approach of constructing controllers that handle constraints while focusing on the main control objective. In this article several controllers are designed for different wind conditions and appropriate switching conditions ensure...
Robust Model Predictive Control Schemes for Tracking Setpoints
Directory of Open Access Journals (Sweden)
Vu Trieu Minh
2010-01-01
Full Text Available This paper briefly reviews the development of nontracking robust model predictive control (RMPC schemes for uncertain systems using linear matrix inequalities (LMIs subject to input saturated and softened state constraints. Then we develop two new tracking setpoint RMPC schemes with common Lyapunov function and with zero terminal equality subject to input saturated and softened state constraints. The novel tracking setpoint RMPC schemes are able to stabilize uncertain systems once the output setpoints lead to the violation of the state constraints. The state violation can be regulated by changing the value of the weighting factor. A brief comparative simulation study of the two tracking setpoint RMPC schemes is done via simple examples to demonstrate the ability of the softened state constraint schemes. Finally, some features of future research from this study are discussed.
Predictive simulation of bidirectional Glenn shunt using a hybrid blood vessel model.
Li, Hao; Leow, Wee Kheng; Chiu, Ing-Sh
2009-01-01
This paper proposes a method for performing predictive simulation of cardiac surgery. It applies a hybrid approach to model the deformation of blood vessels. The hybrid blood vessel model consists of a reference Cosserat rod and a surface mesh. The reference Cosserat rod models the blood vessel's global bending, stretching, twisting and shearing in a physically correct manner, and the surface mesh models the surface details of the blood vessel. In this way, the deformation of blood vessels can be computed efficiently and accurately. Our predictive simulation system can produce complex surgical results given a small amount of user inputs. It allows the surgeon to easily explore various surgical options and evaluate them. Tests of the system using bidirectional Glenn shunt (BDG) as an application example show that the results produc by the system are similar to real surgical results.
Directory of Open Access Journals (Sweden)
Irfan Hussain
2018-06-01
Full Text Available This research work aims at realizing a new compliant robotic actuator for safe human-robotic interaction. In this paper, we present the modeling, control, and numerical simulations of a novel Binary-Controlled Variable Stiffness Actuator (BcVSA aiming to be used for the development of a novel compliant robotic manipulator. BcVSA is the proof of concept of the active revolute joint with the variable recruitment of series-parallel elastic elements. We briefly recall the basic design principle which is based on a stiffness varying mechanism consisting of a motor, three inline clutches, and three torsional springs with stiffness values (K0, 2K0, 4K0 connected to the load shaft and the motor shaft through two planetary sun gear trains with ratios (4:1, 4:1 respectively. We present the design concept, stiffness and dynamic modeling, and control of our BcVSA. We implemented three kinds of Multiple Model Predictive Control (MPC to control our actuator. The main motivation of choosing this controller lies in the fact that working principle of multiple MPC and multiple states space representation (stiffness level of our actuator share similar interests. In particular, we implemented Multiple MPC, Multiple Explicit MPC, and Approximated Multiple Explicit MPC. Numerical simulations are performed in order to evaluate their effectiveness for the future experiments on the prototype of our actuator. The simulation results showed that the Multiple MPC, and the Multiple Explicit MPC have similar results from the robustness point of view. On the other hand, the robustness performance of Approximated Multiple Explicit MPC is not good as compared to other controllers but it works in the offline framework while having the capability to compute the sub-optimal results. We also performed the comparison of MPC based controllers with the Computed Torque Control (CTC, and Linear Quadratic Regulator (LQR. In future, we are planning to test the presented approach on the
Modeling, Prediction, and Control of Heating Temperature for Tube Billet
Directory of Open Access Journals (Sweden)
Yachun Mao
2015-01-01
Full Text Available Annular furnaces have multivariate, nonlinear, large time lag, and cross coupling characteristics. The prediction and control of the exit temperature of a tube billet are important but difficult. We establish a prediction model for the final temperature of a tube billet through OS-ELM-DRPLS method. We address the complex production characteristics, integrate the advantages of PLS and ELM algorithms in establishing linear and nonlinear models, and consider model update and data lag. Based on the proposed model, we design a prediction control algorithm for tube billet temperature. The algorithm is validated using the practical production data of Baosteel Co., Ltd. Results show that the model achieves the precision required in industrial applications. The temperature of the tube billet can be controlled within the required temperature range through compensation control method.
Dynamic modelling and simulation for control of a cylindrical robotic manipulator
International Nuclear Information System (INIS)
Iqbal, A.; Athar, S.M.
1995-03-01
In this report a dynamic model for the three degrees-of-freedom cylindrical manipulator, INFOMATE has been developed. Although the robot dynamics are highly coupled and non-linear, the developed model is relatively straight forward and compact for control engineering and simulation applications. The model has been simulated using the graphical simulation package SIMULINK. Different aspects of INFOMATE associated with forward dynamics, inverse dynamics and control have been investigated by performing various simulation experiments. These simulation experiments confirm the accuracy and applicability of the dynamic robot model. (author) 18 figs
Gain-Scheduled Model Predictive Control of Wind Turbines using Laguerre Functions
DEFF Research Database (Denmark)
Adegas, Fabiano Daher; Wisniewski, Rafal; Larsen, Lars Finn Sloth
2014-01-01
This paper presents a systematic approach to design gain-scheduled predictive controllers for wind turbines. The predictive control law is based on Laguerre functions to parameterize control signals and a parameter-dependent cost function that is analytically determined from turbine data....... These properties facilitate the design of speed controllers by placement of the closed-loop poles (when constraints are not active) and systematic adaptation towards changes in the operating point. Vibration control of undamped modes is achieved by imposing a certain degree of stability to the closed-loop system....... The approach can be utilized to the design of new controllers and to represent existing gain-scheduled controllers as predictive controllers. The numerical example and simulations illustrate the design of a speed controller augmented with active damping of the tower fore-aft displacement....
International Nuclear Information System (INIS)
Chen, Jiaoliao; Xu, Fang; Tan, Dapeng; Shen, Zheng; Zhang, Libin; Ai, Qinglin
2015-01-01
Highlights: • A novel control method for the heating greenhouse with SWSHPS is proposed. • CFD is employed to predict the priorities of FCU loops for thermal performance. • EPM is act as an on-line tool to predict the total energy demand of greenhouse. • The CFD–EPM-based method can save energy and improve control accuracy. • The energy savings potential is between 8.7% and 15.1%. - Abstract: As energy heating is one of the main production costs, many efforts have been made to reduce the energy consumption of agricultural greenhouses. Herein, a novel control method of greenhouse heating using computational fluid dynamics (CFD) and energy prediction model (EPM) is proposed for energy savings and system performance. Based on the low-Reynolds number k–ε turbulence principle, a CFD model of heating greenhouse is developed, applying the discrete ordinates model for the radiative heat transfers and porous medium approach for plants considering plants sensible and latent heat exchanges. The CFD simulations have been validated, and used to analyze the greenhouse thermal performance and the priority of fan coil units (FCU) loops under the various heating conditions. According to the heating efficiency and temperature uniformity, the priorities of each FCU loop can be predicted to generate a database with priorities for control system. EPM is built up based on the thermal balance, and used to predict and optimize the energy demand of the greenhouse online. Combined with the priorities of FCU loops from CFD simulations offline, we have developed the CFD–EPM-based heating control system of greenhouse with surface water source heat pumps system (SWSHPS). Compared with conventional multi-zone independent control (CMIC) method, the energy savings potential is between 8.7% and 15.1%, and the control temperature deviation is decreased to between 0.1 °C and 0.6 °C in the investigated greenhouse. These results show the CFD–EPM-based method can improve system
Model predictive control of a crude oil distillation column
Directory of Open Access Journals (Sweden)
Morten Hovd
1999-04-01
Full Text Available The project of designing and implementing model based predictive control on the vacuum distillation column at the Nynäshamn Refinery of Nynäs AB is described in this paper. The paper describes in detail the modeling for the model based control, covers the controller implementation, and documents the benefits gained from the model based controller.
Siddiqui, Bilal A.; El-Ferik, Sami; Abdelkader, Mohamed
2016-01-01
In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.
Siddiqui, Bilal A.
2016-07-26
In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.
Zhang, Langwen; Xie, Wei; Wang, Jingcheng
2017-11-01
In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min-max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.
Nandola, Naresh N.; Rivera, Daniel E.
2011-01-01
This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character. PMID:21874087
Applying model predictive control to power system frequency control
Ersdal, AM; Imsland, L; Cecilio, IM; Fabozzi, D; Thornhill, NF
2013-01-01
16.07.14 KB Ok to add accepted version to Spiral Model predictive control (MPC) is investigated as a control method which may offer advantages in frequency control of power systems than the control methods applied today, especially in presence of increased renewable energy penetration. The MPC includes constraints on both generation amount and generation rate of change, and it is tested on a one-area system. The proposed MPC is tested against a conventional proportional-integral (PI) cont...
Nonlinear model predictive control theory and algorithms
Grüne, Lars
2017-01-01
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...
A predictive pilot model for STOL aircraft landing
Kleinman, D. L.; Killingsworth, W. R.
1974-01-01
An optimal control approach has been used to model pilot performance during STOL flare and landing. The model is used to predict pilot landing performance for three STOL configurations, each having a different level of automatic control augmentation. Model predictions are compared with flight simulator data. It is concluded that the model can be effective design tool for studying analytically the effects of display modifications, different stability augmentation systems, and proposed changes in the landing area geometry.
Frequency-Weighted Model Predictive Control of Trailing Edge Flaps on a Wind Turbine Blade
DEFF Research Database (Denmark)
Castaignet, Damien; Couchman, Ian; Poulsen, Niels Kjølstad
2013-01-01
flapwise blade root moment and trailing edge flap deflection. Frequency-weighted MPC is chosen for its ability to handle constraints on the trailing edge flaps deflection, and to target at loads with given frequencies only. The controller is first tested in servo-aeroelastic simulations, before being......This paper presents the load reduction achieved with trailing edge flaps during a full-scale test on a Vestas V27 wind turbine. The trailing edge flap controller is a frequency-weighted linear model predictive control (MPC) where the quadratic cost consists of costs on the zero-phase filtered...
Decentralized model predictive based load frequency control in an interconnected power system
Energy Technology Data Exchange (ETDEWEB)
Mohamed, T.H., E-mail: tarekhie@yahoo.co [High Institute of Energy, South Valley University (Egypt); Bevrani, H., E-mail: bevrani@ieee.or [Dept. of Electrical Engineering and Computer Science, University of Kurdistan (Iran, Islamic Republic of); Hassan, A.A., E-mail: aahsn@yahoo.co [Faculty of Engineering, Dept. of Electrical Engineering, Minia University, Minia (Egypt); Hiyama, T., E-mail: hiyama@cs.kumamoto-u.ac.j [Dept. of Electrical Engineering and Computer Science, Kumamoto University, Kumamoto (Japan)
2011-02-15
This paper presents a new load frequency control (LFC) design using the model predictive control (MPC) technique in a multi-area power system. The MPC technique has been designed such that the effect of the uncertainty due to governor and turbine parameters variation and load disturbance is reduced. Each local area controller is designed independently such that stability of the overall closed-loop system is guaranteed. A frequency response model of multi-area power system is introduced, and physical constraints of the governors and turbines are considered. The model was employed in the MPC structures. Digital simulations for both two and three-area power systems are provided to validate the effectiveness of the proposed scheme. The results show that, with the proposed MPC technique, the overall closed-loop system performance demonstrated robustness in the face of uncertainties due to governors and turbines parameters variation and loads disturbances. A performance comparison between the proposed controller and a classical integral control scheme is carried out confirming the superiority of the proposed MPC technique.
Decentralized model predictive based load frequency control in an interconnected power system
International Nuclear Information System (INIS)
Mohamed, T.H.; Bevrani, H.; Hassan, A.A.; Hiyama, T.
2011-01-01
This paper presents a new load frequency control (LFC) design using the model predictive control (MPC) technique in a multi-area power system. The MPC technique has been designed such that the effect of the uncertainty due to governor and turbine parameters variation and load disturbance is reduced. Each local area controller is designed independently such that stability of the overall closed-loop system is guaranteed. A frequency response model of multi-area power system is introduced, and physical constraints of the governors and turbines are considered. The model was employed in the MPC structures. Digital simulations for both two and three-area power systems are provided to validate the effectiveness of the proposed scheme. The results show that, with the proposed MPC technique, the overall closed-loop system performance demonstrated robustness in the face of uncertainties due to governors and turbines parameters variation and loads disturbances. A performance comparison between the proposed controller and a classical integral control scheme is carried out confirming the superiority of the proposed MPC technique.
Model Predictive Control of Sewer Networks
DEFF Research Database (Denmark)
Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik
2016-01-01
The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....
Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current
Directory of Open Access Journals (Sweden)
Wenbiao Li
2016-04-01
Full Text Available Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC balancing. To solve these problems, multi-objective predictive balancing control (MOPBC based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process.
Wheel slip control with torque blending using linear and nonlinear model predictive control
Basrah, M. Sofian; Siampis, Efstathios; Velenis, Efstathios; Cao, Dongpu; Longo, Stefano
2017-11-01
Modern hybrid electric vehicles employ electric braking to recuperate energy during deceleration. However, currently anti-lock braking system (ABS) functionality is delivered solely by friction brakes. Hence regenerative braking is typically deactivated at a low deceleration threshold in case high slip develops at the wheels and ABS activation is required. If blending of friction and electric braking can be achieved during ABS events, there would be no need to impose conservative thresholds for deactivation of regenerative braking and the recuperation capacity of the vehicle would increase significantly. In addition, electric actuators are typically significantly faster responding and would deliver better control of wheel slip than friction brakes. In this work we present a control strategy for ABS on a fully electric vehicle with each wheel independently driven by an electric machine and friction brake independently applied at each wheel. In particular we develop linear and nonlinear model predictive control strategies for optimal performance and enforcement of critical control and state constraints. The capability for real-time implementation of these controllers is assessed and their performance is validated in high fidelity simulation.
Economic Model Predictive Control for Smart Energy Systems
DEFF Research Database (Denmark)
Halvgaard, Rasmus
Model Predictive Control (MPC) can be used to control the energy distribution in a Smart Grid with a high share of stochastic energy production from renewable energy sources like wind. Heat pumps for heating residential buildings can exploit the slow heat dynamics of a building to store heat and ...... and hereby shift the heat pump power consumption to periods with both low electricity prices and a high fraction of green energy in the grid.......Model Predictive Control (MPC) can be used to control the energy distribution in a Smart Grid with a high share of stochastic energy production from renewable energy sources like wind. Heat pumps for heating residential buildings can exploit the slow heat dynamics of a building to store heat...
DYNAMIC MODELLING AND ADVANCED PREDICTIVE CONTROL OF A CONTINUOUS PROCESS OF ENZYME PURIFICATION
Directory of Open Access Journals (Sweden)
Dechechi E.C.
1997-01-01
Full Text Available A dynamic mathematical model, simulation and computer control of a Continuous Affinity Recycle Extraction (CARE process, a protein purification technique based on protein adsorption on solid-phase adsorbents is described in this work. This process, consisting of three reactors, is a multivariable process with considerable time delay in the on-line analyses of the controlled variable. An advanced predictive control configuration, specifically the Dynamic Matrix Control (DMC, was applied. The DMC algorithm was applied in process schemes where the aim was to maintain constant the enzyme concentration in the outlet of the third reactor. The performance of the DMC controller was analyzed in the feed-flow disturbances and the results are presented.
Directory of Open Access Journals (Sweden)
Guo Jiuwang
2015-01-01
Full Text Available Because of the randomness and fluctuation of wind energy, as well as the impact of strongly nonlinear characteristic of variable speed constant frequency (VSCF wind power generation system with doubly fed induction generators (DFIG, traditional active power control strategies are difficult to achieve high precision control and the output power of wind turbines is more fluctuated. In order to improve the quality of output electric energy of doubly fed wind turbines, on the basis of analyzing the operating principles and dynamic characteristics of doubly fed wind turbines, this paper proposes a new active power optimal control method of doubly fed wind turbines based on predictive control theory. This method uses state space model of wind turbines, based on the prediction of the future state of wind turbines, moves horizon optimization, and meanwhile, gets the control signals of pitch angle and generator torque. Simulation results show that the proposed control strategies can guarantee the utilization efficiency for wind energy. Simultaneously, they can improve operation stability of wind turbines and the quality of electric energy.
Tube Model Predictive Control with an Auxiliary Sliding Mode Controller
Directory of Open Access Journals (Sweden)
Miodrag Spasic
2016-07-01
Full Text Available This paper studies Tube Model Predictive Control (MPC with a Sliding Mode Controller (SMC as an auxiliary controller. It is shown how to calculate the tube widths under SMC control, and thus how much the constraints of the nominal MPC have to be tightened in order to achieve robust stability and constraint fulfillment. The analysis avoids the assumption of infinitely fast switching in the SMC controller.
Simulation model of an active stall wind turbine controller
Energy Technology Data Exchange (ETDEWEB)
Jauch, C.; Hansen, A.D.; Soerensen, P. [Risoe National Lab., Wind Energy Dept., Rosilde (Denmark); Blaabjerg, F. [Aalborg Univ., Inst. of Energy Technology (Denmark)
2004-07-01
This paper describes an active stall wind turbine controller. The objective is to develop a general model of an active stall controller in order to simulate the operation of grid connected active stall wind turbines. The active stall turbine concept and its control strategies are presented and evaluated on the basis of simulations. The presented controller is described for continuous operation under all wind speeds from start-up wind speed to shut doven wind speed. Due to its parametric implementation it is general i.e. it can represent different active stall wind turbine controllers and can be implemented in different simulation tools. (au)
A review of mathematical modeling and simulation of controlled-release fertilizers.
Irfan, Sayed Ameenuddin; Razali, Radzuan; KuShaari, KuZilati; Mansor, Nurlidia; Azeem, Babar; Ford Versypt, Ashlee N
2018-02-10
Nutrients released into soils from uncoated fertilizer granules are lost continuously due to volatilization, leaching, denitrification, and surface run-off. These issues have caused economic loss due to low nutrient absorption efficiency and environmental pollution due to hazardous emissions and water eutrophication. Controlled-release fertilizers (CRFs) can change the release kinetics of the fertilizer nutrients through an abatement strategy to offset these issues by providing the fertilizer content in synchrony with the metabolic needs of the plants. Parametric analysis of release characteristics of CRFs is of paramount importance for the design and development of new CRFs. However, the experimental approaches are not only time consuming, but they are also cumbersome and expensive. Scientists have introduced mathematical modeling techniques to predict the release of nutrients from the CRFs to elucidate fundamental understanding of the dynamics of the release processes and to design new CRFs in a shorter time and with relatively lower cost. This paper reviews and critically analyzes the latest developments in the mathematical modeling and simulation techniques that have been reported for the characteristics and mechanisms of nutrient release from CRFs. The scope of this review includes the modeling and simulations techniques used for coated, controlled-release fertilizers. Copyright © 2017 Elsevier B.V. All rights reserved.
Robust Output Model Predictive Control of an Unstable Rijke Tube
Directory of Open Access Journals (Sweden)
Fabian Jarmolowitz
2012-01-01
Full Text Available This work investigates the active control of an unstable Rijke tube using robust output model predictive control (RMPC. As internal model a polytopic linear system with constraints is assumed to account for uncertainties. For guaranteed stability, a linear state feedback controller is designed using linear matrix inequalities and used within a feedback formulation of the model predictive controller. For state estimation a robust gain-scheduled observer is developed. It is shown that the proposed RMPC ensures robust stability under constraints over the considered operating range.
Finite-Control-Set Model Predictive Control (FCS-MPC) for Islanded Hybrid Microgrids
Yi, Zhehan; Babqi, Abdulrahman J.; Wang, Yishen; Shi, Di; Etemadi, Amir H.; Wang, Zhiwei; Huang, Bibin
2018-01-01
Microgrids consisting of multiple distributed energy resources (DERs) provide a promising solution to integrate renewable energies, e.g., solar photovoltaic (PV) systems. Hybrid AC/DC microgrids leverage the merits of both AC and DC power systems. In this paper, a control strategy for islanded multi-bus hybrid microgrids is proposed based on the Finite-Control-Set Model Predictive Control (FCS-MPC) technologies. The control loops are expedited by predicting the future states and determining t...
Zhao, Dong; Sakoda, Hideyuki; Sawyer, W Gregory; Banks, Scott A; Fregly, Benjamin J
2008-02-01
Wear of ultrahigh molecular weight polyethylene remains a primary factor limiting the longevity of total knee replacements (TKRs). However, wear testing on a simulator machine is time consuming and expensive, making it impractical for iterative design purposes. The objectives of this paper were first, to evaluate whether a computational model using a wear factor consistent with the TKR material pair can predict accurate TKR damage measured in a simulator machine, and second, to investigate how choice of surface evolution method (fixed or variable step) and material model (linear or nonlinear) affect the prediction. An iterative computational damage model was constructed for a commercial knee implant in an AMTI simulator machine. The damage model combined a dynamic contact model with a surface evolution model to predict how wear plus creep progressively alter tibial insert geometry over multiple simulations. The computational framework was validated by predicting wear in a cylinder-on-plate system for which an analytical solution was derived. The implant damage model was evaluated for 5 million cycles of simulated gait using damage measurements made on the same implant in an AMTI machine. Using a pin-on-plate wear factor for the same material pair as the implant, the model predicted tibial insert wear volume to within 2% error and damage depths and areas to within 18% and 10% error, respectively. Choice of material model had little influence, while inclusion of surface evolution affected damage depth and area but not wear volume predictions. Surface evolution method was important only during the initial cycles, where variable step was needed to capture rapid geometry changes due to the creep. Overall, our results indicate that accurate TKR damage predictions can be made with a computational model using a constant wear factor obtained from pin-on-plate tests for the same material pair, and furthermore, that surface evolution method matters only during the initial
Model Predictive Control for Offset-Free Reference Tracking
Czech Academy of Sciences Publication Activity Database
Belda, Květoslav
2016-01-01
Roč. 5, č. 1 (2016), s. 8-13 ISSN 1805-3386 Institutional support: RVO:67985556 Keywords : offset-free reference tracking * predictive control * ARX model * state-space model * multi-input multi-output system * robotic system * mechatronic system Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2016/AS/belda-0458355.pdf
Control-relevant modeling and simulation of a SOFC-GT hybrid system
Rambabu Kandepu; Lars Imsland; Christoph Stiller; Bjarne A. Foss; Vinay Kariwala
2006-01-01
In this paper, control-relevant models of the most important components in a SOFC-GT hybrid system are described. Dynamic simulations are performed on the overall hybrid system. The model is used to develop a simple control structure, but the simulations show that more elaborate control is needed.
Generalized Predictive Control of Dynamic Systems with Rigid-Body Modes
Kvaternik, Raymond G.
2013-01-01
Numerical simulations to assess the effectiveness of Generalized Predictive Control (GPC) for active control of dynamic systems having rigid-body modes are presented. GPC is a linear, time-invariant, multi-input/multi-output predictive control method that uses an ARX model to characterize the system and to design the controller. Although the method can accommodate both embedded (implicit) and explicit feedforward paths for incorporation of disturbance effects, only the case of embedded feedforward in which the disturbances are assumed to be unknown is considered here. Results from numerical simulations using mathematical models of both a free-free three-degree-of-freedom mass-spring-dashpot system and the XV-15 tiltrotor research aircraft are presented. In regulation mode operation, which calls for zero system response in the presence of disturbances, the simulations showed reductions of nearly 100%. In tracking mode operations, where the system is commanded to follow a specified path, the GPC controllers produced the desired responses, even in the presence of disturbances.
Efficient predictive model-based and fuzzy control for green urban mobility
Jamshidnejad, A.
2017-01-01
In this thesis, we develop efficient predictive model-based control approaches, including model-predictive control (MPC) andmodel-based fuzzy control, for application in urban traffic networks with the aim of reducing a combination of the total time spent by the vehicles within the network and the
Offset Free Tracking Predictive Control Based on Dynamic PLS Framework
Directory of Open Access Journals (Sweden)
Jin Xin
2017-10-01
Full Text Available This paper develops an offset free tracking model predictive control based on a dynamic partial least square (PLS framework. First, state space model is used as the inner model of PLS to describe the dynamic system, where subspace identification method is used to identify the inner model. Based on the obtained model, multiple independent model predictive control (MPC controllers are designed. Due to the decoupling character of PLS, these controllers are running separately, which is suitable for distributed control framework. In addition, the increment of inner model output is considered in the cost function of MPC, which involves integral action in the controller. Hence, the offset free tracking performance is guaranteed. The results of an industry background simulation demonstrate the effectiveness of proposed method.
Alcator C-Mod predictive modeling
International Nuclear Information System (INIS)
Pankin, Alexei; Bateman, Glenn; Kritz, Arnold; Greenwald, Martin; Snipes, Joseph; Fredian, Thomas
2001-01-01
Predictive simulations for the Alcator C-mod tokamak [I. Hutchinson et al., Phys. Plasmas 1, 1511 (1994)] are carried out using the BALDUR integrated modeling code [C. E. Singer et al., Comput. Phys. Commun. 49, 275 (1988)]. The results are obtained for temperature and density profiles using the Multi-Mode transport model [G. Bateman et al., Phys. Plasmas 5, 1793 (1998)] as well as the mixed-Bohm/gyro-Bohm transport model [M. Erba et al., Plasma Phys. Controlled Fusion 39, 261 (1997)]. The simulated discharges are characterized by very high plasma density in both low and high modes of confinement. The predicted profiles for each of the transport models match the experimental data about equally well in spite of the fact that the two models have different dimensionless scalings. Average relative rms deviations are less than 8% for the electron density profiles and 16% for the electron and ion temperature profiles
Tong, Juxiu; Hu, Bill X; Yang, Jinzhong; Zhu, Yan
2016-06-01
The mixing layer theory is not suitable for predicting solute transfer from initially saturated soil to surface runoff water under controlled drainage conditions. By coupling the mixing layer theory model with the numerical model Hydrus-1D, a hybrid solute transfer model has been proposed to predict soil solute transfer from an initially saturated soil into surface water, under controlled drainage water conditions. The model can also consider the increasing ponding water conditions on soil surface before surface runoff. The data of solute concentration in surface runoff and drainage water from a sand experiment is used as the reference experiment. The parameters for the water flow and solute transfer model and mixing layer depth under controlled drainage water condition are identified. Based on these identified parameters, the model is applied to another initially saturated sand experiment with constant and time-increasing mixing layer depth after surface runoff, under the controlled drainage water condition with lower drainage height at the bottom. The simulation results agree well with the observed data. Study results suggest that the hybrid model can accurately simulate the solute transfer from initially saturated soil into surface runoff under controlled drainage water condition. And it has been found that the prediction with increasing mixing layer depth is better than that with the constant one in the experiment with lower drainage condition. Since lower drainage condition and deeper ponded water depth result in later runoff start time, more solute sources in the mixing layer are needed for the surface water, and larger change rate results in the increasing mixing layer depth.
Selection of References in Wind Turbine Model Predictive Control Design
DEFF Research Database (Denmark)
Odgaard, Peter Fogh; Hovgaard, Tobias
2015-01-01
a model predictive controller for a wind turbine. One of the important aspects for a tracking control problem is how to setup the optimal reference tracking problem, as it might be relevant to track, e.g., the three concurrent references: optimal pitch angle, optimal rotational speed, and optimal power......Lowering the cost of energy is one of the major focus areas in the wind turbine industry. Recent research has indicated that wind turbine controllers based on model predictive control methods can be useful in obtaining this objective. A number of design considerations have to be made when designing....... The importance if the individual references differ depending in particular on the wind speed. In this paper we investigate the performance of a reference tracking model predictive controller with two different setups of the used optimal reference signals. The controllers are evaluated using an industrial high...
Zheng, Yang; Zhou, Jianzhong; Xu, Yanhe; Zhang, Yuncheng; Qian, Zhongdong
2017-05-01
This paper proposes a distributed model predictive control based load frequency control (MPC-LFC) scheme to improve control performances in the frequency regulation of power system. In order to reduce the computational burden in the rolling optimization with a sufficiently large prediction horizon, the orthonormal Laguerre functions are utilized to approximate the predicted control trajectory. The closed-loop stability of the proposed MPC scheme is achieved by adding a terminal equality constraint to the online quadratic optimization and taking the cost function as the Lyapunov function. Furthermore, the treatments of some typical constraints in load frequency control have been studied based on the specific Laguerre-based formulations. Simulations have been conducted in two different interconnected power systems to validate the effectiveness of the proposed distributed MPC-LFC as well as its superiority over the comparative methods. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Control-relevant modeling and simulation of a SOFC-GT hybrid system
Directory of Open Access Journals (Sweden)
Rambabu Kandepu
2006-07-01
Full Text Available In this paper, control-relevant models of the most important components in a SOFC-GT hybrid system are described. Dynamic simulations are performed on the overall hybrid system. The model is used to develop a simple control structure, but the simulations show that more elaborate control is needed.
Numerical flow simulation and efficiency prediction for axial turbines by advanced turbulence models
International Nuclear Information System (INIS)
Jošt, D; Škerlavaj, A; Lipej, A
2012-01-01
Numerical prediction of an efficiency of a 6-blade Kaplan turbine is presented. At first, the results of steady state analysis performed by different turbulence models for different operating regimes are compared to the measurements. For small and optimal angles of runner blades the efficiency was quite accurately predicted, but for maximal blade angle the discrepancy between calculated and measured values was quite large. By transient analysis, especially when the Scale Adaptive Simulation Shear Stress Transport (SAS SST) model with zonal Large Eddy Simulation (ZLES) in the draft tube was used, the efficiency was significantly improved. The improvement was at all operating points, but it was the largest for maximal discharge. The reason was better flow simulation in the draft tube. Details about turbulent structure in the draft tube obtained by SST, SAS SST and SAS SST with ZLES are illustrated in order to explain the reasons for differences in flow energy losses obtained by different turbulence models.
Numerical flow simulation and efficiency prediction for axial turbines by advanced turbulence models
Jošt, D.; Škerlavaj, A.; Lipej, A.
2012-11-01
Numerical prediction of an efficiency of a 6-blade Kaplan turbine is presented. At first, the results of steady state analysis performed by different turbulence models for different operating regimes are compared to the measurements. For small and optimal angles of runner blades the efficiency was quite accurately predicted, but for maximal blade angle the discrepancy between calculated and measured values was quite large. By transient analysis, especially when the Scale Adaptive Simulation Shear Stress Transport (SAS SST) model with zonal Large Eddy Simulation (ZLES) in the draft tube was used, the efficiency was significantly improved. The improvement was at all operating points, but it was the largest for maximal discharge. The reason was better flow simulation in the draft tube. Details about turbulent structure in the draft tube obtained by SST, SAS SST and SAS SST with ZLES are illustrated in order to explain the reasons for differences in flow energy losses obtained by different turbulence models.
Distributed Model Predictive Control for Smart Energy Systems
DEFF Research Database (Denmark)
Halvgaard, Rasmus Fogtmann; Vandenberghe, Lieven; Poulsen, Niels Kjølstad
2016-01-01
Integration of a large number of flexible consumers in a smart grid requires a scalable power balancing strategy. We formulate the control problem as an optimization problem to be solved repeatedly by the aggregator in a model predictive control framework. To solve the large-scale control problem...
Koh, Vivian C A; Yong Kuen Ho; Stevens, Michael C; Salamonsen, Robert F; Lovell, Nigel H; Lim, Einly
2017-07-01
Control for dual rotary left ventricular assist devices (LVADs) used as a biventricular assist device (BiVAD) is challenging. If the control system fails, flow imbalance between the systemic and the pulmonary circulations would result, subsequently leading to ventricular suction or pulmonary congestion. With the expectation that advanced control approaches such as model predictive control could address the challenges naturally and effectively, we developed a synergistic first principles model predictive controller (MPC) for the BiVAD. The internal model of the MPC is a simplified state-space model that has been developed and validated in a previous study. A single Frank-Starling (FS) control curve was used to define the target pump flow corresponding to the preload on each side of the heart. The MPC was evaluated in a validated numerical model using three clinical scenarios: blood loss, myocardial recovery, and exercise. Simulation results showed that the MPC was effective in adapting to changes in physiological states without causing ventricular suction or pulmonary congestion. The use of MPC for a BiVAD eliminates the need for two controllers of dual LVADs thus making the task of controller tuning easier.
