Optimal Model-Based Control in HVAC Systems
DEFF Research Database (Denmark)
Komareji, Mohammad; Stoustrup, Jakob; Rasmussen, Henrik;
2008-01-01
This paper presents optimal model-based control of a heating, ventilating, and air-conditioning (HVAC) system. This HVAC system is made of two heat exchangers: an air-to-air heat exchanger (a rotary wheel heat recovery) and a water-to- air heat exchanger. First dynamic model of the HVAC system...
Model-based dynamic control and optimization of gas networks
Energy Technology Data Exchange (ETDEWEB)
Hofsten, Kai
2001-07-01
This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum
Model-based dynamic control and optimization of gas networks
Energy Technology Data Exchange (ETDEWEB)
Hofsten, Kai
2001-07-01
This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum
Model-based control of fuel cells (2): Optimal efficiency
Energy Technology Data Exchange (ETDEWEB)
Golbert, Joshua; Lewin, Daniel R. [PSE Research Group, Wolfson Department of Chemical Engineering, Technion IIT, Haifa 32000 (Israel)
2007-11-08
A dynamic PEM fuel cell model has been developed, taking into account spatial dependencies of voltage, current, material flows, and temperatures. The voltage, current, and therefore, the efficiency are dependent on the temperature and other variables, which can be optimized on the fly to achieve optimal efficiency. In this paper, we demonstrate that a model predictive controller, relying on a reduced-order approximation of the dynamic PEM fuel cell model can satisfy setpoint changes in the power demand, while at the same time, minimize fuel consumption to maximize the efficiency. The main conclusion of the paper is that by appropriate formulation of the objective function, reliable optimization of the performance of a PEM fuel cell can be performed in which the main tunable parameter is the prediction and control horizons, V and U, respectively. We have demonstrated that increased fuel efficiency can be obtained at the expense of slower responses, by increasing the values of these parameters. (author)
Developments in model-based optimization and control distributed control and industrial applications
Grancharova, Alexandra; Pereira, Fernando
2015-01-01
This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: · complexity and structure in model predictive control (MPC); · collaborative MPC; · distributed MPC; · optimization-based analysis and desi...
Energy Technology Data Exchange (ETDEWEB)
Sandvig, J.
2009-11-15
The project's overall objective has been to use methods in model-based control and online optimization to increase industrial energy efficiency. Model-based regulation is a relatively new technology that combines knowledge of processes and systems, theoretical methods and computer processing power in intelligent, advanced control solutions and methods. The methods have so far been successfully applied in some of the largest process industries, but virtually not in small and medium-sized industries. A major reason for this is that no standard solutions have existed, and therefore it has required significant resources to develop and implement. The goal of this project is to contribute to model-based control being disseminated among the SMEs. This can be done by finding out whether it is possible to adjust and standardize the methods so that they are suitable for deployment in these segments. (ln)
Model-Based Control of an Aircraft Engine using an Optimal Tuner Approach
Connolly, Joseph W.; Chicatelli, Amy; Garg, Sanjay
2012-01-01
This paper covers the development of a model-based engine control (MBEC) method- ology applied to an aircraft turbofan engine. Here, a linear model extracted from the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40k) at a cruise operating point serves as the engine and the on-board model. The on-board model is up- dated using an optimal tuner Kalman Filter (OTKF) estimation routine, which enables the on-board model to self-tune to account for engine performance variations. The focus here is on developing a methodology for MBEC with direct control of estimated parameters of interest such as thrust and stall margins. MBEC provides the ability for a tighter control bound of thrust over the entire life cycle of the engine that is not achievable using traditional control feedback, which uses engine pressure ratio or fan speed. CMAPSS40k is capable of modeling realistic engine performance, allowing for a verification of the MBEC tighter thrust control. In addition, investigations of using the MBEC to provide a surge limit for the controller limit logic are presented that could provide benefits over a simple acceleration schedule that is currently used in engine control architectures.
Energy Technology Data Exchange (ETDEWEB)
Velut, Stephane; Raaberg, Martin; Wendel, Hans (Grontmij AB (SE))
2007-12-15
Thermal power plants are complex processes in which many variables must be monitored and controlled in real-time for a safe and economic operation. The complex interactions between actuators and controlled variables as well as the load dependent dynamics make the design and tuning of all controllers a challenging task. A mathematical model of the process that describes critical characteristics such as dynamics, interactions, and nonlinearities might greatly facilitate the task of the control engineer. Such controllers can be designed in a rather systematic way to achieve good performance in terms of response time and robustness. This enables the operator to run the process closer to its limits while minimizing damage risks. The goal of the project was threefold. The first objective was to describe the available methods to compute process models directly from experimental data and illustrate how those models can be used for control design. The second objective was to apply some of the fore mentioned methods on a specific process, namely a feed water heater train to control the level in each preheater. The third objective was to analyze how the level in each preheater affects the thermal efficiency of the plant and derive adequate set-points for the model-based controllers. The project started at the end of the production season, which resulted in a tight schedule for the planning and the realization of experiments. Informative data could however be collected and models could be derived for some specific loads. Unfortunately the effect of the changes in the level set point could not be verified because of the limited length of the experiments. The project results can be summarized as follows: The way the condensate level should be chosen in every preheater has been formulated as a simple optimization problem that aims as maximizing the thermal efficiency of the plant. Even though the model used in the optimization was simple, the results were pretty intuitive. The
Boyer, Mark D.; Barton, Justin; Schuster, Eugenio; Luce, Tim C.; Ferron, John R.; Walker, Michael L.; Humphreys, David A.; Penaflor, Ben G.; Johnson, Robert D.
2013-10-01
In tokamak fusion plasmas, control of the spatial distribution profile of the toroidal plasma current plays an important role in realizing certain advanced operating scenarios. These scenarios, characterized by improved confinement, magnetohydrodynamic stability, and a high fraction of non-inductively driven plasma current, could enable steady-state reactor operation with high fusion gain. Current profile control experiments at the DIII-D tokamak focus on using a combination of feedforward and feedback control to achieve a targeted current profile during the ramp-up and early flat-top phases of the shot and then to actively maintain this profile during the rest of the discharge. The dynamic evolution of the current profile is nonlinearly coupled with several plasma parameters, motivating the design of model-based control algorithms that can exploit knowledge of the system to achieve desired performance. In this work, we use a first-principles-driven, control-oriented model of the current profile evolution in low confinement mode (L-mode) discharges in DIII-D to design a feedback control law for regulating the profile around a desired trajectory. The model combines the magnetic diffusion equations with empirical correlations for the electron temperature, resistivity, and non-inductive current drive. To improve tracking performance of the system, a nonlinear input transformation is combined with a linear-quadratic-integral (LQI) optimal controller designed to minimize a weighted combination of the tracking error and controller effort. The resulting control law utilizes the total plasma current, total external heating power, and line averaged plasma density as actuators. A simulation study was used to test the controller's performance and ensure correct implementation in the DIII-D plasma control system prior to experimental testing. Experimental results are presented that show the first-principles-driven model-based control scheme's successful rejection of input
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...
Scheller, Johannes; Braza, Marianna; Triantafyllou, Michael
2016-11-01
Bats and other animals rapidly change their wingspan in order to control the aerodynamic forces. A NACA0013 type airfoil with dynamically changing span is proposed as a simple model to experimentally study these biomimetic morphing wings. Combining this large-scale morphing with inline motion allows to control both force magnitude and direction. Force measurements are conducted in order to analyze the impact of the 4 degree of freedom flapping motion on the flow. A blade-element theory augmented unsteady aerodynamic model is then used to derive optimal flapping trajectories.
Model based optimization of wind erosion control by tree shelterbelt for suitable land management
Bartus, M.; Farsang, A.; Szatmári, J.; Barta, K.
2012-04-01
The degradation of soil by wind erosion causes huge problem in many parts of the world. The wind erodes the upper, nutrition rich part of the soil, therefore erosion causes soil productivity loss. The length of tree shelterbelts was significantly reduced by the collectivisation (1960-1989) and the wind erosion affected areas expanded in Hungary. The tree shelterbelt is more than just a tool of wind erosion control; by good planning it can increase the yield. The tree shelterbelt reduces the wind speed and changes the microclimate providing better condition to plant growth. The aim of our work is to estimate wind erosion risk and to find the way to reduce it by tree shelterbelts. A GIS based model was created to calculate the risk and the optimal windbreak position was defined to reduce the wind erosion risk to the minimum. The model is based on the DIN 19706 (Ermitlung der Erosiongefährdung von Böden durch Wind, Estimation of Wind Erosion Risk) German standard. The model uses five input data: structure and carbon content of soil, average yearly wind speed at 10 meters height, the cultivated plants and the height and position of windbreak. The study field (16km2) was chosen near Szeged (SE Hungary). In our investigation, the cultivated plant species and the position and height of windbreaks were modified. Different scenarios were made using the data of the land management in the last few years. The best case scenario (zero wind erosion) and the worst case scenario (with no tree shelter belt and the worst land use) were made to find the optimal windbreak position. Finally, the research proved that the tree shelterbelts can provide proper protection against wind erosion, but for optimal land management the cultivated plant types should also controlled. As a result of the research, a land management plan was defined to reduce the wind erosion risk on the study field, which contains the positions of new tree shelterbelts planting and the optimal cultivation.
Model-based Optimization and Feedback Control of the Current Density Profile Evolution in NSTX-U
Ilhan, Zeki Okan
Nuclear fusion research is a highly challenging, multidisciplinary field seeking contributions from both plasma physics and multiple engineering areas. As an application of plasma control engineering, this dissertation mainly explores methods to control the current density profile evolution within the National Spherical Torus eXperiment-Upgrade (NSTX-U), which is a substantial upgrade based on the NSTX device, which is located in Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ. Active control of the toroidal current density profile is among those plasma control milestones that the NSTX-U program must achieve to realize its next-step operational goals, which are characterized by high-performance, long-pulse, MHD-stable plasma operation with neutral beam heating. Therefore, the aim of this work is to develop model-based, feedforward and feedback controllers that can enable time regulation of the current density profile in NSTX-U by actuating the total plasma current, electron density, and the powers of the individual neutral beam injectors. Motivated by the coupled, nonlinear, multivariable, distributed-parameter plasma dynamics, the first step towards control design is the development of a physics-based, control-oriented model for the current profile evolution in NSTX-U in response to non-inductive current drives and heating systems. Numerical simulations of the proposed control-oriented model show qualitative agreement with the high-fidelity physics code TRANSP. The next step is to utilize the proposed control-oriented model to design an open-loop actuator trajectory optimizer. Given a desired operating state, the optimizer produces the actuator trajectories that can steer the plasma to such state. The objective of the feedforward control design is to provide a more systematic approach to advanced scenario planning in NSTX-U since the development of such scenarios is conventionally carried out experimentally by modifying the tokamak's actuator
Van den Hof, P.M.J.; Jansen, J.D.; Van Essen, G.M.; Bosgra, O.H.
2009-01-01
Due to urgent needs to increase efficiency in oil recovery from subsurface reservoirs new technology is developed that allows more detailed sensing and actuation of multiphase flow properties in oil reservoirs. One of the examples is the controlled injection of water through injection wells with the
Model based development of engine control algorithms
Dekker, H.J.; Sturm, W.L.
1996-01-01
Model based development of engine control systems has several advantages. The development time and costs are strongly reduced because much of the development and optimization work is carried out by simulating both engine and control system. After optimizing the control algorithm it can be executed b
Connolly, Joseph W.; Csank, Jeffrey Thomas; Chicatelli, Amy; Kilver, Jacob
2013-01-01
This paper covers the development of a model-based engine control (MBEC) methodology featuring a self tuning on-board model applied to an aircraft turbofan engine simulation. Here, the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40k) serves as the MBEC application engine. CMAPSS40k is capable of modeling realistic engine performance, allowing for a verification of the MBEC over a wide range of operating points. The on-board model is a piece-wise linear model derived from CMAPSS40k and updated using an optimal tuner Kalman Filter (OTKF) estimation routine, which enables the on-board model to self-tune to account for engine performance variations. The focus here is on developing a methodology for MBEC with direct control of estimated parameters of interest such as thrust and stall margins. Investigations using the MBEC to provide a stall margin limit for the controller protection logic are presented that could provide benefits over a simple acceleration schedule that is currently used in traditional engine control architectures.
Mentink, P.R.; Willems, F.P.T.; Kupper, F.; Eijnden, E.A.C. van den
2013-01-01
This paper presents a model-based control and calibration design method for online cost-based optimization of engine-aftertreatment operation under all operating conditions. The so-called Integrated Emission Management (IEM) strategy online minimizes the fuel and AbBlue consumption. Based on the
Mentink, P.R.; Willems, F.P.T.; Kupper, F.; Eijnden, E.A.C. van den
2013-01-01
This paper presents a model-based control and calibration design method for online cost-based optimization of engine-aftertreatment operation under all operating conditions. The so-called Integrated Emission Management (IEM) strategy online minimizes the fuel and AbBlue consumption. Based on the act
Model Based Control of Refrigeration Systems
DEFF Research Database (Denmark)
Larsen, Lars Finn Sloth
of the supermarket refrigeration systems therefore greatly relies on a human operator to detect and accommodate failures, and to optimize system performance under varying operational condition. Today these functions are maintained by monitoring centres located all over the world. Initiated by the growing need...... for automation of these procedures, that is to incorporate some "intelligence" in the control system, this project was started up. The main emphasis of this work has been on model based methods for system optimizing control in supermarket refrigeration systems. The idea of implementing a system optimizing.......e. by degrading the performance. The method has been successfully applied on a test frigeration system for minimization of the power consumption; the hereby gained experimental results will be presented. The present control structure in a supermarket refrigeration system is distributed, which means...
Model-based control of networked systems
Garcia, Eloy; Montestruque, Luis A
2014-01-01
This monograph introduces a class of networked control systems (NCS) called model-based networked control systems (MB-NCS) and presents various architectures and control strategies designed to improve the performance of NCS. The overall performance of NCS considers the appropriate use of network resources, particularly network bandwidth, in conjunction with the desired response of the system being controlled. The book begins with a detailed description of the basic MB-NCS architecture that provides stability conditions in terms of state feedback updates . It also covers typical problems in NCS such as network delays, network scheduling, and data quantization, as well as more general control problems such as output feedback control, nonlinear systems stabilization, and tracking control. Key features and topics include: Time-triggered and event-triggered feedback updates Stabilization of uncertain systems subject to time delays, quantization, and extended absence of feedback Optimal control analysis and ...
Model-Based Power Plant Master Control
Energy Technology Data Exchange (ETDEWEB)
Boman, Katarina; Thomas, Jean; Funkquist, Jonas
2010-08-15
good indications of robustness. - The Master MPC controller does not require excessive computational requirements and should be possible to implement in a control operation environment. On a standard desktop simulation computer, computation of a control input takes around 40 ms, which is 75 times faster than the sampling time of 3 seconds. - Even though the tuning of an MPC controller is straightforward in many respects, for example balancing the different controlled variables, the procedure also include some less obvious parameter settings and some experience on the power plants and on MPC is needed to tune the optimization problem parameters in the right way. The target groups for this study are both plant management, showing the potential of model-based solutions for the plant master control, and process engineers, giving valuable information on implementation issues for model-based predictive control solutions
Model based control of refrigeration systems
Energy Technology Data Exchange (ETDEWEB)
Sloth Larsen, L.F.
2005-11-15
The subject for this Ph.D. thesis is model based control of refrigeration systems. Model based control covers a variety of different types of controls, that incorporates mathematical models. In this thesis the main subject therefore has been restricted to deal with system optimizing control. The optimizing control is divided into two layers, where the system oriented top layers deals with set-point optimizing control and the lower layer deals with dynamical optimizing control in the subsystems. The thesis has two main contributions, i.e. a novel approach for set-point optimization and a novel approach for desynchronization based on dynamical optimization. The focus in the development of the proposed set-point optimizing control has been on deriving a simple and general method, that with ease can be applied on various compositions of the same class of systems, such as refrigeration systems. The method is based on a set of parameter depended static equations describing the considered process. By adapting the parameters to the given process, predict the steady state and computing a steady state gradient of the cost function, the process can be driven continuously towards zero gradient, i.e. the optimum (if the cost function is convex). The method furthermore deals with system constrains by introducing barrier functions, hereby the best possible performance taking the given constrains in to account can be obtained, e.g. under extreme operational conditions. The proposed method has been applied on a test refrigeration system, placed at Aalborg University, for minimization of the energy consumption. Here it was proved that by using general static parameter depended system equations it was possible drive the set-points close to the optimum and thus reduce the power consumption with up to 20%. In the dynamical optimizing layer the idea is to optimize the operation of the subsystem or the groupings of subsystems, that limits the obtainable system performance. In systems
Zervas, P. L.; Sarimveis, H.; Palyvos, J. A.; Markatos, N. C. G.
Hybrid renewable energy systems are expected to become competitive to conventional power generation systems in the near future and, thus, optimization of their operation is of particular interest. In this work, a hybrid power generation system is studied consisting of the following main components: photovoltaic array (PV), electrolyser, metal hydride tanks, and proton exchange membrane fuel cells (PEMFC). The key advantage of the hybrid system compared to stand-alone photovoltaic systems is that it can store efficiently solar energy by transforming it to hydrogen, which is the fuel supplied to the fuel cell. However, decision making regarding the operation of this system is a rather complicated task. A complete framework is proposed for managing such systems that is based on a rolling time horizon philosophy.
Directory of Open Access Journals (Sweden)
L. I. Rozonoer
1999-01-01
Full Text Available Necessary and sufficient conditions for existence of optimal control for all initial data are proved for LQ-optimization problem. If these conditions are fulfilled, necessary and sufficient conditions of optimality are formulated. Basing on the results, some general hypotheses on optimal control in terms of Pontryagin's maximum condition and Bellman's equation are proposed.
An Optimization Model Based on Game Theory
Directory of Open Access Journals (Sweden)
Yang Shi
2014-04-01
Full Text Available Game Theory has a wide range of applications in department of economics, but in the field of computer science, especially in the optimization algorithm is seldom used. In this paper, we integrate thinking of game theory into optimization algorithm, and then propose a new optimization model which can be widely used in optimization processing. This optimization model is divided into two types, which are called “the complete consistency” and “the partial consistency”. In these two types, the partial consistency is added disturbance strategy on the basis of the complete consistency. When model’s consistency is satisfied, the Nash equilibrium of the optimization model is global optimal and when the model’s consistency is not met, the presence of perturbation strategy can improve the application of the algorithm. The basic experiments suggest that this optimization model has broad applicability and better performance, and gives a new idea for some intractable problems in the field of artificial intelligence
Warehouse Optimization Model Based on Genetic Algorithm
Directory of Open Access Journals (Sweden)
Guofeng Qin
2013-01-01
Full Text Available This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366 m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.
RPOA Model-Based Optimal Resource Provisioning
Directory of Open Access Journals (Sweden)
Noha El. Attar
2014-01-01
Full Text Available Optimal utilization of resources is the core of the provisioning process in the cloud computing. Sometimes the local resources of a data center are not adequate to satisfy the users’ requirements. So, the providers need to create several data centers at different geographical area around the world and spread the users’ applications on these resources to satisfy both service providers and customers QoS requirements. By considering the expansion of the resources and applications, the transmission cost and time have to be concerned as significant factors in the allocation process. According to the work of our previous paper, a Resource Provision Optimal Algorithm (RPOA based on Particle Swarm Optimization (PSO has been introduced to find the near optimal resource utilization with considering the customer budget and suitable for deadline time. This paper is considered an enhancement to RPOA algorithm to find the near optimal resource utilization with considering the data transfer time and cost, in addition to the customer budget and deadline time, in the performance measurement.
Model based optimization of EMC input filters
Energy Technology Data Exchange (ETDEWEB)
Raggl, K; Kolar, J. W. [Swiss Federal Institute of Technology, Power Electronic Systems Laboratory, Zuerich (Switzerland); Nussbaumer, T. [Levitronix GmbH, Zuerich (Switzerland)
2008-07-01
Input filters of power converters for compliance with regulatory electromagnetic compatibility (EMC) standards are often over-dimensioned in practice due to a non-optimal selection of number of filter stages and/or the lack of solid volumetric models of the inductor cores. This paper presents a systematic filter design approach based on a specific filter attenuation requirement and volumetric component parameters. It is shown that a minimal volume can be found for a certain optimal number of filter stages for both the differential mode (DM) and common mode (CM) filter. The considerations are carried out exemplarily for an EMC input filter of a single phase power converter for the power levels of 100 W, 300 W, and 500 W. (author)
Electrochemical model based charge optimization for lithium-ion batteries
Pramanik, Sourav; Anwar, Sohel
2016-05-01
In this paper, we propose the design of a novel optimal strategy for charging the lithium-ion battery based on electrochemical battery model that is aimed at improved performance. A performance index that aims at minimizing the charging effort along with a minimum deviation from the rated maximum thresholds for cell temperature and charging current has been defined. The method proposed in this paper aims at achieving a faster charging rate while maintaining safe limits for various battery parameters. Safe operation of the battery is achieved by including the battery bulk temperature as a control component in the performance index which is of critical importance for electric vehicles. Another important aspect of the performance objective proposed here is the efficiency of the algorithm that would allow higher charging rates without compromising the internal electrochemical kinetics of the battery which would prevent abusive conditions, thereby improving the long term durability. A more realistic model, based on battery electro-chemistry has been used for the design of the optimal algorithm as opposed to the conventional equivalent circuit models. To solve the optimization problem, Pontryagins principle has been used which is very effective for constrained optimization problems with both state and input constraints. Simulation results show that the proposed optimal charging algorithm is capable of shortening the charging time of a lithium ion cell while maintaining the temperature constraint when compared with the standard constant current charging. The designed method also maintains the internal states within limits that can avoid abusive operating conditions.
Stabilization of model-based networked control systems
Miranda, Francisco; Abreu, Carlos; Mendes, Paulo M.
2016-06-01
A class of networked control systems called Model-Based Networked Control Systems (MB-NCSs) is considered. Stabilization of MB-NCSs is studied using feedback controls and simulation of stabilization for different feedbacks is made with the purpose to reduce the network trafic. The feedback control input is applied in a compensated model of the plant that approximates the plant dynamics and stabilizes the plant even under slow network conditions. Conditions for global exponential stabilizability and for the choosing of a feedback control input for a given constant time between the information moments of the network are derived. An optimal control problem to obtain an optimal feedback control is also presented.
Optimization and Optimal Control
Chinchuluun, Altannar; Enkhbat, Rentsen; Tseveendorj, Ider
2010-01-01
During the last four decades there has been a remarkable development in optimization and optimal control. Due to its wide variety of applications, many scientists and researchers have paid attention to fields of optimization and optimal control. A huge number of new theoretical, algorithmic, and computational results have been observed in the last few years. This book gives the latest advances, and due to the rapid development of these fields, there are no other recent publications on the same topics. Key features: Provides a collection of selected contributions giving a state-of-the-art accou
Model Based Control of Reefer Container Systems
DEFF Research Database (Denmark)
Sørensen, Kresten Kjær
This thesis is concerned with the development of model based control for the Star Cool refrigerated container (reefer) with the objective of reducing energy consumption. This project has been carried out under the Danish Industrial PhD programme and has been financed by Lodam together with the Da......This thesis is concerned with the development of model based control for the Star Cool refrigerated container (reefer) with the objective of reducing energy consumption. This project has been carried out under the Danish Industrial PhD programme and has been financed by Lodam together...
2017-03-21
BrightBox Optimization Modeling Platform ................................................................. 11 Figure 2. BrightBox Software Architecture and...2. BrightBox Software Architecture and Interaction with Building 12 We recognized the need for a dashboard and real-time savings reports for...account for equipment specifications, chilled water load and flow profile, and the coincident weather data. This program tests all of the possible
RISK LOAN PORTFOLIO OPTIMIZATION MODEL BASED ON CVAR RISK MEASURE
Directory of Open Access Journals (Sweden)
Ming-Chang LEE
2015-07-01
Full Text Available In order to achieve commercial banks liquidity, safety and profitability objective requirements, loan portfolio risk analysis based optimization decisions are rational allocation of assets. The risk analysis and asset allocation are the key technology of banking and risk management. The aim of this paper, build a loan portfolio optimization model based on risk analysis. Loan portfolio rate of return by using Value-at-Risk (VaR and Conditional Value-at-Risk (CVaR constraint optimization decision model reflects the bank's risk tolerance, and the potential loss of direct control of the bank. In this paper, it analyze a general risk management model applied to portfolio problems with VaR and CVaR risk measures by using Using the Lagrangian Algorithm. This paper solves the highly difficult problem by matrix operation method. Therefore, the combination of this paper is easy understanding the portfolio problems with VaR and CVaR risk model is a hyperbola in mean-standard deviation space. It is easy calculation in proposed method.
Aschepkov, Leonid T; Kim, Taekyun; Agarwal, Ravi P
2016-01-01
This book is based on lectures from a one-year course at the Far Eastern Federal University (Vladivostok, Russia) as well as on workshops on optimal control offered to students at various mathematical departments at the university level. The main themes of the theory of linear and nonlinear systems are considered, including the basic problem of establishing the necessary and sufficient conditions of optimal processes. In the first part of the course, the theory of linear control systems is constructed on the basis of the separation theorem and the concept of a reachability set. The authors prove the closure of a reachability set in the class of piecewise continuous controls, and the problems of controllability, observability, identification, performance and terminal control are also considered. The second part of the course is devoted to nonlinear control systems. Using the method of variations and the Lagrange multipliers rule of nonlinear problems, the authors prove the Pontryagin maximum principle for prob...
Hybrid and adaptive meta-model-based global optimization
Gu, J.; Li, G. Y.; Dong, Z.
2012-01-01
As an efficient and robust technique for global optimization, meta-model-based search methods have been increasingly used in solving complex and computation intensive design optimization problems. In this work, a hybrid and adaptive meta-model-based global optimization method that can automatically select appropriate meta-modelling techniques during the search process to improve search efficiency is introduced. The search initially applies three representative meta-models concurrently. Progress towards a better performing model is then introduced by selecting sample data points adaptively according to the calculated values of the three meta-models to improve modelling accuracy and search efficiency. To demonstrate the superior performance of the new algorithm over existing search methods, the new method is tested using various benchmark global optimization problems and applied to a real industrial design optimization example involving vehicle crash simulation. The method is particularly suitable for design problems involving computation intensive, black-box analyses and simulations.
2017-03-21
UNIT NUMBER None 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) B. PERFORMING ORGANIZATION AND ADDRESS(ES) REPORT NUMBER BrightBox...AutoCx Development .................................................................................................... 3 2.2 ADVANTAGES AND LIMITATIONS...control could eventually be deployed. Unfortunately, an adequate market for AutoCx products was not successfully developed despite the clear need and
Model Based Adaptive Piecewise Linear Controller for Complicated Control Systems
Directory of Open Access Journals (Sweden)
Tain-Sou Tsay
2014-01-01
Full Text Available A model based adaptive piecewise linear control scheme for industry processes with specifications on peak overshoots and rise times is proposed. It is a gain stabilized control technique. Large gain is used for large tracking error to get fast response. Small gain is used between large and small tracking error for good performance. Large gain is used again for small tracking error to cope with large disturbance. Parameters of the three-segment piecewise linear controller are found by an automatic regulating time series which is function of output characteristics of the plant and reference model. The time series will be converged to steady values after the time response of the considered system matching that of the reference model. The proposed control scheme is applied to four numerical examples which have been compensated by PID controllers. Parameters of PID controllers are found by optimization method. It gives an almost command independent response and gives significant improvements for response time and performance.
Ilhan, Z.; Wehner, W. P.; Schuster, E.; Boyer, M. D.; Gates, D. A.; Gerhardt, S.; Menard, J.
2015-11-01
Active control of the toroidal current density profile is crucial to achieve and maintain high-performance, MHD-stable plasma operation in NSTX-U. A first-principles-driven, control-oriented model describing the temporal evolution of the current profile has been proposed earlier by combining the magnetic diffusion equation with empirical correlations obtained at NSTX-U for the electron density, electron temperature, and non-inductive current drives. A feedforward + feedback control scheme for the requlation of the current profile is constructed by embedding the proposed nonlinear, physics-based model into the control design process. Firstly, nonlinear optimization techniques are used to design feedforward actuator trajectories that steer the plasma to a desired operating state with the objective of supporting the traditional trial-and-error experimental process of advanced scenario planning. Secondly, a feedback control algorithm to track a desired current profile evolution is developed with the goal of adding robustness to the overall control scheme. The effectiveness of the combined feedforward + feedback control algorithm for current profile regulation is tested in predictive simulations carried out in TRANSP. Supported by PPPL.
Optimal pricing decision model based on activity-based costing
Institute of Scientific and Technical Information of China (English)
王福胜; 常庆芳
2003-01-01
In order to find out the applicability of the optimal pricing decision model based on conventional costbehavior model after activity-based costing has given strong shock to the conventional cost behavior model andits assumptions, detailed analyses have been made using the activity-based cost behavior and cost-volume-profitanalysis model, and it is concluded from these analyses that the theory behind the construction of optimal pri-cing decision model is still tenable under activity-based costing, but the conventional optimal pricing decisionmodel must be modified as appropriate to the activity-based costing based cost behavior model and cost-volume-profit analysis model, and an optimal pricing decision model is really a product pricing decision model construc-ted by following the economic principle of maximizing profit.
Model-based multiobjective evolutionary algorithm optimization for HCCI engines
Ma, He; Xu, Hongming; Wang, Jihong; Schnier, Thorsten; Neaves, Ben; Tan, Cheng; Wang, Zhi
2014-01-01
Modern engines feature a considerable number of adjustable control parameters. With this increasing number of Degrees of Freedom (DoF) for engines, and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated engine optimization approach is desired. In this paper, a self-learning evolutionary algorithm based multi-objective globally optimization approach for a H...
Model-based Optimization of Oil Recovery: Robust Operational Strategies
Van Essen, G.M.
2015-01-01
The process of depleting an oil reservoir can be poured into an optimal control problem with the objective to maximize economic performance over the life of the ﬁeld. Despite its large potential, life-cycle optimization has not yet found its way into operational environments. The objective of this t
Model-based optimization of tapered free-electron lasers
Directory of Open Access Journals (Sweden)
Alan Mak
2015-04-01
Full Text Available The energy extraction efficiency is a figure of merit for a free-electron laser (FEL. It can be enhanced by the technique of undulator tapering, which enables the sustained growth of radiation power beyond the initial saturation point. In the development of a single-pass x-ray FEL, it is important to exploit the full potential of this technique and optimize the taper profile a_{w}(z. Our approach to the optimization is based on the theoretical model by Kroll, Morton, and Rosenbluth, whereby the taper profile a_{w}(z is not a predetermined function (such as linear or exponential but is determined by the physics of a resonant particle. For further enhancement of the energy extraction efficiency, we propose a modification to the model, which involves manipulations of the resonant particle’s phase. Using the numerical simulation code GENESIS, we apply our model-based optimization methods to a case of the future FEL at the MAX IV Laboratory (Lund, Sweden, as well as a case of the LCLS-II facility (Stanford, USA.
Towards automatic model based controller design for reconfigurable plants
DEFF Research Database (Denmark)
Michelsen, Axel Gottlieb; Stoustrup, Jakob; Izadi-Zamanabadi, Roozbeh
2008-01-01
This paper introduces model-based Plug and Play Process Control, a novel concept for process control, which allows a model-based control system to be reconfigured when a sensor or an actuator is plugged into a controlled process. The work reported in this paper focuses on composing a monolithic m...
Optimality principles for model-based prediction of human gait.
Ackermann, Marko; van den Bogert, Antonie J
2010-04-19
Although humans have a large repertoire of potential movements, gait patterns tend to be stereotypical and appear to be selected according to optimality principles such as minimal energy. When applied to dynamic musculoskeletal models such optimality principles might be used to predict how a patient's gait adapts to mechanical interventions such as prosthetic devices or surgery. In this paper we study the effects of different performance criteria on predicted gait patterns using a 2D musculoskeletal model. The associated optimal control problem for a family of different cost functions was solved utilizing the direct collocation method. It was found that fatigue-like cost functions produced realistic gait, with stance phase knee flexion, as opposed to energy-related cost functions which avoided knee flexion during the stance phase. We conclude that fatigue minimization may be one of the primary optimality principles governing human gait.
Peng, Haijun; Wang, Wei
2016-10-01
An adaptive surrogate model-based multi-objective optimization strategy that combines the benefits of invariant manifolds and low-thrust control toward developing a low-computational-cost transfer trajectory between libration orbits around the L1 and L2 libration points in the Sun-Earth system has been proposed in this paper. A new structure for a multi-objective transfer trajectory optimization model that divides the transfer trajectory into several segments and gives the dominations for invariant manifolds and low-thrust control in different segments has been established. To reduce the computational cost of multi-objective transfer trajectory optimization, a mixed sampling strategy-based adaptive surrogate model has been proposed. Numerical simulations show that the results obtained from the adaptive surrogate-based multi-objective optimization are in agreement with the results obtained using direct multi-objective optimization methods, and the computational workload of the adaptive surrogate-based multi-objective optimization is only approximately 10% of that of direct multi-objective optimization. Furthermore, the generating efficiency of the Pareto points of the adaptive surrogate-based multi-objective optimization is approximately 8 times that of the direct multi-objective optimization. Therefore, the proposed adaptive surrogate-based multi-objective optimization provides obvious advantages over direct multi-objective optimization methods.