International Nuclear Information System (INIS)
Takagi, S; Onoue, S; Iyanagi, K; Nishitani, K; Shinmura, T; Kanoh, M; Itoh, H; Shioyama, Y; Akiyama, T; Kishigami, D
2003-01-01
We have developed a simulation for predicting reactive ion etching (RIE) topography, which is a combination of plasma simulation, the gas reaction model, the sheath model and the surface reaction model. The simulation is applied to the SiO 2 etching process of a high-aspect-ratio contact hole using C 5 F 8 gas. A capacitively coupled plasma (CCP) reactor of an 8-in. wafer was used in the etching experiments. The baseline conditions are RF power of 1500 W and gas pressure of 4.0 Pa in a gas mixture of Ar, O 2 and C 5 F 8 . The plasma simulation reproduces the tendency that CF 2 radical density increases rapidly and the electron density decreases gradually with increasing gas flow rate of C 5 F 8 . In the RIE topography simulation, the etching profiles such as bowing and taper shape at the bottom are reproduced in deep holes with aspect ratios greater than 19. Moreover, the etching profile, the dependence of the etch depth on the etching time, and the bottom diameter can be predicted by this simulation
Modeling and control simulation of the China CLEAR-IB
International Nuclear Information System (INIS)
Yan, Shoujun; Wan, Jiashuang; Wang, Pengfei; Fang, Huawei; Sun, Changyi; Zhao, Fuyu
2014-01-01
Highlights: • A model for the reactor for CLEAR-IB was developed. • A PI controller was designed to control the power. • A control strategy was adopted to control the water enthalpy of air cooler. • Dynamic simulation of the whole system was performed. - Abstract: To investigate the dynamic and control characteristics of the plant, a model for the main components of the reactor and the most relevant interactions among them is developed. The system comprises of the primary system with lead bismuth eutectic (LBE) as the coolant, the secondary circuit with steam water mixture as the coolant and the associated air cooling system for an effective rejection of thermal power to the environment as a final heat sink. A Proportional-Integral (PI) controller is designed to keep the power following the set value as quickly as possible. To keep outlet coolant of air coolers and inlet coolant of HXs being saturated water, a control strategy based on a simultaneous feed-forward and feedback scheme has been adopted. Based on the developed model and control strategy, dynamic simulation of the whole system in the cases of step changes of external source and load is performed. The simulation results show that the proposed model is accurate enough to describe the dynamic behaviors of the plant in spite of its simplicity. It has also been demonstrated that the developed controllers for the CLEAR-IB can provide superior reactor control capabilities due to the efficiency of the control strategy adopted
Model Predictive Control Algorithms for Pen and Pump Insulin Administration
DEFF Research Database (Denmark)
Boiroux, Dimitri
at mealtime, and the case where the insulin sensitivity increases during the night. This thesis consists of a summary report, glucose and insulin proles of the clinical studies and research papers submitted, peer-reviewed and/or published in the period September 2009 - September 2012....... of current closed-loop controllers. In this thesis, we present different control strategies based on Model Predictive Control (MPC) for an artificial pancreas. We use Nonlinear Model Predictive Control (NMPC) in order to determine the optimal insulin and blood glucose profiles. The optimal control problem...
Modeling, Simulation and Position Control of 3 Degree of Freedom Articulated Manipulator
Directory of Open Access Journals (Sweden)
Hossein Sadegh Lafmejani
2013-09-01
Full Text Available In this paper, the modeling, simulation and control of 3 degree of freedom articulated robotic manipulator have been studied. First, we extracted kinematics and dynamics equations of the mentioned manipulator by using the Lagrange method. In order to validate the analytical model of the manipulator we compared the model simulated in the simulation environment of Matlab with the model was simulated with the SimMechanics toolbox. A sample path has been designed for analyzing the tracking subject. The system has been linearized with feedback linearization and then a PID controller was applied to track a reference trajectory. Finally, the control results have been compared with a nonlinear PID controller.
DEFF Research Database (Denmark)
Boiroux, Dimitri; Duun-Henriksen, Anne Katrine; Schmidt, Signe
2014-01-01
This paper addresses overnight blood glucose stabilization in people with type 1 diabetes using a Model Predictive Controller (MPC). We use a control strategy based on an adaptive ARMAX model in which we use a Recursive Extended Least Squares (RELS) method to estimate parameters of the stochastic...... and reduce the risk of hypoglycemia. We test our control strategies on a virtual clinic of 100 randomly generated patients with a representative inter-subject variability. This virtual clinic is based on the Hovorka model. We consider the case where only half of the meal bolus is administered at mealtime......, and the case where the insulin sensitivity varies during the night. The simulation results demonstrate that the adaptive control strategy can reduce the risks of hypoglycemia and hyperglycemia during the night....
In-the-loop simulation of electronic automatic temperature control systems: HVAC modeling
Energy Technology Data Exchange (ETDEWEB)
Domschke, R.; Matthes, M. [Visteon Deutschland GmbH, Kerpen (Germany)
2006-07-01
The Electronic Automatic Temperature Control (EATC) ensures the occupant comfort and provides safety features like rapid defrost and demist protection. Doing this, the EATC controller provides a direct interface to the end consumer and has a considerable impact on customer satisfaction. The In-the-loop (IL) simulation process is an integral part of Visteons model-based development process. It helps to design and calibrate the EATC controller. It consists of several IL simulation techniques like Model-in-the-loop (MIL), Software-in-the-loop (SIL) and Hardware-in-the-loop (HIL). In this article, we will focus on MIL/SIL Simulations. MIL/SIL allows simulation of the EATC controller in a virtual vehicle environment from the early states of and throughout the development process. This ensures a rapid, high quality and robust development process. The MIL/SIL model contains a thermal vehicle model, a heating, ventilation and air conditioning (HVAC) unit model and a model of the EATC controller itself. The thermal vehicle model simulates transient temperature and humidity conditions in the passenger compartment of a vehicle, settings from the controller, heat fluxes through the vehicle shell and windows, solar load and several further boundary conditions. Whereas the thermal vehicle model of a specific vehicle can be adapted from a default data base, one has to pay special attention to the HVAC unit model. Visteon has developed a special, physically based HVAC unit model to be adapted and implemented into the MIL/SIL simulation. This HVAC model enables a straightforward implementation of different HVAC architectures into the MIL/SIL simulation. Moreover, changes in the HVAC settings (i.e. different blower/scroll assemblies) can be assessed and the influence on passenger comfort can be quantified. Examples of the MIL/SIL simulation demonstrate the benefits of this approach. Results are discussed and a further outlook provided. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Draxl, C.; Churchfield, M.; Mirocha, J.; Lee, S.; Lundquist, J.; Michalakes, J.; Moriarty, P.; Purkayastha, A.; Sprague, M.; Vanderwende, B.
2014-06-01
Wind plant aerodynamics are influenced by a combination of microscale and mesoscale phenomena. Incorporating mesoscale atmospheric forcing (e.g., diurnal cycles and frontal passages) into wind plant simulations can lead to a more accurate representation of microscale flows, aerodynamics, and wind turbine/plant performance. Our goal is to couple a numerical weather prediction model that can represent mesoscale flow [specifically the Weather Research and Forecasting model] with a microscale LES model (OpenFOAM) that can predict microscale turbulence and wake losses.
Generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB.
Lee, Leng-Feng; Umberger, Brian R
2016-01-01
Computer modeling, simulation and optimization are powerful tools that have seen increased use in biomechanics research. Dynamic optimizations can be categorized as either data-tracking or predictive problems. The data-tracking approach has been used extensively to address human movement problems of clinical relevance. The predictive approach also holds great promise, but has seen limited use in clinical applications. Enhanced software tools would facilitate the application of predictive musculoskeletal simulations to clinically-relevant research. The open-source software OpenSim provides tools for generating tracking simulations but not predictive simulations. However, OpenSim includes an extensive application programming interface that permits extending its capabilities with scripting languages such as MATLAB. In the work presented here, we combine the computational tools provided by MATLAB with the musculoskeletal modeling capabilities of OpenSim to create a framework for generating predictive simulations of musculoskeletal movement based on direct collocation optimal control techniques. In many cases, the direct collocation approach can be used to solve optimal control problems considerably faster than traditional shooting methods. Cyclical and discrete movement problems were solved using a simple 1 degree of freedom musculoskeletal model and a model of the human lower limb, respectively. The problems could be solved in reasonable amounts of time (several seconds to 1-2 hours) using the open-source IPOPT solver. The problems could also be solved using the fmincon solver that is included with MATLAB, but the computation times were excessively long for all but the smallest of problems. The performance advantage for IPOPT was derived primarily by exploiting sparsity in the constraints Jacobian. The framework presented here provides a powerful and flexible approach for generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB. This
Directory of Open Access Journals (Sweden)
Hiroyuki Goto
2013-07-01
Full Text Available A model predictive control-based scheduler for a class of discrete event systems is designed and developed. We focus on repetitive, multiple-input, multiple-output, and directed acyclic graph structured systems on which capacity constraints can be imposed. The target system’s behaviour is described by linear equations in max-plus algebra, referred to as state-space representation. Assuming that the system’s performance can be improved by paying additional cost, we adjust the system parameters and determine control inputs for which the reference output signals can be observed. The main contribution of this research is twofold, 1: For systems with capacity constraints, we derived an output prediction equation as functions of adjustable variables in a recursive form, 2: Regarding the construct for the system’s representation, we improved the structure to accomplish general operations which are essential for adjusting the system parameters. The result of numerical simulation in a later section demonstrates the effectiveness of the developed controller.
Directory of Open Access Journals (Sweden)
Gimara Rajapakse
2017-10-01
Full Text Available Despite the predictability and availability at large scale, wave energy conversion (WEC has still not become a mainstream renewable energy technology. One of the main reasons is the large variations in the extracted power which could lead to instabilities in the power grid. In addition, maintaining the speed of the turbine within optimal range under changing wave conditions is another control challenge, especially in oscillating water column (OWC type WEC systems. As a solution to the first issue, this paper proposes the direct connection of a battery bank into the dc-link of the back-to-back power converter system, thereby smoothening the power delivered to the grid. For the second issue, model predictive controllers (MPCs are developed for the rectifier and the inverter of the back-to-back converter system aiming to maintain the turbine speed within its optimum range. In addition, MPC controllers are designed to control the battery current as well, in both charging and discharging conditions. Operations of the proposed battery direct integration scheme and control solutions are verified through computer simulations. Simulation results show that the proposed integrated energy storage and control solutions are capable of delivering smooth power to the grid while maintaining the turbine speed within its optimum range under varying wave conditions.
Repetitive model predictive approach to individual pitch control of wind turbines
DEFF Research Database (Denmark)
Adegas, Fabiano Daher; Stoustrup, Jakob; Odgaard, Peter Fogh
2011-01-01
prediction. As a consequence, individual pitch feed-forward control action is generated by the controller, taking ”future” wind disturbance into account. Information about the estimated wind spatial distribution one blade experience can be used in the prediction model to better control the next passing blade......Wind turbines are inherently exposed to nonuniform wind fields with of wind shear, tower shadow, and possible wake contributions. Asymmetrical aerodynamic rotor loads are a consequence of such periodic, repetitive wind disturbances experienced by the blades. A controller may estimate and use...... this peculiar disturbance pattern to better attenuate loads and regulate power by controlling the blade pitch angles individually. A novel model predictive (MPC) approach for individual pitch control of wind turbines is proposed in this paper. A repetitive wind disturbance model is incorporated into the MPC...
Control System Design for Cylindrical Tank Process Using Neural Model Predictive Control Technique
Directory of Open Access Journals (Sweden)
M. Sridevi
2010-10-01
Full Text Available Chemical manufacturing and process industry requires innovative technologies for process identification. This paper deals with model identification and control of cylindrical process. Model identification of the process was done using ARMAX technique. A neural model predictive controller was designed for the identified model. The performance of the controllers was evaluated using MATLAB software. The performance of NMPC controller was compared with Smith Predictor controller and IMC controller based on rise time, settling time, overshoot and ISE and it was found that the NMPC controller is better suited for this process.
Fault Tolerant Control Using Gaussian Processes and Model Predictive Control
Directory of Open Access Journals (Sweden)
Yang Xiaoke
2015-03-01
Full Text Available Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control.
Robot trajectory tracking with self-tuning predicted control
Cui, Xianzhong; Shin, Kang G.
1988-01-01
A controller that combines self-tuning prediction and control is proposed for robot trajectory tracking. The controller has two feedback loops: one is used to minimize the prediction error, and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated online to account for the model uncertainty and the time-varying property of the system. The authors describe the principle of STPC, analyze the system performance, and discuss the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.
Computationally efficient model predictive control algorithms a neural network approach
Ławryńczuk, Maciej
2014-01-01
This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: · A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. · Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. · The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). · The MPC algorithms with neural approximation with no on-line linearization. · The MPC algorithms with guaranteed stability and robustness. · Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require d...
Interpreting Disruption Prediction Models to Improve Plasma Control
Parsons, Matthew
2017-10-01
In order for the tokamak to be a feasible design for a fusion reactor, it is necessary to minimize damage to the machine caused by plasma disruptions. Accurately predicting disruptions is a critical capability for triggering any mitigative actions, and a modest amount of attention has been given to efforts that employ machine learning techniques to make these predictions. By monitoring diagnostic signals during a discharge, such predictive models look for signs that the plasma is about to disrupt. Typically these predictive models are interpreted simply to give a `yes' or `no' response as to whether a disruption is approaching. However, it is possible to extract further information from these models to indicate which input signals are more strongly correlated with the plasma approaching a disruption. If highly accurate predictive models can be developed, this information could be used in plasma control schemes to make better decisions about disruption avoidance. This work was supported by a Grant from the 2016-2017 Fulbright U.S. Student Program, administered by the Franco-American Fulbright Commission in France.
Tu, Chia-Ying; Tseng, Wan-Ling; Kuo, Pei-Hsuan; Lan, Yung-Yao; Tsuang, Ben-Jei; Hsu, Huang-Hsiung
2017-04-01
Precipitation in Taiwan area is significantly influenced by MJO (Madden-Julian Oscillation) in the boreal winter. This study is therefore conducted by toggling the MJO prediction and simulation with a unique model structure. The one-dimensional TKE (Turbulence Kinetic Energy) type ocean model SIT (Snow, Ice, Thermocline) with refined vertical resolution near surface is able to resolve cool skin, as well as diurnal warm layer. SIT can simulate accurate SST and hence give precise air-sea interaction. By coupling SIT with ECHAM5 (MPI-Meteorology), CAM5 (NCAR) and HiRAM (GFDL), the MJO simulations in 20-yrs climate integrations conducted by three SIT-coupled AGCMs are significant improved comparing to those driven by prescribed SST. The horizontal resolutions in ECHAM5, CAM5 and HiRAM are 2-deg., 1-deg and 0.5-deg., respectively. This suggests that the improvement of MJO simulation by coupling SIT is AGCM-resolution independent. This study further utilizes HiRAM coupled SIT to evaluate its MJO forecast skill. HiRAM has been recognized as one of the best model for seasonal forecasts of hurricane/typhoon activity (Zhao et al., 2009; Chen & Lin, 2011; 2013), but was not as successful in MJO forecast. The preliminary result of the HiRAM-SIT experiment during DYNAMO period shows improved success in MJO forecast. These improvements of MJO prediction and simulation in both hindcast experiments and climate integrations are mainly from better-simulated SST diurnal cycle and diurnal amplitude, which is contributed by the refined vertical resolution near ocean surface in SIT. Keywords: MJO Predictability, DYNAMO
A Building Model Framework for a Genetic Algorithm Multi-objective Model Predictive Control
DEFF Research Database (Denmark)
Arendt, Krzysztof; Ionesi, Ana; Jradi, Muhyiddine
2016-01-01
Model Predictive Control (MPC) of building systems is a promising approach to optimize building energy performance. In contrast to traditional control strategies which are reactive in nature, MPC optimizes the utilization of resources based on the predicted effects. It has been shown that energy ...
Model predictive control of trailing edge flaps on a wind turbine blade
Energy Technology Data Exchange (ETDEWEB)
Castaignet, D.B.
2011-11-15
Trailing edge flaps on wind turbine blades have been investigated for several years. Aero-servoelastic simulations carried out with different simulation tools, trailing edge flaps configurations and controller designs proved that trailing edge flaps are a suitable solution for reducing some of the wind turbine fatigue and extreme loads. This potential was confirmed with wind tunnel tests made on blade sections with trailing edge flaps and on a scaled two-bladed wind turbine in a wind tunnel. The work presented in this thesis includes a full-scale test run on a Vestas V27 wind turbine equipped with three trailing edge flaps on one blade, located on DTU's Risoe Campus in Roskilde, Denmark. This thesis is divided into three parts: the controller design, results from simulations, and results from the experiments. The trailing edge flaps controller designed for this project is based on a frequency-weighted model predictive control, tuned in order to target only the flapwise blade root loads at the frequencies contributing the most to blade root fatigue damage (the 1P, 2P and 3P frequencies), and to avoid unnecessary wear and tear of the actuators at high frequencies. A disturbance model consisting in periodic disturbances at the rotor speed harmonic frequencies and a quasi-steady input disturbance is aggregated to an analytical model of a spinning blade with trailing edge flaps. Simulations on a multi-megawatt wind turbine show the potential of the trailing edge flaps to reduce the flapwise blade root fatigue loads by 23%, but also the main shaft and the tower fatigue loads by up to 32%. Extreme loads during normal production also benefit from the trailing edge flaps. At last, the same controller was run on the Vestas V27 wind turbine located at the Risoe Campus of the Technical University of Denmark, in Roskilde, Denmark. One blade of the turbine was equipped with three independent trailing edge flaps. In spite of the failure of several sensors and actuators, the
Directory of Open Access Journals (Sweden)
Kaijiang YU
2015-10-01
Full Text Available As the conventional control method for hybrid electric vehicle doesn’t consider the effect of known traffic light information on the vehicle energy management, this paper proposes a model predictive control intelligent optimization strategies based on traffic light information for hybrid electric vehicles. By building the simplified model of the hybrid electric vehicle and adopting the continuation/generalized minimum residual method, the model prediction problem is solved. The simulation is conducted by using MATLAB/Simulink platform. The simulation results show the effectiveness of the proposed model of the traffic light information, and that the proposed model predictive control method can improve fuel economy and the real-time control performance significantly. The research conclusions show that the proposed control strategy can achieve optimal control of the vehicle trajectory, significantly improving fuel economy of the vehicle, and meet the system requirements for the real-time optimal control.
Directory of Open Access Journals (Sweden)
Xuan Wu
2015-01-01
Full Text Available In order to control the permanent-magnet synchronous motor system (PMSM with different disturbances and nonlinearity, an improved current control algorithm for the PMSM systems using recursive model predictive control (RMPC is developed in this paper. As the conventional MPC has to be computed online, its iterative computational procedure needs long computing time. To enhance computational speed, a recursive method based on recursive Levenberg-Marquardt algorithm (RLMA and iterative learning control (ILC is introduced to solve the learning issue in MPC. RMPC is able to significantly decrease the computation cost of traditional MPC in the PMSM system. The effectiveness of the proposed algorithm has been verified by simulation and experimental results.
Hardiansyah, Deni; Attarwala, Ali Asgar; Kletting, Peter; Mottaghy, Felix M; Glatting, Gerhard
2017-10-01
To investigate the accuracy of predicted time-integrated activity coefficients (TIACs) in peptide-receptor radionuclide therapy (PRRT) using simulated dynamic PET data and a physiologically based pharmacokinetic (PBPK) model. PBPK parameters were estimated using biokinetic data of 15 patients after injection of (152±15)MBq of 111 In-DTPAOC (total peptide amount (5.78±0.25)nmol). True mathematical phantoms of patients (MPPs) were the PBPK model with the estimated parameters. Dynamic PET measurements were simulated as being done after bolus injection of 150MBq 68 Ga-DOTATATE using the true MPPs. Dynamic PET scans around 35min p.i. (P 1 ), 4h p.i. (P 2 ) and the combination of P 1 and P 2 (P 3 ) were simulated. Each measurement was simulated with four frames of 5min each and 2 bed positions. PBPK parameters were fitted to the PET data to derive the PET-predicted MPPs. Therapy was simulated assuming an infusion of 5.1GBq of 90 Y-DOTATATE over 30min in both true and PET-predicted MPPs. TIACs of simulated therapy were calculated, true MPPs (true TIACs) and predicted MPPs (predicted TIACs) followed by the calculation of variabilities v. For P 1 and P 2 the population variabilities of kidneys, liver and spleen were acceptable (v10%). Treatment planning of PRRT based on dynamic PET data seems possible for the kidneys, liver and spleen using a PBPK model and patient specific information. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Alejandro Marquez
2013-01-01
Full Text Available This paper studies the application of proper orthogonal decomposition (POD to reduce the order of distributed reactor models with axial and radial diffusion and the implementation of model predictive control (MPC based on discrete-time linear time invariant (LTI reduced-order models. In this paper, the control objective is to keep the operation of the reactor at a desired operating condition in spite of the disturbances in the feed flow. This operating condition is determined by means of an optimization algorithm that provides the optimal temperature and concentration profiles for the system. Around these optimal profiles, the nonlinear partial differential equations (PDEs, that model the reactor are linearized, and afterwards the linear PDEs are discretized in space giving as a result a high-order linear model. POD and Galerkin projection are used to derive the low-order linear model that captures the dominant dynamics of the PDEs, which are subsequently used for controller design. An MPC formulation is constructed on the basis of the low-order linear model. The proposed approach is tested through simulation, and it is shown that the results are good with regard to keep the operation of the reactor.
Distributed Model Predictive Control via Dual Decomposition
DEFF Research Database (Denmark)
Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle
2014-01-01
This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...
Multimodel Predictive Control Approach for UAV Formation Flight
Directory of Open Access Journals (Sweden)
Chang-jian Ru
2014-01-01
Full Text Available Formation flight problem is the most important and interesting problem of multiple UAVs (unmanned aerial vehicles cooperative control. In this paper, a novel approach for UAV formation flight based on multimodel predictive control is designed. Firstly, the state equation of relative motion is obtained and then discretized. By the geometrical method, the characteristic points of state are determined. Afterwards, based on the linearization technique, the standard linear discrete model is obtained at each characteristic state point. Then, weighted model set is proposed using the idea of T-S (Takagi-Sugeno fuzzy control and the predictive control is carried out based on the multimodel method. Finally, to verify the performance of the proposed method, two different simulation scenarios are performed.
Theoretical Tools and Software for Modeling, Simulation and Control Design of Rocket Test Facilities
Richter, Hanz
2004-01-01
A rocket test stand and associated subsystems are complex devices whose operation requires that certain preparatory calculations be carried out before a test. In addition, real-time control calculations must be performed during the test, and further calculations are carried out after a test is completed. The latter may be required in order to evaluate if a particular test conformed to specifications. These calculations are used to set valve positions, pressure setpoints, control gains and other operating parameters so that a desired system behavior is obtained and the test can be successfully carried out. Currently, calculations are made in an ad-hoc fashion and involve trial-and-error procedures that may involve activating the system with the sole purpose of finding the correct parameter settings. The goals of this project are to develop mathematical models, control methodologies and associated simulation environments to provide a systematic and comprehensive prediction and real-time control capability. The models and controller designs are expected to be useful in two respects: 1) As a design tool, a model is the only way to determine the effects of design choices without building a prototype, which is, in the context of rocket test stands, impracticable; 2) As a prediction and tuning tool, a good model allows to set system parameters off-line, so that the expected system response conforms to specifications. This includes the setting of physical parameters, such as valve positions, and the configuration and tuning of any feedback controllers in the loop.
Development of a Closed Loop Simulator for Poloidal Field Control in DIII-D
International Nuclear Information System (INIS)
J.A. Leuer; M.L. Walker; D.A. Humphreys; J.R. Ferron; A. Nerem; B.G. Penaflor
1999-01-01
The design of a model-based simulator of the DIII-D poloidal field system is presented. The simulator is automatically configured to match a particular DIII-D discharge circuit. The simulator can be run in a data input mode, in which prior acquired DIII-D shot data is input to the simulator, or in a stand-alone predictive mode, in which the model operates in closed loop with the plasma control system. The simulator is used to design and validate a multi-input-multi-output controller which has been implemented on DIII-D to control plasma shape. Preliminary experimental controller results are presented
Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs
Institute of Scientific and Technical Information of China (English)
Yi Zhang; Xiangjie Liu; Bin Qu
2017-01-01
Reliable load frequency control(LFC) is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control(DMPC) based on coordination scheme.The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints(GRCs), load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed–loop performance, and computational burden with the physical constraints.
Outdoor flocking of quadcopter drones with decentralized model predictive control.
Yuan, Quan; Zhan, Jingyuan; Li, Xiang
2017-11-01
In this paper, we present a multi-drone system featured with a decentralized model predictive control (DMPC) flocking algorithm. The drones gather localized information from neighbors and update their velocities using the DMPC flocking algorithm. In the multi-drone system, data packages are transmitted through XBee ® wireless modules in broadcast mode, yielding such an anonymous and decentralized system where all the calculations and controls are completed on an onboard minicomputer of each drone. Each drone is a double-layered agent system with the coordination layer running multi-drone flocking algorithms and the flight control layer navigating the drone, and the final formation of the flock relies on both the communication range and the desired inter-drone distance. We give both numerical simulations and field tests with a flock of five drones, showing that the DMPC flocking algorithm performs well on the presented multi-drone system in both the convergence rate and the ability of tracking a desired path. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Rider, William J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Witkowski, Walter R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Mousseau, Vincent Andrew [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2016-04-13
The importance of credible, trustworthy numerical simulations is obvious especially when using the results for making high-consequence decisions. Determining the credibility of such numerical predictions is much more difficult and requires a systematic approach to assessing predictive capability, associated uncertainties and overall confidence in the computational simulation process for the intended use of the model. This process begins with an evaluation of the computational modeling of the identified, important physics of the simulation for its intended use. This is commonly done through a Phenomena Identification Ranking Table (PIRT). Then an assessment of the evidence basis supporting the ability to computationally simulate these physics can be performed using various frameworks such as the Predictive Capability Maturity Model (PCMM). There were several critical activities that follow in the areas of code and solution verification, validation and uncertainty quantification, which will be described in detail in the following sections. Here, we introduce the subject matter for general applications but specifics are given for the failure prediction project. In addition, the first task that must be completed in the verification & validation procedure is to perform a credibility assessment to fully understand the requirements and limitations of the current computational simulation capability for the specific application intended use. The PIRT and PCMM are tools used at Sandia National Laboratories (SNL) to provide a consistent manner to perform such an assessment. Ideally, all stakeholders should be represented and contribute to perform an accurate credibility assessment. PIRTs and PCMMs are both described in brief detail below and the resulting assessments for an example project are given.
Nonlinear Economic Model Predictive Control Strategy for Active Smart Buildings
DEFF Research Database (Denmark)
Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.
2016-01-01
Nowadays, the development of advanced and innovative intelligent control techniques for energy management in buildings is a key issue within the smart grid topic. A nonlinear economic model predictive control (EMPC) scheme, based on the branch-and-bound tree search used as optimization algorithm ...... controller is shown very reliable keeping the comfort levels in the two considered seasons and shifting the load away from peak hours in order to achieve the desired flexible electricity consumption.......Nowadays, the development of advanced and innovative intelligent control techniques for energy management in buildings is a key issue within the smart grid topic. A nonlinear economic model predictive control (EMPC) scheme, based on the branch-and-bound tree search used as optimization algorithm...
Progress and improvement of KSTAR plasma control using model-based control simulators
Energy Technology Data Exchange (ETDEWEB)
Hahn, Sang-hee, E-mail: hahn76@nfri.re.kr [National Fusion Research Institute, 169-148 Gwahak-ro, yuseong-gu, Daejeon (Korea, Republic of); Welander, A.S. [General Atomics, San Diego, CA (United States); Yoon, S.W.; Bak, J.G. [National Fusion Research Institute, 169-148 Gwahak-ro, yuseong-gu, Daejeon (Korea, Republic of); Eidietis, N.W. [General Atomics, San Diego, CA (United States); Han, H.S. [National Fusion Research Institute, 169-148 Gwahak-ro, yuseong-gu, Daejeon (Korea, Republic of); Humphreys, D.A.; Hyatt, A. [General Atomics, San Diego, CA (United States); Jeon, Y.M. [National Fusion Research Institute, 169-148 Gwahak-ro, yuseong-gu, Daejeon (Korea, Republic of); Johnson, R.D. [General Atomics, San Diego, CA (United States); Kim, H.S.; Kim, J. [National Fusion Research Institute, 169-148 Gwahak-ro, yuseong-gu, Daejeon (Korea, Republic of); Kolemen, E.; Mueller, D. [Princeton Plasma Physics Laboratory, Princeton, NJ (United States); Penaflor, B.G.; Piglowski, D.A. [General Atomics, San Diego, CA (United States); Shin, G.W. [University of Science and Technology, Daejeon (Korea, Republic of); Walker, M.L. [General Atomics, San Diego, CA (United States); Woo, M.H. [National Fusion Research Institute, 169-148 Gwahak-ro, yuseong-gu, Daejeon (Korea, Republic of)
2014-05-15
Superconducting tokamaks like KSTAR, EAST and ITER need elaborate magnetic controls mainly due to either the demanding experiment schedule or tighter hardware limitations caused by the superconducting coils. In order to reduce the operation runtime requirements, two types of plasma simulators for the KSTAR plasma control system (PCS) have been developed for improving axisymmetric magnetic controls. The first one is an open-loop type, which can reproduce the control done in an old shot by loading the corresponding diagnostics data and PCS setup. The other one, a closed-loop simulator based on a linear nonrigid plasma model, is designed to simulate dynamic responses of the plasma equilibrium and plasma current (I{sub p}) due to changes of the axisymmetric poloidal field (PF) coil currents, poloidal beta, and internal inductance. The closed-loop simulator is the one that actually can test and enable alteration of the feedback control setup for the next shot. The simulators have been used routinely in 2012 plasma campaign, and the experimental performances of the axisymmetric shape control algorithm are enhanced. Quality of the real-time EFIT has been enhanced by utilizations of the open-loop type. Using the closed-loop type, the decoupling scheme of the plasma current control and axisymmetric shape controls are verified through both the simulations and experiments. By combining with the relay feedback tuning algorithm, the improved controls helped to maintain the shape suitable for longer H-mode (10–16 s) with the number of required commissioning shots largely reduced.
Validation of a predictive model for smart control of electrical energy storage
Homan, Bart; van Leeuwen, Richard Pieter; Smit, Gerardus Johannes Maria; Zhu, Lei; de Wit, Jan B.
2016-01-01
The purpose of this paper is to investigate the applicability of a relatively simple model which is based on energy conservation for model predictions as part of smart control of thermal and electric storage. The paper reviews commonly used predictive models. Model predictions of charging and
Model Predictive Control of Buoy Type Wave Energy Converter
DEFF Research Database (Denmark)
Soltani, Mohsen N.; Sichani, Mahdi T.; Mirzaei, Mahmood
2014-01-01
by forcing this condition. In the paper the theoretical framework for this principal is shown. The optimal controller requires information of the sea state for infinite horizon which is not applicable. Model Predictive Controllers (MPC) can have finite horizon which crosses out this requirement....... This approach is then taken into account and an MPC controller is designed for a model wave energy converter and implemented on a numerical example. Further, the power outtake of this controller is compared to the optimal controller as an indicator of the performance of the designed controller....
Model Predictive Control of Buoy Type Wave Energy Converter
DEFF Research Database (Denmark)
Soltani, Mohsen; Sichani, Mahdi Teimouri; Mirzaei, Mahmood
2014-01-01
by forcing this condition. In the paper the theoretical framework for this principal is shown. The optimal controller requires information of the sea state for infinite horizon which is not applicable. Model Predictive Controllers (MPC) can have finite horizon which crosses out this requirement....... This approach is then taken into account and an MPC controller is designed for a model WEC and implemented on a numerical example. Further, the power outtake of this controller is compared to the optimal controller as an indicator of the performance of the designed controller....
Model predictive control as a tool for improving the process operation of MSW combustion plants
International Nuclear Information System (INIS)
Leskens, M.; Kessel, L.B.M. van; Bosgra, O.H.
2005-01-01
In this paper a feasibility study is presented on the application of the advanced control strategy called model predictive control (MPC) as a tool for obtaining improved process operation performance for municipal solid waste (MSW) combustion plants. The paper starts with a discussion of the operational objectives and control of such plants, from which a motivation follows for applying MPC to them. This is followed by a discussion on the basic idea behind this advanced control strategy. After that, an MPC-based combustion control system is proposed aimed at tackling a typical MSW combustion control problem and, using this proposed control system, an assessment is made of the improvement in performance that an MPC-based MSW combustion control system can provide in comparison to conventional MSW combustion control systems. This assessment is based on simulations using an experimentally obtained process and disturbance model of a real-life large-scale MSW combustion plant
Control-Oriented Models for Real-Time Simulation of Automotive Transmission Systems
Directory of Open Access Journals (Sweden)
Cavina N.
2015-01-01
Full Text Available A control-oriented model of a Dual Clutch Transmission (DCT was developed for real-time Hardware In the Loop (HIL applications, to support model-based development of the DCT controller and to systematically test its performance. The model is an innovative attempt to reproduce the fast dynamics of the actuation system while maintaining a simulation step size large enough for real-time applications. The model comprehends a detailed physical description of hydraulic circuit, clutches, synchronizers and gears, and simplified vehicle and internal combustion engine sub-models. As the oil circulating in the system has a large bulk modulus, the pressure dynamics are very fast, possibly causing instability in a real-time simulation; the same challenge involves the servo valves dynamics, due to the very small masses of the moving elements. Therefore, the hydraulic circuit model has been modified and simplified without losing physical validity, in order to adapt it to the real-time simulation requirements. The results of offline simulations have been compared to on-board measurements to verify the validity of the developed model, which was then implemented in a HIL system and connected to the Transmission Control Unit (TCU. Several tests have been performed on the HIL simulator, to verify the TCU performance: electrical failure tests on sensors and actuators, hydraulic and mechanical failure tests on hydraulic valves, clutches and synchronizers, and application tests comprehending all the main features of the control actions performed by the TCU. Being based on physical laws, in every condition the model simulates a plausible reaction of the system. A test automation procedure has finally been developed to permit the execution of a pattern of tests without the interaction of the user; perfectly repeatable tests can be performed for non-regression verification, allowing the testing of new software releases in fully automatic mode.
Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine
Directory of Open Access Journals (Sweden)
Hang-cheong Wong
2012-01-01
Full Text Available Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM, to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN and decremental least-squares support vector machine (DLSSVM. Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportional-integral (PI controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control.