Mechanics and model-based control of advanced engineering systems
Irschik, Hans; Krommer, Michael
2014-01-01
Mechanics and Model-Based Control of Advanced Engineering Systems collects 32 contributions presented at the International Workshop on Advanced Dynamics and Model Based Control of Structures and Machines, which took place in St. Petersburg, Russia in July 2012. The workshop continued a series of international workshops, which started with a Japan-Austria Joint Workshop on Mechanics and Model Based Control of Smart Materials and Structures and a Russia-Austria Joint Workshop on Advanced Dynamics and Model Based Control of Structures and Machines. In the present volume, 10 full-length papers based on presentations from Russia, 9 from Austria, 8 from Japan, 3 from Italy, one from Germany and one from Taiwan are included, which represent the state of the art in the field of mechanics and model based control, with particular emphasis on the application of advanced structures and machines.
Model-based control of hopper dredgers
Braaksma, J.
2008-01-01
The modern trailing suction hopper dredgers are advanced ships that are equipped with many automation systems that can be controlled with integrated computer systems from the bridge. From the operators it is expected that they generate the right set-points for all these systems. The latest ships are
Adaptive model-based control systems and methods for controlling a gas turbine
Brunell, Brent Jerome (Inventor); Mathews, Jr., Harry Kirk (Inventor); Kumar, Aditya (Inventor)
2004-01-01
Adaptive model-based control systems and methods are described so that performance and/or operability of a gas turbine in an aircraft engine, power plant, marine propulsion, or industrial application can be optimized under normal, deteriorated, faulted, failed and/or damaged operation. First, a model of each relevant system or component is created, and the model is adapted to the engine. Then, if/when deterioration, a fault, a failure or some kind of damage to an engine component or system is detected, that information is input to the model-based control as changes to the model, constraints, objective function, or other control parameters. With all the information about the engine condition, and state and directives on the control goals in terms of an objective function and constraints, the control then solves an optimization so the optimal control action can be determined and taken. This model and control may be updated in real-time to account for engine-to-engine variation, deterioration, damage, faults and/or failures using optimal corrective control action command(s).
Internal Model Based Active Disturbance Rejection Control
Pan, Jinwen; Wang, Yong
2016-01-01
The basic active disturbance rejection control (BADRC) algorithm with only one order higher extended state observer (ESO) proves to be robust to both internal and external disturbances. An advantage of BADRC is that in many applications it can achieve high disturbance attenuation level without requiring a detailed model of the plant or disturbance. However, this can be regarded as a disadvantage when the disturbance characteristic is known since the BADRC algorithm cannot exploit such informa...
Energy Technology Data Exchange (ETDEWEB)
Kortela, U.; Mononen, J.; Leppaekoski, K.; Hiltunen, J.; Jouppila, M.; Karppinen, R. [Oulu Univ. (Finland). Systems Engineering Lab.
1997-10-01
The aims of the project are to develop the combustion control strategies and to minimize the flue gas emissions. The common goal of the studies has been the reduction of flue gas emissions by using advanced control and optimization methods. The behaviour of different kind of boilers and fuels has been modelled using experimental data from fullscale plants, such as a 42 MW bubbling fluidized bed boiler, 23 MW bubbling fluidized bed boiler and a 300 MW circulating fluidized bed boiler. Many of the individual observations and modelled correlations between control variables and flue gas emissions have lead to operation instructions and/or re-organized control schemes which help to control total emissions. The most part of this knowledge can be formed to the standard IF- THEN - type rules which contain some uncertainty or fuzziness. Rule-based instruction system for the reduction of flue gas emissions is under work. (orig.)
Cascaded process model based control: packed absorption column application.
Govindarajan, Anand; Jayaraman, Suresh Kumar; Sethuraman, Vijayalakshmi; Raul, Pramod R; Rhinehart, R Russell
2014-03-01
Nonlinear, adaptive, process-model based control is demonstrated in a cascaded single-input-single-output mode for pressure drop control in a pilot-scale packed absorption column. The process is shown to be nonlinear. Control is demonstrated in both servo and regulatory modes, for no wind-up in a constrained situation, and for bumpless transfer. Model adaptation is demonstrated and shown to provide process insight. The application procedure is revealed as a design guide to aid others in implementing process-model based control.
An Industrial Model Based Disturbance Feedback Control Scheme
DEFF Research Database (Denmark)
Kawai, Fukiko; Nakazawa, Chikashi; Vinther, Kasper
2014-01-01
This paper presents a model based disturbance feedback control scheme. Industrial process systems have been traditionally controlled by using relay and PID controller. However these controllers are affected by disturbances and model errors and these effects degrade control performance. The authors...... propose a new control method that can decrease the negative impact of disturbance and model errors. The control method is motivated by industrial practice by Fuji Electric. Simulation tests are examined with a conventional PID controller and the disturbance feedback control. The simulation results...
Model-Based Traffic Control for Sustainable Mobility
Zegeye, S.K.
2011-01-01
Computationally efficient dynamic fuel consumption, emissions, and dispersion of emissions models are developed. Fast and practically feasible model-based controller is proposed. Using the developed models, the controller steers the traffic flow in such a way that a balanced trade-off between the t
Model based control of dynamic atomic force microscope
Energy Technology Data Exchange (ETDEWEB)
Lee, Chibum [Department of Mechanical System Design Engineering, Seoul National University of Science and Technology, Seoul 139-743 (Korea, Republic of); Salapaka, Srinivasa M., E-mail: salapaka@illinois.edu [Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 (United States)
2015-04-15
A model-based robust control approach is proposed that significantly improves imaging bandwidth for the dynamic mode atomic force microscopy. A model for cantilever oscillation amplitude and phase dynamics is derived and used for the control design. In particular, the control design is based on a linearized model and robust H{sub ∞} control theory. This design yields a significant improvement when compared to the conventional proportional-integral designs and verified by experiments.
Model based control of dynamic atomic force microscope.
Lee, Chibum; Salapaka, Srinivasa M
2015-04-01
A model-based robust control approach is proposed that significantly improves imaging bandwidth for the dynamic mode atomic force microscopy. A model for cantilever oscillation amplitude and phase dynamics is derived and used for the control design. In particular, the control design is based on a linearized model and robust H(∞) control theory. This design yields a significant improvement when compared to the conventional proportional-integral designs and verified by experiments.
Model-Based Development of Control Systems for Forestry Cranes
Directory of Open Access Journals (Sweden)
Pedro La Hera
2015-01-01
Full Text Available Model-based methods are used in industry for prototyping concepts based on mathematical models. With our forest industry partners, we have established a model-based workflow for rapid development of motion control systems for forestry cranes. Applying this working method, we can verify control algorithms, both theoretically and practically. This paper is an example of this workflow and presents four topics related to the application of nonlinear control theory. The first topic presents the system of differential equations describing the motion dynamics. The second topic presents nonlinear control laws formulated according to sliding mode control theory. The third topic presents a procedure for model calibration and control tuning that are a prerequisite to realize experimental tests. The fourth topic presents the results of tests performed on an experimental crane specifically equipped for these tasks. Results of these studies show the advantages and disadvantages of these control algorithms, and they highlight their performance in terms of robustness and smoothness.
Model-based Optimal Evacuation Planning anticipating Traveler Compliance Behavior
Pel, A.J.; Huibregtse, O.L.; Hoogendoorn, S.P.; Bliemer, M.C.J.
2010-01-01
Instructing evacuees on their departure time, destination, and route can lead to more efficient evacuation traffic operations. While current evacuation plan optimization techniques are limited to assessing mandatory evacuation where travelers strictly follow the instructions, in reality a share of
Sensor Optimization Selection Model Based on Testability Constraint
Institute of Scientific and Technical Information of China (English)
YANG Shuming; QIU Jing; LIU Guanjun
2012-01-01
Sensor selection and optimization is one of the important parts in design for testability.To address the problems that the traditional sensor optimization selection model does not take the requirements of prognostics and health management especially fault prognostics for testability into account and does not consider the impacts of sensor actual attributes on fault detectability,a novel sensor optimization selection model is proposed.Firstly,a universal architecture for sensor selection and optimization is provided.Secondly,a new testability index named fault predictable rate is defined to describe fault prognostics requirements for testability.Thirdly,a sensor selection and optimization model for prognostics and health management is constructed,which takes sensor cost as objective finction and the defined testability indexes as constraint conditions.Due to NP-hard property of the model,a generic algorithm is designed to obtain the optimal solution.At last,a case study is presented to demonstrate the sensor selection approach for a stable tracking servo platform.The application results and comparison analysis show the proposed model and algorithm are effective and feasible.This approach can be used to select sensors for prognostics and health management of any system.
Janardhanan, S.; Datta, B.
2011-12-01
Surrogate models are widely used to develop computationally efficient simulation-optimization models to solve complex groundwater management problems. Artificial intelligence based models are most often used for this purpose where they are trained using predictor-predictand data obtained from a numerical simulation model. Most often this is implemented with the assumption that the parameters and boundary conditions used in the numerical simulation model are perfectly known. However, in most practical situations these values are uncertain. Under these circumstances the application of such approximation surrogates becomes limited. In our study we develop a surrogate model based coupled simulation optimization methodology for determining optimal pumping strategies for coastal aquifers considering parameter uncertainty. An ensemble surrogate modeling approach is used along with multiple realization optimization. The methodology is used to solve a multi-objective coastal aquifer management problem considering two conflicting objectives. Hydraulic conductivity and the aquifer recharge are considered as uncertain values. Three dimensional coupled flow and transport simulation model FEMWATER is used to simulate the aquifer responses for a number of scenarios corresponding to Latin hypercube samples of pumping and uncertain parameters to generate input-output patterns for training the surrogate models. Non-parametric bootstrap sampling of this original data set is used to generate multiple data sets which belong to different regions in the multi-dimensional decision and parameter space. These data sets are used to train and test multiple surrogate models based on genetic programming. The ensemble of surrogate models is then linked to a multi-objective genetic algorithm to solve the pumping optimization problem. Two conflicting objectives, viz, maximizing total pumping from beneficial wells and minimizing the total pumping from barrier wells for hydraulic control of
Optimal obstacle control problem
Institute of Scientific and Technical Information of China (English)
ZHU Li; LI Xiu-hua; GUO Xing-ming
2008-01-01
In the paper we discuss some properties of the state operators of the optimal obstacle control problem for elliptic variational inequality. Existence, uniqueness and regularity of the optimal control problem are established. In addition, the approximation of the optimal obstacle problem is also studied.
Model-based Control of a Bottom Fired Marine Boiler
DEFF Research Database (Denmark)
Solberg, Brian; Karstensen, Claus M. S.; Andersen, Palle;
This paper focuses on applying model based MIMO control to minimize variations in water level for a specific boiler type. A first principles model is put up. The model is linearized and an LQG controller is designed. Furthermore the benefit of using a steam °ow measurement is compared to a strategy...... relying on estimates of the disturbance. Preliminary tests at the boiler system show that the designed controller is able to control the boiler process. Furthermore it can be concluded that relying on estimates of the steam flow in the control strategy does not decrease the controller performance...
Model-based Control of a Bottom Fired Marine Boiler
DEFF Research Database (Denmark)
Solberg, Brian; Karstensen, Claus M. S.; Andersen, Palle;
2005-01-01
This paper focuses on applying model based MIMO control to minimize variations in water level for a specific boiler type. A first principles model is put up. The model is linearized and an LQG controller is designed. Furthermore the benefit of using a steam °ow measurement is compared to a strategy...... relying on estimates of the disturbance. Preliminary tests at the boiler system show that the designed controller is able to control the boiler process. Furthermore it can be concluded that relying on estimates of the steam flow in the control strategy does not decrease the controller performance...
Model-based Optimal Evacuation Planning anticipating Traveler Compliance Behavior
Pel, A.J.; Huibregtse, O.L.; Hoogendoorn, S.P.; Bliemer, M.C.J.
2010-01-01
Instructing evacuees on their departure time, destination, and route can lead to more efficient evacuation traffic operations. While current evacuation plan optimization techniques are limited to assessing mandatory evacuation where travelers strictly follow the instructions, in reality a share of t
Model based active power control of a wind turbine
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Soltani, Mohsen; Poulsen, Niels Kjølstad;
2014-01-01
in the electricity market that selling the reserve power is more profitable than producing with the full capacity. Therefore wind turbines can be down-regulated and sell the differential capacity as the reserve power. In this paper we suggest a model based approach to control wind turbines for active power reference...
Control and optimization system
Xinsheng, Lou
2013-02-12
A system for optimizing a power plant includes a chemical loop having an input for receiving an input parameter (270) and an output for outputting an output parameter (280), a control system operably connected to the chemical loop and having a multiple controller part (230) comprising a model-free controller. The control system receives the output parameter (280), optimizes the input parameter (270) based on the received output parameter (280), and outputs an optimized input parameter (270) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
Production Cost Optimization Model Based on CODP in Mass Customization
Directory of Open Access Journals (Sweden)
Yanhong Qin
2013-01-01
Full Text Available The key for enterprises to implement the postponement strategy is the right decision on the location of Customer Order Decoupling Point (CODP so as to achieve the scope economics of mass customization and scale economics of mass production fully. To deal with production cost optimization problem of postponement system based on various situation of CODP, a basic model of production cost and its M/M/1 extended model are proposed and compared so as to optimize the overall production cost of the postponement system. The production modes can be classified as MTS (make to stock, ATO (assemble to order, MTO (make to order and ETO (engineering to order according to the inventory location, and the postponed production system considered here includes manufacturing cost, semi-finished inventory cost and customer waiting cost caused by delaying delivery. By Matlab simulation, we can compute the optimal location of CODP in each production mode, which can provide some management insight for the manufacturer to decide the right production mode and utilize the resources efficiently.
Model-based optimization of phased array ultrasonic testing
Institute of Scientific and Technical Information of China (English)
Sung-Jin; Song; Hak-Joon; Kim; Suk-Chull; Kang; Sung-Sik; Kang; Kyungcho; Kim; Myung-Ho; Song
2010-01-01
Simulation of phased array beams in dovetail and austenitic welds is conducted to optimize the setup of phased array ultrasonic testing(PAUT).To simulate the beam in such material with complex geometry or with characteristic of anisotropy and inhomogeneity, firstly,linear phased multi-Gaussian beam(LPMGB) models are introduced and discussed. Then,in the case of dovetail,wedge is designed to maximize the stable amplitude of the beam along the steering path;in the case of austenitic weld,modified focal law...
Schaft, A.J. van der
1987-01-01
It is argued that the existence of symmetries may simplify, as in classical mechanics, the solution of optimal control problems. A procedure for obtaining symmetries for the optimal Hamiltonian resulting from the Maximum Principle is given; this avoids the actual calculation of the optimal
ARX-NNPLS Model Based Optimization Strategy and Its Application in Polymer Grade Transition Process
Institute of Scientific and Technical Information of China (English)
费正顺; 胡斌; 叶鲁彬; 梁军
2012-01-01
Since it is often difficult to build differential algebraic equations (DAEs) for chemical processes, a new data-based modeling approach is proposed using ARX (AutoRegressive with eXogenous inputs) combined with neural network under partial least squares framework (ARX-NNPLS), in which less specific knowledge of the process is required but the input and output data. To represent the dynamic and nonlinear behavior of the process, the ARX combined with neural network is used in the partial least squares (PLS) inner model between input and output latent variables. In the proposed dynamic optimization strategy based on the ARX-NNPLS model, neither parameterization nor iterative solving process for DAEs is needed as the ARX-NNPLS model gives a proper representation for the dynamic behavior of the process, and the computing time is greatly reduced compared to conventional control vector parameterization method. To demonstrate the ARX-NNPLS model based optimization strategy, the polyethylene grade transition in gas phase fluidized-bed reactor is taken into account. The optimization results show that the final optimal trajectory of quality index determined by the new approach moves faster to the target values and the computing time is much less.
Model-based control of district heating supply temperature
Energy Technology Data Exchange (ETDEWEB)
Saarinen, Linn
2010-11-15
A model-based control strategy for the supply temperature to a district heating network was tested during three weeks at Idbaecken's CHP plant. The aim was to increase the electricity efficiency by a lower supply temperature, without risking the delivery reliability of heat to the district heating customers. Simulations and tests showed that at high loads, the mean supply temperature could be reduced by 4 deg C and the electricity production could be increased by 2.5%
Optimal control computer programs
Kuo, F.
1992-01-01
The solution of the optimal control problem, even with low order dynamical systems, can usually strain the analytical ability of most engineers. The understanding of this subject matter, therefore, would be greatly enhanced if a software package existed that could simulate simple generic problems. Surprisingly, despite a great abundance of commercially available control software, few, if any, address the part of optimal control in its most generic form. The purpose of this paper is, therefore, to present a simple computer program that will perform simulations of optimal control problems that arise from the first necessary condition and the Pontryagin's maximum principle.
Embedded Control System Design A Model Based Approach
Forrai, Alexandru
2013-01-01
Control system design is a challenging task for practicing engineers. It requires knowledge of different engineering fields, a good understanding of technical specifications and good communication skills. The current book introduces the reader into practical control system design, bridging the gap between theory and practice. The control design techniques presented in the book are all model based., considering the needs and possibilities of practicing engineers. Classical control design techniques are reviewed and methods are presented how to verify the robustness of the design. It is how the designed control algorithm can be implemented in real-time and tested, fulfilling different safety requirements. Good design practices and the systematic software development process are emphasized in the book according to the generic standard IEC61508. The book is mainly addressed to practicing control and embedded software engineers - working in research and development – as well as graduate students who are face...
Application of model based control to robotic manipulators
Petrosky, Lyman J.; Oppenheim, Irving J.
1988-01-01
A robot that can duplicate humam motion capabilities in such activities as balancing, reaching, lifting, and moving has been built and tested. These capabilities are achieved through the use of real time Model-Based Control (MBC) techniques which have recently been demonstrated. MBC accounts for all manipulator inertial forces and provides stable manipulator motion control even at high speeds. To effectively demonstrate the unique capabilities of MBC, an experimental robotic manipulator was constructed, which stands upright, balancing on a two wheel base. The mathematical modeling of dynamics inherent in MBC permit the control system to perform functions that are impossible with conventional non-model based methods. These capabilities include: (1) Stable control at all speeds of operation; (2) Operations requiring dynamic stability such as balancing; (3) Detection and monitoring of applied forces without the use of load sensors; (4) Manipulator safing via detection of abnormal loads. The full potential of MBC has yet to be realized. The experiments performed for this research are only an indication of the potential applications. MBC has no inherent stability limitations and its range of applicability is limited only by the attainable sampling rate, modeling accuracy, and sensor resolution. Manipulators could be designed to operate at the highest speed mechanically attainable without being limited by control inadequacies. Manipulators capable of operating many times faster than current machines would certainly increase productivity for many tasks.
Integrated controls design optimization
Lou, Xinsheng; Neuschaefer, Carl H.
2015-09-01
A control system (207) for optimizing a chemical looping process of a power plant includes an optimizer (420), an income algorithm (230) and a cost algorithm (225) and a chemical looping process models. The process models are used to predict the process outputs from process input variables. Some of the process in puts and output variables are related to the income of the plant; and some others are related to the cost of the plant operations. The income algorithm (230) provides an income input to the optimizer (420) based on a plurality of input parameters (215) of the power plant. The cost algorithm (225) provides a cost input to the optimizer (420) based on a plurality of output parameters (220) of the power plant. The optimizer (420) determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant.
Colonius, Fritz
1988-01-01
This research monograph deals with optimal periodic control problems for systems governed by ordinary and functional differential equations of retarded type. Particular attention is given to the problem of local properness, i.e. whether system performance can be improved by introducing periodic motions. Using either Ekeland's Variational Principle or optimization theory in Banach spaces, necessary optimality conditions are proved. In particular, complete proofs of second-order conditions are included and the result is used for various versions of the optimal periodic control problem. Furthermore a scenario for local properness (related to Hopf bifurcation) is drawn up, giving hints as to where to look for optimal periodic solutions. The book provides mathematically rigorous proofs for results which are potentially of importance in chemical engineering and aerospace engineering.
Model Based Control of Moisture Sorption in a Historical Interior
Directory of Open Access Journals (Sweden)
P. Zítek
2005-01-01
Full Text Available This paper deals with a novel scheme for microclimate control in historical exhibition rooms, inhibiting moisture sorption phenomena that are inadmissible from the preventive conservation point of view. The impact of air humidity is the most significant harmful exposure for a great deal of the cultural heritage deposited in remote historical buildings. Leaving the interior temperature to run almost its spontaneous yearly cycle, the proposed non-linear model-based control protects exhibits from harmful variations in moisture content by compensating the temperature drifts with an adequate adjustment of the air humidity. Already implemented in a medieval interior since 1999, the proposed microclimate control has proved capable of permanently maintaining constant a desirable moisture content in organic or porous materials in the interior of a building.
Model-based Sliding Mode Controller of Anti-lock Braking System
Zheng, Lin; Luo, Yue-Gang; Kang, Jing; Shi, Zhan-Qun
2016-05-01
The anti-lock braking system (ABS) used in automobiles is used to prevent wheel from lockup and to maintain the steering ability and stability. The sliding mode controller is able to control nonlinear system steadily. In this research, a one-wheel dynamic model with ABS control is built up using model-based method. Using the sliding model controller, the simulation results by using Matlab/Simulink show qualified data compared with optimal slip rate. By using this method, the ABS brake efficiency is improved efficiently.
Discrete Variational Optimal Control
Jimenez, Fernando; de Diego, David Martin
2012-01-01
This paper develops numerical methods for optimal control of mechanical systems in the Lagrangian setting. It extends the theory of discrete mechanics to enable the solutions of optimal control problems through the discretization of variational principles. The key point is to solve the optimal control problem as a variational integrator of a specially constructed higher-dimensional system. The developed framework applies to systems on tangent bundles, Lie groups, underactuated and nonholonomic systems with symmetries, and can approximate either smooth or discontinuous control inputs. The resulting methods inherit the preservation properties of variational integrators and result in numerically robust and easily implementable algorithms. Several theoretical and a practical examples, e.g. the control of an underwater vehicle, will illustrate the application of the proposed approach.
Discrete Variational Optimal Control
Jiménez, Fernando; Kobilarov, Marin; Martín de Diego, David
2013-06-01
This paper develops numerical methods for optimal control of mechanical systems in the Lagrangian setting. It extends the theory of discrete mechanics to enable the solutions of optimal control problems through the discretization of variational principles. The key point is to solve the optimal control problem as a variational integrator of a specially constructed higher dimensional system. The developed framework applies to systems on tangent bundles, Lie groups, and underactuated and nonholonomic systems with symmetries, and can approximate either smooth or discontinuous control inputs. The resulting methods inherit the preservation properties of variational integrators and result in numerically robust and easily implementable algorithms. Several theoretical examples and a practical one, the control of an underwater vehicle, illustrate the application of the proposed approach.
On the model-based optimization of secreting mammalian cell (GS-NS0) cultures.
Kiparissides, A; Pistikopoulos, E N; Mantalaris, A
2015-03-01
The global bio-manufacturing industry requires improved process efficiency to satisfy the increasing demands for biochemicals, biofuels, and biologics. The use of model-based techniques can facilitate the reduction of unnecessary experimentation and reduce labor and operating costs by identifying the most informative experiments and providing strategies to optimize the bioprocess at hand. Herein, we investigate the potential of a research methodology that combines model development, parameter estimation, global sensitivity analysis, and selection of optimal feeding policies via dynamic optimization methods to improve the efficiency of an industrially relevant bioprocess. Data from a set of batch experiments was used to estimate values for the parameters of an unstructured model describing monoclonal antibody (mAb) production in GS-NS0 cell cultures. Global Sensitivity Analysis (GSA) highlighted parameters with a strong effect on the model output and data from a fed-batch experiment were used to refine their estimated values. Model-based optimization was used to identify a feeding regime that maximized final mAb titer. An independent fed-batch experiment was conducted to validate both the results of the optimization and the predictive capabilities of the developed model. The successful integration of wet-lab experimentation and mathematical model development, analysis, and optimization represents a unique, novel, and interdisciplinary approach that addresses the complicated research and industrial problem of model-based optimization of cell based processes.
Model based control charts in stage 1 quality control
A.J. Koning (Alex)
1999-01-01
textabstractIn this paper a general method of constructing control charts for preliminary analysis of individual observations is presented, which is based on recursive score residuals. A simulation study shows that certain implementations of these charts are highly effective in detecting assignable
Ripamonti, Francesco; Orsini, Lorenzo; Resta, Ferruccio
2015-04-01
Non-linear behavior is present in many mechanical system operating conditions. In these cases, a common engineering practice is to linearize the equation of motion around a particular operating point, and to design a linear controller. The main disadvantage is that the stability properties and validity of the controller are local. In order to improve the controller performance, non-linear control techniques represent a very attractive solution for many smart structures. The aim of this paper is to compare non-linear model-based and non-model-based control techniques. In particular the model-based sliding-mode-control (SMC) technique is considered because of its easy implementation and the strong robustness of the controller even under heavy model uncertainties. Among the non-model-based control techniques, the fuzzy control (FC), allowing designing the controller according to if-then rules, has been considered. It defines the controller without a system reference model, offering many advantages such as an intrinsic robustness. These techniques have been tested on the pendulum nonlinear system.
A nonlinear regression model-based predictive control algorithm.
Dubay, R; Abu-Ayyad, M; Hernandez, J M
2009-04-01
This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.
Construction project investment control model based on instant information
Institute of Scientific and Technical Information of China (English)
WANG Xue-tong
2006-01-01
Change of construction conditions always influences project investment by causing the loss of construction work time and extending the duration. To resolve such problem as difficult dynamic control in work construction plan, this article presents a concept of instant optimization by ways of adjustment operation time of each working procedure to minimize investment change. Based on this concept, its mathematical model is established and a strict mathematical justification is performed. An instant optimization model takes advantage of instant information in the construction process to duly complete adjustment of construction; thus we maximize cost efficiency of project investment.
Model-based plant-wide optimization of large-scale lignocellulosic bioethanol plants
DEFF Research Database (Denmark)
Prunescu, Remus Mihail; Blanke, Mogens; Jakobsen, Jon Geest
2017-01-01
with respect to maximum economic profit of a large scale biorefinery plant using a systematic model-based plantwide optimization methodology. The following key process parameters are identified as decision variables: pretreatment temperature, enzyme dosage in enzymatic hydrolysis, and yeast loading per batch...... in fermentation. The plant is treated in an integrated manner taking into account the interactions and trade-offs between the conversion steps. A sensitivity and uncertainty analysis follows at the optimal solution considering both model and feed parameters. It is found that the optimal point is more sensitive...
Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
Ahmed, Gulnaz; Zou, Jianhua; Zhao, Xi; Sadiq Fareed, Mian Muhammad
2017-01-01
The longer network lifetime of Wireless Sensor Networks (WSNs) is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs) selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED) clustering, Artificial Bee Colony (ABC), Zone Based Routing (ZBR), and Centralized Energy Efficient Clustering (CEEC) using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps) greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput. PMID:28241492
Aeroservoelastic model based active control for large civil aircraft
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
A modeling and control approach for an advanced configured large civil aircraft with aeroservoelasticity via the LQG method and control allocation is presented.Mathematical models and implementation issues for the multi-input/multi-output(MIMO) aeroservoelastic system simulation developed for a flexible wing with multi control surfaces are described.A fuzzy logic based optimization approach is employed to solve the constrained control allocation problem via intelligently adjusting the components of output vector and find a proper vector in the attainable moment set(AMS) autonomously.The basic idea is to minimize the L2 norm of error between the desired moment and achievable moment using the designing freedom provided by redundantly allocated actuators and control surfaces.Considering the constraints of control surfaces,in order to obtain acceptable performance of aircraft such as stability and maneuverability,the fuzzy weights are updated by the learning algorithm,which makes the closed-loop system self-adaptation.Finally,an application example of flight control designing for the advanced civil aircraft is discussed as a demonstration.The studies we have performed showed that the advanced configured large civil aircraft has good performance with the proper designed control law designed via the proposed approach.The gust alleviation and flutter suppression are applied with the synergetic effects of elevator,ailerons,equivalent rudders and flaps.The results show good closed loop performance and meet the requirement of constraint of control surfaces.
On Symmetries in Optimal Control
van der Schaft, A. J.
1986-01-01
We discuss the use of symmetries in solving optimal control problems. In particular a procedure for obtaining symmetries is given which can be performed before the actual calculation of the optimal control and optimal Hamiltonian.
On Symmetries in Optimal Control
Schaft, A.J. van der
1986-01-01
We discuss the use of symmetries in solving optimal control problems. In particular a procedure for obtaining symmetries is given which can be performed before the actual calculation of the optimal control and optimal Hamiltonian.
Optimized joystick controller.
Ding, D; Cooper, R A; Spaeth, D
2004-01-01
The purpose of the study was to develop an optimized joystick control interface for electric powered wheelchairs and thus provide safe and effective control of electric powered wheelchairs to people with severe physical disabilities. The interface enables clinicians to tune joystick parameters for each individual subject through selecting templates, dead zones, and bias axes. In terms of hand tremor usually associated with people with traumatic brain injury, cerebral palsy, and multiple sclerosis, fuzzy logic rules were applied to suppress erratic hand movements and extract the intended motion from the joystick. Simulation results were presented to show the graphical tuning interface as well as the performance of the fuzzy logic controller.
DEFF Research Database (Denmark)
Abd.Hamid, Mohd-Kamaruddin; Sin, Gürkan; Gani, Rafiqul
2010-01-01
In this paper, a novel systematic model-based methodology for performing integrated process design and controller design (IPDC) for chemical processes is presented. The methodology uses a decomposition method to solve the IPDC typically formulated as a mathematical programming (optimization...... with constraints) problem. Accordingly the optimization problem is decomposed into four sub-problems: (i) pre-analysis, (ii) design analysis, (iii) controller design analysis, and (iv) final selection and verification, which are relatively easier to solve. The methodology makes use of thermodynamic-process...... insights and the reverse design approach to arrive at the final process design–controller design decisions. The developed methodology is illustrated through the design of: (a) a single reactor, (b) a single separator, and (c) a reactor–separator-recycle system and shown to provide effective solutions...
Energy Optimal Control of Induction Motor Drives
DEFF Research Database (Denmark)
Abrahamsen, Flemming
This thesis deals with energy optimal control of small and medium-size variable speed induction motor drives for especially Heating, Ventilation and Air-Condition (HVAC) applications. Optimized efficiency is achieved by adapting the magnetization level in the motor to the load, and the basic...... purpose is demonstrate how this can be done for low-cost PWM-VSI drives without bringing the robustness of the drive below an acceptable level. Four drives are investigated with respect to energy optimal control: 2.2 kW standard and high-efficiency motor drives, 22 kW and 90 kW standard motor drives....... The method has been to make extensive efficiency measurements within the specified operating area with optimized efficiency and with constant air-gap flux, and to establish reliable converter and motor loss models based on those measurements. The loss models have been used to analyze energy optimal control...
Directory of Open Access Journals (Sweden)
Fu-Kwun Wang
2012-01-01
Full Text Available It is important for executives to predict the future trends. Otherwise, their companies cannot make profitable decisions and investments. The Bass diffusion model can describe the empirical adoption curve for new products and technological innovations. The Grey model provides short-term forecasts using four data points. This study develops a combined model based on the rolling Grey model (RGM and the Bass diffusion model to forecast motherboard shipments. In addition, we investigate evolutionary optimization algorithms to determine the optimal parameters. Our results indicate that the combined model using a hybrid algorithm outperforms other methods for the fitting and forecasting processes in terms of mean absolute percentage error.