Model predictive control of wind energy conversion systems
Yaramasu, Venkata Narasimha R
2017-01-01
The authors provide a comprehensive analysis on the model predictive control of power converters employed in a wide variety of variable-speed wind energy conversion systems (WECS). The contents of this book includes an overview of wind energy system configurations, power converters for variable-speed WECS, digital control techniques, MPC, modeling of power converters and wind generators for MPC design. Other topics include the mapping of continuous-time models to discrete-time models by various exact, approximate, and quasi-exact discretization methods, modeling and control of wind turbine grid-side two-level and multilevel voltage source converters. The authors also focus on the MPC of several power converter configurations for full variable-speed permanent magnet synchronous generator based WECS, squirrel-cage induction generator based WECS, and semi-variable-speed doubly fed induction generator based WECS.
Quality by control: Towards model predictive control of mammalian cell culture bioprocesses.
Sommeregger, Wolfgang; Sissolak, Bernhard; Kandra, Kulwant; von Stosch, Moritz; Mayer, Martin; Striedner, Gerald
2017-07-01
The industrial production of complex biopharmaceuticals using recombinant mammalian cell lines is still mainly built on a quality by testing approach, which is represented by fixed process conditions and extensive testing of the end-product. In 2004 the FDA launched the process analytical technology initiative, aiming to guide the industry towards advanced process monitoring and better understanding of how critical process parameters affect the critical quality attributes. Implementation of process analytical technology into the bio-production process enables moving from the quality by testing to a more flexible quality by design approach. The application of advanced sensor systems in combination with mathematical modelling techniques offers enhanced process understanding, allows on-line prediction of critical quality attributes and subsequently real-time product quality control. In this review opportunities and unsolved issues on the road to a successful quality by design and dynamic control implementation are discussed. A major focus is directed on the preconditions for the application of model predictive control for mammalian cell culture bioprocesses. Design of experiments providing information about the process dynamics upon parameter change, dynamic process models, on-line process state predictions and powerful software environments seem to be a prerequisite for quality by control realization. © 2017 The Authors. Biotechnology Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Lai, Hanh; McJunkin, Timothy R.; Miller, Carla J.; Scott, Jill R.; Almirall, José R.
2008-09-01
The combined use of SIMION 7.0 and the statistical diffusion simulation (SDS) user program in conjunction with SolidWorks® with COSMSOSFloWorks® fluid dynamics software to model a complete, commercial ion mobility spectrometer (IMS) was demonstrated for the first time and compared to experimental results for tests using compounds of immediate interest in the security industry (e.g., 2,4,6-trinitrotoluene, 2,7-dinitrofluorene, and cocaine). The effort of this research was to evaluate the predictive power of SIMION/SDS for application to IMS instruments. The simulation was evaluated against experimental results in three studies: (1) a drift:carrier gas flow rates study assesses the ability of SIMION/SDS to correctly predict the ion drift times; (2) a drift gas composition study evaluates the accuracy in predicting the resolution; (3) a gate width study compares the simulated peak shape and peak intensity with the experimental values. SIMION/SDS successfully predicted the correct drift time, intensity, and resolution trends for the operating parameters studied. Despite the need for estimations and assumptions in the construction of the simulated instrument, SIMION/SDS was able to predict the resolution between two ion species in air within 3% accuracy. The preliminary success of IMS simulations using SIMION/SDS software holds great promise for the design of future instruments with enhanced performance.
International Nuclear Information System (INIS)
Eliasi, H.; Menhaj, M.B.; Davilu, H.
2011-01-01
Research highlights: → In this work, a robust nonlinear model predictive control algorithm is developed. → This algorithm is applied to control the power level for load following. → The state constraints are imposed on the predicted trajectory during optimization. → The xenon oscillations are the main constraint for the load following problem. → In this algorithm, xenon oscillations are bounded within acceptable limits. - Abstract: One of the important operations in nuclear power plants is load-following in which imbalance of axial power distribution induces xenon oscillations. These oscillations must be maintained within acceptable limits otherwise the nuclear power plant could become unstable. Therefore, bounded xenon oscillation considered to be a constraint for the load-following operation. In this paper, a robust nonlinear model predictive control for the load-following operation problem is proposed that ensures xenon oscillations are kept bounded within acceptable limits. The proposed controller uses constant axial offset (AO) strategy to maintain xenon oscillations to be bounded. The constant AO is a robust state constraint for load-following problem. The controller imposes restricted state constraints on the predicted trajectory during optimization which guarantees robust satisfaction of state constraints without restoring to a min-max optimization problem. Simulation results show that the proposed controller for the load-following operation is so effective so that the xenon oscillations kept bounded in the given region.
Philip J. Radtke; Nathan D. Herring; David L. Loftis; Chad E. Keyser
2012-01-01
Prediction accuracy for projected basal area and trees per acre was assessed for the growth and yield model of the Forest Vegetation Simulator Southern Variant (FVS-Sn). Data for comparison with FVS-Sn predictions were compiled from a collection of n
Designing Closed-Loop Brain-Machine Interfaces Using Model Predictive Control
Directory of Open Access Journals (Sweden)
Gautam Kumar
2016-06-01
Full Text Available Brain-machine interfaces (BMIs are broadly defined as systems that establish direct communications between living brain tissue and external devices, such as artificial arms. By sensing and interpreting neuronal activities to actuate an external device, BMI-based neuroprostheses hold great promise in rehabilitating motor disabled subjects, such as amputees. In this paper, we develop a control-theoretic analysis of a BMI-based neuroprosthetic system for voluntary single joint reaching task in the absence of visual feedback. Using synthetic data obtained through the simulation of an experimentally validated psycho-physiological cortical circuit model, both the Wiener filter and the Kalman filter based linear decoders are developed. We analyze the performance of both decoders in the presence and in the absence of natural proprioceptive feedback information. By performing simulations, we show that the performance of both decoders degrades significantly in the absence of the natural proprioception. To recover the performance of these decoders, we propose two problems, namely tracking the desired position trajectory and tracking the firing rate trajectory of neurons which encode the proprioception, in the model predictive control framework to design optimal artificial sensory feedback. Our results indicate that while the position trajectory based design can only recover the position and velocity trajectories, the firing rate trajectory based design can recover the performance of the motor task along with the recovery of firing rates in other cortical regions. Finally, we extend our design by incorporating a network of spiking neurons and designing artificial sensory feedback in the form of a charged balanced biphasic stimulating current.
Directory of Open Access Journals (Sweden)
Nicolas Sockeel
2017-04-01
Full Text Available Power management strategies have impacts on fuel economy, greenhouse gasses (GHG emission, as well as effects on the durability of power-train components. This is why different off-line and real-time optimal control approaches are being developed. However, real-time control seems to be more attractive than off-line control because it can be directly implemented for managing power and energy flows inside an actual vehicle. One interesting illustration of these power management strategies is the model predictive control (MPC based algorithm. Inside a MPC, a cost function is optimized while system constraints are validated in real time. The MPC algorithm relies on dynamic models of the vehicle and the battery. The complexity and accuracy of the battery model are usually neglected to benefit the development of new cost functions or better MPC algorithms. The contribution of this manuscript consists of developing and evaluating a high-fidelity battery model of a plug-in hybrid electric vehicle (PHEV that has been used for MPC. Via empirical work and simulation, the impact of a high-fidelity battery model has been evaluated and compared to a simpler model in the context of MPC. It is proven that the new battery model reduces the absolute voltage, state of charge (SoC, and battery power loss error by a factor of 3.2, 1.9 and 2.1 on average respectively, compared to the simpler battery model.
An improved robust model predictive control for linear parameter-varying input-output models
Abbas, H.S.; Hanema, J.; Tóth, R.; Mohammadpour, J.; Meskin, N.
2018-01-01
This paper describes a new robust model predictive control (MPC) scheme to control the discrete-time linear parameter-varying input-output models subject to input and output constraints. Closed-loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal
Model Predictive Control for a Small Scale Unmanned Helicopter
Directory of Open Access Journals (Sweden)
Jianfu Du
2008-11-01
Full Text Available Kinematical and dynamical equations of a small scale unmanned helicoper are presented in the paper. Based on these equations a model predictive control (MPC method is proposed for controlling the helicopter. This novel method allows the direct accounting for the existing time delays which are used to model the dynamics of actuators and aerodynamics of the main rotor. Also the limits of the actuators are taken into the considerations during the controller design. The proposed control algorithm was verified in real flight experiments where good perfomance was shown in postion control mode.
Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin
2017-10-01
Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.
Generic Model Predictive Control Framework for Advanced Driver Assistance Systems
Wang, M.
2014-01-01
This thesis deals with a model predictive control framework for control design of Advanced Driver Assistance Systems, where car-following tasks are under control. The framework is applied to design several autonomous and cooperative controllers and to examine the controller properties at the
Hypersonic Control Modeling and Simulation Tool for Lifting Towed Ballutes, Phase I
National Aeronautics and Space Administration — Global Aerospace Corporation proposes to develop a hypersonic control modeling and simulation tool for hypersonic aeroassist vehicles. Our control and simulation...
DEFF Research Database (Denmark)
Solberg, Brian; Andersen, Palle; Maciejowski, Jan
2008-01-01
This paper discusses the application of hybrid model predictive control to control switching between different burner modes in a novel compact marine boiler design. A further purpose of the present work is to point out problems with finite horizon model predictive control applied to systems for w...
Distributed model predictive control made easy
Negenborn, Rudy
2014-01-01
The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems. This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...
DEFF Research Database (Denmark)
Koch-Ciobotaru, Cosmin; Isleifsson, Fridrik Rafn; Gehrke, Oliver
2012-01-01
This paper presents a model predictive controller developed in order to minimize the cost of grid energy consumption and maximize the amount of energy consumed from a local photovoltaic (PV) installation. The usage of as much locally produced renewable energy sources (RES) as possible, diminishes...... the effects of their large penetration in the distribution grid and reduces overloading the grid capacity, which is an increasing problem for the power system. The controller uses 24 hour prediction data for the ambient temperature, the solar irradiance, and for the PV output power. Simulation results...
Predicting environmental restoration activities through static simulation
International Nuclear Information System (INIS)
Ross, T.L.; King, D.A.; Wilkins, M.L.; Forward, M.F.
1994-12-01
This paper discusses a static simulation model that predicts several performance measures of environmental restoration activities over different remedial strategies. Basic model operation consists of manipulating and processing waste streams via selecting and applying remedial technologies according to the strategy. Performance measure prediction is possible for contaminated soil, solid waste, surface water, groundwater, storage tank, and facility sites. Simulations are performed for the U.S. Department of Energy in support of its Programmatic Environmental Impact Statement
Simulation, optimization and control of a thermal cracking furnace
International Nuclear Information System (INIS)
Masoumi, M.E.; Sadrameli, S.M.; Towfighi, J.; Niaei, A.
2006-01-01
The ethylene production process is one of the most important aspect of a petrochemical plant and the cracking furnace is the heart of the process. Since, ethylene is one of the raw materials in the chemical industry and the market situation of not only the feed and the product, but also the utility is rapidly changing, the optimal operation and control of the plant is important. A mathematical model, which describes the static and dynamic operations of a pilot plant furnace, was developed. The static simulation was used to predict the steady-state profiles of temperature, pressure and products yield. The dynamic simulation of the process was used to predict the transient behavior of thermal cracking reactor. Using a dynamic programming technique, an optimal temperature profile was developed along the reactor. Performances of temperature control loop were tested for different controller parameters and disturbances. The results of the simulation were tested experimentally in a computer control pilot plant
Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation
Energy Technology Data Exchange (ETDEWEB)
Abbas, Nikhar [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Tom, Nathan M [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-06-03
Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalman filter and autoregressive model to evaluate model predictive control performance.
Directory of Open Access Journals (Sweden)
Amir-Hasan Kakaee
2018-03-01
Full Text Available In the current study, a comparative study is performed using Large Eddy Simulation (LES and Reynolds-averaged Navier–Stokes (RANS turbulence models on a natural gas/diesel Reactivity Controlled Compression Ignition (RCCI engine. The numerical results are validated against the available research work in the literature. The RNG (Re-Normalization Group k − ε and dynamic structure models are employed to model turbulent flow for RANS and LES simulations, respectively. Parameters like the premixed natural gas mass fraction, the second start of injection timing (SOI2 of diesel and the engine speed are studied to compare performance of RANS and LES models on combustion and pollutant emissions prediction. The results obtained showed that the LES and RANS model give almost similar predictions of cylinder pressure and heat release rate at lower natural gas mass fractions and late SOI2 timings. However, the LES showed improved capability to predict the natural gas auto-ignition and pollutant emissions prediction compared to RANS model especially at higher natural gas mass fractions.
Predictive Maturity of Multi-Scale Simulation Models for Fuel Performance
International Nuclear Information System (INIS)
Atamturktur, Sez; Unal, Cetin; Hemez, Francois; Williams, Brian; Tome, Carlos
2015-01-01
The project proposed to provide a Predictive Maturity Framework with its companion metrics that (1) introduce a formalized, quantitative means to communicate information between interested parties, (2) provide scientifically dependable means to claim completion of Validation and Uncertainty Quantification (VU) activities, and (3) guide the decision makers in the allocation of Nuclear Energy's resources for code development and physical experiments. The project team proposed to develop this framework based on two complimentary criteria: (1) the extent of experimental evidence available for the calibration of simulation models and (2) the sophistication of the physics incorporated in simulation models. The proposed framework is capable of quantifying the interaction between the required number of physical experiments and degree of physics sophistication. The project team has developed this framework and implemented it with a multi-scale model for simulating creep of a core reactor cladding. The multi-scale model is composed of the viscoplastic self-consistent (VPSC) code at the meso-scale, which represents the visco-plastic behavior and changing properties of a highly anisotropic material and a Finite Element (FE) code at the macro-scale to represent the elastic behavior and apply the loading. The framework developed takes advantage of the transparency provided by partitioned analysis, where independent constituent codes are coupled in an iterative manner. This transparency allows model developers to better understand and remedy the source of biases and uncertainties, whether they stem from the constituents or the coupling interface by exploiting separate-effect experiments conducted within the constituent domain and integral-effect experiments conducted within the full-system domain. The project team has implemented this procedure with the multi- scale VPSC-FE model and demonstrated its ability to improve the predictive capability of the model. Within this
Predictive Maturity of Multi-Scale Simulation Models for Fuel Performance
Energy Technology Data Exchange (ETDEWEB)
Atamturktur, Sez [Clemson Univ., SC (United States); Unal, Cetin [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Hemez, Francois [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Williams, Brian [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Tome, Carlos [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2015-03-16
The project proposed to provide a Predictive Maturity Framework with its companion metrics that (1) introduce a formalized, quantitative means to communicate information between interested parties, (2) provide scientifically dependable means to claim completion of Validation and Uncertainty Quantification (VU) activities, and (3) guide the decision makers in the allocation of Nuclear Energy’s resources for code development and physical experiments. The project team proposed to develop this framework based on two complimentary criteria: (1) the extent of experimental evidence available for the calibration of simulation models and (2) the sophistication of the physics incorporated in simulation models. The proposed framework is capable of quantifying the interaction between the required number of physical experiments and degree of physics sophistication. The project team has developed this framework and implemented it with a multi-scale model for simulating creep of a core reactor cladding. The multi-scale model is composed of the viscoplastic self-consistent (VPSC) code at the meso-scale, which represents the visco-plastic behavior and changing properties of a highly anisotropic material and a Finite Element (FE) code at the macro-scale to represent the elastic behavior and apply the loading. The framework developed takes advantage of the transparency provided by partitioned analysis, where independent constituent codes are coupled in an iterative manner. This transparency allows model developers to better understand and remedy the source of biases and uncertainties, whether they stem from the constituents or the coupling interface by exploiting separate-effect experiments conducted within the constituent domain and integral-effect experiments conducted within the full-system domain. The project team has implemented this procedure with the multi- scale VPSC-FE model and demonstrated its ability to improve the predictive capability of the model. Within this
Predictive control and identification: Applications to steering dynamics
DEFF Research Database (Denmark)
Hansen, Anca Daniela
1996-01-01
and of the loss function, which defines the optimality of the control. Some guidelines on how to choose the design parameters, depending on the type of process to be controlled and on the required control performance, are presented. A predictive track keeping system for a Mariner Class Vessel is formulated based...... the under- standing of the connection between identification and control, analysed in Chapter 7. Chapter 7 focuses on how to make the on-line identification for predictive control more robust towards unmodelled dynamics. The theory is verified via simulation studies on a Mariner Class Vessel. The effects...... and the need of a prefilter in the estimation are analysed and illustrated. Based on the idea that the control criterion must be dual to the estimation criterion, an iterative optimal prefilter is designed. This seems to be an appealing way to tune the model towards the objective for which the model...
DEFF Research Database (Denmark)
Nadimi, Esmaeil Sharak; Bak, Thomas; Izadi-Zamanabadi, Roozbeh
2006-01-01
The main objective of this paper is to investigate the erformance and applicability of two GPC (generalized predictive control) based control methods on a complete benchmark model of the Stewart platform made in MATLAB V6.5. The first method involves an LQG controller (Linear Quadratic Gaussian...
Model Predictive Control of a Nonlinear System with Known Scheduling Variable
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2012-01-01
Model predictive control (MPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Consequently...... the control problem of the nonlinear system is simplied into a quadratic programming. Wind turbine is chosen as the case study and we choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon....
Levy, R.; Mcginness, H.
1976-01-01
Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.
Simulation-Based Prediction of Equivalent Continuous Noises during Construction Processes.
Zhang, Hong; Pei, Yun
2016-08-12
Quantitative prediction of construction noise is crucial to evaluate construction plans to help make decisions to address noise levels. Considering limitations of existing methods for measuring or predicting the construction noise and particularly the equivalent continuous noise level over a period of time, this paper presents a discrete-event simulation method for predicting the construction noise in terms of equivalent continuous level. The noise-calculating models regarding synchronization, propagation and equivalent continuous level are presented. The simulation framework for modeling the noise-affected factors and calculating the equivalent continuous noise by incorporating the noise-calculating models into simulation strategy is proposed. An application study is presented to demonstrate and justify the proposed simulation method in predicting the equivalent continuous noise during construction. The study contributes to provision of a simulation methodology to quantitatively predict the equivalent continuous noise of construction by considering the relevant uncertainties, dynamics and interactions.
MODELLING OF DYNAMIC SPEED LIMITS USING THE MODEL PREDICTIVE CONTROL
Directory of Open Access Journals (Sweden)
Andrey Borisovich Nikolaev
2017-09-01
Full Text Available The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS, which consist of interacting intelligent vehicles (IV and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering. Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC. The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.
Development of a Model Following Control Law for Inflight Simulation and Flight Controls Research
Takahashi, Mark; Fletcher, Jay; Aiken, Edwin W. (Technical Monitor)
1994-01-01
The U.S. Army and NASA are currently developing the Rotorcraft Aircrew Systems Concepts Airborne Laboratory (RASCAL) at the Ames Research Center. RASCAL, shown in Figure 1, is a UH-60, which is being modified in a phased development program to have a research fly-by-wire flight control system, and an advanced navigation research platform. An important part of the flight controls and handling qualities research on RASCAL will be an FCS design for the aircraft to achieve high bandwidth control responses and disturbance rejection characteristics. Initially, body states will be used as feedbacks, but research into the use of rotor states will also be considered in later stages to maximize agility and maneuverability. In addition to supporting flight controls research, this FCS design will serve as the inflight simulation control law to support basic handling qualities, guidance, and displays research. Research in high bandwidth controls laws is motivated by the desire to improve the handling qualities in aggressive maneuvering and in severely degraded weather conditions. Naturally, these advantages will also improve the quality of the model following, thereby improving the inflight simulation capabilities of the research vehicle. High bandwidth in the control laws provides tighter tracking allowing for higher response bandwidths which can meet handling qualities requirements for aggressive maneuvering. System sensitivity is also reduced preventing variations in the response from the vehicle due to changing flight conditions. In addition, improved gust rejection will result from this reduced sensitivity. The gust rejection coupled with a highly stable system will make more precise maneuvering and pointing possible in severely degraded weather conditions. The difficulty in achieving higher bandwidths from the control laws in the feedback and in the responses arises from the complexity of the models that are needed to produce a satisfactory design. In this case, high
Simulation model of an active-stall fixed-speed wind turbine controller
Energy Technology Data Exchange (ETDEWEB)
Jauch, C.; Hansen, A.D.; Sorensen, P.; Blaabjerg, F.
2004-07-01
This paper describes an active-stall wind turbine controller. The objective is to develop a general model of an active stall controller in order to simulate the operation of grid connected active stall wind turbines. The active stall turbine concept and its control strategies are presented and evaluated by simulations. The presented controller is described for continuous operation under all wind speeds from start-up wind speed to shut down wind speed. Due to its parametric implementation it is general i. e. it can represent different active stall wind turbine controllers and can be implemented in different simulation tools. (author)
Direct numerical simulation of turbulent combustion: fundamental insights towards predictive models
International Nuclear Information System (INIS)
Hawkes, Evatt R; Sankaran, Ramanan; Sutherland, James C; Chen, Jacqueline H
2005-01-01
The advancement of our basic understanding of turbulent combustion processes and the development of physics-based predictive tools for design and optimization of the next generation of combustion devices are strategic areas of research for the development of a secure, environmentally sound energy infrastructure. In direct numerical simulation (DNS) approaches, all scales of the reacting flow problem are resolved. However, because of the magnitude of this task, DNS of practical high Reynolds number turbulent hydrocarbon flames is out of reach of even terascale computing. For the foreseeable future, the approach to this complex multi-scale problem is to employ distinct but synergistic approaches to tackle smaller sub-ranges of the complete problem, which then require models for the small scale interactions. With full access to the spatially and temporally resolved fields, DNS can play a major role in the development of these models and in the development of fundamental understanding of the micro-physics of turbulence-chemistry interactions. Two examples, from simulations performed at terascale Office of Science computing facilities, are presented to illustrate the role of DNS in delivering new insights to advance the predictive capability of models. Results are presented from new three-dimensional DNS with detailed chemistry of turbulent non-premixed jet flames, revealing the differences between mixing of passive and reacting scalars, and determining an optimal lower dimensional representation of the full thermochemical state space
Predicting Flory-Huggins χ from Simulations
Zhang, Wenlin; Gomez, Enrique D.; Milner, Scott T.
2017-07-01
We introduce a method, based on a novel thermodynamic integration scheme, to extract the Flory-Huggins χ parameter as small as 10-3k T for polymer blends from molecular dynamics (MD) simulations. We obtain χ for the archetypical coarse-grained model of nonpolar polymer blends: flexible bead-spring chains with different Lennard-Jones interactions between A and B monomers. Using these χ values and a lattice version of self-consistent field theory (SCFT), we predict the shape of planar interfaces for phase-separated binary blends. Our SCFT results agree with MD simulations, validating both the predicted χ values and our thermodynamic integration method. Combined with atomistic simulations, our method can be applied to predict χ for new polymers from their chemical structures.
A LIDAR-assisted model predictive controller added on a traditional wind turbine controller
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Hansen, Morten Hartvig
2016-01-01
control and opens the market of retrofitting existing wind turbines with the new technology. In this paper, we suggest a model predictive controller (MPC) that is added to the basic gain scheduled PI controller of a WT to enhance the performance of the closed loop system using LIDAR measurements...
Tran, Tri; Ha, Q P
2018-01-01
A perturbed cooperative-state feedback (PSF) strategy is presented for the control of interconnected systems in this paper. The subsystems of an interconnected system can exchange data via the communication network that has multiple connection topologies. The PSF strategy can resolve both issues, the sensor data losses and the communication network breaks, thanks to the two components of the control including a cooperative-state feedback and a perturbation variable, e.g., u i =K ij x j +w i . The PSF is implemented in a decentralized model predictive control scheme with a stability constraint and a non-monotonic storage function (ΔV(x(k))≥0), derived from the dissipative systems theory. Numerical simulation for the automatic generation control problem in power systems is studied to illustrate the effectiveness of the presented PSF strategy. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Iterated non-linear model predictive control based on tubes and contractive constraints.
Murillo, M; Sánchez, G; Giovanini, L
2016-05-01
This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Fiorentini, Massimo; Wall, Josh; Ma, Zhenjun; Braslavsky, Julio H.; Cooper, Paul
2017-01-01
Highlights: • A comprehensive approach to managing thermal energy in residential buildings. • Solar-assisted HVAC system with on-site energy generation and storage. • Mixed logic-dynamical building model identified using experimental data. • Design and implementation of a logic-dynamical model predictive control strategy. • MPC applied to the Net-Zero Energy house winner of the Solar Decathlon China 2013. - Abstract: This paper describes the development, implementation and experimental investigation of a Hybrid Model Predictive Control (HMPC) strategy to control solar-assisted heating, ventilation and air-conditioning (HVAC) systems with on-site thermal energy generation and storage. A comprehensive approach to the thermal energy management of a residential building is presented to optimise the scheduling of the available thermal energy resources to meet a comfort objective. The system has a hybrid nature with both continuous variables and discrete, logic-driven operating modes. The proposed control strategy is organized in two hierarchical levels. At the high-level, an HMPC controller with a 24-h prediction horizon and a 1-h control step is used to select the operating mode of the HVAC system. At the low-level, each operating mode is optimised using a 1-h rolling prediction horizon with a 5-min control step. The proposed control strategy has been practically implemented on the Building Management and Control System (BMCS) of a Net Zero-Energy Solar Decathlon house. This house features a sophisticated HVAC system comprising of an air-based photovoltaic thermal (PVT) collector and a phase change material (PCM) thermal storage integrated with the air-handling unit (AHU) of a ducted reverse-cycle heat pump system. The simulation and experimental results demonstrated the high performance achievable using an HMPC approach to optimising complex multimode HVAC systems in residential buildings, illustrating efficient selection of the appropriate operating modes
Nonlinear Predictive Sliding Mode Control for Active Suspension System
Directory of Open Access Journals (Sweden)
Dazhuang Wang
2018-01-01
Full Text Available An active suspension system is important in meeting the requirements of the ride comfort and handling stability for vehicles. In this work, a nonlinear model of active suspension system and a corresponding nonlinear robust predictive sliding mode control are established for the control problem of active suspension. Firstly, a seven-degree-of-freedom active suspension model is established considering the nonlinear effects of springs and dampers; and secondly, the dynamic model is expanded in the time domain, and the corresponding predictive sliding mode control is established. The uncertainties in the controller are approximated by the fuzzy logic system, and the adaptive controller reduces the approximation error to increase the robustness of the control system. Finally, the simulation results show that the ride comfort and handling stability performance of the active suspension system is better than that of the passive suspension system and the Skyhook active suspension. Thus, the system can obviously improve the shock absorption performance of vehicles.
Finite Control Set Model Predictive Control for Multiple Distributed Generators Microgrids
Babqi, Abdulrahman Jamal
This dissertation proposes two control strategies for AC microgrids that consist of multiple distributed generators (DGs). The control strategies are valid for both grid-connected and islanded modes of operation. In general, microgrid can operate as a stand-alone system (i.e., islanded mode) or while it is connected to the utility grid (i.e., grid connected mode). To enhance the performance of a micrgorid, a sophisticated control scheme should be employed. The control strategies of microgrids can be divided into primary and secondary controls. The primary control regulates the output active and reactive powers of each DG in grid-connected mode as well as the output voltage and frequency of each DG in islanded mode. The secondary control is responsible for regulating the microgrid voltage and frequency in the islanded mode. Moreover, it provides power sharing schemes among the DGs. In other words, the secondary control specifies the set points (i.e. reference values) for the primary controllers. In this dissertation, Finite Control Set Model Predictive Control (FCS-MPC) was proposed for controlling microgrids. FCS-MPC was used as the primary controller to regulate the output power of each DG (in the grid-connected mode) or the voltage of the point of DG coupling (in the islanded mode of operation). In the grid-connected mode, Direct Power Model Predictive Control (DPMPC) was implemented to manage the power flow between each DG and the utility grid. In the islanded mode, Voltage Model Predictive Control (VMPC), as the primary control, and droop control, as the secondary control, were employed to control the output voltage of each DG and system frequency. The controller was equipped with a supplementary current limiting technique in order to limit the output current of each DG in abnormal incidents. The control approach also enabled smooth transition between the two modes. The performance of the control strategy was investigated and verified using PSCAD/EMTDC software
Image-Based Visual Servoing for Manipulation Via Predictive Control – A Survey of Some Results
Directory of Open Access Journals (Sweden)
Corneliu Lazăr
2016-09-01
Full Text Available In this paper, a review of predictive control algorithms developed by the authors for visual servoing of robots in manipulation applications is presented. Using these algorithms, a control predictive framework was created for image-based visual servoing (IBVS systems. Firstly, considering the point features, in the year 2008 we introduced an internal model predictor based on the interaction matrix. Secondly, distinctly from the set-point trajectory, we introduced in 2011 the reference trajectory using the concept from predictive control. Finally, minimizing a sum of squares of predicted errors, the optimal input trajectory was obtained. The new concept of predictive control for IBVS systems was employed to develop a cascade structure for motion control of robot arms. Simulation results obtained with a simulator for predictive IBVS systems are also presented.
Esna-Ashari, Mojgan; Zekri, Maryam; Askari, Masood; Khalili, Noushin
2017-01-01
Because of increasing risk of diabetes, the measurement along with control of blood sugar has been of great importance in recent decades. In type I diabetes, because of the lack of insulin secretion, the cells cannot absorb glucose leading to low level of glucose. To control blood glucose (BG), the insulin must be injected to the body. This paper proposes a method for BG level regulation in type I diabetes. The control strategy is based on nonlinear model predictive control. The aim of the proposed controller optimized with genetics algorithms is to measure BG level each time and predict it for the next time interval. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. Consequently, this method can decrease the risk of hypoglycemia, a lethal phenomenon in regulating BG level in diabetes caused by a low BG level. Two delay differential equation models, namely Wang model and Enhanced Wang model, are applied as controller model and plant, respectively. The simulation results exhibit an acceptable performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. As a result, if the nutrition of the person decreases instantly, the hypoglycemia will not happen. Furthermore, comparing this method with other works, it was shown that the new method outperforms previous studies.
Enhanced Voltage Control of VSC-HVDC Connected Offshore Wind Farms Based on Model Predictive Control
DEFF Research Database (Denmark)
Guo, Yifei; Gao, Houlei; Wu, Qiuwei
2018-01-01
This paper proposes an enhanced voltage control strategy (EVCS) based on model predictive control (MPC) for voltage source converter based high voltage direct current (VSCHVDC) connected offshore wind farms (OWFs). In the proposed MPC based EVCS, all wind turbine generators (WTGs) as well...... as the wind farm side VSC are optimally coordinated to keep voltages within the feasible range and reduce system power losses. Considering the high ratio of the OWF collector system, the effects of active power outputs of WTGs on voltage control are also taken into consideration. The predictive model of VSC...
Predictive Models of Procedural Human Supervisory Control Behavior
2011-01-01
821708, Brest , France. Page 139 of 150 Boussemart, Y. and M. L. Cummings (2010). "Predicting Supervisory Control Behavior with Hidden Markov Models...Strategies for Strike Planning. COGIS 2006 - Cognitive Systems with Interactive Sensors, Paris . Burges, C. (1998). "A Tutorial on Support Vector Machines
Distributed Model Predictive Control for Active Power Control of Wind Farm
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei; Rasmussen, Claus Nygaard
2014-01-01
This paper presents the active power control of a wind farm using the Distributed Model Predictive Controller (D- MPC) via dual decomposition. Different from the conventional centralized wind farm control, multiple objectives such as power reference tracking performance and wind turbine load can...... be considered to achieve a trade-off between them. Additionally, D- MPC is based on communication among the subsystems. Through the interaction among the neighboring subsystems, the global optimization could be achieved, which significantly reduces the computation burden. It is suitable for the modern large......-scale wind farm control....
Model predictive control of a high speed switched reluctance generator system
Marinkov, Sava; De Jager, Bram; Steinbuch, Maarten
2013-01-01
This paper presents a novel voltage control strategy for the high-speed operation of a Switched Reluctance Generator. It uses a linear Model Predictive Control law based on the average system model. The controller computes the DC-link current needed to achieve the tracking of a desired voltage
Hu, Dawei; Liu, Hong; Yang, Chenliang; Hu, Enzhu
As a subsystem of the bioregenerative life support system (BLSS), light-algae bioreactor (LABR) has properties of high reaction rate, efficiently synthesizing microalgal biomass, absorbing CO2 and releasing O2, so it is significant for BLSS to provide food and maintain gas balance. In order to manipulate the LABR properly, it has been designed as a closed-loop control system, and technology of Artificial Neural Network-Model Predictive Control (ANN-MPC) is applied to design the controller for LABR in which green microalgae, Spirulina platensis is cultivated continuously. The conclusion is drawn by computer simulation that ANN-MPC controller can intelligently learn the complicated dynamic performances of LABR, and automatically, robustly and self-adaptively regulate the light intensity illuminating on the LABR, hence make the growth of microalgae in the LABR be changed in line with the references, meanwhile provide appropriate damping to improve markedly the transient response performance of LABR.
A Case Study Using Modeling and Simulation to Predict Logistics Supply Chain Issues
Tucker, David A.
2007-01-01
Optimization of critical supply chains to deliver thousands of parts, materials, sub-assemblies, and vehicle structures as needed is vital to the success of the Constellation Program. Thorough analysis needs to be performed on the integrated supply chain processes to plan, source, make, deliver, and return critical items efficiently. Process modeling provides simulation technology-based, predictive solutions for supply chain problems which enable decision makers to reduce costs, accelerate cycle time and improve business performance. For example, United Space Alliance, LLC utilized this approach in late 2006 to build simulation models that recreated shuttle orbiter thruster failures and predicted the potential impact of thruster removals on logistics spare assets. The main objective was the early identification of possible problems in providing thruster spares for the remainder of the Shuttle Flight Manifest. After extensive analysis the model results were used to quantify potential problems and led to improvement actions in the supply chain. Similarly the proper modeling and analysis of Constellation parts, materials, operations, and information flows will help ensure the efficiency of the critical logistics supply chains and the overall success of the program.
Adaptive Model Predictive Vibration Control of a Cantilever Beam with Real-Time Parameter Estimation
Directory of Open Access Journals (Sweden)
Gergely Takács
2014-01-01
Full Text Available This paper presents an adaptive-predictive vibration control system using extended Kalman filtering for the joint estimation of system states and model parameters. A fixed-free cantilever beam equipped with piezoceramic actuators serves as a test platform to validate the proposed control strategy. Deflection readings taken at the end of the beam have been used to reconstruct the position and velocity information for a second-order state-space model. In addition to the states, the dynamic system has been augmented by the unknown model parameters: stiffness, damping constant, and a voltage/force conversion constant, characterizing the actuating effect of the piezoceramic transducers. The states and parameters of this augmented system have been estimated in real time, using the hybrid extended Kalman filter. The estimated model parameters have been applied to define the continuous state-space model of the vibrating system, which in turn is discretized for the predictive controller. The model predictive control algorithm generates state predictions and dual-mode quadratic cost prediction matrices based on the updated discrete state-space models. The resulting cost function is then minimized using quadratic programming to find the sequence of optimal but constrained control inputs. The proposed active vibration control system is implemented and evaluated experimentally to investigate the viability of the control method.
Model Predictive Control of Wind Turbines using Uncertain LIDAR Measurements
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Soltani, Mohsen; Poulsen, Niels Kjølstad
2013-01-01
, we simplify state prediction for the MPC. Consequently, the control problem of the nonlinear system is simplified into a quadratic programming. We consider uncertainty in the wind propagation time, which is the traveling time of wind from the LIDAR measurement point to the rotor. An algorithm based......The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined...... on wind speed estimation and measurements from the LIDAR is devised to find an estimate of the delay and compensate for it before it is used in the controller. Comparisons between the MPC with error compensation, the MPC without error compensation and an MPC with re-linearization at each sample point...
Model predictive control in light naphtha distillation column of gasoline hydrogenation process
Directory of Open Access Journals (Sweden)
Kornkrit Chiewchanchairat
2015-03-01
Full Text Available The main scope of this research is for designing and implementing of model predictive control (MPC on the light naphtha distillation column of gasoline hydrogenation process. This model is designed by using robust multivariable predictive control technology (RMPCT. The performance of MPC controller is better than PID controllers 32.1 % those are comparing by using as the same of objective function and also in the MPC controller can be used for steam optimization that is shown in this research, stream consumption is reduced 6.6 Kg/ m3 of fresh feed.
A control-oriented simulation model of a power-split hybrid electric vehicle
International Nuclear Information System (INIS)
Cipek, Mihael; Pavković, Danijel; Petrić, Joško
2013-01-01
Highlights: ► A simulation model of a two mode power-split hybrid electric vehicle (HEV) is proposed. ► Modeling the energy losses in the HEV transmission components are presented. ► The control optimization model implementation aspects are discussed. -- Abstract: A simulation model of a two mode power-split hybrid electric vehicle (HEV) is proposed in this paper for the purpose of HEV dynamics analysis and control system design. The bond graph methodology is used to model dominant dynamic effects of the mechanical part of the HEV transmission. Simple quasi-static battery model, the environment model, the tire and the power losses model of a vehicle are included, as well. A low-level electric generator speed control loop is designed, which includes a PI controller tuned according to the symmetrical optimum tuning procedure. Finally, off-line optimization by conjugate gradient-based BPTT-like optimal control algorithm, which is based on the presented mathematical model, is also given in the paper.
Model predictions and control of conditions in a CA-reefer container
Sman, van der R.G.M.; Verdijck, G.J.C.
2003-01-01
In this paper a concept for energy saving for refrigerated container transport is presented. The concept is based on model-predictive control of the set points of the cooling unit. These models predict energy consumption of the cooling unit, climatic conditions inside the cargo space, and the change
AUTHOR|(SzGeCERN)673023; Blanco Viñuela, Enrique
In each of eight arcs of the 27 km circumference Large Hadron Collider (LHC), 2.5 km long strings of super-conducting magnets are cooled with superfluid Helium II at 1.9 K. The temperature stabilisation is a challenging control problem due to complex non-linear dynamics of the magnets temperature and presence of multiple operational constraints. Strong nonlinearities and variable dead-times of the dynamics originate at strongly heat-flux dependent effective heat conductivity of superfluid that varies three orders of magnitude over the range of possible operational conditions. In order to improve the temperature stabilisation, a proof of concept on-line economic output-feedback Non-linear Model Predictive Controller (NMPC) is presented in this thesis. The controller is based on a novel complex first-principles distributed parameters numerical model of the temperature dynamics over a 214 m long sub-sector of the LHC that is characterized by very low computational cost of simulation needed in real-time optimizat...
Simulation model of a transient fault controller for an active-stall wind turbine
Energy Technology Data Exchange (ETDEWEB)
Jauch, C.; Soerensen, P.; Bak Jensen, B.
2005-01-01
This paper describes the simulation model of a controller that enables an active-stall wind turbine to ride through transient faults. The simulated wind turbine is connected to a simple model of a power system. Certain fault scenarios are specified and the turbine shall be able to sustain operation in case of such faults. The design of the controller is described and its performance assessed by simulations. The control strategies are explained and the behaviour of the turbine discussed. (author)
Optimization control of LNG regasification plant using Model Predictive Control
Wahid, A.; Adicandra, F. F.
2018-03-01
Optimization of liquified natural gas (LNG) regasification plant is important to minimize costs, especially operational costs. Therefore, it is important to choose optimum LNG regasification plant design and maintaining the optimum operating conditions through the implementation of model predictive control (MPC). Optimal tuning parameter for MPC such as P (prediction horizon), M (control of the horizon) and T (sampling time) are achieved by using fine-tuning method. The optimal criterion for design is the minimum amount of energy used and for control is integral of square error (ISE). As a result, the optimum design is scheme 2 which is developed by Devold with an energy savings of 40%. To maintain the optimum conditions, required MPC with P, M and T as follows: tank storage pressure: 90, 2, 1; product pressure: 95, 2, 1; temperature vaporizer: 65, 2, 2; and temperature heater: 35, 6, 5, with ISE value at set point tracking respectively 0.99, 1792.78, 34.89 and 7.54, or improvement of control performance respectively 4.6%, 63.5%, 3.1% and 58.2% compared to PI controller performance. The energy savings that MPC controllers can make when there is a disturbance in temperature rise 1°C of sea water is 0.02 MW.
Predictive IP controller for robust position control of linear servo system.
Lu, Shaowu; Zhou, Fengxing; Ma, Yajie; Tang, Xiaoqi
2016-07-01
Position control is a typical application of linear servo system. In this paper, to reduce the system overshoot, an integral plus proportional (IP) controller is used in the position control implementation. To further improve the control performance, a gain-tuning IP controller based on a generalized predictive control (GPC) law is proposed. Firstly, to represent the dynamics of the position loop, a second-order linear model is used and its model parameters are estimated on-line by using a recursive least squares method. Secondly, based on the GPC law, an optimal control sequence is obtained by using receding horizon, then directly supplies the IP controller with the corresponding control parameters in the real operations. Finally, simulation and experimental results are presented to show the efficiency of proposed scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Predictive Simulations of ITER Including Neutral Beam Driven Toroidal Rotation
International Nuclear Information System (INIS)
Halpern, Federico D.; Kritz, Arnold H.; Bateman, G.; Pankin, Alexei Y.; Budny, Robert V.; McCune, Douglas C.
2008-01-01
Predictive simulations of ITER [R. Aymar et al., Plasma Phys. Control. Fusion 44, 519 2002] discharges are carried out for the 15 MA high confinement mode (H-mode) scenario using PTRANSP, the predictive version of the TRANSP code. The thermal and toroidal momentum transport equations are evolved using turbulent and neoclassical transport models. A predictive model is used to compute the temperature and width of the H-mode pedestal. The ITER simulations are carried out for neutral beam injection (NBI) heated plasmas, for ion cyclotron resonant frequency (ICRF) heated plasmas, and for plasmas heated with a mix of NBI and ICRF. It is shown that neutral beam injection drives toroidal rotation that improves the confinement and fusion power production in ITER. The scaling of fusion power with respect to the input power and to the pedestal temperature is studied. It is observed that, in simulations carried out using the momentum transport diffusivity computed using the GLF23 model [R.Waltz et al., Phys. Plasmas 4, 2482 (1997)], the fusion power increases with increasing injected beam power and central rotation frequency. It is found that the ITER target fusion power of 500 MW is produced with 20 MW of NBI power when the pedesta temperature is 3.5 keV. 2008 American Institute of Physics. [DOI: 10.1063/1.2931037
Introducing Model Predictive Control for Improving Power Plant Portfolio Performance
DEFF Research Database (Denmark)
Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon
2008-01-01
This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...
Simulation modelling for new gas turbine fuel controller creation.
Vendland, L. E.; Pribylov, V. G.; Borisov, Yu A.; Arzamastsev, M. A.; Kosoy, A. A.
2017-11-01
State of the art gas turbine fuel flow control systems are based on throttle principle. Major disadvantage of such systems is that they require high pressure fuel intake. Different approach to fuel flow control is to use regulating compressor. And for this approach because of controller and gas turbine interaction a specific regulating compressor is required. Difficulties emerge as early as the requirement definition stage. To define requirements for new object, his properties must be known. Simulation modelling helps to overcome these difficulties. At the requirement definition stage the most simplified mathematical model is used. Mathematical models will get more complex and detailed as we advance in planned work. If future adjusting of regulating compressor physical model to work with virtual gas turbine and physical control system is planned.
International Nuclear Information System (INIS)
Nunes de Faria, Mário M.; Vargas Machuca Bueno, Juan P.; Ayad, Sami M.M. Elmassalami; Belchior, Carlos R. Pereira
2017-01-01
Highlights: • A 0-D model for performance prediction of SI ICE fueled with biogas is proposed. • Relative difference between simulated and experimental values was under 5%. • Can be adapted for different biogas compositions and operating ranges. • Could be a valuable tool for predicting trends and guiding experimentation. • Is suitable for use with biogas supplies in developing regions. - Abstract: Biogas found its way from developing countries and is now an alternative to fossil fuels in internal combustion engines and with the advantage of lower greenhouse gas emissions. However, its use in gas engines requires engine modifications or adaptations that may be costly. This paper reports the results of experimental performance and emissions tests of an engine-generator unit fueled with biogas produced in a sewage plant in Brazil, operating under different loads, and with suitable engine modifications. These emissions and performance results were in agreement with the literature and it was confirmed that the penalties to engine performance were more significant than emission reduction in the operating range tested. Furthermore, a zero dimensional simulation model was employed to predict performance characteristics. Moreover, a differential thermodynamic equation system was solved, obtaining the pressure inside the cylinder as a function of the crank angle for different engine conditions. Mean effective pressure and indicated power were also obtained. The results of simulation and experimental tests of the engine in similar conditions were compared and the model validated. Although several simplifying assumptions were adopted and empirical correlations were used for Wiebe function, the model was adequate in predicting engine performance as the relative difference between simulated and experimental values was lower than 5%. The model can be adapted for use with different raw or enriched biogas compositions and could prove to be a valuable tool to guide
Supervisory Model Predictive Control of the Heat Integrated Distillation Column
DEFF Research Database (Denmark)
Meyer, Kristian; Bisgaard, Thomas; Huusom, Jakob Kjøbsted
2017-01-01
This paper benchmarks a centralized control system based on model predictive control for the operation of the heat integrated distillation column (HIDiC) against a fully decentralized control system using the most complete column model currently available in the literature. The centralized control...... system outperforms the decentralized system, because it handles the interactions in the HIDiC process better. The integral absolute error (IAE) is reduced by a factor of 2 and a factor of 4 for control of the top and bottoms compositions, respectively....
A model predictive controller for the water level of nuclear steam generators
International Nuclear Information System (INIS)
Na, Man Gyun
2001-01-01
In this work, the model predictive control method was applied to a linear model and a nonlinear model of steam generators. The parameters of a linear model for steam generators are very different according to the power levels. The model predictive controller was designed for the linear steam generator model at a fixed power level. The proposed controller designed at the fixed power level showed good performance for any other power levels by changing only the input-weighting factor. As the input-weighting factor usually increases, its relative stability does so. The stem generator has some nonlinear characteristics. Therefore, the proposed algorithm has been implemented for a nonlinear model of the nuclear steam generator to verify its real performance and also, showed good performance. (author)
CONTROL PREDICTIVO DE UN ROBOT TIPO SCARA PREDICTIVE CONTROL OF A SCARA ROBOT
Directory of Open Access Journals (Sweden)
Oscar Andrés Vivas Albán
2006-08-01
Full Text Available Este artículo presenta una aplicación eficiente de un control por modelo de referencia sobre un robot de tipo SCARA. El control estudiado es un control predictivo funcional, el que hace uso de un modelo dinámico simplificado del robot. Los ensayos simulados se realizan sobre un robot de cuatro grados de libertad, tipo SCARA. Con el fin de comparar diferentes estrategias de control, se diseña un controlador clásico tipo PID y dos controladores basados en el modelo de referencia. En este último caso el sistema se linealiza y se desacoplada por realimentación, lo que transforma el sistema a controlar en un simple par de integradores. Al sistema lineal y desacoplado resultante se le aplica el control por par calculado y el control predictivo funcional. Los tres controladores estudiados se simulan sobre el robot SCARA con valores numéricos reales. Las pruebas permiten valorar las respuestas de estos controladores en seguimiento de trayectoria, rechazo de perturbaciones y presencia de errores en el modelado con consignas complejas similares a las utilizadas en procesos de fabricación.This paper describes an efficient approach for model based control, applied on a SCARA robot. The studied control is the predictive functional control which uses a simplified dynamical model of the robot. The simulated tests are made on a SCARA type robot, with four DOF. To compare several control strategies, a classical PID control and two model based controllers are designed. In the last case, the model is first linearized and decoupled by feedback, transforming the system into a double set of integrators. Computed torque control and predictive functional control are applied to the linear and decoupled system. The three studied controllers are simulated on the SCARA robot with real numerical values. Tracking performance, disturbance rejection and model robot mismatch are enlightened, using complex machining tasks trajectories and error presence in the modelling
On Practical tuning of Model Uncertainty in Wind Turbine Model Predictive Control
DEFF Research Database (Denmark)
Odgaard, Peter Fogh; Hovgaard, Tobias
2015-01-01
Model predictive control (MPC) has in previous works been applied on wind turbines with promising results. These results apply linear MPC, i.e., linear models linearized at different operational points depending on the wind speed. The linearized models are derived from a nonlinear first principles...... model of a wind turbine. In this paper, we investigate the impact of this approach on the performance of a wind turbine. In particular, we focus on the most non-linear operational ranges of a wind turbine. The MPC controller is designed for, tested, and evaluated at an industrial high fidelity wind...
Directory of Open Access Journals (Sweden)
M’hamed Sekour
2017-01-01
Full Text Available In order to improve the driving performance and the stability of electric vehicles (EVs, a new multimachine robust control, which realizes the acceleration slip regulation (ASR and antilock braking system (ABS functions, based on nonlinear model predictive (NMP direct torque control (DTC, is proposed for four permanent magnet synchronous in-wheel motors. The in-wheel motor provides more possibilities of wheel control. One of its advantages is that it has low response time and almost instantaneous torque generation. Moreover, it can be independently controlled, enhancing the limits of vehicular control. For an EV equipped with four in-wheel electric motors, an advanced control may be envisaged. Taking advantage of the fast and accurate torque of in-wheel electric motors which is directly transmitted to the wheels, a new approach for longitudinal control realized by ASR and ABS is presented in this paper. In order to achieve a high-performance torque control for EVs, the NMP-DTC strategy is proposed. It uses the fuzzy logic control technique that determines online the accurate values of the weighting factors and generates the optimal switching states that optimize the EV drives’ decision. The simulation results built in Matlab/Simulink indicate that the EV can achieve high-performance vehicle longitudinal stability control.
Energy Technology Data Exchange (ETDEWEB)
Abbas, Nikhar; Tom, Nathan
2017-09-01
Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalman filter and autoregressive model to evaluate model predictive control performance.
Directory of Open Access Journals (Sweden)
Kurnyta-Mazurek Paulina
2015-12-01
Full Text Available Conventional controllers are usually synthesized on the basis of already known parameters associated with the model developed for the object to be controlled. However, sometimes it proves extremely difficult or even infeasible to find out these parameters, in particular when they subject to changes during the exploitation lifetime. If so, much more sophisticated control methods have to be applied, e.g. the method of predictive control. Thus, the paper deals with application of the predictive control approach to follow-up tracking of an active magnetic suspension where the mathematical and simulation models for such a control system are disclosed with preliminary results from simulation investigations of the control system in question.
NCS--a software for visual modeling and simulation of PWR nuclear power plant control system
International Nuclear Information System (INIS)
Cui Zhenhua
1998-12-01
The modeling and simulation of nuclear power plant control system has been investigated. Some mathematical models for rapid and accurate simulation are derived, including core models, pressurizer model, steam generator model, etc. Several numerical methods such as Runge-Kutta Method and Treanor Method are adopted to solve the above system models. In order to model the control system conveniently, a block diagram-oriented visual modeling platform is designed. And the Discrete Similarity Method is used to calculate the control system models. A corresponding simulating software, NCS, is developed for researching on the control systems of commercial nuclear power plant. And some satisfactory results are obtained. The research works will be of referential and applying value to design and analysis of nuclear power plant control system
Nonlinear Dynamic Inversion Baseline Control Law: Architecture and Performance Predictions
Miller, Christopher J.
2011-01-01
A model reference dynamic inversion control law has been developed to provide a baseline control law for research into adaptive elements and other advanced flight control law components. This controller has been implemented and tested in a hardware-in-the-loop simulation; the simulation results show excellent handling qualities throughout the limited flight envelope. A simple angular momentum formulation was chosen because it can be included in the stability proofs for many basic adaptive theories, such as model reference adaptive control. Many design choices and implementation details reflect the requirements placed on the system by the nonlinear flight environment and the desire to keep the system as basic as possible to simplify the addition of the adaptive elements. Those design choices are explained, along with their predicted impact on the handling qualities.
Model-Based Power Plant Master Control
Energy Technology Data Exchange (ETDEWEB)
Boman, Katarina; Thomas, Jean; Funkquist, Jonas
2010-08-15
The main goal of the project has been to evaluate the potential of a coordinated master control for a solid fuel power plant in terms of tracking capability, stability and robustness. The control strategy has been model-based predictive control (MPC) and the plant used in the case study has been the Vattenfall power plant Idbaecken in Nykoeping. A dynamic plant model based on nonlinear physical models was used to imitate the true plant in MATLAB/SIMULINK simulations. The basis for this model was already developed in previous Vattenfall internal projects, along with a simulation model of the existing control implementation with traditional PID controllers. The existing PID control is used as a reference performance, and it has been thoroughly studied and tuned in these previous Vattenfall internal projects. A turbine model was developed with characteristics based on the results of steady-state simulations of the plant using the software EBSILON. Using the derived model as a representative for the actual process, an MPC control strategy was developed using linearization and gain-scheduling. The control signal constraints (rate of change) and constraints on outputs were implemented to comply with plant constraints. After tuning the MPC control parameters, a number of simulation scenarios were performed to compare the MPC strategy with the existing PID control structure. The simulation scenarios also included cases highlighting the robustness properties of the MPC strategy. From the study, the main conclusions are: - The proposed Master MPC controller shows excellent set-point tracking performance even though the plant has strong interactions and non-linearity, and the controls and their rate of change are bounded. - The proposed Master MPC controller is robust, stable in the presence of disturbances and parameter variations. Even though the current study only considered a very small number of the possible disturbances and modelling errors, the considered cases are
Moment based model predictive control for systems with additive uncertainty
Saltik, M.B.; Ozkan, L.; Weiland, S.; Ludlage, J.H.A.
2017-01-01
In this paper, we present a model predictive control (MPC) strategy based on the moments of the state variables and the cost functional. The statistical properties of the state predictions are calculated through the open loop iteration of dynamics and used in the formulation of MPC cost function. We
Zhu, Kaiqun; Song, Yan; Zhang, Sunjie; Zhong, Zhaozhun
2017-07-01
In this paper, a non-fragile observer-based output feedback control problem for the polytopic uncertain system under distributed model predictive control (MPC) approach is discussed. By decomposing the global system into some subsystems, the computation complexity is reduced, so it follows that the online designing time can be saved.Moreover, an observer-based output feedback control algorithm is proposed in the framework of distributed MPC to deal with the difficulties in obtaining the states measurements. In this way, the presented observer-based output-feedback MPC strategy is more flexible and applicable in practice than the traditional state-feedback one. What is more, the non-fragility of the controller has been taken into consideration in favour of increasing the robustness of the polytopic uncertain system. After that, a sufficient stability criterion is presented by using Lyapunov-like functional approach, meanwhile, the corresponding control law and the upper bound of the quadratic cost function are derived by solving an optimisation subject to convex constraints. Finally, some simulation examples are employed to show the effectiveness of the method.
Liacouras, Peter C; Wayne, Jennifer S
2007-12-01
Computational models of musculoskeletal joints and limbs can provide useful information about joint mechanics. Validated models can be used as predictive devices for understanding joint function and serve as clinical tools for predicting the outcome of surgical procedures. A new computational modeling approach was developed for simulating joint kinematics that are dictated by bone/joint anatomy, ligamentous constraints, and applied loading. Three-dimensional computational models of the lower leg were created to illustrate the application of this new approach. Model development began with generating three-dimensional surfaces of each bone from CT images and then importing into the three-dimensional solid modeling software SOLIDWORKS and motion simulation package COSMOSMOTION. Through SOLIDWORKS and COSMOSMOTION, each bone surface file was filled to create a solid object and positioned necessary components added, and simulations executed. Three-dimensional contacts were added to inhibit intersection of the bones during motion. Ligaments were represented as linear springs. Model predictions were then validated by comparison to two different cadaver studies, syndesmotic injury and repair and ankle inversion following ligament transection. The syndesmotic injury model was able to predict tibial rotation, fibular rotation, and anterior/posterior displacement. In the inversion simulation, calcaneofibular ligament extension and angles of inversion compared well. Some experimental data proved harder to simulate accurately, due to certain software limitations and lack of complete experimental data. Other parameters that could not be easily obtained experimentally can be predicted and analyzed by the computational simulations. In the syndesmotic injury study, the force generated in the tibionavicular and calcaneofibular ligaments reduced with the insertion of the staple, indicating how this repair technique changes joint function. After transection of the calcaneofibular
Economic Model Predictive Control for Spray Drying Plants
DEFF Research Database (Denmark)
Petersen, Lars Norbert
and a complexity reduced control model is used for state estimation and prediction in the controllers. These models facilitate development and comparison of control strategies. We develop two MPC strategies; a linear tracking MPC with a Real-Time Optimization layer (MPC with RTO) and an Economic Nonlinear MPC (E...... horizon, out of which only the first input is applied to the dryer. This procedure is repeated at each sample instant and is solved numerically in real-time. The MPC with RTO tracks a target that optimizes the cost of operation at steady-state. The E-MPC optimizes the cost of operation directly by having...... this objective directly in the controller. The need for the RTO layer is then eliminated. We demonstrate the application of the proposed MPC with RTO to control an industrial GEA MSDTM-1250 spray dryer, which produces approximately 7500 kg/hr of enriched milk powder. Compared to the conventional PI controller...
Dynamic Model of Centrifugal Compressor for Prediction of Surge Evolution and Performance Variations
International Nuclear Information System (INIS)
Jung, Mooncheong; Han, Jaeyoung; Yu, Sangseok
2016-01-01
When a control algorithm is developed to protect automotive compressor surges, the simulation model typically selects an empirically determined look-up table. However, it is difficult for a control oriented empirical model to show surge characteristics of the super charger. In this study, a dynamic supercharger model is developed to predict the performance of a centrifugal compressor under dynamic load follow-up. The model is developed using Simulink® environment, and is composed of a compressor, throttle body, valves, and chamber. Greitzer’s compressor model is used, and the geometric parameters are achieved by the actual supercharger. The simulation model is validated with experimental data. It is shown that compressor surge is effectively predicted by this dynamic compressor model under various operating conditions.
Dynamic Model of Centrifugal Compressor for Prediction of Surge Evolution and Performance Variations
Energy Technology Data Exchange (ETDEWEB)
Jung, Mooncheong; Han, Jaeyoung; Yu, Sangseok [Chungnam National Univ., Daejeon (Korea, Republic of)
2016-05-15
When a control algorithm is developed to protect automotive compressor surges, the simulation model typically selects an empirically determined look-up table. However, it is difficult for a control oriented empirical model to show surge characteristics of the super charger. In this study, a dynamic supercharger model is developed to predict the performance of a centrifugal compressor under dynamic load follow-up. The model is developed using Simulink® environment, and is composed of a compressor, throttle body, valves, and chamber. Greitzer’s compressor model is used, and the geometric parameters are achieved by the actual supercharger. The simulation model is validated with experimental data. It is shown that compressor surge is effectively predicted by this dynamic compressor model under various operating conditions.
Automatic Power Control for Daily Load-following Operation using Model Predictive Control Method
Energy Technology Data Exchange (ETDEWEB)
Yu, Keuk Jong; Kim, Han Gon [KH, Daejeon (Korea, Republic of)
2009-10-15
Under the circumstances that nuclear power occupies more than 50%, nuclear power plants are required to be operated on load-following operation in order to make the effective management of electric grid system and enhanced responsiveness to rapid changes in power demand. Conventional reactors such as the OPR1000 and APR1400 have a regulating system that controls the average temperature of the reactor core relation to the reference temperature. This conventional method has the advantages of proven technology and ease of implementation. However, this method is unsuitable for controlling the axial power shape, particularly the load following operation. Accordingly, this paper reports on the development of a model predictive control method which is able to control the reactor power and the axial shape index. The purpose of this study is to analyze the behavior of nuclear reactor power and the axial power shape by using a model predictive control method when the power is increased and decreased for a daily load following operation. The study confirms that deviations in the axial shape index (ASI) are within the operating limit.
Energy Technology Data Exchange (ETDEWEB)
Flueck, Alex [Illinois Inst. of Technology, Chicago, IL (United States)
2017-07-14
The “High Fidelity, Faster than RealTime Simulator for Predicting Power System Dynamic Behavior” was designed and developed by Illinois Institute of Technology with critical contributions from Electrocon International, Argonne National Laboratory, Alstom Grid and McCoy Energy. Also essential to the project were our two utility partners: Commonwealth Edison and AltaLink. The project was a success due to several major breakthroughs in the area of largescale power system dynamics simulation, including (1) a validated faster than real time simulation of both stable and unstable transient dynamics in a largescale positive sequence transmission grid model, (2) a threephase unbalanced simulation platform for modeling new grid devices, such as independently controlled singlephase static var compensators (SVCs), (3) the world’s first high fidelity threephase unbalanced dynamics and protection simulator based on Electrocon’s CAPE program, and (4) a firstofits kind implementation of a singlephase induction motor model with stall capability. The simulator results will aid power grid operators in their true time of need, when there is a significant risk of cascading outages. The simulator will accelerate performance and enhance accuracy of dynamics simulations, enabling operators to maintain reliability and steer clear of blackouts. In the longterm, the simulator will form the backbone of the newly conceived hybrid realtime protection and control architecture that will coordinate local controls, widearea measurements, widearea controls and advanced realtime prediction capabilities. The nation’s citizens will benefit in several ways, including (1) less down time from power outages due to the fasterthanrealtime simulator’s predictive capability, (2) higher levels of reliability due to the detailed dynamics plus protection simulation capability, and (3) more resiliency due to the three phase unbalanced simulator’s ability to
Pilot-model analysis and simulation study of effect of control task desired control response
Adams, J. J.; Gera, J.; Jaudon, J. B.
1978-01-01
A pilot model analysis was performed that relates pilot control compensation, pilot aircraft system response, and aircraft response characteristics for longitudinal control. The results show that a higher aircraft short period frequency is required to achieve superior pilot aircraft system response in an altitude control task than is required in an attitude control task. These results were confirmed by a simulation study of target tracking. It was concluded that the pilot model analysis provides a theoretical basis for determining the effect of control task on pilot opinions.
Liu, Shuming; Brazier, Richard; Heathwaite, Louise
2005-05-15
A simple catchment scale model simulating diffuse phosphorus (P) loss from agricultural land to water, the Phosphorus Indicators Tool (PIT), has been developed. Previous research has shown that this model worked well in simulating the average annual P lost from two catchments: Windermere and Windrush, but it was not known which drivers in the model had the greatest control on predicted P delivery to water from agricultural land. In order to simulate the P export from each catchment source via each hydrological pathway specified individually, 108 coefficients are used in the model code. A univariate sensitivity analysis was conducted to evaluate which coefficient exerted the greatest control on the model output. Results from the univariate analysis suggest that the model is sensitive to a number of coefficients, but importantly, not all of the coefficients that were varied in the sensitivity analysis, altered the model output. The PIT model has been calibrated by optimizing results from the univariate analysis against observed data in the Windermere catchment. The simulated results from model calibration fit the observed data well, at the 95% level. This paper describes the methodology developed for the univariate analysis and evaluates the model calibration procedure against observed data from the Windermere catchment.
Design and Application of Offset-Free Model Predictive Control Disturbance Observation Method
Directory of Open Access Journals (Sweden)
Xue Wang
2016-01-01
Full Text Available Model predictive control (MPC with its lower request to the mathematical model, excellent control performance, and convenience online calculation has developed into a very important subdiscipline with rich theory foundation and practical application. However, unmeasurable disturbance is widespread in industrial processes, which is difficult to deal with directly at present. In most of the implemented MPC strategies, the method of incorporating a constant output disturbance into the process model is introduced to solve this problem, but it fails to achieve offset-free control once the unmeasured disturbances access the process. Based on the Kalman filter theory, the problem is solved by using a more general disturbance model which is superior to the constant output disturbance model. This paper presents the necessary conditions for offset-free model predictive control based on the model. By applying disturbance model, the unmeasurable disturbance vectors are augmented as the states of control system, and the Kalman filer is used to estimate unmeasurable disturbance and its effect on the output. Then, the dynamic matrix control (DMC algorithm is improved by utilizing the feed-forward compensation control strategy with the disturbance estimated.
Directory of Open Access Journals (Sweden)
Abdul Mannan Dadu
2017-12-01
Full Text Available A high computational burden is required in conventional model predictive control, as all of the voltage vectors of a power inverter are used to predict the future behavior of the system. Apart from that, the common mode voltage (CMV of a three-phase four-leg inverter utilizes up to half of the DC-link voltage due to the use of all of the available voltage vectors. Thus, this paper proposes a near state vector selection-based model predictive control (NSV-MPC scheme to mitigate the CMV and reduce computational burden. In the proposed technique, only six active voltage vectors are used in the predictive model, and the vectors are selected based on the position of the future reference vector. In every sampling period, the position of the reference current is used to detect the voltage vectors surrounding the reference voltage vector. Besides the six active vectors, one of the zero vectors is also used. The proposed technique is compared with the conventional control scheme in terms of execution time, CMV variation, and load current ripple in both simulation and an experimental setup. The LabVIEW Field programmable gate array rapid prototyping controller is used to validate the proposed control scheme experimentally, and demonstrate that the CMV can be bounded within one-fourth of the DC-link voltage.
Explicit Nonlinear Model Predictive Control Theory and Applications
Grancharova, Alexandra
2012-01-01
Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behind explicit NMPC is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real time. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation and the possibility to design embedded control systems with low software and hardware complexity. This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations: Ø Nonlinear systems described by first-principles models and nonlinear systems described by black-box models; �...
International Nuclear Information System (INIS)
Montoya Andrade, Dan-El; Villa Jaén, Antonio de la; García Santana, Agustín
2014-01-01
Highlights: • We considered the linear generator copper losses in the proposed MPC strategy. • We maximized the power transferred to the generator side power converter. • The proposed MPC increases the useful average power injected into the grid. • The stress level of the PTO system can be reduced by the proposed MPC. - Abstract: The amount of energy that a wave energy converter can extract depends strongly on the control strategy applied to the power take-off system. It is well known that, ideally, the reactive control allows for maximum energy extraction from waves. However, the reactive control is intrinsically noncausal in practice and requires some kind of causal approach to be applied. Moreover, this strategy does not consider physical constraints and this could be a problem because the system could achieve unacceptable dynamic values. These, and other control techniques have focused on the wave energy extraction problem in order to maximize the energy absorbed by the power take-off device without considering the possible losses in intermediate devices. In this sense, a reactive control that considers the linear generator copper losses has been recently proposed to increase the useful power injected into the grid. Among the control techniques that have emerged recently, the model predictive control represents a promising strategy. This approach performs an optimization process on a time prediction horizon incorporating dynamic constraints associated with the physical features of the power take-off system. This paper proposes a model predictive control technique that considers the copper losses in the control optimization process of point absorbers with direct drive linear generators. This proposal makes the most of reactive control as it considers the copper losses, and it makes the most of the model predictive control, as it considers the system constraints. This means that the useful power transferred from the linear generator to the power
High Quality Model Predictive Control for Single Phase Grid Connected Photovoltaic Inverters
DEFF Research Database (Denmark)
Zangeneh Bighash, Esmaeil; Sadeghzadeh, Seyed Mohammad; Ebrahimzadeh, Esmaeil
2018-01-01
Single phase grid-connected inverters with LCL filter are widely used to connect the photovoltaic systems to the utility grid. Among the presented control schemes, predictive control methods are faster and more accurate but are more complex to implement. Recently, the model-predictive control...... algorithm for single-phase inverter has been presented, where the algorithm implementation is straightforward. In the proposed approach, all switching states are tested in each switching period to achieve the control objectives. However, since the number of the switching states in single-phase inverter...... is low, the inverter output current has a high total harmonic distortions. In order to reduce the total harmonic distortions of the injected current, this paper presents a high-quality model-predictive control for one of the newest structure of the grid connected photovoltaic inverter, i.e., HERIC...