Institute of Scientific and Technical Information of China (English)
高小永; 江永亨; 黄德先
2016-01-01
随着全球化市场竞争日趋激烈，炼油生产过程的系统工程方法引起学术界和工业界的普遍关注。由于炼油生产过程的复杂性，现有方法远未有效解决过程模型准确描述问题，直接导致系统工程应用效果欠佳；过程模型的准确描述成为阻碍过程系统工程成功应用的关键。从困扰过程模型准确描述的症结出发，提出了一种基于装置级优化控制与厂级调度优化集成策略的模型描述方法，意在打破当前装置级底层控制系统和上层过程系统工程应用相互孤立情况下带来的系统间互为排斥、相互抑制的困局。在炼油生产过程装置分类的基础上，基于装置级优化控制的操作运行大数据所蕴含的多个最优操作模态信息，提出分装置类别和分操作模式的多模型描述方法，解决变化原料及操作工况的影响；基于该模型描述机制，将能够为智能炼油提供基础。%The process system engineering methods for refinery production process have drawn increasing concerns in both academic and industrial communities due to the fierce global competition. Due to the complexity of refinery production process, the effective process model is still an open problem, which hampers process system engineering application. In some senses, the process model is the fundamental basis for successful application. To break this bottleneck, an integration between unit-wide optimal process control system and plant-wide scheduling system based modelling framework is proposed. The whole refinery production processes are divided into several different classes, and each class unit has a unique and well-designed model structure. Based on the big operational data collected from the unit-wide optimal control system, the multi-mode models are obtained to take varying crudes and operating conditions into account. This modelling mechanism can provide the concrete model for smart or intelligent
Study on Model Based Combustion Control of Diesel Engine with Multi Fuel Injection
Ikemura, R.; Yamasaki, Y.; Kaneko, S.
2016-09-01
A controller for model-based control of diesel engine with triple injection were developed with a combustion model. In the combustion model, an engine cycle is discretized into several representative points in order to improve calculation speed, while physical equations are employed to expand the versatility. The combustion model can predict in-cylinder pressure and temperature in these discrete points. Prediction accuracy of the combustion model was evaluated by comparison with experimental result. A controller was designed with the combustion model in order to calculate optimal fuel injection pattern for controlling in-cylinder pressure peak timing. The controller's performance was evaluated through simulation in which the combustion model was used as a plant model.
Product Form Design Model Based on Multiobjective Optimization and Multicriteria Decision-Making
Directory of Open Access Journals (Sweden)
Meng-Dar Shieh
2017-01-01
Full Text Available Affective responses concern customers’ affective needs and have received increasing attention in consumer-focused research. To design a product that appeals to consumers, designers should consider multiple affective responses (MARs. Designing products capable of satisfying MARs falls into the category of multiobjective optimization (MOO. However, when exploring optimal product form design, most relevant studies have transformed multiple objectives into a single objective, which limits their usefulness to designers and consumers. To optimize product form design for MARs, this paper proposes an integrated model based on MOO and multicriteria decision-making (MCDM. First, design analysis is applied to identify design variables and MARs; quantification theory type I is then employed to build the relationship models between them; on the basis of these models, an MOO model for optimization of product form design is constructed. Next, we use nondominated sorting genetic algorithm-II (NSGA-II as a multiobjective evolutionary algorithm (MOEA to solve the MOO model and thereby derive Pareto optimal solutions. Finally, we adopt the fuzzy analytic hierarchy process (FAHP to obtain the optimal design from the Pareto solutions. A case study of car form design is conducted to demonstrate the proposed approach. The results suggest that this approach is feasible and effective in obtaining optimal designs and can provide great insight for product form design.
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...
Optimal control for chemical engineers
Upreti, Simant Ranjan
2013-01-01
Optimal Control for Chemical Engineers gives a detailed treatment of optimal control theory that enables readers to formulate and solve optimal control problems. With a strong emphasis on problem solving, the book provides all the necessary mathematical analyses and derivations of important results, including multiplier theorems and Pontryagin's principle.The text begins by introducing various examples of optimal control, such as batch distillation and chemotherapy, and the basic concepts of optimal control, including functionals and differentials. It then analyzes the notion of optimality, de
Power, control and optimization
Vasant, Pandian; Barsoum, Nader
2013-01-01
The book consists of chapters based on selected papers of international conference „Power, Control and Optimization 2012”, held in Las Vegas, USA. Readers can find interesting chapters discussing various topics from the field of power control, its distribution and related fields. Book discusses topics like energy consumption impacted by climate, mathematical modeling of the influence of thermal power plant on the aquatic environment, investigation of cost reduction in residential electricity bill using electric vehicle at peak times or allocation and size evaluation of distributed generation using ANN model and others. Chapter authors are to the best of our knowledge the originators or closely related to the originators of presented ideas and its applications. Hence, this book certainly is one of the few books discussing the benefit from intersection of those modern and fruitful scientific fields of research with very tight and deep impact on real life and industry. This book is devoted to the studies o...
Oil Reservoir Production Optimization using Optimal Control
DEFF Research Database (Denmark)
Völcker, Carsten; Jørgensen, John Bagterp; Stenby, Erling Halfdan
2011-01-01
Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using the adjo......Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using...... the adjoint method. We use an Explicit Singly Diagonally Implicit Runge-Kutta (ESDIRK) method for the integration and a quasi-Newton Sequential Quadratic Programming (SQP) algorithm for the constrained optimization. We use this algorithm in a numerical case study to optimize the production of oil from an oil...... reservoir using water ooding and smart well technology. Compared to the uncontrolled case, the optimal operation increases the Net Present Value of the oil field by 10%....
Csank, Jeffrey T.; Connolly, Joseph W.
2016-01-01
This paper discusses the design and application of model-based engine control (MBEC) for use during emergency operation of the aircraft. The MBEC methodology is applied to the Commercial Modular Aero-Propulsion System Simulation 40k (CMAPSS40k) and features an optimal tuner Kalman Filter (OTKF) to estimate unmeasured engine parameters, which can then be used for control. During an emergency scenario, normally-conservative engine operating limits may be relaxed to increase the performance of the engine and overall survivability of the aircraft; this comes at the cost of additional risk of an engine failure. The MBEC architecture offers the advantage of estimating key engine parameters that are not directly measureable. Estimating the unknown parameters allows for tighter control over these parameters, and on the level of risk the engine will operate at. This will allow the engine to achieve better performance than possible when operating to more conservative limits on a related, measurable parameter.
Optimal Control of Mechanical Systems
Directory of Open Access Journals (Sweden)
Vadim Azhmyakov
2007-01-01
Full Text Available In the present work, we consider a class of nonlinear optimal control problems, which can be called “optimal control problems in mechanics.” We deal with control systems whose dynamics can be described by a system of Euler-Lagrange or Hamilton equations. Using the variational structure of the solution of the corresponding boundary-value problems, we reduce the initial optimal control problem to an auxiliary problem of multiobjective programming. This technique makes it possible to apply some consistent numerical approximations of a multiobjective optimization problem to the initial optimal control problem. For solving the auxiliary problem, we propose an implementable numerical algorithm.
Research on optimal driver steering model based on Multi-Point preview
Gu, Jun; Ma, Aijing
2017-08-01
In this paper, multi-point preview control algorithm is applied to driver steering control model. This paper builds multi-point preview road model in the form of state shift register. Based on the linear quadratic regulator (LQR) optimal control theory it optimizes driver steering control model with multi-point preview. Meanwhile, in the Matlab Simulink environment, based on vehicle system dynamics and optimal control theory, different preview points and weighted coefficients are simulated to study the influence of driver steering model. The simulation results show that the multi-point preview control mode has excellent driving performance. And in this paper, the main parameters affecting the preview control algorithm such as speed, preview weighted coefficients and the number of preview points and so on are discussed.
Model Based Optimal Sensor Network Design for Condition Monitoring in an IGCC Plant
Energy Technology Data Exchange (ETDEWEB)
Kumar, Rajeeva; Kumar, Aditya; Dai, Dan; Seenumani, Gayathri; Down, John; Lopez, Rodrigo
2012-12-31
This report summarizes the achievements and final results of this program. The objective of this program is to develop a general model-based sensor network design methodology and tools to address key issues in the design of an optimal sensor network configuration: the type, location and number of sensors used in a network, for online condition monitoring. In particular, the focus in this work is to develop software tools for optimal sensor placement (OSP) and use these tools to design optimal sensor network configuration for online condition monitoring of gasifier refractory wear and radiant syngas cooler (RSC) fouling. The methodology developed will be applicable to sensing system design for online condition monitoring for broad range of applications. The overall approach consists of (i) defining condition monitoring requirement in terms of OSP and mapping these requirements in mathematical terms for OSP algorithm, (ii) analyzing trade-off of alternate OSP algorithms, down selecting the most relevant ones and developing them for IGCC applications (iii) enhancing the gasifier and RSC models as required by OSP algorithms, (iv) applying the developed OSP algorithm to design the optimal sensor network required for the condition monitoring of an IGCC gasifier refractory and RSC fouling. Two key requirements for OSP for condition monitoring are desired precision for the monitoring variables (e.g. refractory wear) and reliability of the proposed sensor network in the presence of expected sensor failures. The OSP problem is naturally posed within a Kalman filtering approach as an integer programming problem where the key requirements of precision and reliability are imposed as constraints. The optimization is performed over the overall network cost. Based on extensive literature survey two formulations were identified as being relevant to OSP for condition monitoring; one based on LMI formulation and the other being standard INLP formulation. Various algorithms to solve
Energy Technology Data Exchange (ETDEWEB)
Cho, Su Gil; Jang, Jun Yong; Kim, Ji Hoon; Lee, Tae Hee [Hanyang University, Seoul (Korea, Republic of); Lee, Min Uk [Romax Technology Ltd., Seoul (Korea, Republic of); Choi, Jong Su; Hong, Sup [Korea Research Institute of Ships and Ocean Engineering, Daejeon (Korea, Republic of)
2015-04-15
Sequential surrogate model-based global optimization algorithms, such as super-EGO, have been developed to increase the efficiency of commonly used global optimization technique as well as to ensure the accuracy of optimization. However, earlier studies have drawbacks because there are three phases in the optimization loop and empirical parameters. We propose a united sampling criterion to simplify the algorithm and to achieve the global optimum of problems with constraints without any empirical parameters. It is able to select the points located in a feasible region with high model uncertainty as well as the points along the boundary of constraint at the lowest objective value. The mean squared error determines which criterion is more dominant among the infill sampling criterion and boundary sampling criterion. Also, the method guarantees the accuracy of the surrogate model because the sample points are not located within extremely small regions like super-EGO. The performance of the proposed method, such as the solvability of a problem, convergence properties, and efficiency, are validated through nonlinear numerical examples with disconnected feasible regions.
Optimal Control of Evolutionary Dynamics
Chakrabarti, Raj; McLendon, George
2008-01-01
Elucidating the fitness measures optimized during the evolution of complex biological systems is a major challenge in evolutionary theory. We present experimental evidence and an analytical framework demonstrating how biochemical networks exploit optimal control strategies in their evolutionary dynamics. Optimal control theory explains a striking pattern of extremization in the redox potentials of electron transport proteins, assuming only that their fitness measure is a control objective functional with bounded controls.
Optimal Control of Mechanical Systems
Vadim Azhmyakov
2007-01-01
In the present work, we consider a class of nonlinear optimal control problems, which can be called “optimal control problems in mechanics.” We deal with control systems whose dynamics can be described by a system of Euler-Lagrange or Hamilton equations. Using the variational structure of the solution of the corresponding boundary-value problems, we reduce the initial optimal control problem to an auxiliary problem of multiobjective programming. This technique makes it possible to apply some ...
Model-based analysis of control performance in sewer systems
DEFF Research Database (Denmark)
Mollerup, Ane Høyer; Mauricio Iglesias, Miguel; Johansen, N.B.;
2012-01-01
Design and assessment of control in wastewater systems has to be tackled at all levels, including supervisory and regulatory level. We present here an integrated approach to assessment of control in sewer systems based on modelling and the use of process control tools to assess the controllability...... of the process. A case study of a subcatchment area in Copenhagen (Denmark) is used to illustrate the combined approach in modelling of the system and control assessment....
Gong, Wei; Duan, Qingyun; Li, Jianduo; Wang, Chen; Di, Zhenhua; Ye, Aizhong; Miao, Chiyuan; Dai, Yongjiu
2016-03-01
Parameter specification is an important source of uncertainty in large, complex geophysical models. These models generally have multiple model outputs that require multiobjective optimization algorithms. Although such algorithms have long been available, they usually require a large number of model runs and are therefore computationally expensive for large, complex dynamic models. In this paper, a multiobjective adaptive surrogate modeling-based optimization (MO-ASMO) algorithm is introduced that aims to reduce computational cost while maintaining optimization effectiveness. Geophysical dynamic models usually have a prior parameterization scheme derived from the physical processes involved, and our goal is to improve all of the objectives by parameter calibration. In this study, we developed a method for directing the search processes toward the region that can improve all of the objectives simultaneously. We tested the MO-ASMO algorithm against NSGA-II and SUMO with 13 test functions and a land surface model - the Common Land Model (CoLM). The results demonstrated the effectiveness and efficiency of MO-ASMO.
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
The recurrent neural network (RNN) model based on projective operator was studied. Different from the former study, the value region of projective operator in the neural network in this paper is a general closed convex subset of n-dimensional Euclidean space and it is not a compact convex set in general, that is, the value region of projective operator is probably unbounded. It was proved that the network has a global solution and its solution trajectory converges to some equilibrium set whenever objective function satisfies some conditions. After that, the model was applied to continuously differentiable optimization and nonlinear or implicit complementarity problems. In addition, simulation experiments confirm the efficiency of the RNN.
Model Based Monitoring and Control of Chemical and Biochemical Processes
DEFF Research Database (Denmark)
Huusom, Jakob Kjøbsted
This presentation will give an overview of the work performed at the department of Chemical and Biochemical Engineering related to process control. A research vision is formulated and related to a number of active projects at the department. In more detail a project describing model estimation...... and controller tuning in Model Predictive Control application is discussed....
Model based Control of a Continuous Yeast Fermentation
DEFF Research Database (Denmark)
Andersen, Maria Yolanda; Brabrand, Henrik; Jørgensen, Sten Bay
1991-01-01
Control of a continuous fermentation with Saccharomyces cerevisiae is performed by manipulation of the feed flow rate using an ethanol measurement in the exit gas The process is controlled at the critical dilution rate with a low ethanol concentration of 40-50 mg/l. A standard PI controller is able...
Pressure Control in Distillation Columns: A Model-Based Analysis
DEFF Research Database (Denmark)
Mauricio Iglesias, Miguel; Bisgaard, Thomas; Kristensen, Henrik
2014-01-01
A comprehensive assessment of pressure control in distillation columns is presented, including the consequences for composition control and energy consumption. Two types of representative control structures are modeled, analyzed, and benchmarked. A detailed simulation test, based on a real...... industrial distillation column, is used to assess the differences between the two control structures and to demonstrate the benefits of pressure control in the operation. In the second part of the article, a thermodynamic analysis is carried out to establish the influence of pressure on relative volatility...
Optimisation of Marine Boilers using Model-based Multivariable Control
DEFF Research Database (Denmark)
Solberg, Brian
Traditionally, marine boilers have been controlled using classical single loop controllers. To optimise marine boiler performance, reduce new installation time and minimise the physical dimensions of these large steel constructions, a more comprehensive and coherent control strategy is needed. Th......). In the thesis the pressure control is based on this new method when on/off burner switching is required while the water level control is handled by a model predictive controller........ This research deals with the application of advanced control to a specific class of marine boilers combining well-known design methods for multivariable systems. This thesis presents contributions for modelling and control of the one-pass smoke tube marine boilers as well as for hybrid systems control. Much...... of the focus has been directed towards water level control which is complicated by the nature of the disturbances acting on the system as well as by low frequency sensor noise. This focus was motivated by an estimated large potential to minimise the boiler geometry by reducing water level fluctuations...
Investigation into Model-Based Fuzzy Logic Control
1993-12-01
Logic, in this context, will be used to bridge the gap between linear systems theory and nonlinear control application. Said another way, the language of...to demonstrate the value of applying both Fuzzy Set theory and linear systems theory to the control of nonlinear plants. It is conjectured that the... linear systems theory , to the extent possible. * The plant should be as simple as possible to dearly demonstrate the the developed controller. The
Mdluli, Thembi; Buzzard, Gregery T; Rundell, Ann E
2015-09-01
This model-based design of experiments (MBDOE) method determines the input magnitudes of an experimental stimuli to apply and the associated measurements that should be taken to optimally constrain the uncertain dynamics of a biological system under study. The ideal global solution for this experiment design problem is generally computationally intractable because of parametric uncertainties in the mathematical model of the biological system. Others have addressed this issue by limiting the solution to a local estimate of the model parameters. Here we present an approach that is independent of the local parameter constraint. This approach is made computationally efficient and tractable by the use of: (1) sparse grid interpolation that approximates the biological system dynamics, (2) representative parameters that uniformly represent the data-consistent dynamical space, and (3) probability weights of the represented experimentally distinguishable dynamics. Our approach identifies data-consistent representative parameters using sparse grid interpolants, constructs the optimal input sequence from a greedy search, and defines the associated optimal measurements using a scenario tree. We explore the optimality of this MBDOE algorithm using a 3-dimensional Hes1 model and a 19-dimensional T-cell receptor model. The 19-dimensional T-cell model also demonstrates the MBDOE algorithm's scalability to higher dimensions. In both cases, the dynamical uncertainty region that bounds the trajectories of the target system states were reduced by as much as 86% and 99% respectively after completing the designed experiments in silico. Our results suggest that for resolving dynamical uncertainty, the ability to design an input sequence paired with its associated measurements is particularly important when limited by the number of measurements.