Multi input single output model predictive control of non-linear bio-polymerization process
Energy Technology Data Exchange (ETDEWEB)
Arumugasamy, Senthil Kumar; Ahmad, Z. [School of Chemical Engineering, Univerisiti Sains Malaysia, Engineering Campus, Seri Ampangan,14300 Nibong Tebal, Seberang Perai Selatan, Pulau Pinang (Malaysia)
2015-05-15
This paper focuses on Multi Input Single Output (MISO) Model Predictive Control of bio-polymerization process in which mechanistic model is developed and linked with the feedforward neural network model to obtain a hybrid model (Mechanistic-FANN) of lipase-catalyzed ring-opening polymerization of ε-caprolactone (ε-CL) for Poly (ε-caprolactone) production. In this research, state space model was used, in which the input to the model were the reactor temperatures and reactor impeller speeds and the output were the molecular weight of polymer (M{sub n}) and polymer polydispersity index. State space model for MISO created using System identification tool box of Matlab™. This state space model is used in MISO MPC. Model predictive control (MPC) has been applied to predict the molecular weight of the biopolymer and consequently control the molecular weight of biopolymer. The result shows that MPC is able to track reference trajectory and give optimum movement of manipulated variable.
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Frison, Gianluca; Skajaa, Anders
2015-01-01
We develop an efficient homogeneous and self-dual interior-point method (IPM) for the linear programs arising in economic model predictive control of constrained linear systems with linear objective functions. The algorithm is based on a Riccati iteration procedure, which is adapted to the linear...... system of equations solved in homogeneous and self-dual IPMs. Fast convergence is further achieved using a warm-start strategy. We implement the algorithm in MATLAB and C. Its performance is tested using a conceptual power management case study. Closed loop simulations show that 1) the proposed algorithm...
International Nuclear Information System (INIS)
Edelbaum, T.N.; Serben, S.; Var, R.E.
1976-01-01
A computer model of a tokamak experimental power reactor and its control system is being constructed. This simulator will allow the exploration of various open loop and closed loop strategies for reactor control. This paper provides a brief description of the simulator and some of the potential control problems associated with this class of tokamaks
Predictive Eco-Cruise Control (ECC) system : model development, modeling and potential benefits.
2013-02-01
The research develops a reference model of a predictive eco-cruise control (ECC) system that intelligently modulates vehicle speed within a pre-set speed range to minimize vehicle fuel consumption levels using roadway topographic information. The stu...
Modeling and control for closed environment plant production systems
Fleisher, David H.; Ting, K. C.; Janes, H. W. (Principal Investigator)
2002-01-01
A computer program was developed to study multiple crop production and control in controlled environment plant production systems. The program simulates crop growth and development under nominal and off-nominal environments. Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller. The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling. The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting. The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations. Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.
Economic model predictive control theory, formulations and chemical process applications
Ellis, Matthew; Christofides, Panagiotis D
2017-01-01
This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes: Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples. The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes. In addition to being...
Stability of a neural predictive controller scheme on a neural model
DEFF Research Database (Denmark)
Luther, Jim Benjamin; Sørensen, Paul Haase
2009-01-01
In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue....... The resulting controller is tested on a nonlinear pneumatic servo system.......In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue...... has not been addressed specifically for these controllers. On the other hand a number of results concerning the stability of receding horizon controllers on a nonlinear system exist. In this paper we present a proof of stability for a predictive controller controlling a neural network model...
Offset-Free Direct Power Control of DFIG Under Continuous-Time Model Predictive Control
DEFF Research Database (Denmark)
Errouissi, Rachid; Al-Durra, Ahmed; Muyeen, S.M.
2017-01-01
This paper presents a robust continuous-time model predictive direct power control for doubly fed induction generator (DFIG). The proposed approach uses Taylor series expansion to predict the stator current in the synchronous reference frame over a finite time horizon. The predicted stator current...... is directly used to compute the required rotor voltage in order to minimize the difference between the actual stator currents and their references over the predictive time. However, as the proposed strategy is sensitive to parameter variations and external disturbances, a disturbance observer is embedded...... into the control loop to remove the steady-state error of the stator current. It turns out that the steady-state and the transient performances can be identified by simple design parameters. In this paper, the reference of the stator current is directly calculated from the desired stator active and reactive powers...
Predictive models of glucose control: roles for glucose-sensing neurones
Kosse, C.; Gonzalez, A.; Burdakov, D.
2018-01-01
The brain can be viewed as a sophisticated control module for stabilizing blood glucose. A review of classical behavioural evidence indicates that central circuits add predictive (feedforward/anticipatory) control to the reactive (feedback/compensatory) control by peripheral organs. The brain/cephalic control is constructed and engaged, via associative learning, by sensory cues predicting energy intake or expenditure (e.g. sight, smell, taste, sound). This allows rapidly measurable sensory information (rather than slowly generated internal feedback signals, e.g. digested nutrients) to control food selection, glucose supply for fight-or-flight responses or preparedness for digestion/absorption. Predictive control is therefore useful for preventing large glucose fluctuations. We review emerging roles in predictive control of two classes of widely projecting hypothalamic neurones, orexin/hypocretin (ORX) and melanin-concentrating hormone (MCH) cells. Evidence is cited that ORX neurones (i) are activated by sensory cues (e.g. taste, sound), (ii) drive hepatic production, and muscle uptake, of glucose, via sympathetic nerves, (iii) stimulate wakefulness and exploration via global brain projections and (iv) are glucose-inhibited. MCH neurones are (i) glucose-excited, (ii) innervate learning and reward centres to promote synaptic plasticity, learning and memory and (iii) are critical for learning associations useful for predictive control (e.g. using taste to predict nutrient value of food). This evidence is unified into a model for predictive glucose control. During associative learning, inputs from some glucose-excited neurones may promote connections between the ‘fast’ senses and reward circuits, constructing neural shortcuts for efficient action selection. In turn, glucose-inhibited neurones may engage locomotion/exploration and coordinate the required fuel supply. Feedback inhibition of the latter neurones by glucose would ensure that glucose fluxes they
Predictive models of glucose control: roles for glucose-sensing neurones.
Kosse, C; Gonzalez, A; Burdakov, D
2015-01-01
The brain can be viewed as a sophisticated control module for stabilizing blood glucose. A review of classical behavioural evidence indicates that central circuits add predictive (feedforward/anticipatory) control to the reactive (feedback/compensatory) control by peripheral organs. The brain/cephalic control is constructed and engaged, via associative learning, by sensory cues predicting energy intake or expenditure (e.g. sight, smell, taste, sound). This allows rapidly measurable sensory information (rather than slowly generated internal feedback signals, e.g. digested nutrients) to control food selection, glucose supply for fight-or-flight responses or preparedness for digestion/absorption. Predictive control is therefore useful for preventing large glucose fluctuations. We review emerging roles in predictive control of two classes of widely projecting hypothalamic neurones, orexin/hypocretin (ORX) and melanin-concentrating hormone (MCH) cells. Evidence is cited that ORX neurones (i) are activated by sensory cues (e.g. taste, sound), (ii) drive hepatic production, and muscle uptake, of glucose, via sympathetic nerves, (iii) stimulate wakefulness and exploration via global brain projections and (iv) are glucose-inhibited. MCH neurones are (i) glucose-excited, (ii) innervate learning and reward centres to promote synaptic plasticity, learning and memory and (iii) are critical for learning associations useful for predictive control (e.g. using taste to predict nutrient value of food). This evidence is unified into a model for predictive glucose control. During associative learning, inputs from some glucose-excited neurones may promote connections between the 'fast' senses and reward circuits, constructing neural shortcuts for efficient action selection. In turn, glucose-inhibited neurones may engage locomotion/exploration and coordinate the required fuel supply. Feedback inhibition of the latter neurones by glucose would ensure that glucose fluxes they stimulate
MODEL PREDICTIVE CONTROL FUNDAMENTALS
African Journals Online (AJOL)
2012-07-02
Jul 2, 2012 ... signal based on a process model, coping with constraints on inputs and ... paper, we will present an introduction to the theory and application of MPC with Matlab codes ... section 5 presents the simulation results and section 6.
Simulation-Based Internal Models for Safer Robots
Directory of Open Access Journals (Sweden)
Christian Blum
2018-01-01
Full Text Available In this paper, we explore the potential of mobile robots with simulation-based internal models for safety in highly dynamic environments. We propose a robot with a simulation of itself, other dynamic actors and its environment, inside itself. Operating in real time, this simulation-based internal model is able to look ahead and predict the consequences of both the robot’s own actions and those of the other dynamic actors in its vicinity. Hence, the robot continuously modifies its own actions in order to actively maintain its own safety while also achieving its goal. Inspired by the problem of how mobile robots could move quickly and safely through crowds of moving humans, we present experimental results which compare the performance of our internal simulation-based controller with a purely reactive approach as a proof-of-concept study for the practical use of simulation-based internal models.
Stability of the spreading in small-world network with predictive controller
International Nuclear Information System (INIS)
Bao, Z.J.; Jiang, Q.Y.; Yan, W.J.; Cao, Y.J.
2010-01-01
In this Letter, we apply the predictive control strategy to suppress the propagation of diseases or viruses in small-world network. The stability of small-world spreading model with predictive controller is investigated. The sufficient and necessary stability condition is given, which is closely related to the controller parameters and small-world rewiring probability p. Our simulations discover a phenomenon that, with the fixed predictive controller parameters, the spreading dynamics become more and more stable when p decreases from a larger value to a smaller one, and the suitable controller parameters can effectively suppress the spreading behaviors even when p varies within the whole spectrum, and the unsuitable controller parameters can lead to oscillation when p lies within a certain range.
Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction
Li, Zhencai; Wang, Yang; Liu, Zhen
2016-01-01
The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703
Saltik, M.B.; Özkan, L.; Ludlage, J.H.A.; Weiland, S.; Van den Hof, P.M.J.
2018-01-01
In this paper, we discuss the model predictive control algorithms that are tailored for uncertain systems. Robustness notions with respect to both deterministic (or set based) and stochastic uncertainties are discussed and contributions are reviewed in the model predictive control literature. We
Model predictive control of hybrid systems : stability and robustness
Lazar, M.
2006-01-01
This thesis considers the stabilization and the robust stabilization of certain classes of hybrid systems using model predictive control. Hybrid systems represent a broad class of dynamical systems in which discrete behavior (usually described by a finite state machine) and continuous behavior
Bye, Robin T; Neilson, Peter D
2010-10-01
Physiological tremor during movement is characterized by ∼10 Hz oscillation observed both in the electromyogram activity and in the velocity profile. We propose that this particular rhythm occurs as the direct consequence of a movement response planning system that acts as an intermittent predictive controller operating at discrete intervals of ∼100 ms. The BUMP model of response planning describes such a system. It forms the kernel of Adaptive Model Theory which defines, in computational terms, a basic unit of motor production or BUMP. Each BUMP consists of three processes: (1) analyzing sensory information, (2) planning a desired optimal response, and (3) execution of that response. These processes operate in parallel across successive sequential BUMPs. The response planning process requires a discrete-time interval in which to generate a minimum acceleration trajectory to connect the actual response with the predicted future state of the target and compensate for executional error. We have shown previously that a response planning time of 100 ms accounts for the intermittency observed experimentally in visual tracking studies and for the psychological refractory period observed in double stimulation reaction time studies. We have also shown that simulations of aimed movement, using this same planning interval, reproduce experimentally observed speed-accuracy tradeoffs and movement velocity profiles. Here we show, by means of a simulation study of constant velocity tracking movements, that employing a 100 ms planning interval closely reproduces the measurement discontinuities and power spectra of electromyograms, joint-angles, and angular velocities of physiological tremor reported experimentally. We conclude that intermittent predictive control through sequential operation of BUMPs is a fundamental mechanism of 10 Hz physiological tremor in movement. Copyright © 2010 Elsevier B.V. All rights reserved.
Chilibroste, P.; Dijkstra, J.; Robinson, P.H.; Tamminga, S.
2008-01-01
A simulation rumen model has been developed to function under non-steady state conditions in order to allow prediction of nutrient availability in dairy cows managed under discontinuous feeding systems. The model simulates availability of glycogenic, aminogenic and lipogenic nutrients to lactating
Application of model predictive control for optimal operation of wind turbines
Yuan, Yuan; Cao, Pei; Tang, J.
2017-04-01
For large-scale wind turbines, reducing maintenance cost is a major challenge. Model predictive control (MPC) is a promising approach to deal with multiple conflicting objectives using the weighed sum approach. In this research, model predictive control method is applied to wind turbine to find an optimal balance between multiple objectives, such as the energy capture, loads on turbine components, and the pitch actuator usage. The actuator constraints are integrated into the objective function at the control design stage. The analysis is carried out in both the partial load region and full load region, and the performances are compared with those of a baseline gain scheduling PID controller. The application of this strategy achieves enhanced balance of component loads, the average power and actuator usages in partial load region.
A Model Predictive Algorithm for Active Control of Nonlinear Noise Processes
Directory of Open Access Journals (Sweden)
Qi-Zhi Zhang
2005-01-01
Full Text Available In this paper, an improved nonlinear Active Noise Control (ANC system is achieved by introducing an appropriate secondary source. For ANC system to be successfully implemented, the nonlinearity of the primary path and time delay of the secondary path must be overcome. A nonlinear Model Predictive Control (MPC strategy is introduced to deal with the time delay in the secondary path and the nonlinearity in the primary path of the ANC system. An overall online modeling technique is utilized for online secondary path and primary path estimation. The secondary path is estimated using an adaptive FIR filter, and the primary path is estimated using a Neural Network (NN. The two models are connected in parallel with the two paths. In this system, the mutual disturbances between the operation of the nonlinear ANC controller and modeling of the secondary can be greatly reduced. The coefficients of the adaptive FIR filter and weight vector of NN are adjusted online. Computer simulations are carried out to compare the proposed nonlinear MPC method with the nonlinear Filter-x Least Mean Square (FXLMS algorithm. The results showed that the convergence speed of the proposed nonlinear MPC algorithm is faster than that of nonlinear FXLMS algorithm. For testing the robust performance of the proposed nonlinear ANC system, the sudden changes in the secondary path and primary path of the ANC system are considered. Results indicated that the proposed nonlinear ANC system can rapidly track the sudden changes in the acoustic paths of the nonlinear ANC system, and ensure the adaptive algorithm stable when the nonlinear ANC system is time variable.
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Standardi, Laura; Edlund, Kristian
2014-01-01
This paper presents a warm-started Dantzig–Wolfe decomposition algorithm tailored to economic model predictive control of dynamically decoupled subsystems. We formulate the constrained optimal control problem solved at each sampling instant as a linear program with state space constraints, input...... limits, input rate limits, and soft output limits. The objective function of the linear program is related directly to the cost of operating the subsystems, and the cost of violating the soft output constraints. Simulations for large-scale economic power dispatch problems show that the proposed algorithm...... is significantly faster than both state-of-the-art linear programming solvers, and a structure exploiting implementation of the alternating direction method of multipliers. It is also demonstrated that the control strategy presented in this paper can be tuned using a weighted ℓ1-regularization term...
Stauch, V. J.; Gwerder, M.; Gyalistras, D.; Oldewurtel, F.; Schubiger, F.; Steiner, P.
2010-09-01
The high proportion of the total primary energy consumption by buildings has increased the public interest in the optimisation of buildings' operation and is also driving the development of novel control approaches for the indoor climate. In this context, the use of weather forecasts presents an interesting and - thanks to advances in information and predictive control technologies and the continuous improvement of numerical weather prediction (NWP) models - an increasingly attractive option for improved building control. Within the research project OptiControl (www.opticontrol.ethz.ch) predictive control strategies for a wide range of buildings, heating, ventilation and air conditioning (HVAC) systems, and representative locations in Europe are being investigated with the aid of newly developed modelling and simulation tools. Grid point predictions for radiation, temperature and humidity of the high-resolution limited area NWP model COSMO-7 (see www.cosmo-model.org) and local measurements are used as disturbances and inputs into the building system. The control task considered consists in minimizing energy consumption whilst maintaining occupant comfort. In this presentation, we use the simulation-based OptiControl methodology to investigate the impact of COSMO-7 forecasts on the performance of predictive building control and the resulting energy savings. For this, we have selected building cases that were shown to benefit from a prediction horizon of up to 3 days and therefore, are particularly suitable for the use of numerical weather forecasts. We show that the controller performance is sensitive to the quality of the weather predictions, most importantly of the incident radiation on differently oriented façades. However, radiation is characterised by a high temporal and spatial variability in part caused by small scale and fast changing cloud formation and dissolution processes being only partially represented in the COSMO-7 grid point predictions. On the
Model Predictive Voltage Control of Wind Power Plants
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei
2018-01-01
the efficacy of the proposed WFVC, two case scenarios were designed: the wind farm is under normal operating conditions and the internal wind power fluctuation is considered; and besides internal power fluctuation, the impact of the external grid on the wind farm is considered.......This chapter proposes an autonomous wind farm voltage controller (WFVC) based on model predictive control (MPC). It also introduces the analytical expressions for the voltage sensitivity to tap positions of a transformer. The chapter then describes the discrete models for the wind turbine...... generators (WTGs) and static var compensators (SVCs)/static var generators (SVGs). Next, it describes the implementation of the on‐load tap changing (OLTC) in the MPC. Furthermore, the chapter examines the cost function as well as the constraints of the MPC‐based WFVC for both control modes. In order to test...
Mahoney, D. T.; al Aamery, N. M. H.; Fox, J.
2017-12-01
The authors find that sediment (dis)connectivity has seldom taken precedence within watershed models, and the present study advances this modeling framework and applies the modeling within a bedrock-controlled system. Sediment (dis)connectivity, defined as the detachment and transport of sediment from source to sink between geomorphic zones, is a major control on sediment transport. Given the availability of high resolution geospatial data, coupling sediment connectivity concepts within sediment prediction models offers an approach to simulate sediment sources and pathways within a watershed's sediment cascade. Bedrock controlled catchments are potentially unique due to the presence of rock outcrops causing longitudinal impedance to sediment transport pathways in turn impacting the longitudinal distribution of the energy gradient responsible for conveying sediment. Therefore, the authors were motivated by the need to formulate a sediment transport model that couples sediment (dis)connectivity knowledge to predict sediment flux for bedrock controlled catchments. A watershed-scale sediment transport model was formulated that incorporates sediment (dis)connectivity knowledge collected via field reconnaissance and predicts sediment flux through coupling with the Partheniades equation and sediment continuity model. Sediment (dis)connectivity was formulated by coupling probabilistic upland lateral connectivity prediction with instream longitudinal connectivity assessments via discretization of fluid and sediment pathways. Flux predictions from the upland lateral connectivity model served as an input to the instream longitudinal connectivity model. Disconnectivity in the instream model was simulated via the discretization of stream reaches due to barriers such as bedrock outcroppings and man-made check dams. The model was tested for a bedrock controlled catchment in Kentucky, USA for which extensive historic water and sediment flux data was available. Predicted sediment
Simulated annealing model of acupuncture
Shang, Charles; Szu, Harold
2015-05-01
The growth control singularity model suggests that acupuncture points (acupoints) originate from organizers in embryogenesis. Organizers are singular points in growth control. Acupuncture can cause perturbation of a system with effects similar to simulated annealing. In clinical trial, the goal of a treatment is to relieve certain disorder which corresponds to reaching certain local optimum in simulated annealing. The self-organizing effect of the system is limited and related to the person's general health and age. Perturbation at acupoints can lead a stronger local excitation (analogous to higher annealing temperature) compared to perturbation at non-singular points (placebo control points). Such difference diminishes as the number of perturbed points increases due to the wider distribution of the limited self-organizing activity. This model explains the following facts from systematic reviews of acupuncture trials: 1. Properly chosen single acupoint treatment for certain disorder can lead to highly repeatable efficacy above placebo 2. When multiple acupoints are used, the result can be highly repeatable if the patients are relatively healthy and young but are usually mixed if the patients are old, frail and have multiple disorders at the same time as the number of local optima or comorbidities increases. 3. As number of acupoints used increases, the efficacy difference between sham and real acupuncture often diminishes. It predicted that the efficacy of acupuncture is negatively correlated to the disease chronicity, severity and patient's age. This is the first biological - physical model of acupuncture which can predict and guide clinical acupuncture research.
An Iterative Approach for Distributed Model Predictive Control of Irrigation Canals
Doan, D.; Keviczky, T.; Negenborn, R.R.; De Schutter, B.
2009-01-01
Optimization techniques have played a fundamental role in designing automatic control systems for the most part of the past half century. This dependence is ever more obvious in today’s wide-spread use of online optimization-based control methods, such as Model Predictive Control (MPC) [1]. The
Johnson, Erik A.; Elhaddad, Wael M.; Wojtkiewicz, Steven F.
2016-04-01
A variety of strategies have been developed over the past few decades to determine controllable damping device forces to mitigate the response of structures and mechanical systems to natural hazards and other excitations. These "smart" damping devices produce forces through passive means but have properties that can be controlled in real time, based on sensor measurements of response across the structure, to dramatically reduce structural motion by exploiting more than the local "information" that is available to purely passive devices. A common strategy is to design optimal damping forces using active control approaches and then try to reproduce those forces with the smart damper. However, these design forces, for some structures and performance objectives, may achieve high performance by selectively adding energy, which cannot be replicated by a controllable damping device, causing the smart damper performance to fall far short of what an active system would provide. The authors have recently demonstrated that a model predictive control strategy using hybrid system models, which utilize both continuous and binary states (the latter to capture the switching behavior between dissipative and non-dissipative forces), can provide reductions in structural response on the order of 50% relative to the conventional clipped-optimal design strategy. This paper explores the robustness of this newly proposed control strategy through evaluating controllable damper performance when the structure model differs from the nominal one used to design the damping strategy. Results from the application to a two-degree-of-freedom structure model confirms the robustness of the proposed strategy.
Directory of Open Access Journals (Sweden)
V. Balaji
2016-12-01
Full Text Available pH control plays a important role in any chemical plant and process industries. For the past four decades the classical PID controller has been occupied by the industries. Due to the faster computing technology in the industry demands a tighter advanced control strategy. To fulfill the needs and requirements Model Predictive Control (MPC is the best among all the advanced control algorithms available in the present scenario. The study and analysis has been done for First Order plus Delay Time (FOPDT model controlled by Proportional Integral Derivative (PID and MPC using the Matlab software. This paper explores the capability of the MPC strategy, analyze and compare the control effects with conventional control strategy in pH control. A comparison results between the PID and MPC is plotted using the software. The results clearly show that MPC provide better performance than the classical controller.
Model Predictive Control for the acquisition queue and related queueing networks
Leeuwaarden, van J.S.H.; Lefeber, A.A.J.; Nazarathy, J.; Rooda, J.E.
2010-01-01
Model Predictive Control (MPC) is a well established method in control theory and engineering practice. It is often the method of choice for systems that need to be controlled in view of constraints. The main idea of MPC is to solve an optimization problem over a given time horizon at each control
Energy Technology Data Exchange (ETDEWEB)
Bourgeois, D. [Laval Univ., Quebec City, PQ (Canada). School of Architecture; Hand, J.; Macdonald, I. [Strathclyde Univ., Glasgow (United Kingdom). Energy Systems Research Unit; Reinhart, C. [National Research Council of Canada, Ottawa, ON (Canada). Institute for Research in Construction
2004-07-01
There are several whole building energy simulation programs that require input of casual gain loads such as heat discharged from occupants or lighting and equipment loads. Variations in casual gains in offices are attributed to absenteeism, occupant environmental preference and energy management features. This paper discussed energy simulation programs which offer sub-hourly simulation time-steps, diversity profiles of occupancy and associated gains such as lighting and equipment. In particular, it focused on the different events that occur in buildings at sub-hourly frequencies, such as manually adjusting blinds and lights in response to illuminance variations. These short-term changes affect electrical energy demand because they generate evident shifts in instantaneous solar and equipment loads. However, it is a challenge to model these complex sub-hourly changes. This paper reviews various models predicting occupancy, occupancy-sensing control and manual environmental controls. Their addition to whole building energy simulations were then outlined with particular reference to the ESP-r. 34 refs., 1 fig.
Energy Technology Data Exchange (ETDEWEB)
Jin, Xin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Baker, Kyri A. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Christensen, Dane T. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Isley, Steven C. [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2017-08-21
This paper presents a user-preference-driven home energy management system (HEMS) for demand response (DR) with residential building loads and battery storage. The HEMS is based on a multi-objective model predictive control algorithm, where the objectives include energy cost, thermal comfort, and carbon emission. A multi-criterion decision making method originating from social science is used to quickly determine user preferences based on a brief survey and derive the weights of different objectives used in the optimization process. Besides the residential appliances used in the traditional DR programs, a home battery system is integrated into the HEMS to improve the flexibility and reliability of the DR resources. Simulation studies have been performed on field data from a residential building stock data set. Appliance models and usage patterns were learned from the data to predict the DR resource availability. Results indicate the HEMS was able to provide a significant amount of load reduction with less than 20% prediction error in both heating and cooling cases.
Model Predictive Control for Linear Complementarity and Extended Linear Complementarity Systems
Directory of Open Access Journals (Sweden)
Bambang Riyanto
2005-11-01
Full Text Available In this paper, we propose model predictive control method for linear complementarity and extended linear complementarity systems by formulating optimization along prediction horizon as mixed integer quadratic program. Such systems contain interaction between continuous dynamics and discrete event systems, and therefore, can be categorized as hybrid systems. As linear complementarity and extended linear complementarity systems finds applications in different research areas, such as impact mechanical systems, traffic control and process control, this work will contribute to the development of control design method for those areas as well, as shown by three given examples.
A conceptual modeling framework for discrete event simulation using hierarchical control structures.
Furian, N; O'Sullivan, M; Walker, C; Vössner, S; Neubacher, D
2015-08-01
Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for applying CM to DES have emerged and discussion of CM for DES is increasing. However, both the organization of model-components and the identification of behavior and system control from standard CM approaches have shortcomings that limit CM's applicability to DES. Therefore, we discuss the different aspects of previous CM frameworks and identify their limitations. Further, we present the Hierarchical Control Conceptual Modeling framework that pays more attention to the identification of a models' system behavior, control policies and dispatching routines and their structured representation within a conceptual model. The framework guides the user step-by-step through the modeling process and is illustrated by a worked example.
International Nuclear Information System (INIS)
Bruni, G.; Cordiner, S.; Mulone, V.; Rocco, V.; Spagnolo, F.
2015-01-01
Highlights: • Development of a domestic microgrid and house thermal model. • Model Predictive Control for simultaneous management of power flow and thermal comfort. • Modeling of summer and winter typical conditions. • Comparison with standard rule based controller results. • Fuel cell downsizing potential of output is up to 60%. - Abstract: In this paper a Model Predictive Control (MPC) logic, based on weather forecasts, has been applied to the analysis of power management in a domestic off-grid system. The system is laid out as the integration of renewable energy conversion devices (Photovoltaic, PV), a high efficiency energy conversion programmable system (a Fuel Cell, FC) and an electrochemical energy storage (batteries). The control strategy has the objective of minimizing energy costs, while maintaining the optimal environmental comfort in the house, thus optimizing the use of renewable sources. To that aim, a validated numerical model of the whole system has been developed, and simulations have been carried out for winter and summer periods. Performances attainable with a MPC-based logic have been evaluated in comparison with a standard Rule Based Control logic, by means of costs and efficiency parameters of the micro-grid. Temperature violations have been taken into account to represent the impact of the control on comfort. Results show an improvement of the house comfort conditions and a lower use (on average 14.5%) of primary fossil energy. This is due both to a reduction of required energy, and to an increased use of renewable energy sources. Moreover, the modulation of the HVAC load and of the FC operation gives a reduction of requested power by approximately 40%. Smoother battery pack charge and discharge processes are also obtained. As a main positive effect, a reduction of the FC powerplant size and an increase of its durability seems feasible, leading to an overall reduction of capital costs
Monte Carlo simulation as a tool to predict blasting fragmentation based on the Kuz Ram model
Morin, Mario A.; Ficarazzo, Francesco
2006-04-01
Rock fragmentation is considered the most important aspect of production blasting because of its direct effects on the costs of drilling and blasting and on the economics of the subsequent operations of loading, hauling and crushing. Over the past three decades, significant progress has been made in the development of new technologies for blasting applications. These technologies include increasingly sophisticated computer models for blast design and blast performance prediction. Rock fragmentation depends on many variables such as rock mass properties, site geology, in situ fracturing and blasting parameters and as such has no complete theoretical solution for its prediction. However, empirical models for the estimation of size distribution of rock fragments have been developed. In this study, a blast fragmentation Monte Carlo-based simulator, based on the Kuz-Ram fragmentation model, has been developed to predict the entire fragmentation size distribution, taking into account intact and joints rock properties, the type and properties of explosives and the drilling pattern. Results produced by this simulator were quite favorable when compared with real fragmentation data obtained from a blast quarry. It is anticipated that the use of Monte Carlo simulation will increase our understanding of the effects of rock mass and explosive properties on the rock fragmentation by blasting, as well as increase our confidence in these empirical models. This understanding will translate into improvements in blasting operations, its corresponding costs and the overall economics of open pit mines and rock quarries.
Jongeneelen, Frans; ten Berge, Wil
2012-08-01
A physiologically based toxicokinetic (PBTK) model can predict blood and urine concentrations, given a certain exposure scenario of inhalation, dermal and/or oral exposure. The recently developed PBTK-model IndusChemFate is a unified model that mimics the uptake, distribution, metabolism and elimination of a chemical in a reference human of 70 kg. Prediction of the uptake by inhalation is governed by pulmonary exchange to blood. Oral uptake is simulated as a bolus dose that is taken up at a first-order rate. Dermal uptake is estimated by the use of a novel dermal physiologically based module that considers dermal deposition rate and duration of deposition. Moreover, evaporation during skin contact is fully accounted for and related to the volatility of the substance. Partitioning of the chemical and metabolite(s) over blood and tissues is estimated by a Quantitative Structure-Property Relationship (QSPR) algorithm. The aim of this study was to test the generic PBTK-model by comparing measured urinary levels of 1-hydroxypyrene in various inhalation and dermal exposure scenarios with the result of model simulations. In the last three decades, numerous biomonitoring studies of PAH-exposed humans were published that used the bioindicator 1-hydroxypyrene (1-OH-pyrene) in urine. Longitudinal studies that encompass both dosimetry and biomonitoring with repeated sampling in time were selected to test the accuracy of the PBTK-model by comparing the reported concentrations of 1-OHP in urine with the model-predicted values. Two controlled human volunteer studies and three field studies of workers exposed to polycyclic aromatic hydrocarbons (PAH) were included. The urinary pyrene-metabolite levels of a controlled human inhalation study, a transdermal uptake study of bitumen fume, efficacy of respirator use in electrode paste workers, cokery workers in shale oil industry and a longitudinal study of five coke liquefaction workers were compared to the PBTK-predicted values. The
Coordinated Voltage Control of a Wind Farm based on Model Predictive Control
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai
2016-01-01
This paper presents an autonomous wind farm voltage controller based on Model Predictive Control (MPC). The reactive power compensation and voltage regulation devices of the wind farm include Static Var Compensators (SVCs), Static Var Generators (SVGs), Wind Turbine Generators (WTGs) and On...... are calculated based on an analytical method to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both voltage violated and normal operation conditions. A wind farm with 20 wind turbines was used to conduct case studies to verify the proposed coordinated...
Applied Distributed Model Predictive Control for Energy Efficient Buildings and Ramp Metering
Koehler, Sarah Muraoka
Industrial large-scale control problems present an interesting algorithmic design challenge. A number of controllers must cooperate in real-time on a network of embedded hardware with limited computing power in order to maximize system efficiency while respecting constraints and despite communication delays. Model predictive control (MPC) can automatically synthesize a centralized controller which optimizes an objective function subject to a system model, constraints, and predictions of disturbance. Unfortunately, the computations required by model predictive controllers for large-scale systems often limit its industrial implementation only to medium-scale slow processes. Distributed model predictive control (DMPC) enters the picture as a way to decentralize a large-scale model predictive control problem. The main idea of DMPC is to split the computations required by the MPC problem amongst distributed processors that can compute in parallel and communicate iteratively to find a solution. Some popularly proposed solutions are distributed optimization algorithms such as dual decomposition and the alternating direction method of multipliers (ADMM). However, these algorithms ignore two practical challenges: substantial communication delays present in control systems and also problem non-convexity. This thesis presents two novel and practically effective DMPC algorithms. The first DMPC algorithm is based on a primal-dual active-set method which achieves fast convergence, making it suitable for large-scale control applications which have a large communication delay across its communication network. In particular, this algorithm is suited for MPC problems with a quadratic cost, linear dynamics, forecasted demand, and box constraints. We measure the performance of this algorithm and show that it significantly outperforms both dual decomposition and ADMM in the presence of communication delay. The second DMPC algorithm is based on an inexact interior point method which is
Human motion simulation predictive dynamics
Abdel-Malek, Karim
2013-01-01
Simulate realistic human motion in a virtual world with an optimization-based approach to motion prediction. With this approach, motion is governed by human performance measures, such as speed and energy, which act as objective functions to be optimized. Constraints on joint torques and angles are imposed quite easily. Predicting motion in this way allows one to use avatars to study how and why humans move the way they do, given specific scenarios. It also enables avatars to react to infinitely many scenarios with substantial autonomy. With this approach it is possible to predict dynamic motion without having to integrate equations of motion -- rather than solving equations of motion, this approach solves for a continuous time-dependent curve characterizing joint variables (also called joint profiles) for every degree of freedom. Introduces rigorous mathematical methods for digital human modelling and simulation Focuses on understanding and representing spatial relationships (3D) of biomechanics Develops an i...
Moloto, K. D.; Engelbrecht, N. E.; Burger, R. A.
2018-06-01
A simplified ab initio approach is followed to model cosmic-ray proton modulation, using a steady-state three-dimensional stochastic solver of the Parker transport equation that simulates some effects of time dependence. Standard diffusion coefficients based on Quasilinear Theory and Nonlinear Guiding Center Theory are employed. The spatial and temporal dependences of the various turbulence quantities required as inputs for the diffusion, as well as the turbulence-reduced drift coefficients, follow from parametric fits to results from a turbulence transport model as well as from spacecraft observations of these turbulence quantities. Effective values are used for the solar wind speed, magnetic field magnitude, and tilt angle in the modulation model to simulate temporal effects due to changes in the large-scale heliospheric plasma. The unusually high cosmic-ray intensities observed during the 2009 solar minimum follow naturally from the current model for most of the energies considered. This demonstrates that changes in turbulence contribute significantly to the high intensities during that solar minimum. We also discuss and illustrate how this model can be used to predict future cosmic-ray intensities, and comment on the reliability of such predictions.
Dynamic optimization and robust explicit model predictive control of hydrogen storage tank
Panos, C.
2010-09-01
We present a general framework for the optimal design and control of a metal-hydride bed under hydrogen desorption operation. The framework features: (i) a detailed two-dimension dynamic process model, (ii) a design and operational dynamic optimization step, and (iii) an explicit/multi-parametric model predictive controller design step. For the controller design, a reduced order approximate model is obtained, based on which nominal and robust multi-parametric controllers are designed. © 2010 Elsevier Ltd.
Dynamic optimization and robust explicit model predictive control of hydrogen storage tank
Panos, C.; Kouramas, K.I.; Georgiadis, M.C.; Pistikopoulos, E.N.
2010-01-01
We present a general framework for the optimal design and control of a metal-hydride bed under hydrogen desorption operation. The framework features: (i) a detailed two-dimension dynamic process model, (ii) a design and operational dynamic optimization step, and (iii) an explicit/multi-parametric model predictive controller design step. For the controller design, a reduced order approximate model is obtained, based on which nominal and robust multi-parametric controllers are designed. © 2010 Elsevier Ltd.
Robust model predictive control for constrained continuous-time nonlinear systems
Sun, Tairen; Pan, Yongping; Zhang, Jun; Yu, Haoyong
2018-02-01
In this paper, a robust model predictive control (MPC) is designed for a class of constrained continuous-time nonlinear systems with bounded additive disturbances. The robust MPC consists of a nonlinear feedback control and a continuous-time model-based dual-mode MPC. The nonlinear feedback control guarantees the actual trajectory being contained in a tube centred at the nominal trajectory. The dual-mode MPC is designed to ensure asymptotic convergence of the nominal trajectory to zero. This paper extends current results on discrete-time model-based tube MPC and linear system model-based tube MPC to continuous-time nonlinear model-based tube MPC. The feasibility and robustness of the proposed robust MPC have been demonstrated by theoretical analysis and applications to a cart-damper springer system and a one-link robot manipulator.
A conceptual modeling framework for discrete event simulation using hierarchical control structures
Furian, N.; O’Sullivan, M.; Walker, C.; Vössner, S.; Neubacher, D.
2015-01-01
Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for applying CM to DES have emerged and discussion of CM for DES is increasing. However, both the organization of model-components and the identification of behavior and system control from standard CM approaches have shortcomings that limit CM’s applicability to DES. Therefore, we discuss the different aspects of previous CM frameworks and identify their limitations. Further, we present the Hierarchical Control Conceptual Modeling framework that pays more attention to the identification of a models’ system behavior, control policies and dispatching routines and their structured representation within a conceptual model. The framework guides the user step-by-step through the modeling process and is illustrated by a worked example. PMID:26778940
International Nuclear Information System (INIS)
Xia, Dunzhu; Kong, Lun; Hu, Yiwei; Ni, Peizhen
2015-01-01
We present a novel silicon microgyroscope (SMG) temperature prediction and control system in a narrow space. As the temperature of SMG is closely related to its drive mode frequency and driving voltage, a temperature prediction model can be established based on the BP neural network. The simulation results demonstrate that the established temperature prediction model can estimate the temperature in the range of −40 to 60 °C with an error of less than ±0.05 °C. Then, a temperature control system based on the combination of fuzzy logic controller and the increment PID control method is proposed. The simulation results prove that the Fuzzy-PID controller has a smaller steady state error, less rise time and better robustness than the PID controller. This is validated by experimental results that show the Fuzzy-PID control method can achieve high precision in keeping the SMG temperature stable at 55 °C with an error of less than 0.2 °C. The scale factor can be stabilized at 8.7 mV/°/s with a temperature coefficient of 33 ppm °C −1 . ZRO (zero rate output) instability is decreased from 1.10°/s (9.5 mV) to 0.08°/s (0.7 mV) when the temperature control system is implemented over an ambient temperature range of −40 to 60 °C. (paper)
Directory of Open Access Journals (Sweden)
Glen P. Martin
2017-01-01
Full Text Available Abstract Background Clinical prediction models (CPMs are increasingly deployed to support healthcare decisions but they are derived inconsistently, in part due to limited data. An emerging alternative is to aggregate existing CPMs developed for similar settings and outcomes. This simulation study aimed to investigate the impact of between-population-heterogeneity and sample size on aggregating existing CPMs in a defined population, compared with developing a model de novo. Methods Simulations were designed to mimic a scenario in which multiple CPMs for a binary outcome had been derived in distinct, heterogeneous populations, with potentially different predictors available in each. We then generated a new ‘local’ population and compared the performance of CPMs developed for this population by aggregation, using stacked regression, principal component analysis or partial least squares, with redevelopment from scratch using backwards selection and penalised regression. Results While redevelopment approaches resulted in models that were miscalibrated for local datasets of less than 500 observations, model aggregation methods were well calibrated across all simulation scenarios. When the size of local data was less than 1000 observations and between-population-heterogeneity was small, aggregating existing CPMs gave better discrimination and had the lowest mean square error in the predicted risks compared with deriving a new model. Conversely, given greater than 1000 observations and significant between-population-heterogeneity, then redevelopment outperformed the aggregation approaches. In all other scenarios, both aggregation and de novo derivation resulted in similar predictive performance. Conclusion This study demonstrates a pragmatic approach to contextualising CPMs to defined populations. When aiming to develop models in defined populations, modellers should consider existing CPMs, with aggregation approaches being a suitable modelling
Model Predictive Control for Load Frequency Control with Wind Turbines
Directory of Open Access Journals (Sweden)
Yi Zhang
2015-01-01
Full Text Available Reliable load frequency (LFC control is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control (DMPC based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The scheme incorporates the two critical nonlinear constraints, for example, the generation rate constraint (GRC and the valve limit, into convex optimization problems. Furthermore, the algorithm reduces the impact on the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and that without the participation of the wind turbines is carried out. Good performance is obtained in the presence of power system nonlinearities due to the governors and turbines constraints and load change disturbances.
Hierarchical Model Predictive Control for Resource Distribution
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob
2010-01-01
units. The approach is inspired by smart-grid electric power production and consumption systems, where the flexibility of a large number of power producing and/or power consuming units can be exploited in a smart-grid solution. The objective is to accommodate the load variation on the grid, arising......This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...... on one hand from varying consumption, on the other hand by natural variations in power production e.g. from wind turbines. The approach presented is based on quadratic optimization and possess the properties of low algorithmic complexity and of scalability. In particular, the proposed design methodology...
Modeling and Simulation of Claus Unit Reaction Furnace
Directory of Open Access Journals (Sweden)
Maryam Pahlavan
2016-01-01
Full Text Available Reaction furnace is the most important part of the Claus sulfur recovery unit and its performance has a significant impact on the process efficiency. Too many reactions happen in the furnace and their kinetics and mechanisms are not completely understood; therefore, modeling reaction furnace is difficult and several works have been carried out on in this regard so far. Equilibrium models are commonly used to simulate the furnace, but the related literature states that the outlet of furnace is not in equilibrium and the furnace reactions are controlled by kinetic laws; therefore, in this study, the reaction furnace is simulated by a kinetic model. The predicted outlet temperature and concentrations by this model are compared with experimental data published in the literature and the data obtained by PROMAX V2.0 simulator. The results show that the accuracy of the proposed kinetic model and PROMAX simulator is almost similar, but the kinetic model used in this paper has two importance abilities. Firstly, it is a distributed model and can be used to obtain the temperature and concentration profiles along the furnace. Secondly, it is a dynamic model and can be used for analyzing the transient behavior and designing the control system.
Directory of Open Access Journals (Sweden)
Ramanpreet Kaur
2017-02-01
Full Text Available Intelligent prediction of neighboring node (k well defined neighbors as specified by the dht protocol dynamism is helpful to improve the resilience and can reduce the overhead associated with topology maintenance of structured overlay networks. The dynamic behavior of overlay nodes depends on many factors such as underlying user’s online behavior, geographical position, time of the day, day of the week etc. as reported in many applications. We can exploit these characteristics for efficient maintenance of structured overlay networks by implementing an intelligent predictive framework for setting stabilization parameters appropriately. Considering the fact that human driven behavior usually goes beyond intermittent availability patterns, we use a hybrid Neuro-fuzzy based predictor to enhance the accuracy of the predictions. In this paper, we discuss our predictive stabilization approach, implement Neuro-fuzzy based prediction in MATLAB simulation and apply this predictive stabilization model in a chord based overlay network using OverSim as a simulation tool. The MATLAB simulation results present that the behavior of neighboring nodes is predictable to a large extent as indicated by the very small RMSE. The OverSim based simulation results also observe significant improvements in the performance of chord based overlay network in terms of lookup success ratio, lookup hop count and maintenance overhead as compared to periodic stabilization approach.
Tanoshima, Reo; Bournissen, Facundo Garcia; Tanigawara, Yusuke; Kristensen, Judith H; Taddio, Anna; Ilett, Kenneth F; Begg, Evan J; Wallach, Izhar; Ito, Shinya
2014-10-01
Population pharmacokinetic (pop PK) modelling can be used for PK assessment of drugs in breast milk. However, complex mechanistic modelling of a parent and an active metabolite using both blood and milk samples is challenging. We aimed to develop a simple predictive pop PK model for milk concentration-time profiles of a parent and a metabolite, using data on fluoxetine (FX) and its active metabolite, norfluoxetine (NFX), in milk. Using a previously published data set of drug concentrations in milk from 25 women treated with FX, a pop PK model predictive of milk concentration-time profiles of FX and NFX was developed. Simulation was performed with the model to generate FX and NFX concentration-time profiles in milk of 1000 mothers. This milk concentration-based pop PK model was compared with the previously validated plasma/milk concentration-based pop PK model of FX. Milk FX and NFX concentration-time profiles were described reasonably well by a one compartment model with a FX-to-NFX conversion coefficient. Median values of the simulated relative infant dose on a weight basis (sRID: weight-adjusted daily doses of FX and NFX through breastmilk to the infant, expressed as a fraction of therapeutic FX daily dose per body weight) were 0.028 for FX and 0.029 for NFX. The FX sRID estimates were consistent with those of the plasma/milk-based pop PK model. A predictive pop PK model based on only milk concentrations can be developed for simultaneous estimation of milk concentration-time profiles of a parent (FX) and an active metabolite (NFX). © 2014 The British Pharmacological Society.
Coyne, Kevin Anthony
The safe operation of complex systems such as nuclear power plants requires close coordination between the human operators and plant systems. In order to maintain an adequate level of safety following an accident or other off-normal event, the operators often are called upon to perform complex tasks during dynamic situations with incomplete information. The safety of such complex systems can be greatly improved if the conditions that could lead operators to make poor decisions and commit erroneous actions during these situations can be predicted and mitigated. The primary goal of this research project was the development and validation of a cognitive model capable of simulating nuclear plant operator decision-making during accident conditions. Dynamic probabilistic risk assessment methods can improve the prediction of human error events by providing rich contextual information and an explicit consideration of feedback arising from man-machine interactions. The Accident Dynamics Simulator paired with the Information, Decision, and Action in a Crew context cognitive model (ADS-IDAC) shows promise for predicting situational contexts that might lead to human error events, particularly knowledge driven errors of commission. ADS-IDAC generates a discrete dynamic event tree (DDET) by applying simple branching rules that reflect variations in crew responses to plant events and system status changes. Branches can be generated to simulate slow or fast procedure execution speed, skipping of procedure steps, reliance on memorized information, activation of mental beliefs, variations in control inputs, and equipment failures. Complex operator mental models of plant behavior that guide crew actions can be represented within the ADS-IDAC mental belief framework and used to identify situational contexts that may lead to human error events. This research increased the capabilities of ADS-IDAC in several key areas. The ADS-IDAC computer code was improved to support additional
Maaoui-Ben Hassine, Ikram; Naouar, Mohamed Wissem; Mrabet-Bellaaj, Najiba
2016-05-01
In this paper, Model Predictive Control and Dead-beat predictive control strategies are proposed for the control of a PMSG based wind energy system. The proposed MPC considers the model of the converter-based system to forecast the possible future behavior of the controlled variables. It allows selecting the voltage vector to be applied that leads to a minimum error by minimizing a predefined cost function. The main features of the MPC are low current THD and robustness against parameters variations. The Dead-beat predictive control is based on the system model to compute the optimum voltage vector that ensures zero-steady state error. The optimum voltage vector is then applied through Space Vector Modulation (SVM) technique. The main advantages of the Dead-beat predictive control are low current THD and constant switching frequency. The proposed control techniques are presented and detailed for the control of back-to-back converter in a wind turbine system based on PMSG. Simulation results (under Matlab-Simulink software environment tool) and experimental results (under developed prototyping platform) are presented in order to show the performances of the considered control strategies. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Stabilizing model predictive control : on the enlargement of the terminal set
Brunner, F.D.; Lazar, M.; Allgöwer, F.
2015-01-01
It is well known that a large terminal set leads to a large region where the model predictive control problem is feasible without the need for a long prediction horizon. This paper proposes a new method for the enlargement of the terminal set. Different from existing approaches, the method uses the
Computer-aided and predictive models for design of controlled release of pesticides
DEFF Research Database (Denmark)
Suné, Nuria Muro; Gani, Rafiqul
2004-01-01
In the field of pesticide controlled release technology, a computer based model that can predict the delivery of the Active Ingredient (AI) from fabricated units is important for purposes of product design and marketing. A model for the release of an M from a microcapsule device is presented...... in this paper, together with a specific case study application to highlight its scope and significance. The paper also addresses the need for predictive models and proposes a computer aided modelling framework for achieving it through the development and introduction of reliable and predictive constitutive...... models. A group-contribution based model for one of the constitutive variables (AI solubility in polymers) is presented together with examples of application and validation....
Adaptive model predictive process control using neural networks
Buescher, K.L.; Baum, C.C.; Jones, R.D.
1997-08-19
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.
Improved model predictive control for high voltage quality in microgrid applications
DEFF Research Database (Denmark)
Dragicevic, T.; Al hasheem, Mohamed; Lu, M.
2017-01-01
This paper proposes an improvement of the finite control set model predictive control (FCS-MPC) strategy for enhancing the voltage regulation performance of a voltage source converter (VSC) used for standalone microgrid and uninterrupted power supply (UPS) applications. The modification is based...
Robust Predictive Functional Control for Flight Vehicles Based on Nonlinear Disturbance Observer
Directory of Open Access Journals (Sweden)
Yinhui Zhang
2015-01-01
Full Text Available A novel robust predictive functional control based on nonlinear disturbance observer is investigated in order to address the control system design for flight vehicles with significant uncertainties, external disturbances, and measurement noise. Firstly, the nonlinear longitudinal dynamics of the flight vehicle are transformed into linear-like state-space equations with state-dependent coefficient matrices. And then the lumped disturbances are considered in the linear structure predictive model of the predictive functional control to increase the precision of the predictive output and resolve the intractable mismatched disturbance problem. As the lumped disturbances cannot be derived or measured directly, the nonlinear disturbance observer is applied to estimate the lumped disturbances, which are then introduced to the predictive functional control to replace the unknown actual lumped disturbances. Consequently, the robust predictive functional control for the flight vehicle is proposed. Compared with the existing designs, the effectiveness and robustness of the proposed flight control are illustrated and validated in various simulation conditions.
Wind Turbine Generator Modeling and Simulation Where Rotational Speed is the Controlled Variable
DEFF Research Database (Denmark)
Mihet-Popa, Lucian; Blaabjerg, Frede; Boldea, Ion
2004-01-01
the interaction between a wind turbine and the power system. The model is intended to simulate the behaviour of the wind turbine using induction generators both during normal operation. Sample simulation results for two induction generators (2/0.5 MW) validate the fundamental issues.......To optimise the power produced in a wind turbine, the speed of the turbine should vary with the wind speed. A simple control method is proposed that will allow an induction machine to run a turbine at its maximum power coefficient. Various types of power control strategies have been suggested...... for application in variable speed wind turbines. The usual strategy is to control the power or the torque acting on the wind turbine shafts. This paper presents an alternative control strategy, where the rotational speed is the controlled variable. The paper describes a model, which is being developed to simulate...
Learning and Control Model of the Arm for Loading
Kim, Kyoungsik; Kambara, Hiroyuki; Shin, Duk; Koike, Yasuharu
We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.
International Nuclear Information System (INIS)
Chen, Xiao; Wang, Qian; Srebric, Jelena
2016-01-01
Highlights: • This study evaluates an occupant-feedback driven Model Predictive Controller (MPC). • The MPC adjusts indoor temperature based on a dynamic thermal sensation (DTS) model. • A chamber model for predicting chamber air temperature is developed and validated. • Experiments show that MPC using DTS performs better than using Predicted Mean Vote. - Abstract: In current centralized building climate control, occupants do not have much opportunity to intervene the automated control system. This study explores the benefit of using thermal comfort feedback from occupants in the model predictive control (MPC) design based on a novel dynamic thermal sensation (DTS) model. This DTS model based MPC was evaluated in chamber experiments. A hierarchical structure for thermal control was adopted in the chamber experiments. At the high level, an MPC controller calculates the optimal supply air temperature of the chamber heating, ventilation, and air conditioning (HVAC) system, using the feedback of occupants’ votes on thermal sensation. At the low level, the actual supply air temperature is controlled by the chiller/heater using a PI control to achieve the optimal set point. This DTS-based MPC was also compared to an MPC designed based on the Predicted Mean Vote (PMV) model for thermal sensation. The experiment results demonstrated that the DTS-based MPC using occupant feedback allows significant energy saving while maintaining occupant thermal comfort compared to the PMV-based MPC.
Health-aware Model Predictive Control of Wind Turbines using Fatigue Prognosis
DEFF Research Database (Denmark)
Sardi, Hector Eloy Sanchez; Escobet, Teressa; Puig, Vicenc
2015-01-01
management module with the control provides a mechanism for the wind turbine to operate safely and optimize the trade-off between components life and energy production. The research presented in this paper explores the integration of model predictive control (MPC) with fatigue-based prognosis approach...
Ren, Lei; Howard, David; Ren, Luquan; Nester, Chris; Tian, Limei
2010-01-19
The objective of this paper is to develop an analytical framework to representing the ankle-foot kinematics by modelling the foot as a rollover rocker, which cannot only be used as a generic tool for general gait simulation but also allows for case-specific modelling if required. Previously, the rollover models used in gait simulation have often been based on specific functions that have usually been of a simple form. In contrast, the analytical model described here is in a general form that the effective foot rollover shape can be represented by any polar function rho=rho(phi). Furthermore, a normalized generic foot rollover model has been established based on a normative foot rollover shape dataset of 12 normal healthy subjects. To evaluate model accuracy, the predicted ankle motions and the centre of pressure (CoP) were compared with measurement data for both subject-specific and general cases. The results demonstrated that the ankle joint motions in both vertical and horizontal directions (relative RMSE approximately 10%) and CoP (relative RMSE approximately 15% for most of the subjects) are accurately predicted over most of the stance phase (from 10% to 90% of stance). However, we found that the foot cannot be very accurately represented by a rollover model just after heel strike (HS) and just before toe off (TO), probably due to shear deformation of foot plantar tissues (ankle motion can occur without any foot rotation). The proposed foot rollover model can be used in both inverse and forward dynamics gait simulation studies and may also find applications in rehabilitation engineering. Copyright 2009 Elsevier Ltd. All rights reserved.
Wahid, A.; Putra, I. G. E. P.
2018-03-01
Dimethyl ether (DME) as an alternative clean energy has attracted a growing attention in the recent years. DME production via reactive distillation has potential for capital cost and energy requirement savings. However, combination of reaction and distillation on a single column makes reactive distillation process a very complex multivariable system with high non-linearity of process and strong interaction between process variables. This study investigates a multivariable model predictive control (MPC) based on two-point temperature control strategy for the DME reactive distillation column to maintain the purities of both product streams. The process model is estimated by a first order plus dead time model. The DME and water purity is maintained by controlling a stage temperature in rectifying and stripping section, respectively. The result shows that the model predictive controller performed faster responses compared to conventional PI controller that are showed by the smaller ISE values. In addition, the MPC controller is able to handle the loop interactions well.
Sresht, Vishnu; Lewandowski, Eric P; Blankschtein, Daniel; Jusufi, Arben
2017-08-22
A molecular modeling approach is presented with a focus on quantitative predictions of the surface tension of aqueous surfactant solutions. The approach combines classical Molecular Dynamics (MD) simulations with a molecular-thermodynamic theory (MTT) [ Y. J. Nikas, S. Puvvada, D. Blankschtein, Langmuir 1992 , 8 , 2680 ]. The MD component is used to calculate thermodynamic and molecular parameters that are needed in the MTT model to determine the surface tension isotherm. The MD/MTT approach provides the important link between the surfactant bulk concentration, the experimental control parameter, and the surfactant surface concentration, the MD control parameter. We demonstrate the capability of the MD/MTT modeling approach on nonionic alkyl polyethylene glycol surfactants at the air-water interface and observe reasonable agreement of the predicted surface tensions and the experimental surface tension data over a wide range of surfactant concentrations below the critical micelle concentration. Our modeling approach can be extended to ionic surfactants and their mixtures with both ionic and nonionic surfactants at liquid-liquid interfaces.
Predictive modeling of mosquito abundance and dengue transmission in Kenya
Caldwell, J.; Krystosik, A.; Mutuku, F.; Ndenga, B.; LaBeaud, D.; Mordecai, E.
2017-12-01
Approximately 390 million people are exposed to dengue virus every year, and with no widely available treatments or vaccines, predictive models of disease risk are valuable tools for vector control and disease prevention. The aim of this study was to modify and improve climate-driven predictive models of dengue vector abundance (Aedes spp. mosquitoes) and viral transmission to people in Kenya. We simulated disease transmission using a temperature-driven mechanistic model and compared model predictions with vector trap data for larvae, pupae, and adult mosquitoes collected between 2014 and 2017 at four sites across urban and rural villages in Kenya. We tested predictive capacity of our models using four temperature measurements (minimum, maximum, range, and anomalies) across daily, weekly, and monthly time scales. Our results indicate seasonal temperature variation is a key driving factor of Aedes mosquito abundance and disease transmission. These models can help vector control programs target specific locations and times when vectors are likely to be present, and can be modified for other Aedes-transmitted diseases and arboviral endemic regions around the world.
Advanced Models and Controls for Prediction and Extension of Battery Lifetime (Presentation)
Energy Technology Data Exchange (ETDEWEB)
Smith, K.; Wood, E.; Santhanagopalan, S.; Kim, G.; Pesaran, A.
2014-02-01
Predictive models of capacity and power fade must consider a multiplicity of degradation modes experienced by Li-ion batteries in the automotive environment. Lacking accurate models and tests, lifetime uncertainty must presently be absorbed by overdesign and excess warranty costs. To reduce these costs and extend life, degradation models are under development that predict lifetime more accurately and with less test data. The lifetime models provide engineering feedback for cell, pack and system designs and are being incorporated into real-time control strategies.
Evaluation of wave runup predictions from numerical and parametric models
Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.
2014-01-01
Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.
Directory of Open Access Journals (Sweden)
Muayad Al-Qaisy
2015-02-01
Full Text Available In this article, multi-input multi-output (MIMO linear model predictive controller (LMPC based on state space model and nonlinear model predictive controller based on neural network (NNMPC are applied on a continuous stirred tank reactor (CSTR. The idea is to have a good control system that will be able to give optimal performance, reject high load disturbance, and track set point change. In order to study the performance of the two model predictive controllers, MIMO Proportional-Integral-Derivative controller (PID strategy is used as benchmark. The LMPC, NNMPC, and PID strategies are used for controlling the residual concentration (CA and reactor temperature (T. NNMPC control shows a superior performance over the LMPC and PID controllers by presenting a smaller overshoot and shorter settling time.
Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler
Directory of Open Access Journals (Sweden)
Zhenhao Tang
2017-01-01
Full Text Available To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time-varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process.
Parameters Online Detection and Model Predictive Control during the Grain Drying Process
Directory of Open Access Journals (Sweden)
Lihui Zhang
2013-01-01
Full Text Available In order to improve the grain drying quality and automation level, combined with the structural characteristics of the cross-flow circulation grain dryer designed and developed by us, the temperature, moisture, and other parameters measuring sensors were placed on the dryer, to achieve online automatic detection of process parameters during the grain drying process. A drying model predictive control system was set up. A grain dry predictive control model at constant velocity and variable temperature was established, in which the entire process was dried at constant velocity (i.e., precipitation rate per hour is a constant and variable temperature. Combining PC with PLC, and based on LabVIEW, a system control platform was designed.
Directory of Open Access Journals (Sweden)
Hojjat A. Izadi
2011-01-01
Full Text Available The decentralized model predictive control (DMPC of multiple cooperative vehicles with the possibility of communication loss/delay is investigated. The neighboring vehicles exchange their predicted trajectories at every sample time to maintain the cooperation objectives. In the event of a communication loss (packet dropout, the most recent available information, which is potentially delayed, is used. Then the communication loss problem changes to a cooperative problem when random large communication delays are present. Such large communication delays can lead to poor cooperation performance and unsafe behaviors such as collisions. A new DMPC approach is developed to improve the cooperation performance and achieve safety in the presence of the large communication delays. The proposed DMPC architecture estimates the tail of neighbor's trajectory which is not available due to the large communication delays for improving the performance. The concept of the tube MPC is also employed to provide the safety of the fleet against collisions, in the presence of large intervehicle communication delays. In this approach, a tube shaped trajectory set is assumed around the trajectory of the neighboring vehicles whose trajectory is delayed/lost. The radius of tube is a function of the communication delay and vehicle's maneuverability (in the absence of model uncertainty. The simulation of formation problem of multiple vehicles is employed to illustrate the effectiveness of the proposed approach.
Model predictive control approach for a CPAP-device
Directory of Open Access Journals (Sweden)
Scheel Mathias
2017-09-01
Full Text Available The obstructive sleep apnoea syndrome (OSAS is characterized by a collapse of the upper respiratory tract, resulting in a reduction of the blood oxygen- and an increase of the carbon dioxide (CO2 - concentration, which causes repeated sleep disruptions. The gold standard to treat the OSAS is the continuous positive airway pressure (CPAP therapy. The continuous pressure keeps the upper airway open and prevents the collapse of the upper respiratory tract and the pharynx. Most of the available CPAP-devices cannot maintain the pressure reference [1]. In this work a model predictive control approach is provided. This control approach has the possibility to include the patient’s breathing effort into the calculation of the control variable. Therefore a patient-individualized control strategy can be developed.
Adaptive Disturbance Estimation for Offset-Free SISO Model Predictive Control
DEFF Research Database (Denmark)
Huusom, Jakob Kjøbsted; Poulsen, Niels Kjølstad; Jørgensen, Sten Bay
2011-01-01
Offset free tracking in Model Predictive Control requires estimation of unmeasured disturbances or the inclusion of an integrator. An algorithm for estimation of an unknown disturbance based on adaptive estimation with time varying forgetting is introduced and benchmarked against the classical...
The Integrated Medical Model: A Probabilistic Simulation Model Predicting In-Flight Medical Risks
Keenan, Alexandra; Young, Millennia; Saile, Lynn; Boley, Lynn; Walton, Marlei; Kerstman, Eric; Shah, Ronak; Goodenow, Debra A.; Myers, Jerry G., Jr.
2015-01-01
The Integrated Medical Model (IMM) is a probabilistic model that uses simulation to predict mission medical risk. Given a specific mission and crew scenario, medical events are simulated using Monte Carlo methodology to provide estimates of resource utilization, probability of evacuation, probability of loss of crew, and the amount of mission time lost due to illness. Mission and crew scenarios are defined by mission length, extravehicular activity (EVA) schedule, and crew characteristics including: sex, coronary artery calcium score, contacts, dental crowns, history of abdominal surgery, and EVA eligibility. The Integrated Medical Evidence Database (iMED) houses the model inputs for one hundred medical conditions using in-flight, analog, and terrestrial medical data. Inputs include incidence, event durations, resource utilization, and crew functional impairment. Severity of conditions is addressed by defining statistical distributions on the dichotomized best and worst-case scenarios for each condition. The outcome distributions for conditions are bounded by the treatment extremes of the fully treated scenario in which all required resources are available and the untreated scenario in which no required resources are available. Upon occurrence of a simulated medical event, treatment availability is assessed, and outcomes are generated depending on the status of the affected crewmember at the time of onset, including any pre-existing functional impairments or ongoing treatment of concurrent conditions. The main IMM outcomes, including probability of evacuation and loss of crew life, time lost due to medical events, and resource utilization, are useful in informing mission planning decisions. To date, the IMM has been used to assess mission-specific risks with and without certain crewmember characteristics, to determine the impact of eliminating certain resources from the mission medical kit, and to design medical kits that maximally benefit crew health while meeting
Directory of Open Access Journals (Sweden)
Frances Hutchings
2015-12-01
Full Text Available Temporal lobe epilepsy (TLE is a prevalent neurological disorder resulting in disruptive seizures. In the case of drug resistant epilepsy resective surgery is often considered. This is a procedure hampered by unpredictable success rates, with many patients continuing to have seizures even after surgery. In this study we apply a computational model of epilepsy to patient specific structural connectivity derived from diffusion tensor imaging (DTI of 22 individuals with left TLE and 39 healthy controls. We validate the model by examining patient-control differences in simulated seizure onset time and network location. We then investigate the potential of the model for surgery prediction by performing in silico surgical resections, removing nodes from patient networks and comparing seizure likelihood post-surgery to pre-surgery simulations. We find that, first, patients tend to transit from non-epileptic to epileptic states more often than controls in the model. Second, regions in the left hemisphere (particularly within temporal and subcortical regions that are known to be involved in TLE are the most frequent starting points for seizures in patients in the model. In addition, our analysis also implicates regions in the contralateral and frontal locations which may play a role in seizure spreading or surgery resistance. Finally, the model predicts that patient-specific surgery (resection areas chosen on an individual, model-prompted, basis and not following a predefined procedure may lead to better outcomes than the currently used routine clinical procedure. Taken together this work provides a first step towards patient specific computational modelling of epilepsy surgery in order to inform treatment strategies in individuals.
Model predictive control for a dual active bridge inverter with a floating bridge
Chowdhury, Shajjad; Wheeler, Patrick W.; Gerada, C.; Patel, Chintan
2016-01-01
This paper presents a Model Predictive Control technique applied to a dual active bridge inverter where one of the bridges is floating. The proposed floating bridge topology eliminates the need for isolation transformer in a dual inverter system and therefore reduces the size, weight and losses in the system. To achieve multilevel output voltage waveforms the floating inverter DC link capacitor is charged to the half of the main DC link voltage. A finite-set Model Predictive Control technique...
Dynamic modeling and simulation of EBR-II steam generator system
International Nuclear Information System (INIS)
Berkan, R.C.; Upadhyaya, B.R.
1989-01-01
This paper presents a low order dynamic model of the Experimental breeder Reactor-II (EBR-II) steam generator system. The model development includes the application of energy, mass and momentum balance equations in state-space form. The model also includes a three-element controller for the drum water level control problem. The simulation results for low-level perturbations exhibit the inherently stable characteristics of the steam generator. The predictions of test transients also verify the consistency of this low order model
Bakker, M.; Verberk, J.Q.J.C.; Palmen, L.J.; Sperber, V.; Bakker, G.