Predictive-model-based dynamic coordination control strategy for power-split hybrid electric bus
Zeng, Xiaohua; Yang, Nannan; Wang, Junnian; Song, Dafeng; Zhang, Nong; Shang, Mingli; Liu, Jianxin
2015-08-01
Parameter-matching methods and optimal control strategies of the top-selling hybrid electric vehicle (HEV), namely, power-split HEV, are widely studied. In particular, extant research on control strategy focuses on the steady-state energy management strategy to obtain better fuel economy. However, given that multi-power sources are highly coupled in power-split HEVs and influence one another during mode shifting, conducting research on dynamic coordination control strategy (DCCS) to achieve riding comfort is also important. This paper proposes a predictive-model-based DCCS. First, the dynamic model of the objective power-split HEV is built and the mode shifting process is analyzed based on the developed model to determine the reason for the system shock generated. Engine torque estimation algorithm is then designed according to the principle of the nonlinear observer, and the prediction model of the degree of shock is established based on the theory of model predictive control. Finally, the DCCS with adaptation for a complex driving cycle is realized by combining the feedback control and the predictive model. The presented DCCS is validated on the co-simulation platform of AMESim and Simulink. Results show that the shock during mode shifting is well controlled, thereby improving riding comfort.
Gang, Grace J.; Siewerdsen, Jeffrey H.; Webster Stayman, J.
2017-06-01
Tube current modulation (TCM) is routinely adopted on diagnostic CT scanners for dose reduction. Conventional TCM strategies are generally designed for filtered-backprojection (FBP) reconstruction to satisfy simple image quality requirements based on noise. This work investigates TCM designs for model-based iterative reconstruction (MBIR) to achieve optimal imaging performance as determined by a task-based image quality metric. Additionally, regularization is an important aspect of MBIR that is jointly optimized with TCM, and includes both the regularization strength that controls overall smoothness as well as directional weights that permits control of the isotropy/anisotropy of the local noise and resolution properties. Initial investigations focus on a known imaging task at a single location in the image volume. The framework adopts Fourier and analytical approximations for fast estimation of the local noise power spectrum (NPS) and modulation transfer function (MTF)—each carrying dependencies on TCM and regularization. For the single location optimization, the local detectability index (d‧) of the specific task was directly adopted as the objective function. A covariance matrix adaptation evolution strategy (CMA-ES) algorithm was employed to identify the optimal combination of imaging parameters. Evaluations of both conventional and task-driven approaches were performed in an abdomen phantom for a mid-frequency discrimination task in the kidney. Among the conventional strategies, the TCM pattern optimal for FBP using a minimum variance criterion yielded a worse task-based performance compared to an unmodulated strategy when applied to MBIR. Moreover, task-driven TCM designs for MBIR were found to have the opposite behavior from conventional designs for FBP, with greater fluence assigned to the less attenuating views of the abdomen and less fluence to the more attenuating lateral views. Such TCM patterns exaggerate the intrinsic anisotropy of the MTF and NPS
An Enhanced Preventive Maintenance Optimization Model Based on a Three-Stage Failure Process
Directory of Open Access Journals (Sweden)
Ruifeng Yang
2015-01-01
Full Text Available Nuclear power plants are highly complex systems and the issues related to their safety are of primary importance. Probabilistic safety assessment is regarded as the most widespread methodology for studying the safety of nuclear power plants. As maintenance is one of the most important factors for affecting the reliability and safety, an enhanced preventive maintenance optimization model based on a three-stage failure process is proposed. Preventive maintenance is still a dominant maintenance policy due to its easy implementation. In order to correspond to the three-color scheme commonly used in practice, the lifetime of system before failure is divided into three stages, namely, normal, minor defective, and severe defective stages. When the minor defective stage is identified, two measures are considered for comparison: one is that halving the inspection interval only when the minor defective stage is identified at the first time; the other one is that if only identifying the minor defective stage, the subsequent inspection interval is halved. Maintenance is implemented immediately once the severe defective stage is identified. Minimizing the expected cost per unit time is our objective function to optimize the inspection interval. Finally, a numerical example is presented to illustrate the effectiveness of the proposed models.
Directory of Open Access Journals (Sweden)
Haibo Zhang
2016-08-01
Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.
Jayaweera, H. M. P. C.; Muhtaroğlu, Ali
2016-11-01
A novel model based methodology is presented to determine optimal device parameters for the fully integrated ultra low voltage DC-DC converter for energy harvesting applications. The proposed model feasibly contributes to determine the maximum efficient number of charge pump stages to fulfill the voltage requirement of the energy harvester application. The proposed DC-DC converter based power consumption model enables the analytical derivation of the charge pump efficiency when utilized simultaneously with the known LC tank oscillator behavior under resonant conditions, and voltage step up characteristics of the cross-coupled charge pump topology. The verification of the model has been done using a circuit simulator. The optimized system through the established model achieves more than 40% maximum efficiency yielding 0.45 V output with single stage, 0.75 V output with two stages, and 0.9 V with three stages for 2.5 kΩ, 3.5 kΩ and 5 kΩ loads respectively using 0.2 V input.
Model-based Locomotion Control of Underactuated Snake Robots
Rezapour, Ehsan
2015-01-01
Snake robots are a class of biologically inspired robots which are built to emulate the features of biological snakes. These robots are underactuated, i.e. they have fewer control inputs than degrees of freedom, and are hyper redundant, i.e. they have many degrees of freedom. Furthermore, snake robots utilize complex motion patterns and possess a complicated but highly flexible physical structure. These properties make locomotion control of snake robots a complicated and cha...
Model-based control of mechanical ventilation: design and clinical validation.
Martinoni, E P; Pfister, Ch A; Stadler, K S; Schumacher, P M; Leibundgut, D; Bouillon, T; Böhlen, T; Zbinden, A M
2004-06-01
We developed a model-based control system using end-tidal carbon dioxide fraction (FE'(CO(2))) to adjust a ventilator during clinical anaesthesia. We studied 16 ASA I-II patients (mean age 38 (range 20-59) yr; weight 67 (54-87) kg) during i.v. anaesthesia for elective surgery. After periods of normal ventilation the patients were either hyper- or hypoventilated to assess precision and dynamic behaviour of the control system. These data were compared with a previous group where a fuzzy-logic controller had been used. Responses to different clinical events (invalid carbon dioxide measurement, limb tourniquet release, tube cuff leak, exhaustion of carbon dioxide absorbent, simulation of pulmonary embolism) were also noted. The model-based controller correctly maintained the setpoint. No significant difference was found for the static performance between the two controllers. The dynamic response of the model-based controller was more rapid (Pfuzzy-logic and model-based control, respectively, and after a 1 vol% decrease was 355 (127) s and 177 (36) s, respectively. The new model-based controller had a consistent response to clinical artefacts. A model-based FE'(CO(2)) controller can be used in a clinical setting. It reacts appropriately to artefacts, and has a better dynamic response to setpoint changes than a previously described fuzzy-logic controller.
Model Based Control of Single-Phase Marine Cooling Systems
DEFF Research Database (Denmark)
Hansen, Michael
2014-01-01
”, it is shown that the part of the proposed model relating to the thermodynamics is dynamically accurate and with relatively small steady state deviations. The same is shown for a linear version of the part of the model governing the hydraulics of the cooling system. On the subject of control, the main focus...... in this work is on the development of a nonlinear robust control design. The design is based on principles from feedback. linearization to compensate for nonlinearities as well as transport delays by including a delay estimate in the feedback law. To deal with the uncertainties that emerged from the feedback...
Probabilistic Priority Message Checking Modeling Based on Controller Area Networks
Lin, Cheng-Min
Although the probabilistic model checking tool called PRISM has been applied in many communication systems, such as wireless local area network, Bluetooth, and ZigBee, the technique is not used in a controller area network (CAN). In this paper, we use PRISM to model the mechanism of priority messages for CAN because the mechanism has allowed CAN to become the leader in serial communication for automobile and industry control. Through modeling CAN, it is easy to analyze the characteristic of CAN for further improving the security and efficiency of automobiles. The Markov chain model helps us to model the behaviour of priority messages.
Model based monitoring for industrial and traffic noise control
Eerden, F.J.M. van der; Binnerts, B.; Graafland, F.
2015-01-01
Noise control starts by understanding the actual noise situation. Especially for situations where the distance between industrial and traffic noise sources and a local community is in the order of one kilometer or more, it may not be clear what sources are the main contributors to annoyance. Then a
Human Behavior Model Based Control Program for ACC Mobile Robot
Directory of Open Access Journals (Sweden)
Claudiu Pozna
2006-07-01
Full Text Available Present work is a part of the ACC autonomous car project. This paper will focuson the control program architecture. To design this architecture we will start from thehuman driver behavior model. Using this model we have constructed a three level controlprogram. Preliminary results are presented.
Model based control of grate combustion; Modellbaserad roststyrning
Energy Technology Data Exchange (ETDEWEB)
Broden, Henrik; Kjellstroem, Bjoern; Niklasson, Fredrik; Boecher Poulsen, Kristian
2006-12-15
An existing dynamic model for grate combustion has been further developed. The model has been used for studies of possible advantages that can be gained from utilisation of measurements of grate temperatures and fuel bed height for control of a boiler after disturbances caused by varying fuel moisture and fuel feeding. The objective was to asses the possibilities to develop a control system that would adjust for such disturbances quicker than measurements of steam output and oxygen in the exhaust. The model is based on dividing the fuel bed into three layers, where the different layers include fuel being dried, fuel being pyrolysed and char reacting with oxygen. The grate below the fuel bed is also considered. A mass balance, an energy balance and a volume balance is considered for each layer in 22 cells along the grate. The energy balances give the temperature distribution and the volume balances the bed height. The earlier version of the model could not handle layers that are consumed. This weakness has now been eliminated. Comparisons between predicted grate temperatures and measurements in a 25 MW boiler fuelled with biofuel have been used for validation of the model. The comparisons include effects of variations in primary air temperature, fuel moisture and output power. The model shows good agreement with observations for changes in the air temperature but the ability of the model to predict effects of changed fuel moisture is difficult to judge since the steam dome pressure control caused simultaneous changes of the primary air flow, which probably had a larger influence on the grate temperature. A linearised, tuned and reduced version of the model was used for design of a linear quadratic controller. This was used for studies of advantages of using measurements of grate temperatures and bed height for control of pusher velocity, grate speed, primary air flow and air temperature after disturbances of fuel moisture and fuel flow. Measurements of the grate
A Biopsychosocial Model Based on Negative Feedback and Control
Directory of Open Access Journals (Sweden)
Timothy Andrew Carey
2014-02-01
Full Text Available Although the biopsychosocial model has been a popular topic of discussion for over four decades it has not had the traction in fields of research that might be expected of such an intuitively appealing idea. One reason for this might be the absence of an identified mechanism or a functional architecture that is authentically biopsychosocial. What is needed is a robust mechanism that is equally important to biochemical processes as it is to psychological and social processes. Negative feedback may be the mechanism that is required. Negative feedback has been implicated in the regulation of neurotransmitters as well as important psychological and social processes such as emotional regulation and the relationship between a psychotherapist and a client. Moreover, negative feedback is purported to also govern the activity of all other organisms as well as humans. Perceptual Control Theory (PCT describes the way in which negative feedback establishes control at increasing levels of perceptual complexity. Thus, PCT may be the first biopsychosocial model to be articulated in functional terms. In this paper we outline the working model of PCT and explain how PCT provides an embodied hierarchical neural architecture that utilises negative feedback to control physiological, psychological, and social variables. PCT has major implications for both research and practice and, importantly, provides a guide by which fields of research that are currently separated may be integrated to bring about substantial progress in understanding the way in which the brain alters, and is altered by, its behavioural and environmental context.
Optimal magnetic attitude control
DEFF Research Database (Denmark)
Wisniewski, Rafal; Markley, F.L.
1999-01-01
because control torques can only be generated perpendicular to the local geomagnetic field vector. This has been a serious obstacle for using magnetorquer based control for three-axis stabilization of a low earth orbit satellite. The problem of controlling the spacecraft attitude using only magnetic...
Optimal control studies for steamflooding
Energy Technology Data Exchange (ETDEWEB)
Liu, Wei.
1992-01-01
A system science approach using optimal control theory of distributed parameter systems has been developed to determine operating strategies that maximize the economic attractiveness of the steamflooding Enhanced Oil Recovery (EOR) process. Necessary conditions for optimization are established by using the calculus of variations and Pontryagin's Maximum Principle. The objective criterion is to maximize the difference between oil revenue and injected steam cost. A stable and efficient numerical algorithm, based on an iterative gradient method, is developed. The optimal control model is based on a three-dimensional, three-phase (oil, steam and water) steam injection numerical simulator. A discrete form of the model is formulated. The optimized operating variables are the optimal bottom-hole pressure, the optimal injection rate of steam and water, and the optimal steam quality policies. Another optimal control study is also conducted on a simplified one-dimensional model (the extended Neuman model) to provide quick and reliable preliminary information on the economic feasibility of steamflooding processes. The simplified control model only considers the injection rate of steam as the control variable. The performance of this system science approach is investigated through various one-, two- and three-dimensional steamflooding problems. The effects of reservoir properties and heterogeneity on optimal policies as well as the sensitivity of the control variables are also studied. Results show this approach yields significant insight into the steamflooding EOR process. Improvement of the economic objective is significant under optimal operation conditions. These optimization results are quite important in a successful application of the steamflooding EOR method.
Neural Network Model Based Cluster Head Selection for Power Control
Directory of Open Access Journals (Sweden)
Krishan Kumar
2011-01-01
Full Text Available Mobile ad-hoc network has challenge of the limited power to prolong the lifetime of the network, because power is a valuable resource in mobile ad-hoc network. The status of power consumption should be continuously monitored after network deployment. In this paper, we propose coverage aware neural network based power control routing with the objective of maximizing the network lifetime. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage. The simulation results show that the proposed scheme can be used in wide area of applications in mobile ad-hoc network.
Multiple Model-Based Robot Control: Development and Initial Evaluation
1988-12-01
the control systems must be as precise as possible to ac- count for high speed robot dynamics . Previous research has shown that payload adaptation is...model for robot dynamics is adequate, and that the coefficients of the model are estimated on-line DD79,Ser87,LE97,KG83]. The adaptive per- turbation...eigenvalues of F(a, t) revealed that linearized robot dynamics was a function of the trajectory and had a weak dependence on payload. The slight F(a
Gorzelic, P.; Schiff, S. J.; Sinha, A.
2013-04-01
Objective. To explore the use of classical feedback control methods to achieve an improved deep brain stimulation (DBS) algorithm for application to Parkinson's disease (PD). Approach. A computational model of PD dynamics was employed to develop model-based rational feedback controller design. The restoration of thalamocortical relay capabilities to patients suffering from PD is formulated as a feedback control problem with the DBS waveform serving as the control input. Two high-level control strategies are tested: one that is driven by an online estimate of thalamic reliability, and another that acts to eliminate substantial decreases in the inhibition from the globus pallidus interna (GPi) to the thalamus. Control laws inspired by traditional proportional-integral-derivative (PID) methodology are prescribed for each strategy and simulated on this computational model of the basal ganglia network. Main Results. For control based upon thalamic reliability, a strategy of frequency proportional control with proportional bias delivered the optimal control achieved for a given energy expenditure. In comparison, control based upon synaptic inhibitory output from the GPi performed very well in comparison with those of reliability-based control, with considerable further reduction in energy expenditure relative to that of open-loop DBS. The best controller performance was amplitude proportional with derivative control and integral bias, which is full PID control. We demonstrated how optimizing the three components of PID control is feasible in this setting, although the complexity of these optimization functions argues for adaptive methods in implementation. Significance. Our findings point to the potential value of model-based rational design of feedback controllers for Parkinson's disease.