2011-01-01
Half of all water supply systems in the Netherlands are controlled by model predictive flow control; the other half are controlled by conventional level based control. The differences between conventional level based control and model predictive control were investigated in experiments at five full
Salehifar, Mehdi; Moreno-Equilaz, Manuel
2016-01-01
Due to its fault tolerance, a multiphase brushless direct current (BLDC) motor can meet high reliability demand for application in electric vehicles. The voltage-source inverter (VSI) supplying the motor is subjected to open circuit faults. Therefore, it is necessary to design a fault-tolerant (FT) control algorithm with an embedded fault diagnosis (FD) block. In this paper, finite control set-model predictive control (FCS-MPC) is developed to implement the fault-tolerant control algorithm of a five-phase BLDC motor. The developed control method is fast, simple, and flexible. A FD method based on available information from the control block is proposed; this method is simple, robust to common transients in motor and able to localize multiple open circuit faults. The proposed FD and FT control algorithm are embedded in a five-phase BLDC motor drive. In order to validate the theory presented, simulation and experimental results are conducted on a five-phase two-level VSI supplying a five-phase BLDC motor. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Prediction Model of Battery State of Charge and Control Parameter Optimization for Electric Vehicle
Directory of Open Access Journals (Sweden)
Bambang Wahono
2015-07-01
Full Text Available This paper presents the construction of a battery state of charge (SOC prediction model and the optimization method of the said model to appropriately control the number of parameters in compliance with the SOC as the battery output objectives. Research Centre for Electrical Power and Mechatronics, Indonesian Institute of Sciences has tested its electric vehicle research prototype on the road, monitoring its voltage, current, temperature, time, vehicle velocity, motor speed, and SOC during the operation. Using this experimental data, the prediction model of battery SOC was built. Stepwise method considering multicollinearity was able to efficiently develops the battery prediction model that describes the multiple control parameters in relation to the characteristic values such as SOC. It was demonstrated that particle swarm optimization (PSO succesfully and efficiently calculated optimal control parameters to optimize evaluation item such as SOC based on the model.
A naturalistic decision making model for simulated human combatants
International Nuclear Information System (INIS)
HUNTER, KEITH O.; HART, WILLIAM E.; FORSYTHE, JAMES C.
2000-01-01
The authors describe a naturalistic behavioral model for the simulation of small unit combat. This model, Klein's recognition-primed decision making (RPD) model, is driven by situational awareness rather than a rational process of selecting from a set of action options. They argue that simulated combatants modeled with RPD will have more flexible and realistic responses to a broad range of small-scale combat scenarios. Furthermore, they note that the predictability of a simulation using an RPD framework can be easily controlled to provide multiple evaluations of a given combat scenario. Finally, they discuss computational issues for building an RPD-based behavior engine for fully automated combatants in small conflict scenarios, which are being investigated within Sandia's Next Generation Site Security project
Wind farms production: Control and prediction
El-Fouly, Tarek Hussein Mostafa
and the time delay of the incident wind speed of the different turbines on the farm, and to simulate the fluctuation in the generated power more accurately and more closer to real-time operation. Recently, wind farms with considerable output power ratings have been installed. Their integrating into the utility grid will substantially affect the electricity markets. This thesis investigates the possible impact of wind power variability, wind farm control strategy, wind energy penetration level, wind farm location, and wind power prediction accuracy on the total generation costs and close to real time electricity market prices. These issues are addressed by developing a single auction market model for determining the real-time electricity market prices.
Improved model predictive control of resistive wall modes by error field estimator in EXTRAP T2R
Setiadi, A. C.; Brunsell, P. R.; Frassinetti, L.
2016-12-01
Many implementations of a model-based approach for toroidal plasma have shown better control performance compared to the conventional type of feedback controller. One prerequisite of model-based control is the availability of a control oriented model. This model can be obtained empirically through a systematic procedure called system identification. Such a model is used in this work to design a model predictive controller to stabilize multiple resistive wall modes in EXTRAP T2R reversed-field pinch. Model predictive control is an advanced control method that can optimize the future behaviour of a system. Furthermore, this paper will discuss an additional use of the empirical model which is to estimate the error field in EXTRAP T2R. Two potential methods are discussed that can estimate the error field. The error field estimator is then combined with the model predictive control and yields better radial magnetic field suppression.
Velarde, P.; Valverde, L.; Maestre, J. M.; Ocampo-Martinez, C.; Bordons, C.
2017-03-01
In this paper, a performance comparison among three well-known stochastic model predictive control approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control is presented. To this end, three predictive controllers have been designed and implemented in a real renewable-hydrogen-based microgrid. The experimental set-up includes a PEM electrolyzer, lead-acid batteries, and a PEM fuel cell as main equipment. The real experimental results show significant differences from the plant components, mainly in terms of use of energy, for each implemented technique. Effectiveness, performance, advantages, and disadvantages of these techniques are extensively discussed and analyzed to give some valid criteria when selecting an appropriate stochastic predictive controller.
Helicopter Rotor Load Prediction Using a Geometrically Exact Beam with Multicomponent Model
DEFF Research Database (Denmark)
Lee, Hyun-Ku; Viswamurthy, S.R.; Park, Sang Chul
2010-01-01
In this paper, an accurate structural dynamic analysis was developed for a helicopter rotor system including rotor control components, which was coupled to various aerodynamic and wake models in order to predict an aeroelastic response and the loads acting on the rotor. Its blade analysis was based...... rotor-blade/control-system model was loosely coupled with various inflow and wake models in order to simulate both hover and forward-flight conditions. The resulting rotor blade response and pitch link loads are in good agreement with those predicted byCAMRADII. The present analysis features both model...... on an intrinsic formulation of moving beams implemented in the time domain. The rotor control system was modeled as a combination of rigid and elastic components. A multicomponent analysis was then developed by coupling the beam finite element model with the rotor control system model to obtain a complete rotor-blade/control...
Ouari, Kamel; Rekioua, Toufik; Ouhrouche, Mohand
2014-01-01
In order to make a wind power generation truly cost-effective and reliable, an advanced control techniques must be used. In this paper, we develop a new control strategy, using nonlinear generalized predictive control (NGPC) approach, for DFIG-based wind turbine. The proposed control law is based on two points: NGPC-based torque-current control loop generating the rotor reference voltage and NGPC-based speed control loop that provides the torque reference. In order to enhance the robustness of the controller, a disturbance observer is designed to estimate the aerodynamic torque which is considered as an unknown perturbation. Finally, a real-time simulation is carried out to illustrate the performance of the proposed controller. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Zeng, Xiaohua; Li, Guanghan; Yin, Guodong; Song, Dafeng; Li, Sheng; Yang, Nannan
2018-02-01
Equipping a hydraulic hub-motor auxiliary system (HHMAS), which mainly consists of a hydraulic variable pump, a hydraulic hub-motor, a hydraulic valve block and hydraulic accumulators, with part-time all-wheel-drive functions improves the power performance and fuel economy of heavy commercial vehicles. The coordinated control problem that occurs when HHMAS operates in the auxiliary drive mode is addressed in this paper; the solution to this problem is the key to the maximization of HHMAS. To achieve a reasonable distribution of the engine power between mechanical and hydraulic paths, a nonlinear control scheme based on model predictive control (MPC) is investigated. First, a nonlinear model of HHMAS with vehicle dynamics and tire slip characteristics is built, and a controller-design-oriented model is simplified. Then, a steady-state feedforward + dynamic MPC feedback controller (FMPC) is designed to calculate the control input sequence of engine torque and hydraulic variable pump displacement. Finally, the controller is tested in the MATLAB/Simulink and AMESim co-simulation platform and the hardware-in-the-loop experiment platform, and its performance is compared with that of the existing proportional-integral-derivative controller and the feedforward controller under the same conditions. Simulation results show that the designed FMPC has the best performance, and control performance can be guaranteed in a real-time environment. Compared with the tracking control error of the feedforward controller, that of the designed FMPC is decreased by 85% and the traction efficiency performance is improved by 23% under a low-friction-surface condition. Moreover, under common road conditions for heavy commercial vehicles, the traction force can increase up to 13.4-15.6%.
Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint
Energy Technology Data Exchange (ETDEWEB)
Raszmann, Emma; Baker, Kyri; Shi, Ying; Christensen, Dane
2017-02-22
Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.
Directory of Open Access Journals (Sweden)
Lan-Rong Dung
2016-01-01
Full Text Available A new battery simulator based on a hybrid model is proposed in this paper for dynamic discharging behavior and runtime predictions in existing electronic simulation environments, e.g., PSIM, so it can help power circuit designers to develop and optimize their battery-powered electronic systems. The hybrid battery model combines a diffusion model and a switching overpotential model, which automatically switches overpotential resistance mode or overpotential voltage mode to accurately describe the voltage difference between battery electro-motive force (EMF and terminal voltage. Therefore, this simulator can simply run in an electronic simulation software with less computational efforts and estimate battery performances by further considering nonlinear capacity effects. A linear extrapolation technique is adopted for extracting model parameters from constant current discharging tests, so the EMF hysteresis problem is avoided. For model validation, experiments and simulations in MATLAB and PSIM environments are conducted with six different profiles, including constant loads, an interrupted load, increasing and decreasing loads and a varying load. The results confirm the usefulness and accuracy of the proposed simulator. The behavior and runtime prediction errors can be as low as 3.1% and 1.2%, respectively.
Directory of Open Access Journals (Sweden)
Skrzypczyk Krzysztof
2017-06-01
Full Text Available This paper addresses the problem of navigating an autonomous vehicle using environmental dynamics prediction. The usefulness of the Game Against Nature formalism adapted to modelling environmental prediction uncertainty is discussed. The possibility of the control law synthesis on the basis of strategies against Nature is presented. The properties and effectiveness of the approach presented are verified by simulations carried out in MATLAB.
Benchmark Simulation Model No 2 – finalisation of plant layout and default control strategy
DEFF Research Database (Denmark)
Nopens, I.; Benedetti, L.; Jeppsson, U.
2010-01-01
The COST/IWA Benchmark Simulation Model No 1 (BSM1) has been available for almost a decade. Its primary purpose has been to create a platform for control strategy benchmarking of activated sludge processes. The fact that the research work related to the benchmark simulation models has resulted...... in more than 300 publications worldwide demonstrates the interest in and need of such tools within the research community. Recent efforts within the IWA Task Group on “Benchmarking of control strategies for WWTPs” have focused on an extension of the benchmark simulation model. This extension aims...... be evaluated in a realistic fashion in the one week BSM1 evaluation period. In this paper, the finalised plant layout is summarised and, as was done for BSM1, a default control strategy is proposed. A demonstration of how BSM2 can be used to evaluate control strategies is also given....
Mathematical Modelling and Predictive Control of Permanent Magnet Synchronous Motor Drives
Czech Academy of Sciences Publication Activity Database
Belda, Květoslav
2013-01-01
Roč. 2, č. 4 (2013), s. 114-120 ISSN 1805-3386 R&D Projects: GA ČR(CZ) GAP102/11/0437 Institutional support: RVO:67985556 Keywords : Permanent magnet synchronous motor * mathematical modelling * discrete predictive control * multistep explicit control law * square-root optimization Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2014/AS/belda-0422285.pdf
Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.
Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei
2016-02-01
A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.
Directory of Open Access Journals (Sweden)
Daniar Sabah
2016-12-01
Full Text Available In this paper, a multivariable model based predictive control (MPC is proposed for the solution of load frequency control (LFC in a multi-area interconnected power system. The proposed controller is designed to consider time delay, generation rate constraint and multivariable nature of the LFC system, simultaneously. A new formulation of the MPC is presented to compensate time delay. The generation rate constraint is considered by employing a constrained MPC and economic allocation of the generation is further guaranteed by an innovative modification in the predictive control objective function. The effectiveness of proposed scheme is verified through time-based simulations on the standard 39-bus test system and the responses are then compared with the proportional-integral controller. The evaluation of the results reveals that the proposed control scheme offers satisfactory performance with fast responses.
Tavakoli, M. M.; Assadian, N.
2018-03-01
The problem of controlling an all-thruster spacecraft in the coupled translational-rotational motion in presence of actuators fault and/or failure is investigated in this paper. The nonlinear model predictive control approach is used because of its ability to predict the future behavior of the system. The fault/failure of the thrusters changes the mapping between the commanded forces to the thrusters and actual force/torque generated by the thruster system. Thus, the basic six degree-of-freedom kinetic equations are separated from this mapping and a set of neural networks are trained off-line to learn the kinetic equations. Then, two neural networks are attached to these trained networks in order to learn the thruster commands to force/torque mappings on-line. Different off-nominal conditions are modeled so that neural networks can detect any failure and fault, including scale factor and misalignment of thrusters. A simple model of the spacecraft relative motion is used in MPC to decrease the computational burden. However, a precise model by the means of orbit propagation including different types of perturbation is utilized to evaluate the usefulness of the proposed approach in actual conditions. The numerical simulation shows that this method can successfully control the all-thruster spacecraft with ON-OFF thrusters in different combinations of thruster fault and/or failure.
International Nuclear Information System (INIS)
Hannum, David; Bateman, Glenn; Kinsey, Jon; Kritz, Arnold H.; Onjun, Thawatchai; Pankin, Alexei
2001-01-01
Two different transport models -- the Mixed Bohm/gyro-Bohm [Joint European Torus (JET)] model [Erba , Plasma Phys. Controlled Fusion 39, 261 (1997)] and the Multi-Mode model (MMM95) [Bateman , Phys. Plasmas 5, 1793 (1998)] -- are used in predictive transport simulations of 22 high-mode discharges. Fourteen discharges that include systematic scans in normalized gyroradius (ρ * ), plasma pressure (β), collisionality, and isotope mass in the JET tokamak [Rebut , Nucl. Fusion 25, 1011 (1985)] and eight discharges that include scans in ρ * , elongation (κ), power, and density in the DIII-D tokamak [J. L. Luxon and L. G. Davis, Fusion Technol. 8, 441 (1985)] are considered. When simulation temperature and density profiles are compared with processed experimental data from the International Profile Database, it is found that the results with either the JET or MMM95 transport model match experimental data about equally well. With either model, the average normalized rms deviation is approximately 10%. In the simulations carried out using the JET model, the component of the model with Bohm scaling (which is proportional to gyroradius) dominates over much of the plasma. In contrast, the MMM95 model has purely gyro-Bohm scaling (proportional to gyroradius squared). In spite of the differences in the underlying scaling of these transport models, both models reproduce the global confinement scalings observed in the scans equally well. These results are explained by changes in profile shapes from one end of each scan to the other. These changes in the profile shapes are caused by changes in boundary conditions, heating and particle source profiles, large scale instabilities, and transport
A Comparison between Two Simulation Models for Spread of Foot-and-Mouth Disease
DEFF Research Database (Denmark)
Hisham Beshara Halasa, Tariq; Boklund, Anette; Stockmarr, Anders
2014-01-01
Two widely used simulation models of foot-and-mouth disease (FMD) were used in order to compare the models' predictions in term of disease spread, consequence, and the ranking of the applied control strategies, and to discuss the effect of the way disease spread is modeled on the predicted outcomes...... of each model. The DTU-DADS (version 0.100), and ISP (version 2.001.11) were used to simulate a hypothetical spread of FMD in Denmark. Actual herd type, movements, and location data in the period 1st October 2006 and 30th September 2007 was used. The models simulated the spread of FMD using 3 different...... areas and 1,000 in low density swine areas, and 1,000 sheep herds. Generally, DTU-DADS predicted larger, longer duration and costlier epidemics than ISP, except when epidemics started in cattle herds located in high density cattle areas. ISP supported suppressive vaccination rather than pre...
Directory of Open Access Journals (Sweden)
M. Oloumi
2015-06-01
Full Text Available This paper provides a detailed comparative study concerning the performance of min-projection strategy (MPS and model predictive control (MPC systems to control the three-phase grid connected converters. To do so, first, the converter is modeled as a switched linear system. Then, the feasibility of the MPS technique is investigated and its stability criterion is derived as a lower limit on the DC link voltage. Next, the fundamental equations of the MPS to control a VSC are obtained in the stationary reference frame. The mathematical analysis reveals that the MPS is independent of the load, grid, filter and converter parameters. This feature is a great advantage of MPS over the MPC approach. However, the latter is a well-known model-based control technique, has already developed for controlling the VSC in the stationary reference frame. To control the grid connected VSC, both MPS and MPC approaches are simulated in the PSCAD/EMTDC environment. Simulation results illustrate that the MPS is functioning well and is less sensitive to grid and filter inductances as well as the DC link voltage level. However, the MPC approach renders slightly a better performance in the steady state conditions.
International Nuclear Information System (INIS)
Knee, H.E.; Krois, P.A.; Haas, P.M.; Siegel, A.I.; Ryan, T.G.
1983-01-01
The NRC has developed a structured, quantitative, predictive methodology in the form of a computerized simulation model for assessing maintainer task performance. Objective of the overall program is to develop, validate, and disseminate a practical, useful, and acceptable methodology for the quantitative assessment of NPP maintenance personnel reliability. The program was organized into four phases: (1) scoping study, (2) model development, (3) model evaluation, and (4) model dissemination. The program is currently nearing completion of Phase 2 - Model Development
Energy management in a domestic microgrid by means of model predictive controllers
International Nuclear Information System (INIS)
Bruni, G.; Cordiner, S.; Mulone, V.; Sinisi, V.; Spagnolo, F.
2016-01-01
The need to increase renewable energy sources deployment and to reduce consumption of fossil fuels has led to the diffusion of small-scale DG (Distributed Generation) systems, which may be effectively integrated in micro-grids. The role of control logic in defining microgrid performances and reliability is predominant and can be improved by using advanced control logics such as the ones based on MPC (Model Predictive Control). In a previous paper, a MPC logic, based on the use of weather forecasts to improve the performances, has been applied to the analysis of power management in a domestic micro-grid system composed by: PV (Photovoltaic panels), FC (Fuel Cells) and a battery pack; in that case, the system was not affected by real uncertainties. In this paper the same system has been considered for domestic microgrid applications. The system control logic has been implemented by assuming real weather forecast as input data. DMPC and SMPC (Deterministic and Stochastic Model Predictive Control) concepts have been applied to the system and results have been compared to both MPC and to a standard RBC (Rule Based Control) logic. The impact of forecast uncertainties has been evaluated showing the advantages of a stochastic approach. In that case, the SMPC showed encouraging performances compared to standard control logics, primarily in terms of primary energy savings and downsizing potential of the power-delivering sub-systems using programmable energy sources. - Highlights: • A Model Predictive Controller has been used to optimize the behaviour of a microgrid. • A stochastic approach has been used to model weather forecast uncertainties. • 10% savings of HVAC required energy have been demonstrated. • Savings in primary fossil energy range between 10% and 30%. • Power sub-systems using programmable sources can be consistently downsized.
DEFF Research Database (Denmark)
Nielsen, Kirsten Mølgaard; Nielsen, Jens Frederik Dalsgaard
The subject of this Ph.D. thesis is to investigate and develop methods for modelling, simulation and control applicable in large scale termal industrial plants. An ambition has been to evaluate the results in a physical process. Sugar production is well suited for the purpose. In collaboration...... simulator has been developed. The simulator handles the normal working conditions relevant to control engineers. A non-linear dynamic model based on mass and energy balances has been developed. The model parameters have been adjusted to data measured on a Danish sugar plant. The simulator consists...... of a computer, a data terminal and an electric interface corresponding to the interface at the sugar plant. The simulator is operating in realtime and thus a realistic test of controllers is possible. The idiomatic control methodology has been investigated developing a control concept for the evaporation...
Bürger, Adrian; Sawant, Parantapa; Bohlayer, Markus; Altmann-Dieses, Angelika; Braun, Marco; Diehl, Moritz
2017-10-01
Within this work, the benefits of using predictive control methods for the operation of Adsorption Cooling Machines (ACMs) are shown on a simulation study. Since the internal control decisions of series-manufactured ACMs often cannot be influenced, the work focuses on optimized scheduling of an ACM considering its internal functioning as well as forecasts for load and driving energy occurrence. For illustration, an assumed solar thermal climate system is introduced and a system model suitable for use within gradient-based optimization methods is developed. The results of a system simulation using a conventional scheme for ACM scheduling are compared to the results of a predictive, optimization-based scheduling approach for the same exemplary scenario of load and driving energy occurrence. The benefits of the latter approach are shown and future actions for application of these methods for system control are addressed.
Gur, M.; Zomot, E.; Bahar, I.
2013-09-01
The Anton supercomputing technology recently developed for efficient molecular dynamics simulations permits us to examine micro- to milli-second events at full atomic resolution for proteins in explicit water and lipid bilayer. It also permits us to investigate to what extent the collective motions predicted by network models (that have found broad use in molecular biophysics) agree with those exhibited by full-atomic long simulations. The present study focuses on Anton trajectories generated for two systems: the bovine pancreatic trypsin inhibitor, and an archaeal aspartate transporter, GltPh. The former, a thoroughly studied system, helps benchmark the method of comparative analysis, and the latter provides new insights into the mechanism of function of glutamate transporters. The principal modes of motion derived from both simulations closely overlap with those predicted for each system by the anisotropic network model (ANM). Notably, the ANM modes define the collective mechanisms, or the pathways on conformational energy landscape, that underlie the passage between the crystal structure and substates visited in simulations. In particular, the lowest frequency ANM modes facilitate the conversion between the most probable substates, lending support to the view that easy access to functional substates is a robust determinant of evolutionarily selected native contact topology.
DEFF Research Database (Denmark)
Salo, T J; Palosuo, T; Kersebaum, K C
2016-01-01
Eleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley (Hordeum vulgare L.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen......, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area...... ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi...
Bayesian inference in mass flow simulations - from back calculation to prediction
Kofler, Andreas; Fischer, Jan-Thomas; Hellweger, Valentin; Huber, Andreas; Mergili, Martin; Pudasaini, Shiva; Fellin, Wolfgang; Oberguggenberger, Michael
2017-04-01
Mass flow simulations are an integral part of hazard assessment. Determining the hazard potential requires a multidisciplinary approach, including different scientific fields such as geomorphology, meteorology, physics, civil engineering and mathematics. An important task in snow avalanche simulation is to predict process intensities (runout, flow velocity and depth, ...). The application of probabilistic methods allows one to develop a comprehensive simulation concept, ranging from back to forward calculation and finally to prediction of mass flow events. In this context optimized parameter sets for the used simulation model or intensities of the modeled mass flow process (e.g. runout distances) are represented by probability distributions. Existing deterministic flow models, in particular with respect to snow avalanche dynamics, contain several parameters (e.g. friction). Some of these parameters are more conceptual than physical and their direct measurement in the field is hardly possible. Hence, parameters have to be optimized by matching simulation results to field observations. This inverse problem can be solved by a Bayesian approach (Markov chain Monte Carlo). The optimization process yields parameter distributions, that can be utilized for probabilistic reconstruction and prediction of avalanche events. Arising challenges include the limited amount of observations, correlations appearing in model parameters or observed avalanche characteristics (e.g. velocity and runout) and the accurate handling of ensemble simulations, always taking into account the related uncertainties. Here we present an operational Bayesian simulation framework with r.avaflow, the open source GIS simulation model for granular avalanches and debris flows.
Regan, Timothy F.
2004-01-01
The free-piston Stirling convertor end-to-end modeling effort at the NASA Glenn Research Center has produced a software-based test bed in which free-piston Stirling convertors can be simulated and evaluated. The simulation model includes all the components of the convertor: the Stirling cycle engine, heat source, linear alternator, controller, and load. So far, it has been used in evaluating the performance of electronic controller designs. Three different controller design concepts were simulated using the model: 1) Controllers with parasitic direct current loading. 2) Controllers with parasitic alternating current loading. 3) Controllers that maintain a reference current. The free-piston Stirling convertor is an electromechanical device that operates at resonance. It is the function of the electronic load controller to ensure that the electrical load seen by the machine is always great enough to keep the amplitude of the piston and alternator oscillation at the rated value. This is done by regulating the load on the output bus. The controller monitors the instantaneous voltage, regulating it by switching loads called parasitic loads onto the bus whenever the bus voltage is too high and removing them whenever the voltage is too low. In the first type of controller, the monitor-ing and switching are done on the direct-current (dc) bus. In the second type, the alternating current bus is used. The model allows designers to test a controller concept before investing time in hardware. The simulation code used to develop the model also offers detailed models of digital and analog electronic components so that the resulting designs are realistic enough to translate directly into hardware circuits.
Real time modeling, simulation and control of dynamical systems
Mughal, Asif Mahmood
2016-01-01
This book introduces modeling and simulation of linear time invariant systems and demonstrates how these translate to systems engineering, mechatronics engineering, and biomedical engineering. It is organized into nine chapters that follow the lectures used for a one-semester course on this topic, making it appropriate for students as well as researchers. The author discusses state space modeling derived from two modeling techniques and the analysis of the system and usage of modeling in control systems design. It also contains a unique chapter on multidisciplinary energy systems with a special focus on bioengineering systems and expands upon how the bond graph augments research in biomedical and bio-mechatronics systems.
Dynamics Modeling and Simulation of Large Transport Airplanes in Upset Conditions
Foster, John V.; Cunningham, Kevin; Fremaux, Charles M.; Shah, Gautam H.; Stewart, Eric C.; Rivers, Robert A.; Wilborn, James E.; Gato, William
2005-01-01
As part of NASA's Aviation Safety and Security Program, research has been in progress to develop aerodynamic modeling methods for simulations that accurately predict the flight dynamics characteristics of large transport airplanes in upset conditions. The motivation for this research stems from the recognition that simulation is a vital tool for addressing loss-of-control accidents, including applications to pilot training, accident reconstruction, and advanced control system analysis. The ultimate goal of this effort is to contribute to the reduction of the fatal accident rate due to loss-of-control. Research activities have involved accident analyses, wind tunnel testing, and piloted simulation. Results have shown that significant improvements in simulation fidelity for upset conditions, compared to current training simulations, can be achieved using state-of-the-art wind tunnel testing and aerodynamic modeling methods. This paper provides a summary of research completed to date and includes discussion on key technical results, lessons learned, and future research needs.
Passivity-based model predictive control for mobile vehicle motion planning
Tahirovic, Adnan
2013-01-01
Passivity-based Model Predictive Control for Mobile Vehicle Navigation represents a complete theoretical approach to the adoption of passivity-based model predictive control (MPC) for autonomous vehicle navigation in both indoor and outdoor environments. The brief also introduces analysis of the worst-case scenario that might occur during the task execution. Some of the questions answered in the text include: • how to use an MPC optimization framework for the mobile vehicle navigation approach; • how to guarantee safe task completion even in complex environments including obstacle avoidance and sideslip and rollover avoidance; and • what to expect in the worst-case scenario in which the roughness of the terrain leads the algorithm to generate the longest possible path to the goal. The passivity-based MPC approach provides a framework in which a wide range of complex vehicles can be accommodated to obtain a safer and more realizable tool during the path-planning stage. During task execution, the optimi...
DEFF Research Database (Denmark)
Andersen, Rune Korsholm; Fabi, Valentina; Corgnati, Stefano P.
2016-01-01
with the building controls (windows, thermostats, solar shading etc.). During the last decade, studies about stochastic models of occupants' behaviour in relation to control of the indoor environment have been published. Often the overall aim of these models is to enable more reliable predictions of building...... performance using building energy performance simulations (BEPS). However, the validity of these models has only been sparsely tested. In this paper, stochastic models of occupants' behaviour from literature were tested against measurements in five apartments. In a monitoring campaign, measurements of indoor....... However, comparisons of the average stochastic predictions with the measured temperatures, relative humidity and CO2 concentrations revealed that the models did not predict the actual indoor environmental conditions well....
Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques
Lee, Hanbong
2016-01-01
Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2012-01-01
Robust model predictive control (RMPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Because...... of the special structure of the problem, uncertainty is only in the B matrix (gain) of the state space model. Therefore by taking advantage of this structure, we formulate a tractable minimax optimization problem to solve robust model predictive control problem. Wind turbine is chosen as the case study and we...... choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon....
Stock management in hospital pharmacy using chance-constrained model predictive control.
Jurado, I; Maestre, J M; Velarde, P; Ocampo-Martinez, C; Fernández, I; Tejera, B Isla; Prado, J R Del
2016-05-01
One of the most important problems in the pharmacy department of a hospital is stock management. The clinical need for drugs must be satisfied with limited work labor while minimizing the use of economic resources. The complexity of the problem resides in the random nature of the drug demand and the multiple constraints that must be taken into account in every decision. In this article, chance-constrained model predictive control is proposed to deal with this problem. The flexibility of model predictive control allows taking into account explicitly the different objectives and constraints involved in the problem while the use of chance constraints provides a trade-off between conservativeness and efficiency. The solution proposed is assessed to study its implementation in two Spanish hospitals. Copyright © 2015 Elsevier Ltd. All rights reserved.
Creating a simulation model of software testing using Simulink package
Directory of Open Access Journals (Sweden)
V. M. Dubovoi
2016-12-01
Full Text Available The determination of the solution model of software testing that allows prediction both the whole process and its specific stages is actual for IT-industry. The article focuses on solving this problem. The aim of the article is prediction the time and improvement the quality of software testing. The analysis of the software testing process shows that it can be attributed to the branched cyclic technological processes because it is cyclical with decision-making on control operations. The investigation uses authors' previous works andsoftware testing process method based on Markov model. The proposed method enables execution the prediction for each software module, which leads to better decision-making of each controlled suboperation of all processes. Simulink simulation model shows implementation and verification of results of proposed technique. Results of the research have practically implemented in the IT-industry.
Linear Model-Based Predictive Control of the LHC 1.8 K Cryogenic Loop
Blanco-Viñuela, E; De Prada-Moraga, C
1999-01-01
The LHC accelerator will employ 1800 superconducting magnets (for guidance and focusing of the particle beams) in a pressurized superfluid helium bath at 1.9 K. This temperature is a severely constrained control parameter in order to avoid the transition from the superconducting to the normal state. Cryogenic processes are difficult to regulate due to their highly non-linear physical parameters (heat capacity, thermal conductance, etc.) and undesirable peculiarities like non self-regulating process, inverse response and variable dead time. To reduce the requirements on either temperature sensor or cryogenic system performance, various control strategies have been investigated on a reduced-scale LHC prototype built at CERN (String Test). Model Based Predictive Control (MBPC) is a regulation algorithm based on the explicit use of a process model to forecast the plant output over a certain prediction horizon. This predicted controlled variable is used in an on-line optimization procedure that minimizes an approp...
Model predictive control of PMSG-based wind turbines for frequency regulation in an isolated grid
DEFF Research Database (Denmark)
Wang, Haixin; Yang, Junyou; Ma, Yiming
2017-01-01
This paper proposes a frequency regulation strategy applied to wind turbine generators (WTGs) in an isolated grid. In order to complement active power shortage caused by sudden load or wind speed change, an improved deloading method is proposed to solve inconsistent regulation capabilities...... in different speed regions and provide WTGs a certain capacity of power reserves. Considering the torque compensation may bring about power oscillation, speed reference of conventional pitch control system should be reset. Moreover, to suppress disturbances of load and wind speed as well as overcome dependence...... on system parameters, a model predictive controller (MPC) of wind farm is designed to generate torque compensation for each deloaded WTG. The key feature of this strategy is that each WTG reacts to grid disturbances in different ways, which depends on generator speeds. Hardware-in-the-loop simulation...
Predictive validation of an influenza spread model.
Directory of Open Access Journals (Sweden)
Ayaz Hyder
Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve
Predictive Validation of an Influenza Spread Model
Hyder, Ayaz; Buckeridge, David L.; Leung, Brian
2013-01-01
Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive
Hurley, Susan
2008-02-01
Imitation, deliberation, and mindreading are characteristically human sociocognitive skills. Research on imitation and its role in social cognition is flourishing across various disciplines. Imitation is surveyed in this target article under headings of behavior, subpersonal mechanisms, and functions of imitation. A model is then advanced within which many of the developments surveyed can be located and explained. The shared circuits model (SCM) explains how imitation, deliberation, and mindreading can be enabled by subpersonal mechanisms of control, mirroring, and simulation. It is cast at a middle, functional level of description, that is, between the level of neural implementation and the level of conscious perceptions and intentional actions. The SCM connects shared informational dynamics for perception and action with shared informational dynamics for self and other, while also showing how the action/perception, self/other, and actual/possible distinctions can be overlaid on these shared informational dynamics. It avoids the common conception of perception and action as separate and peripheral to central cognition. Rather, it contributes to the situated cognition movement by showing how mechanisms for perceiving action can be built on those for active perception.;>;>The SCM is developed heuristically, in five layers that can be combined in various ways to frame specific ontogenetic or phylogenetic hypotheses. The starting point is dynamic online motor control, whereby an organism is closely attuned to its embedding environment through sensorimotor feedback. Onto this are layered functions of prediction and simulation of feedback, mirroring, simulation of mirroring, monitored inhibition of motor output, and monitored simulation of input. Finally, monitored simulation of input specifying possible actions plus inhibited mirroring of such possible actions can generate information about the possible as opposed to actual instrumental actions of others, and the
Ganesan, Nandhini; Basu, Suman; Hariharan, Krishnan S.; Kolake, Subramanya Mayya; Song, Taewon; Yeo, Taejung; Sohn, Dong Kee; Doo, Seokgwang
2016-08-01
Lithium-Ion batteries used for electric vehicle applications are subject to large currents and various operation conditions, making battery pack design and life extension a challenging problem. With increase in complexity, modeling and simulation can lead to insights that ensure optimal performance and life extension. In this manuscript, an electrochemical-thermal (ECT) coupled model for a 6 series × 5 parallel pack is developed for Li ion cells with NCA/C electrodes and validated against experimental data. Contribution of the cathode to overall degradation at various operating conditions is assessed. Pack asymmetry is analyzed from a design and an operational perspective. Design based asymmetry leads to a new approach of obtaining the individual cell responses of the pack from an average ECT output. Operational asymmetry is demonstrated in terms of effects of thermal gradients on cycle life, and an efficient model predictive control technique is developed. Concept of reconfigurable battery pack is studied using detailed simulations that can be used for effective monitoring and extension of battery pack life.
Demand Management Based on Model Predictive Control Techniques
Directory of Open Access Journals (Sweden)
Yasser A. Davizón
2014-01-01
Full Text Available Demand management (DM is the process that helps companies to sell the right product to the right customer, at the right time, and for the right price. Therefore the challenge for any company is to determine how much to sell, at what price, and to which market segment while maximizing its profits. DM also helps managers efficiently allocate undifferentiated units of capacity to the available demand with the goal of maximizing revenue. This paper introduces control system approach to demand management with dynamic pricing (DP using the model predictive control (MPC technique. In addition, we present a proper dynamical system analogy based on active suspension and a stability analysis is provided via the Lyapunov direct method.
Model Predictive Control for Flexible Power Consumption of Large-Scale Refrigeration Systems
DEFF Research Database (Denmark)
Shafiei, Seyed Ehsan; Stoustrup, Jakob; Rasmussen, Henrik
2014-01-01
A model predictive control (MPC) scheme is introduced to directly control the electrical power consumption of large-scale refrigeration systems. Deviation from the baseline of the consumption is corresponded to the storing and delivering of thermal energy. By virtue of such correspondence...
Optimal strategy analysis based on robust predictive control for inventory system with random demand
Saputra, Aditya; Widowati, Sutrisno
2017-12-01
In this paper, the optimal strategy for a single product single supplier inventory system with random demand is analyzed by using robust predictive control with additive random parameter. We formulate the dynamical system of this system as a linear state space with additive random parameter. To determine and analyze the optimal strategy for the given inventory system, we use robust predictive control approach which gives the optimal strategy i.e. the optimal product volume that should be purchased from the supplier for each time period so that the expected cost is minimal. A numerical simulation is performed with some generated random inventory data. We simulate in MATLAB software where the inventory level must be controlled as close as possible to a set point decided by us. From the results, robust predictive control model provides the optimal strategy i.e. the optimal product volume that should be purchased and the inventory level was followed the given set point.
Reynolds-Averaged Navier-Stokes Simulation of a 2D Circulation Control Wind Tunnel Experiment
Allan, Brian G.; Jones, Greg; Lin, John C.
2011-01-01
Numerical simulations are performed using a Reynolds-averaged Navier-Stokes (RANS) flow solver for a circulation control airfoil. 2D and 3D simulation results are compared to a circulation control wind tunnel test conducted at the NASA Langley Basic Aerodynamics Research Tunnel (BART). The RANS simulations are compared to a low blowing case with a jet momentum coefficient, C(sub u), of 0:047 and a higher blowing case of 0.115. Three dimensional simulations of the model and tunnel walls show wall effects on the lift and airfoil surface pressures. These wall effects include a 4% decrease of the midspan sectional lift for the C(sub u) 0.115 blowing condition. Simulations comparing the performance of the Spalart Allmaras (SA) and Shear Stress Transport (SST) turbulence models are also made, showing the SST model compares best to the experimental data. A Rotational/Curvature Correction (RCC) to the turbulence model is also evaluated demonstrating an improvement in the CFD predictions.
New Results on Robust Model Predictive Control for Time-Delay Systems with Input Constraints
Directory of Open Access Journals (Sweden)
Qing Lu
2014-01-01
Full Text Available This paper investigates the problem of model predictive control for a class of nonlinear systems subject to state delays and input constraints. The time-varying delay is considered with both upper and lower bounds. A new model is proposed to approximate the delay. And the uncertainty is polytopic type. For the state-feedback MPC design objective, we formulate an optimization problem. Under model transformation, a new model predictive controller is designed such that the robust asymptotical stability of the closed-loop system can be guaranteed. Finally, the applicability of the presented results are demonstrated by a practical example.
Roset, B.J.P.; Lazar, M.; Heemels, W.P.M.H.; Nijmeijer, H.
2007-01-01
Abstract—This paper focuses on the synthesis of nonlinear Model Predictive Controllers that can guarantee robustness with respect to measurement noise. The input-to-state stability framework is employed to analyze the robustness of the resulting Model Predictive Control (MPC) closed-loop system. It
Aydiner, E.; Brunner, F.D.; Heemels, W.P.M.H.; Allgower, F.
2015-01-01
In this paper we present a robust self-triggered model predictive control (MPC) scheme for discrete-time linear time-invariant systems subject to input and state constraints and additive disturbances. In self-triggered model predictive control, at every sampling instant an optimization problem based
Graphical models for simulation and control of robotic systems for waste handling
International Nuclear Information System (INIS)
Drotning, W.D.; Bennett, P.C.
1992-01-01
This paper discusses detailed geometric models which have been used within a graphical simulation environment to study transportation cask facility design and to perform design and analyses of robotic systems for handling of nuclear waste. The models form the basis for a robot control environment which provides safety, flexibility, and reliability for operations which span the spectrum from autonomous control to tasks requiring direct human intervention
Energy Technology Data Exchange (ETDEWEB)
Chen, Pingen [Tennessee Technological University (TTU); Lin, Qinghua [Tennessee Technological University (TTU); Prikhodko, Vitaly Y. [ORNL
2017-10-01
Lean-burn gasoline engines have demonstrated 10–20% engine efficiency gain over stoichiometric engines and are widely considered as a promising technology for meeting the 54.5 miles-per-gallon (mpg) Corporate Average Fuel Economy standard by 2025. Nevertheless, NOx emissions control for lean-burn gasoline for meeting the stringent EPA Tier 3 emission standards has been one of the main challenges towards the commercialization of highly-efficient lean-burn gasoline engines in the United States. Passive selective catalytic reduction (SCR) systems, which consist of a three-way catalyst and SCR, have demonstrated great potentials of effectively reducing NOx emissions for lean gasoline engines but may cause significant fuel penalty due to ammonia generation via rich engine combustion. The purpose of this study is to develop a model-predictive control (MPC) scheme for a lean-burn gasoline engine coupled with a passive SCR system to minimize the fuel penalty associated with passive SCR operation while satisfying stringent NOx and NH3 emissions requirements. Simulation results demonstrate that the MPC-based control can reduce the fuel penalty by 47.7% in a simulated US06 cycle and 32.0% in a simulated UDDS cycle, compared to the baseline control, while achieving over 96% deNOx efficiency and less than 15 ppm tailpipe ammonia slip. The proposed MPC control can potentially enable high engine efficiency gain for highly-efficient lean-burn gasoline engine while meeting the stringent EPA Tier 3 emission standards.
Directory of Open Access Journals (Sweden)
Douglas Halamay
2014-09-01
Full Text Available This paper demonstrates the use of model-based predictive control for energy storage systems to improve the dispatchability of wind power plants. Large-scale wind penetration increases the variability of power flow on the grid, thus increasing reserve requirements. Large energy storage systems collocated with wind farms can improve dispatchability of the wind plant by storing energy during generation over-the-schedule and sourcing energy during generation under-the-schedule, essentially providing on-site reserves. Model predictive control (MPC provides a natural framework for this application. By utilizing an accurate energy storage system model, control actions can be planned in the context of system power and state-of-charge limitations. MPC also enables the inclusion of predicted wind farm performance over a near-term horizon that allows control actions to be planned in anticipation of fast changes, such as wind ramps. This paper demonstrates that model-based predictive control can improve system performance compared with a standard non-predictive, non-model-based control approach. It is also demonstrated that secondary objectives, such as reducing the rate of change of the wind plant output (i.e., ramps, can be considered and successfully implemented within the MPC framework. Specifically, it is shown that scheduling error can be reduced by 81%, reserve requirements can be improved by up to 37%, and the number of ramp events can be reduced by 74%.
Modelling and control of large cryogenic refrigerator
International Nuclear Information System (INIS)
Bonne, Francois
2014-01-01
This manuscript is concern with both the modeling and the derivation of control schemes for large cryogenic refrigerators. The particular case of those which are submitted to highly variable pulsed heat load is studied. A model of each object that normally compose a large cryo-refrigerator is proposed. The methodology to gather objects model into the model of a subsystem is presented. The manuscript also shows how to obtain a linear equivalent model of the subsystem. Based on the derived models, advances control scheme are proposed. Precisely, a linear quadratic controller for warm compression station working with both two and three pressures state is derived, and a predictive constrained one for the cold-box is obtained. The particularity of those control schemes is that they fit the computing and data storage capabilities of Programmable Logic Controllers (PLC) with are well used in industry. The open loop model prediction capability is assessed using experimental data. Developed control schemes are validated in simulation and experimentally on the 400W1.8K SBT's cryogenic test facility and on the CERN's LHC warm compression station. (author) [fr
Whole-building Hygrothermal Simulation Model
DEFF Research Database (Denmark)
Rode, Carsten; Grau, Karl
2003-01-01
An existing integrated simulation tool for dynamic thermal simulation of building was extended with a transient model for moisture release and uptake in building materials. Validation of the new model was begun with comparison against measurements in an outdoor test cell furnished with single...... materials. Almost quasi-steady, cyclic experiments were used to compare the indoor humidity variation and the numerical results of the integrated simulation tool with the new moisture model. Except for the case with chipboard as furnishing, the predictions of indoor humidity with the detailed model were...
Montoye, Alexander H. K.; Conger, Scott A.; Connolly, Christopher P.; Imboden, Mary T.; Nelson, M. Benjamin; Bock, Josh M.; Kaminsky, Leonard A.
2017-01-01
This study compared accuracy of energy expenditure (EE) prediction models from accelerometer data collected in structured and simulated free-living settings. Twenty-four adults (mean age 45.8 years, 50% female) performed two sessions of 11 to 21 activities, wearing four ActiGraph GT9X Link activity monitors (right hip, ankle, both wrists) and a…
Real-time distributed economic model predictive control for complete vehicle energy management
Romijn, Constantijn; Donkers, Tijs; Kessels, John; Weiland, Siep
2017-01-01
In this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving
Sreekanth, J.; Moore, Catherine
2018-04-01
The application of global sensitivity and uncertainty analysis techniques to groundwater models of deep sedimentary basins are typically challenged by large computational burdens combined with associated numerical stability issues. The highly parameterized approaches required for exploring the predictive uncertainty associated with the heterogeneous hydraulic characteristics of multiple aquifers and aquitards in these sedimentary basins exacerbate these issues. A novel Patch Modelling Methodology is proposed for improving the computational feasibility of stochastic modelling analysis of large-scale and complex groundwater models. The method incorporates a nested groundwater modelling framework that enables efficient simulation of groundwater flow and transport across multiple spatial and temporal scales. The method also allows different processes to be simulated within different model scales. Existing nested model methodologies are extended by employing 'joining predictions' for extrapolating prediction-salient information from one model scale to the next. This establishes a feedback mechanism supporting the transfer of information from child models to parent models as well as parent models to child models in a computationally efficient manner. This feedback mechanism is simple and flexible and ensures that while the salient small scale features influencing larger scale prediction are transferred back to the larger scale, this does not require the live coupling of models. This method allows the modelling of multiple groundwater flow and transport processes using separate groundwater models that are built for the appropriate spatial and temporal scales, within a stochastic framework, while also removing the computational burden associated with live model coupling. The utility of the method is demonstrated by application to an actual large scale aquifer injection scheme in Australia.
Prediction based active ramp metering control strategy with mobility and safety assessment
Fang, Jie; Tu, Lili
2018-04-01
Ramp metering is one of the most direct and efficient motorway traffic flow management measures so as to improve traffic conditions. However, owing to short of traffic conditions prediction, in earlier studies, the impact on traffic flow dynamics of the applied RM control was not quantitatively evaluated. In this study, a RM control algorithm adopting Model Predictive Control (MPC) framework to predict and assess future traffic conditions, which taking both the current traffic conditions and the RM-controlled future traffic states into consideration, was presented. The designed RM control algorithm targets at optimizing the network mobility and safety performance. The designed algorithm is evaluated in a field-data-based simulation. Through comparing the presented algorithm controlled scenario with the uncontrolled scenario, it was proved that the proposed RM control algorithm can effectively relieve the congestion of traffic network with no significant compromises in safety aspect.
Improving Permafrost Hydrology Prediction Through Data-Model Integration
Wilson, C. J.; Andresen, C. G.; Atchley, A. L.; Bolton, W. R.; Busey, R.; Coon, E.; Charsley-Groffman, L.
2017-12-01
The CMIP5 Earth System Models were unable to adequately predict the fate of the 16GT of permafrost carbon in a warming climate due to poor representation of Arctic ecosystem processes. The DOE Office of Science Next Generation Ecosystem Experiment, NGEE-Arctic project aims to reduce uncertainty in the Arctic carbon cycle and its impact on the Earth's climate system by improved representation of the coupled physical, chemical and biological processes that drive how much buried carbon will be converted to CO2 and CH4, how fast this will happen, which form will dominate, and the degree to which increased plant productivity will offset increased soil carbon emissions. These processes fundamentally depend on permafrost thaw rate and its influence on surface and subsurface hydrology through thermal erosion, land subsidence and changes to groundwater flow pathways as soil, bedrock and alluvial pore ice and massive ground ice melts. LANL and its NGEE colleagues are co-developing data and models to better understand controls on permafrost degradation and improve prediction of the evolution of permafrost and its impact on Arctic hydrology. The LANL Advanced Terrestrial Simulator was built using a state of the art HPC software framework to enable the first fully coupled 3-dimensional surface-subsurface thermal-hydrology and land surface deformation simulations to simulate the evolution of the physical Arctic environment. Here we show how field data including hydrology, snow, vegetation, geochemistry and soil properties, are informing the development and application of the ATS to improve understanding of controls on permafrost stability and permafrost hydrology. The ATS is being used to inform parameterizations of complex coupled physical, ecological and biogeochemical processes for implementation in the DOE ACME land model, to better predict the role of changing Arctic hydrology on the global climate system. LA-UR-17-26566.
Flight Testing an Iced Business Jet for Flight Simulation Model Validation
Ratvasky, Thomas P.; Barnhart, Billy P.; Lee, Sam; Cooper, Jon
2007-01-01
A flight test of a business jet aircraft with various ice accretions was performed to obtain data to validate flight simulation models developed through wind tunnel tests. Three types of ice accretions were tested: pre-activation roughness, runback shapes that form downstream of the thermal wing ice protection system, and a wing ice protection system failure shape. The high fidelity flight simulation models of this business jet aircraft were validated using a software tool called "Overdrive." Through comparisons of flight-extracted aerodynamic forces and moments to simulation-predicted forces and moments, the simulation models were successfully validated. Only minor adjustments in the simulation database were required to obtain adequate match, signifying the process used to develop the simulation models was successful. The simulation models were implemented in the NASA Ice Contamination Effects Flight Training Device (ICEFTD) to enable company pilots to evaluate flight characteristics of the simulation models. By and large, the pilots confirmed good similarities in the flight characteristics when compared to the real airplane. However, pilots noted pitch up tendencies at stall with the flaps extended that were not representative of the airplane and identified some differences in pilot forces. The elevator hinge moment model and implementation of the control forces on the ICEFTD were identified as a driver in the pitch ups and control force issues, and will be an area for future work.
Keenan, Alexandra; Young, Millennia; Saile, Lynn; Boley, Lynn; Walton, Marlei; Kerstman, Eric; Shah, Ronak; Goodenow, Debra A.; Myers, Jerry G.
2015-01-01
The Integrated Medical Model (IMM) is a probabilistic model that uses simulation to predict mission medical risk. Given a specific mission and crew scenario, medical events are simulated using Monte Carlo methodology to provide estimates of resource utilization, probability of evacuation, probability of loss of crew, and the amount of mission time lost due to illness. Mission and crew scenarios are defined by mission length, extravehicular activity (EVA) schedule, and crew characteristics including: sex, coronary artery calcium score, contacts, dental crowns, history of abdominal surgery, and EVA eligibility. The Integrated Medical Evidence Database (iMED) houses the model inputs for one hundred medical conditions using in-flight, analog, and terrestrial medical data. Inputs include incidence, event durations, resource utilization, and crew functional impairment. Severity of conditions is addressed by defining statistical distributions on the dichotomized best and worst-case scenarios for each condition. The outcome distributions for conditions are bounded by the treatment extremes of the fully treated scenario in which all required resources are available and the untreated scenario in which no required resources are available. Upon occurrence of a simulated medical event, treatment availability is assessed, and outcomes are generated depending on the status of the affected crewmember at the time of onset, including any pre-existing functional impairments or ongoing treatment of concurrent conditions. The main IMM outcomes, including probability of evacuation and loss of crew life, time lost due to medical events, and resource utilization, are useful in informing mission planning decisions. To date, the IMM has been used to assess mission-specific risks with and without certain crewmember characteristics, to determine the impact of eliminating certain resources from the mission medical kit, and to design medical kits that maximally benefit crew health while meeting
van Heeringen, Klaas-Jan; Gooijer, Jan; Knot, Floris; Talsma, Jan
2015-04-01
In the Netherlands, flood protection has always been a key issue to protect settlements against storm surges and riverine floods. Whereas flood protection traditionally focused on structural measures, nowadays the availability of meteorological and hydrological forecasts enable the application of more advanced real-time control techniques for operating the existing hydraulic infrastructure in an anticipatory and more efficient way. Model Predictive Control (MPC) is a powerful technique to derive optimal control variables with the help of model based predictions evaluated against a control objective. In a project for the regional water authority Noorderzijlvest in the north of the Netherlands, it has been shown that MPC can increase the safety level of the system during flood events by an anticipatory pre-release of water. Furthermore, energy costs of pumps can be reduced by making tactical use of the water storage and shifting pump activities during normal operating conditions to off-peak hours. In this way cheap energy is used in combination of gravity flow through gates during low tide periods. MPC has now been implemented for daily operational use of the whole water system of the water authority Noorderzijlvest. The system developed to a real time decision support system which not only supports the daily operation but is able to directly implement the optimal control settings at the structures. We explain how we set-up and calibrated a prediction model (RTC-Tools) that is accurate and fast enough for optimization purposes, and how we integrated it in the operational flood early warning system (Delft-FEWS). Beside the prediction model, the weights and the factors of the objective function are an important element of MPC, since they shape the control objective. We developed special features in Delft-FEWS to allow the operators to adjust the objective function in order to meet changing requirements and to evaluate different control strategies.
Force Control for a Pneumatic Cylinder Using Generalized Predictive Controller Approach
Directory of Open Access Journals (Sweden)
Ahmad ’Athif Mohd Faudzi
2014-01-01
Full Text Available Pneumatic cylinder is a well-known device because of its high power to weight ratio, easy use, and environmental safety. Pneumatic cylinder uses air as its power source and converts it to a possible movement such as linear and rotary movement. In order to control the pneumatic cylinder, controller algorithm is needed to control the on-off solenoid valve with encoder and pressure sensor as the feedback inputs. In this paper, generalized predictive controller (GPC is proposed as the control strategy for the pneumatic cylinder force control. To validate and compare the performance, proportional-integral (PI controller is also presented. Both controllers algorithms GPC and PI are developed using existing linear model of the cylinder from previous research. Results are presented in simulation and experimental approach using MATLAB-Simulink as the platform. The results show that the GPC is capable of fast response with low steady state error and percentage overshoot compared to PI.
Power maximization of a point absorber wave energy converter using improved model predictive control
Milani, Farideh; Moghaddam, Reihaneh Kardehi
2017-08-01
This paper considers controlling and maximizing the absorbed power of wave energy converters for irregular waves. With respect to physical constraints of the system, a model predictive control is applied. Irregular waves' behavior is predicted by Kalman filter method. Owing to the great influence of controller parameters on the absorbed power, these parameters are optimized by imperialist competitive algorithm. The results illustrate the method's efficiency in maximizing the extracted power in the presence of unknown excitation force which should be predicted by Kalman filter.
DEFF Research Database (Denmark)
Boiroux, Dimitri; Hagdrup, Morten; Mahmoudi, Zeinab
2016-01-01
patients with different physiological parameters and a time-varying insulin sensitivity using the Medtronic Virtual Patient (MVP) model. We augment the MVP model with stochastic diffusion terms, time-varying insulin sensitivity and noise-corrupted CGM measurements. We consider meal challenges where......This paper presents a novel ensemble nonlinear model predictive control (NMPC) algorithm for glucose regulation in type 1 diabetes. In this approach, we consider a number of scenarios describing different uncertainties, for instance meals or metabolic variations. We simulate a population of 9...... the uncertainty in meal size is ±50%. Numerical results show that the ensemble NMPC reduces the risk of hypoglycemia compared to standard NMPC in the case where the meal size is overestimated or correctly estimated at the expense of a slightly increased number of hyperglycemia. Therefore, ensemble MPC...
Andreev, Victor P; Head, Trajen; Johnson, Neil; Deo, Sapna K; Daunert, Sylvia; Goldschmidt-Clermont, Pascal J
2013-01-01
Sudden Cardiac Death (SCD) is responsible for at least 180,000 deaths a year and incurs an average cost of $286 billion annually in the United States alone. Herein, we present a novel discrete event simulation model of SCD, which quantifies the chains of events associated with the formation, growth, and rupture of atheroma plaques, and the subsequent formation of clots, thrombosis and on-set of arrhythmias within a population. The predictions generated by the model are in good agreement both with results obtained from pathological examinations on the frequencies of three major types of atheroma, and with epidemiological data on the prevalence and risk of SCD. These model predictions allow for identification of interventions and importantly for the optimal time of intervention leading to high potential impact on SCD risk reduction (up to 8-fold reduction in the number of SCDs in the population) as well as the increase in life expectancy.
Strategy and gaps for modeling, simulation, and control of hybrid systems
Energy Technology Data Exchange (ETDEWEB)
Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States); Garcia, Humberto E. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Hovsapian, Rob [Idaho National Lab. (INL), Idaho Falls, ID (United States); Kinoshita, Robert [Idaho National Lab. (INL), Idaho Falls, ID (United States); Mesina, George L. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Bragg-Sitton, Shannon M. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Boardman, Richard D. [Idaho National Lab. (INL), Idaho Falls, ID (United States)
2015-04-01
The purpose of this report is to establish a strategy for modeling and simulation of candidate hybrid energy systems. Modeling and simulation is necessary to design, evaluate, and optimize the system technical and economic performance. Accordingly, this report first establishes the simulation requirements to analysis candidate hybrid systems. Simulation fidelity levels are established based on the temporal scale, real and synthetic data availability or needs, solution accuracy, and output parameters needed to evaluate case-specific figures of merit. Accordingly, the associated computational and co-simulation resources needed are established; including physical models when needed, code assembly and integrated solutions platforms, mathematical solvers, and data processing. This report first attempts to describe the figures of merit, systems requirements, and constraints that are necessary and sufficient to characterize the grid and hybrid systems behavior and market interactions. Loss of Load Probability (LOLP) and effective cost of Effective Cost of Energy (ECE), as opposed to the standard Levelized Cost of Electricty (LCOE), are introduced as technical and economical indices for integrated energy system evaluations. Financial assessment methods are subsequently introduced for evaluation of non-traditional, hybrid energy systems. Algorithms for coupled and iterative evaluation of the technical and economic performance are subsequently discussed. This report further defines modeling objectives, computational tools, solution approaches, and real-time data collection and processing (in some cases using real test units) that will be required to model, co-simulate, and optimize; (a) an energy system components (e.g., power generation unit, chemical process, electricity management unit), (b) system domains (e.g., thermal, electrical or chemical energy generation, conversion, and transport), and (c) systems control modules. Co-simulation of complex, tightly coupled
A review of model predictive control: moving from linear to nonlinear design methods
International Nuclear Information System (INIS)
Nandong, J.; Samyudia, Y.; Tade, M.O.
2006-01-01
Linear model predictive control (LMPC) has now been considered as an industrial control standard in process industry. Its extension to nonlinear cases however has not yet gained wide acceptance due to many reasons, e.g. excessively heavy computational load and effort, thus, preventing its practical implementation in real-time control. The application of nonlinear MPC (NMPC) is advantageous for processes with strong nonlinearity or when the operating points are frequently moved from one set point to another due to, for instance, changes in market demands. Much effort has been dedicated towards improving the computational efficiency of NMPC as well as its stability analysis. This paper provides a review on alternative ways of extending linear MPC to the nonlinear one. We also highlight the critical issues pertinent to the applications of NMPC and discuss possible solutions to address these issues. In addition, we outline the future research trend in the area of model predictive control by emphasizing on the potential applications of multi-scale process model within NMPC
Kim, Jae-Chang; Moon, Sung-Ki; Kwak, Sangshin
2018-04-01
This paper presents a direct model-based predictive control scheme for voltage source inverters (VSIs) with reduced common-mode voltages (CMVs). The developed method directly finds optimal vectors without using repetitive calculation of a cost function. To adjust output currents with the CMVs in the range of -Vdc/6 to +Vdc/6, the developed method uses voltage vectors, as finite control resources, excluding zero voltage vectors which produce the CMVs in the VSI within ±Vdc/2. In a model-based predictive control (MPC), not using zero voltage vectors increases the output current ripples and the current errors. To alleviate these problems, the developed method uses two non-zero voltage vectors in one sampling step. In addition, the voltage vectors scheduled to be used are directly selected at every sampling step once the developed method calculates the future reference voltage vector, saving the efforts of repeatedly calculating the cost function. And the two non-zero voltage vectors are optimally allocated to make the output current approach the reference current as close as possible. Thus, low CMV, rapid current-following capability and sufficient output current ripple performance are attained by the developed method. The results of a simulation and an experiment verify the effectiveness of the developed method.
DEFF Research Database (Denmark)
Bæk, David; Jensen, Jørgen Arendt; Willatzen, Morten
2010-01-01
FIELD II is a simulation software capable of predicting the field pressure in front of transducers having any complicated geometry. A calibrated prediction with this program is, however, dependent on an exact voltage-to-surface acceleration impulse response of the transducer. Such impulse response...... is not calculated by FIELD II. This work investigates the usability of combining a one-dimensional multilayer transducer modeling principle with the FIELD II software. Multilayer here refers to a transducer composed of several material layers. Measurements of pressure and current from Pz27 piezoceramic disks...... transducer model and the FIELD II software in combination give good agreement with measurements....
Tian, Zhen; Yuan, Jingqi; Zhang, Xiang; Kong, Lei; Wang, Jingcheng
2018-05-01
The coordinated control system (CCS) serves as an important role in load regulation, efficiency optimization and pollutant reduction for coal-fired power plants. The CCS faces with tough challenges, such as the wide-range load variation, various uncertainties and constraints. This paper aims to improve the load tacking ability and robustness for boiler-turbine units under wide-range operation. To capture the key dynamics of the ultra-supercritical boiler-turbine system, a nonlinear control-oriented model is developed based on mechanism analysis and model reduction techniques, which is validated with the history operation data of a real 1000 MW unit. To simultaneously address the issues of uncertainties and input constraints, a discrete-time sliding mode predictive controller (SMPC) is designed with the dual-mode control law. Moreover, the input-to-state stability and robustness of the closed-loop system are proved. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves good tracking performance, disturbance rejection ability and compatibility to input constraints. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Multi-fluid modelling of pulsed discharges for flow control applications
Poggie, J.
2015-02-01
Experimental evidence suggests that short-pulse dielectric barrier discharge actuators are effective for speeds corresponding to take-off and approach of large aircraft, and thus are a fruitful direction for flow control technology development. Large-eddy simulations have reproduced some of the main fluid dynamic effects. The plasma models used in such simulations are semi-empirical, however, and need to be tuned for each flowfield under consideration. In this paper, the discharge physics is examined in more detail with multi-fluid modelling, comparing a five-moment model (continuity, momentum, and energy equations) to a two-moment model (continuity and energy equations). A steady-state, one-dimensional discharge was considered first, and the five-moment model was found to predict significantly lower ionisation rates and number densities than the two-moment model. A two-dimensional, transient discharge problem with an elliptical cathode was studied next. Relative to the two-moment model, the five-moment model predicted a slower response to the activation of the cathode, and lower electron velocities and temperatures as the simulation approached steady-state. The primary reason for the differences in the predictions of the two models can be attributed to the effects of particle inertia, particularly electron inertia in the cathode layer. The computational cost of the five-moment model is only about twice that of the simpler variant, suggesting that it may be feasible to use the more sophisticated model in practical calculations for flow control actuator design.
Guillermo A. Mendoza; Roger J. Meimban; Philip A. Araman; William G. Luppold
1991-01-01
A log inventory model and a real-time hardwood process simulation model were developed and combined into an integrated production planning and control system for hardwood sawmills. The log inventory model was designed to monitor and periodically update the status of the logs in the log yard. The process simulation model was designed to estimate various sawmill...
Delta-Domain Predictive Control and Identification for Control
DEFF Research Database (Denmark)
Lauritsen, Morten Bach
1997-01-01
The present thesis is concerned with different aspects of modelling, control and identification of linear systems. Traditionally, discrete-time sampled-data systems are represented using shift-operator parametrizations. Such parametrizations are not suitable at fast sampling rates. An alternative...... minimum-variance predictor as a special case and to have a well-defined continuous-time limit. By means of this new prediction method a unified framework for discrete-time and continuous-time predictive control algorithms is developed. This contains a continuous-time like discrete-time predictive...... controller which is insensitive to the choice of sampling period and has a well-defined limit in the continuous-time case. Also more conventional discrete-time predictive control methods may be described within the unified approach. The predictive control algorithms are extended to frequency weighted...
International Nuclear Information System (INIS)
Guo, Su; Liu, Deyou; Chen, Xingying; Chu, Yinghao; Xu, Chang; Liu, Qunming; Zhou, Ling
2017-01-01
Highlights: •A nonlinear dynamic model of recirculation DSG parabolic trough is developed. •Collector row, water separator and spray attemperator are modeled, respectively. •The dynamic behaviors of the collector field are simulated and analyzed. •Transfer functions of water level and outlet fluid temperature are derived. •Multi-model switching generalized predictive control strategy is developed. -- Abstract: This work describes and evaluates a new nonlinear dynamic model, and a new generalized predictive control scheme for a collector field of direct steam generation parabolic troughs in recirculation mode. Modeling the dynamic behaviors of collector fields is essential to design, testing and validation of automatic control systems for direct steam generation parabolic troughs. However, the behaviors of two-phase heat transfer fluids impose challenges to simulating and developing process control schemes. In this work, a new nonlinear dynamic model is proposed, based on the nonlinear distributed parameter and the nonlinear lumped parameter methods. The proposed model is used to simulate and analyze the dynamic behaviors of the entire collector field for recirculation mode direct steam generation parabolic troughs under different weather conditions, without excessive computational costs. Based on the proposed model, transfer functions for both the water level of the separator and outlet steam temperatures are derived, and a new multi-model switching generalized predictive control scheme is developed for simulated control of the plant behaviors for a wide region of operational conditions. The proposed control scheme achieves excellent control performance and robustness for systems with long delay, large inertia and time-varying parameters, and efficiently solves the model mismatching problem in direct steam generation parabolic troughs. The performances of the model and control scheme are validated with design data from the project of Integration of Direct
GENERAL REQUIREMENTS FOR SIMULATION MODELS IN WASTE MANAGEMENT
International Nuclear Information System (INIS)
Miller, Ian; Kossik, Rick; Voss, Charlie
2003-01-01
Most waste management activities are decided upon and carried out in a public or semi-public arena, typically involving the waste management organization, one or more regulators, and often other stakeholders and members of the public. In these environments, simulation modeling can be a powerful tool in reaching a consensus on the best path forward, but only if the models that are developed are understood and accepted by all of the parties involved. These requirements for understanding and acceptance of the models constrain the appropriate software and model development procedures that are employed. This paper discusses requirements for both simulation software and for the models that are developed using the software. Requirements for the software include transparency, accessibility, flexibility, extensibility, quality assurance, ability to do discrete and/or continuous simulation, and efficiency. Requirements for the models that are developed include traceability, transparency, credibility/validity, and quality control. The paper discusses these requirements with specific reference to the requirements for performance assessment models that are used for predicting the long-term safety of waste disposal facilities, such as the proposed Yucca Mountain repository
SIMULATION MODELS OF HEAVY TRUCKS TRAFFIC CONTROL WITH ELECTRIC DC DRIVE
Directory of Open Access Journals (Sweden)
N. N. Hurski
2015-01-01
Full Text Available A model of the straight course of movement of the mobile machine with a traction electric motor DC. Traffic management controller provides a closed classical scheme with feedback. The mathematical model of the electric DC motor with the energy dissipation in the rotor bearings. Design scheme of mobile machines include speed dial controller, traction electric motor, gearbox, transmission and progressively moving mass on the elasticdissipative wheel. The results of the simulation of the machine in the form of temporary processes of change control signals, voltage and current in the windings of the motor and traction power developed on the wheel.
Shi, Shengling; Lazar, Mircea
2017-01-01
This paper proposes a distributed model predictive control algorithm for vehicle platooning and more generally networked systems in a chain structure. The distributed models of the vehicle platoon are coupled through the input of the preceding vehicles. Using the principles of robust model
Adaptive filtering prediction and control
Goodwin, Graham C
2009-01-01
Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o
Dual RBFNNs-Based Model-Free Adaptive Control With Aspen HYSYS Simulation.
Zhu, Yuanming; Hou, Zhongsheng; Qian, Feng; Du, Wenli
2017-03-01
In this brief, we propose a new data-driven model-free adaptive control (MFAC) method with dual radial basis function neural networks