1979-12-01
with Uncertain Components 44 13 Component Uncertainty Representation of Uncertain Pole-Zero Locations 46 12 A Feedback Control System 60 i 1 I vii €in...OF FEEDBACK SYSTEM ROBUSTNESS A feedback control system design is said to be robust if it is able to meet design specifications despite differences... feedback control system design problems, the design specifications usually demand that the system be "robust" against the effects of deviations within
Optimization of mask manufacturing rule check constraint for model based assist feature generation
Shim, Seongbo; Kim, Young-chang; Chun, Yong-jin; Lee, Seong-Woo; Lee, Suk-joo; Choi, Seong-woon; Han, Woo-sung; Chang, Seong-hoon; Yoon, Seok-chan; Kim, Hee-bom; Ki, Won-tai; Woo, Sang-gyun; Cho, Han-gu
2008-11-01
space restriction. The test mask for this experimental work includes not only typical split patterns but also real device patterns that are generated by in-house model-based assist feature generation tool. We analyzed the mask writing result for typical patterns and compared the simulation result, and wafer result for real device patterns.
Experimental Model Based Feedback Control for Flutter Suppression and Gust Load Alleviation Project
National Aeronautics and Space Administration — ZONA Technology, Inc. (ZONA) proposes an R&D effort to develop an Experimental Model Based Feedback Control (EMBFC) Framework for the flutter suppression and...
Optimal control in thermal engineering
Badescu, Viorel
2017-01-01
This book is the first major work covering applications in thermal engineering and offering a comprehensive introduction to optimal control theory, which has applications in mechanical engineering, particularly aircraft and missile trajectory optimization. The book is organized in three parts: The first part includes a brief presentation of function optimization and variational calculus, while the second part presents a summary of the optimal control theory. Lastly, the third part describes several applications of optimal control theory in solving various thermal engineering problems. These applications are grouped in four sections: heat transfer and thermal energy storage, solar thermal engineering, heat engines and lubrication.Clearly presented and easy-to-use, it is a valuable resource for thermal engineers and thermal-system designers as well as postgraduate students.
Model-Based Approaches to Active Perception and Control
Directory of Open Access Journals (Sweden)
Giovanni Pezzulo
2017-06-01
Full Text Available There is an on-going debate in cognitive (neuro science and philosophy between classical cognitive theory and embodied, embedded, extended, and enactive (“4-Es” views of cognition—a family of theories that emphasize the role of the body in cognition and the importance of brain-body-environment interaction over and above internal representation. This debate touches foundational issues, such as whether the brain internally represents the external environment, and “infers” or “computes” something. Here we focus on two (4-Es-based criticisms to traditional cognitive theories—to the notions of passive perception and of serial information processing—and discuss alternative ways to address them, by appealing to frameworks that use, or do not use, notions of internal modelling and inference. Our analysis illustrates that: an explicitly inferential framework can capture some key aspects of embodied and enactive theories of cognition; some claims of computational and dynamical theories can be reconciled rather than seen as alternative explanations of cognitive phenomena; and some aspects of cognitive processing (e.g., detached cognitive operations, such as planning and imagination that are sometimes puzzling to explain from enactive and non-representational perspectives can, instead, be captured nicely from the perspective that internal generative models and predictive processing mediate adaptive control loops.
Model-based beam control for illumination of remote objects
Chandler, Susan M.; Lukesh, Gordon W.; Voelz, David; Basu, Santasri; Sjogren, Jon A.
2004-11-01
On September 1, 2003, Nukove Scientific Consulting, together with partner New Mexico State University, began work on a Phase 1 Small Business Technology TRansfer (STTR) grant from the United States Air Force Office of Scientific Research (AFOSR). The purpose of the grant was to show the feasibility of taking Nukove's pointing estimation technique from a post-processing tool for estimation of laser system characteristics to a real-time tool usable in the field. Nukove's techniques for pointing, shape, and OCS estimation do not require an imaging sensor nor a target board, thus estimates may be made very quickly. To prove feasibility, Nukove developed an analysis tool RHINO (Real-time Histogram Interpretation of Numerical Observations) and successfully demonstrated the emulation of real-time, frame-by-frame estimation of laser system characteristics, with data streamed into the tool and the estimates displayed as they are made. The eventual objective will be to use the frame-by-frame estimates to allow for feedback to a fielded system. Closely associated with this, NMSU developed a laboratory testbed to illuminate test objects, collect the received photons, and stream the data into RHINO. The two coupled efforts clearly demonstrate the feasibility of real-time pointing control of a laser system.
Analytical Model-based Fault Detection and Isolation in Control Systems
DEFF Research Database (Denmark)
Vukic, Z.; Ozbolt, H.; Blanke, M.
1998-01-01
The paper gives an introduction and an overview of the field of fault detection and isolation for control systems. The summary of analytical (quantitative model-based) methodds and their implementation are presented. The focus is given to mthe analytical model-based fault-detection and fault...... diagnosis methods, often viewed as the classical or deterministic ones. Emphasis is placed on the algorithms suitable for ship automation, unmanned underwater vehicles, and other systems of automatic control....
Model-Based Engine Control Architecture with an Extended Kalman Filter
Csank, Jeffrey T.; Connolly, Joseph W.
2016-01-01
This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The nonlinear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.
Model-based Layer Estimation using a Hybrid Genetic/Gradient Search Optimization Algorithm
Energy Technology Data Exchange (ETDEWEB)
Chambers, D; Lehman, S; Dowla, F
2007-05-17
A particle swarm optimization (PSO) algorithm is combined with a gradient search method in a model-based approach for extracting interface positions in a one-dimensional multilayer structure from acoustic or radar reflections. The basic approach is to predict the reflection measurement using a simulation of one-dimensional wave propagation in a multi-layer, evaluate the error between prediction and measurement, and then update the simulation parameters to minimize the error. Gradient search methods alone fail due to the number of local minima in the error surface close to the desired global minimum. The PSO approach avoids this problem by randomly sampling the region of the error surface around the global minimum, but at the cost of a large number of evaluations of the simulator. The hybrid approach uses the PSO at the beginning to locate the general area around the global minimum then switches to the gradient search method to zero in on it. Examples of the algorithm applied to the detection of interior walls of a building from reflected ultra-wideband radar signals are shown. Other possible applications are optical inspection of coatings and ultrasonic measurement of multilayer structures.
DEFF Research Database (Denmark)
Salazar, Jorge Andrés González; Santos, Ilmar
2017-01-01
This is part II of a twofold paper series dealing with the design and implementation of model-based controllers meant for assisting the hybrid and developing the feedback-controlled lubrication regimes in active tilting pad journal bearings (active TPJBs). In both papers theoretical and experimen......This is part II of a twofold paper series dealing with the design and implementation of model-based controllers meant for assisting the hybrid and developing the feedback-controlled lubrication regimes in active tilting pad journal bearings (active TPJBs). In both papers theoretical...... and experimental analyses are presented with focus on the reduction of rotor lateral vibration. This part is devoted to synthesising model-based LQG optimal controllers (LQR regulator + Kalman Filter) for the feedback-controlled lubrication and is based upon the mathematical model of the rotor-bearing system...... derived in part I. Results show further suppression of resonant vibrations when using the feedback-controlled or active lubrication, overweighting the reduction already achieved with hybrid lubrication, thus improving the whole machine dynamic performance....
Symposium on Optimal Control Theory
1987-01-01
Control theory can be roughly classified as deterministic or stochastic. Each of these can further be subdivided into game theory and optimal control theory. The central problem of control theory is the so called constrained maximization (which- with slight modifications--is equivalent to minimization). One can then say, heuristically, that the major problem of control theory is to find the maximum of some performance criterion (or criteria), given a set of constraints. The starting point is, of course, a mathematical representation of the performance criterion (or criteria)- sometimes called the objective functional--along with the constraints. When the objective functional is single valued (Le. , when there is only one objective to be maximized), then one is dealing with optimal control theory. When more than one objective is involved, and the objectives are generally incompatible, then one is dealing with game theory. The first paper deals with stochastic optimal control, using the dynamic programming ...
On-line and Model-based Approaches to the Visual Control of Action
Zhao, Huaiyong; Warren, William H.
2014-01-01
Two general approaches to the visual control of action have emerged in last few decades, known as the on-line and model-based approaches. The key difference between them is whether action is controlled by current visual information or on the basis of an internal world model. In this paper, we evaluate three hypotheses: strong on-line control, strong model-based control, and a hybrid solution that combines on-line control with weak off-line strategies. We review experimental research on the co...
Optimal control theory an introduction
Kirk, Donald E
2004-01-01
Optimal control theory is the science of maximizing the returns from and minimizing the costs of the operation of physical, social, and economic processes. Geared toward upper-level undergraduates, this text introduces three aspects of optimal control theory: dynamic programming, Pontryagin's minimum principle, and numerical techniques for trajectory optimization.Chapters 1 and 2 focus on describing systems and evaluating their performances. Chapter 3 deals with dynamic programming. The calculus of variations and Pontryagin's minimum principle are the subjects of chapters 4 and 5, and chapter
Preparation Model Based Control System For Hot Steel Strip Rolling Mill Stands
Bouazza, S. E.; Abbassi, H. A.; Moussaoui, A. K.
2008-06-01
As part of a research project on El-hadjar Hot Steel Rolling Mill Plant Annaba Algeria a new Model based control system is suggested to improve the performance of the hot strip rolling mill process. In this paper off-line model based controllers and a process simulator are described. The process models are based on the laws of physics. these models can predict the future behavior and the stability of the controlled process very reliably. The control scheme consists of a control algorithm. This Model based Control system is evaluated on a simulation model that represents accurately the dynamic of the process. Finally the usefulness to the Steel Industry of the suggested method is highlighted.
Model-based setting of inspiratory pressure and respiratory rate in pressure-controlled ventilation.
Schranz, C; Becher, T; Schädler, D; Weiler, N; Möller, K
2014-03-01
Mechanical ventilation carries the risk of ventilator-induced-lung-injury (VILI). To minimize the risk of VILI, ventilator settings should be adapted to the individual patient properties. Mathematical models of respiratory mechanics are able to capture the individual physiological condition and can be used to derive personalized ventilator settings. This paper presents model-based calculations of inspiration pressure (pI), inspiration and expiration time (tI, tE) in pressure-controlled ventilation (PCV) and a retrospective evaluation of its results in a group of mechanically ventilated patients. Incorporating the identified first order model of respiratory mechanics in the basic equation of alveolar ventilation yielded a nonlinear relation between ventilation parameters during PCV. Given this patient-specific relation, optimized settings in terms of minimal pI and adequate tE can be obtained. We then retrospectively analyzed data from 16 ICU patients with mixed pathologies, whose ventilation had been previously optimized by ICU physicians with the goal of minimization of inspiration pressure, and compared the algorithm's 'optimized' settings to the settings that had been chosen by the physicians. The presented algorithm visualizes the patient-specific relations between inspiration pressure and inspiration time. The algorithm's calculated results highly correlate to the physician's ventilation settings with r = 0.975 for the inspiration pressure, and r = 0.902 for the inspiration time. The nonlinear patient-specific relations of ventilation parameters become transparent and support the determination of individualized ventilator settings according to therapeutic goals. Thus, the algorithm is feasible for a variety of ventilated ICU patients and has the potential of improving lung-protective ventilation by minimizing inspiratory pressures and by helping to avoid the build-up of clinically significant intrinsic positive end-expiratory pressure.
Optimality principles in sensorimotor control.
Todorov, Emanuel
2004-09-01
The sensorimotor system is a product of evolution, development, learning and adaptation-which work on different time scales to improve behavioral performance. Consequently, many theories of motor function are based on 'optimal performance': they quantify task goals as cost functions, and apply the sophisticated tools of optimal control theory to obtain detailed behavioral predictions. The resulting models, although not without limitations, have explained more empirical phenomena than any other class. Traditional emphasis has been on optimizing desired movement trajectories while ignoring sensory feedback. Recent work has redefined optimality in terms of feedback control laws, and focused on the mechanisms that generate behavior online. This approach has allowed researchers to fit previously unrelated concepts and observations into what may become a unified theoretical framework for interpreting motor function. At the heart of the framework is the relationship between high-level goals, and the real-time sensorimotor control strategies most suitable for accomplishing those goals.
Model-based fuzzy control solutions for a laboratory Antilock Braking System
DEFF Research Database (Denmark)
Precup, Radu-Emil; Spataru, Sergiu; Rǎdac, Mircea-Bogdan;
2010-01-01
This paper gives two original model-based fuzzy control solutions dedicated to the longitudinal slip control of Antilock Braking System laboratory equipment. The parallel distributed compensation leads to linear matrix inequalities which guarantee the global stability of the fuzzy control systems...
Directory of Open Access Journals (Sweden)
Devaraj Jayachandran
Full Text Available 6-Mercaptopurine (6-MP is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN through enzymatic reaction involving thiopurine methyltransferase (TPMT. Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP's widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient's ability to metabolize the drug instead of the traditional standard-dose-for-all approach.
Jayachandran, Devaraj; Laínez-Aguirre, José; Rundell, Ann; Vik, Terry; Hannemann, Robert; Reklaitis, Gintaras; Ramkrishna, Doraiswami
2015-01-01
6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP's widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient's ability to metabolize the drug instead of the traditional standard-dose-for-all approach.
Implementation of a Fractional Model-Based Fault Detection Algorithm into a PLC Controller
Kopka, Ryszard
2014-12-01
This paper presents results related to the implementation of model-based fault detection and diagnosis procedures into a typical PLC controller. To construct the mathematical model and to implement the PID regulator, a non-integer order differential/integral calculation was used. Such an approach allows for more exact control of the process and more precise modelling. This is very crucial in model-based diagnostic methods. The theoretical results were verified on a real object in the form of a supercapacitor connected to a PLC controller by a dedicated electronic circuit controlled directly from the PLC outputs.
Optimal actuation in vibration control
Guzzardo, C. A.; Pang, S. S.; Ram, Y. M.
2013-02-01
The paper addresses the problem of finding the optimal location of actuators and their relative gain so that the control effort in an actively controlled vibrating system is minimized. In technical terms the problem is finding the optimal input vector of unit norm that minimizes the norm of the control gain vector. This problem is addressed in the context of the active natural frequency modification problem associated with resonance avoidance in undamped systems, and in the context of the single-input-multi-output pole assignment problem for second order systems.
Optimal Control of Teaching Process
Institute of Scientific and Technical Information of China (English)
BAO Man; ZHANG Guo-zhi
2002-01-01
The authors first put forward quadratic form performance index as a criterion of measuringmerits and demerits of teaching process. On this base, we got a low of optimal control after the quantificationof the teacher's functions. It must play a leading role on how the teacher fully controls the whole teachingprocess.
Optimal control of quantum measurement
Energy Technology Data Exchange (ETDEWEB)
Egger, Daniel; Wilhelm, Frank [Theoretical Physics, Saarland University, 66123 Saarbruecken (Germany)
2015-07-01
Pulses to steer the time evolution of quantum systems can be designed with optimal control theory. In most cases it is the coherent processes that can be controlled and one optimizes the time evolution towards a target unitary process, sometimes also in the presence of non-controllable incoherent processes. Here we show how to extend the GRAPE algorithm in the case where the incoherent processes are controllable and the target time evolution is a non-unitary quantum channel. We perform a gradient search on a fidelity measure based on Choi matrices. We illustrate our algorithm by optimizing a measurement pulse for superconducting phase qubits. We show how this technique can lead to large measurement contrast close to 99%. We also show, within the validity of our model, that this algorithm can produce short 1.4 ns pulses with 98.2% contrast.
Study of Super-Twisting sliding mode control for U model based nonlinear system
Zhang, Jianhua; Li, Yang; Xueli WU; Jianan HUO; Shenyang ZHUANG
2016-01-01
The Super-Twisting control algorithm is adopted to analyze the U model based nonlinear control system in order to solve the controller design problems of non-affine nonlinear systems. The non-affine nonlinear systems are studied, the neural network approximation of the nonlinear function is performed, and the Super-Twisting control algorithm is used to control. The convergence of the Super-Twisting algorithm is proved by selecting an appropriate Lyapunov function. The Matlab simulation is car...
Optimality Conditions for Inventory Control
Feinberg, Eugene A.
2016-01-01
This tutorial describes recently developed general optimality conditions for Markov Decision Processes that have significant applications to inventory control. In particular, these conditions imply the validity of optimality equations and inequalities. They also imply the convergence of value iteration algorithms. For total discounted-cost problems only two mild conditions on the continuity of transition probabilities and lower semi-continuity of one-step costs are needed. For average-cost pr...
International Workshop on Advanced Dynamics and Model Based Control of Structures and Machines
Belyaev, Alexander; Krommer, Michael
2017-01-01
The papers in this volume present and discuss the frontiers in the mechanics of controlled machines and structures. They are based on papers presented at the International Workshop on Advanced Dynamics and Model Based Control of Structures and Machines held in Vienna in September 2015. The workshop continues a series of international workshops held in Linz (2008) and St. Petersburg (2010).
Advanced Dynamics and Model-Based Control of Structures and Machines
Krommer, Michael; Belyaev, Alexander
2012-01-01
The book contains 26 scientific contributions by leading experts from Russia, Austria, Italy, Japan and Taiwan. It presents an overview on recent developments in Advanced Dynamics and Model Based Control of Structures and Machines. Main topics are nonlinear control of structures and systems, sensing and actuation, active and passive damping, nano- and micromechanics, vibrations and waves.
Optimal control linear quadratic methods
Anderson, Brian D O
2007-01-01
This augmented edition of a respected text teaches the reader how to use linear quadratic Gaussian methods effectively for the design of control systems. It explores linear optimal control theory from an engineering viewpoint, with step-by-step explanations that show clearly how to make practical use of the material.The three-part treatment begins with the basic theory of the linear regulator/tracker for time-invariant and time-varying systems. The Hamilton-Jacobi equation is introduced using the Principle of Optimality, and the infinite-time problem is considered. The second part outlines the
Optimal control of motorsport differentials
Tremlett, A. J.; Massaro, M.; Purdy, D. J.; Velenis, E.; Assadian, F.; Moore, A. P.; Halley, M.
2015-12-01
Modern motorsport limited slip differentials (LSD) have evolved to become highly adjustable, allowing the torque bias that they generate to be tuned in the corner entry, apex and corner exit phases of typical on-track manoeuvres. The task of finding the optimal torque bias profile under such varied vehicle conditions is complex. This paper presents a nonlinear optimal control method which is used to find the minimum time optimal torque bias profile through a lane change manoeuvre. The results are compared to traditional open and fully locked differential strategies, in addition to considering related vehicle stability and agility metrics. An investigation into how the optimal torque bias profile changes with reduced track-tyre friction is also included in the analysis. The optimal LSD profile was shown to give a performance gain over its locked differential counterpart in key areas of the manoeuvre where a quick direction change is required. The methodology proposed can be used to find both optimal passive LSD characteristics and as the basis of a semi-active LSD control algorithm.
Dong, Bing; Li, Yan; Han, Xin-li; Hu, Bin
2016-01-01
For high-speed aircraft, a conformal window is used to optimize the aerodynamic performance. However, the local shape of the conformal window leads to large amounts of dynamic aberrations varying with look angle. In this paper, deformable mirror (DM) and model-based wavefront sensorless adaptive optics (WSLAO) are used for dynamic aberration correction of an infrared remote sensor equipped with a conformal window and scanning mirror. In model-based WSLAO, aberration is captured using Lukosz mode, and we use the low spatial frequency content of the image spectral density as the metric function. Simulations show that aberrations induced by the conformal window are dominated by some low-order Lukosz modes. To optimize the dynamic correction, we can only correct dominant Lukosz modes and the image size can be minimized to reduce the time required to compute the metric function. In our experiment, a 37-channel DM is used to mimic the dynamic aberration of conformal window with scanning rate of 10 degrees per second. A 52-channel DM is used for correction. For a 128 × 128 image, the mean value of image sharpness during dynamic correction is 1.436 × 10−5 in optimized correction and is 1.427 × 10−5 in un-optimized correction. We also demonstrated that model-based WSLAO can achieve convergence two times faster than traditional stochastic parallel gradient descent (SPGD) method. PMID:27598161
Dong, Bing; Li, Yan; Han, Xin-Li; Hu, Bin
2016-09-02
For high-speed aircraft, a conformal window is used to optimize the aerodynamic performance. However, the local shape of the conformal window leads to large amounts of dynamic aberrations varying with look angle. In this paper, deformable mirror (DM) and model-based wavefront sensorless adaptive optics (WSLAO) are used for dynamic aberration correction of an infrared remote sensor equipped with a conformal window and scanning mirror. In model-based WSLAO, aberration is captured using Lukosz mode, and we use the low spatial frequency content of the image spectral density as the metric function. Simulations show that aberrations induced by the conformal window are dominated by some low-order Lukosz modes. To optimize the dynamic correction, we can only correct dominant Lukosz modes and the image size can be minimized to reduce the time required to compute the metric function. In our experiment, a 37-channel DM is used to mimic the dynamic aberration of conformal window with scanning rate of 10 degrees per second. A 52-channel DM is used for correction. For a 128 × 128 image, the mean value of image sharpness during dynamic correction is 1.436 × 10(-5) in optimized correction and is 1.427 × 10(-5) in un-optimized correction. We also demonstrated that model-based WSLAO can achieve convergence two times faster than traditional stochastic parallel gradient descent (SPGD) method.
Model based control for run-of-river system. Part 2: Comparison of control structures
Directory of Open Access Journals (Sweden)
Liubomyr Vytvytskyi
2015-10-01
Full Text Available Optimal operation and control of a run-of-river hydro power plant depend on good knowledge of the elements of the plant in the form of models. Both the control architecture of the system, i.e. the choice of inputs and outputs, and to what degree a model is used, will affect the achievable control performance. Here, a model of a river reach based on the Saint Venant equations for open channel flow illustrates the dynamics of the run-of-river system. The hyperbolic partial differential equations are discretized using the Kurganov-Petrova central upwind scheme - see Part I for details. A comparison is given of achievable control performance using two alternative control signals: the inlet or the outlet volumetric flow rates to the system, in combination with a number of different control structures such as PI control, PI control with Smith predictor, and predictive control. The control objective is to keep the level just in front of the dam as high as possible, and with little variation in the level to avoid overflow over the dam. With a step change in the volumetric inflow to the river reach (disturbance and using the volumetric outflow as the control signal, PI control gives quite good performance. Model predictive control (MPC gives superior control in the sense of constraining the variation in the water level, at a cost of longer computational time and thus constraints on possible sample time. Details on controller tuning are given. With volumetric inflow to the river reach as control signal and outflow (production as disturbance, this introduces a considerable time delay in the control signal. Because of nonlinearity in the system (varying time delay, etc., it is difficult to achieve stable closed loop performance using a simple PI controller. However, by combining a PI controller with a Smith predictor based on a simple integrator + fixed time delay model, stable closed loop operation is possible with decent control performance. Still, an MPC
Model-based evaluation of an on-line control strategy for SBRs based on OUR and ORP measurements.
Corominas, Ll; Sin, G; Puig, S; Traore, A; Balaguer, M; Colprim, J; Vanrolleghem, P A
2006-01-01
Application of control strategies for existing wastewater treatment technologies becomes necessary to meet ever-stricter effluent legislations and reduce the associated treatment costs. In the case of SBR technology, controlling the phase scheduling is one of the key aspects of SBR operation. In this study a calibrated mechanistic model based on the ASM1 was used to evaluate an on-line control strategy for the SBR phase-scheduling and compare it with the SBR's performance using no control strategy. To evaluate the performance, reference indices relating to the effluent quality, the required energy for aeration and the treated wastewater volume were used. The results showed that it is possible to maintain optimal SBR performance in the studied system at minimal costs by on-line control of the length of the aerobic and anoxic phases.
Model Based Predictive Control of Thermal Comfort for Integrated Building System
Georgiev, Tz.; Jonkov, T.; Yonchev, E.; Tsankov, D.
2011-12-01
This article deals with the indoor thermal control problem in HVAC (heating, ventilation and air conditioning) systems. Important outdoor and indoor variables in these systems are: air temperature, global and diffuse radiations, wind speed and direction, temperature, relative humidity, mean radiant temperature, and so on. The aim of this article is to obtain the thermal comfort optimisation by model based predictive control algorithms (MBPC) of an integrated building system. The control law is given by a quadratic programming problem and the obtained control action is applied to the process. The derived models and model based predictive control algorithms are investigated based on real—live data. All researches are derived in MATLAB environment. The further research will focus on synthesis of robust energy saving control algorithms.
Control and optimal control theories with applications
Burghes, D N
2004-01-01
This sound introduction to classical and modern control theory concentrates on fundamental concepts. Employing the minimum of mathematical elaboration, it investigates the many applications of control theory to varied and important present-day problems, e.g. economic growth, resource depletion, disease epidemics, exploited population, and rocket trajectories. An original feature is the amount of space devoted to the important and fascinating subject of optimal control. The work is divided into two parts. Part one deals with the control of linear time-continuous systems, using both transfer fun
Optimal control with aerospace applications
Longuski, James M; Prussing, John E
2014-01-01
Want to know not just what makes rockets go up but how to do it optimally? Optimal control theory has become such an important field in aerospace engineering that no graduate student or practicing engineer can afford to be without a working knowledge of it. This is the first book that begins from scratch to teach the reader the basic principles of the calculus of variations, develop the necessary conditions step-by-step, and introduce the elementary computational techniques of optimal control. This book, with problems and an online solution manual, provides the graduate-level reader with enough introductory knowledge so that he or she can not only read the literature and study the next level textbook but can also apply the theory to find optimal solutions in practice. No more is needed than the usual background of an undergraduate engineering, science, or mathematics program: namely calculus, differential equations, and numerical integration. Although finding optimal solutions for these problems is a...
2009-09-01
actuators) as well as control logic and new architectures . Also, control engineers will have to work closely with hardware designers to take advantage of...Similarly, within engine systems themselves, it is becoming necessary to shift toward distributed control architectures to enable weight-neutral...Paulitsch, Michael, “Model-Based Development And The Implications To Design Assurance And Certification”, 0- 7803-9307, IEEE, 2005. MVC , PSC, MPC 7
Moini, A
2002-01-01
In this paper, genetic algorithms are used in the design and robustification various mo el-ba ed/non-model-based fuzzy-logic controllers for robotic manipulators. It is demonstrated that genetic algorithms provide effective means of designing the optimal set of fuzzy rules as well as the optimal domains of associated fuzzy sets in a new class of model-based-fuzzy-logic controllers. Furthermore, it is shown that genetic algorithms are very effective in the optimal design and robustification of non-model-based multivariable fuzzy-logic controllers for robotic manipulators.
Proton Exchange Membrane Fuel Cell Modeling Based on Seeker Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
LI Qi; DAI Chao-hua; Chen Wei-rong; JIA Jun-bo; HAN Ming
2008-01-01
Seeker optimization algorithm (SOA) has applications in continuous space of swarm intelligence. In the fields of proton ex-change membrane fuel cell (PEMFC) modeling, SOA was proposed to research a set of optimized parameters in PEMFC polariza-tion curve model. Experimental result showed that the mean square error of the optimization modeling strategy was only 6.9 × 10-23. Hence, the optimization model could fit the experiment data with high precision.
Optimal control of hybrid vehicles
Jager, Bram; Kessels, John
2013-01-01
Optimal Control of Hybrid Vehicles provides a description of power train control for hybrid vehicles. The background, environmental motivation and control challenges associated with hybrid vehicles are introduced. The text includes mathematical models for all relevant components in the hybrid power train. The power split problem in hybrid power trains is formally described and several numerical solutions detailed, including dynamic programming and a novel solution for state-constrained optimal control problems based on Pontryagin’s maximum principle. Real-time-implementable strategies that can approximate the optimal solution closely are dealt with in depth. Several approaches are discussed and compared, including a state-of-the-art strategy which is adaptive for vehicle conditions like velocity and mass. Two case studies are included in the book: · a control strategy for a micro-hybrid power train; and · experimental results obtained with a real-time strategy implemented in...
Optimization and optimal control in automotive systems
Kolmanovsky, Ilya; Steinbuch, Maarten; Re, Luigi
2014-01-01
This book demonstrates the use of the optimization techniques that are becoming essential to meet the increasing stringency and variety of requirements for automotive systems. It shows the reader how to move away from earlier approaches, based on some degree of heuristics, to the use of more and more common systematic methods. Even systematic methods can be developed and applied in a large number of forms so the text collects contributions from across the theory, methods and real-world automotive applications of optimization. Greater fuel economy, significant reductions in permissible emissions, new drivability requirements and the generally increasing complexity of automotive systems are among the criteria that the contributing authors set themselves to meet. In many cases multiple and often conflicting requirements give rise to multi-objective constrained optimization problems which are also considered. Some of these problems fall into the domain of the traditional multi-disciplinary optimization applie...
Neveu, N.; Larson, J.; Power, J. G.; Spentzouris, L.
2017-07-01
Model-based, derivative-free, trust-region algorithms are increasingly popular for optimizing computationally expensive numerical simulations. A strength of such methods is their efficient use of function evaluations. In this paper, we use one such algorithm to optimize the beam dynamics in two cases of interest at the Argonne Wakefield Accelerator (AWA) facility. First, we minimize the emittance of a 1 nC electron bunch produced by the AWA rf photocathode gun by adjusting three parameters: rf gun phase, solenoid strength, and laser radius. The algorithm converges to a set of parameters that yield an emittance of 1.08 μm. Second, we expand the number of optimization parameters to model the complete AWA rf photoinjector (the gun and six accelerating cavities) at 40 nC. The optimization algorithm is used in a Pareto study that compares the trade-off between emittance and bunch length for the AWA 70MeV photoinjector.
Optimal control of hydroelectric facilities
Zhao, Guangzhi
This thesis considers a simple yet realistic model of pump-assisted hydroelectric facilities operating in a market with time-varying but deterministic power prices. Both deterministic and stochastic water inflows are considered. The fluid mechanical and engineering details of the facility are described by a model containing several parameters. We present a dynamic programming algorithm for optimizing either the total energy produced or the total cash generated by these plants. The algorithm allows us to give the optimal control strategy as a function of time and to see how this strategy, and the associated plant value, varies with water inflow and electricity price. We investigate various cases. For a single pumped storage facility experiencing deterministic power prices and water inflows, we investigate the varying behaviour for an oversimplified constant turbine- and pump-efficiency model with simple reservoir geometries. We then generalize this simple model to include more realistic turbine efficiencies, situations with more complicated reservoir geometry, and the introduction of dissipative switching costs between various control states. We find many results which reinforce our physical intuition about this complicated system as well as results which initially challenge, though later deepen, this intuition. One major lesson of this work is that the optimal control strategy does not differ much between two differing objectives of maximizing energy production and maximizing its cash value. We then turn our attention to the case of stochastic water inflows. We present a stochastic dynamic programming algorithm which can find an on-average optimal control in the face of this randomness. As the operator of a facility must be more cautious when inflows are random, the randomness destroys facility value. Following this insight we quantify exactly how much a perfect hydrological inflow forecast would be worth to a dam operator. In our final chapter we discuss the
Inverse grey-box model-based control of a dielectric elastomer actuator
DEFF Research Database (Denmark)
Jones, Richard William; Sarban, Rahimullah
2012-01-01
An accurate physical-based electromechanical model of a commercially available tubular dielectric elastomer (DE) actuator has been developed and validated. In this contribution, the use of the physical-based electromechanical model to formulate a model-based controller is examined. The choice...
Institute of Scientific and Technical Information of China (English)
ZHAO Peng; MU Xin; YAO Jin-hua; WANG Yong; YANG Xiu-tai
2007-01-01
We established an integrated and optimized model of vehicle scheduling problem and vehicle filling problem for solving an extremely complex delivery mode-multi-type vehicles, non-full loads, pickup and delivery in logistics and delivery system. The integrated and optimized model is based on our previous research result-effective space method. An integrated algorithm suitable for the integrated and optimized model was proposed and corresponding computer programs were designed to solve practical problems. The results indicates the programs can work out optimized delivery routes and concrete loading projects. The model and algorithm have many virtues and are valuable in practice.
Fu-Kwun Wang; Yu-Yao Hsiao; Ku-Kuang Chang
2012-01-01
It is important for executives to predict the future trends. Otherwise, their companies cannot make profitable decisions and investments. The Bass diffusion model can describe the empirical adoption curve for new products and technological innovations. The Grey model provides short-term forecasts using four data points. This study develops a combined model based on the rolling Grey model (RGM) and the Bass diffusion model to forecast motherboard shipments. In addition, we investigate evolutio...
Fu-Kwun Wang; Yu-Yao Hsiao; Ku-Kuang Chang
2012-01-01
It is important for executives to predict the future trends. Otherwise, their companies cannot make profitable decisions and investments. The Bass diffusion model can describe the empirical adoption curve for new products and technological innovations. The Grey model provides short-term forecasts using four data points. This study develops a combined model based on the rolling Grey model (RGM) and the Bass diffusion model to forecast motherboard shipments. In addition, we investigate evolutio...
Lam, Hak-Keung
2016-01-01
This book presents recent research on the stability analysis of polynomial-fuzzy-model-based control systems where the concept of partially/imperfectly matched premises and membership-function dependent analysis are considered. The membership-function-dependent analysis offers a new research direction for fuzzy-model-based control systems by taking into account the characteristic and information of the membership functions in the stability analysis. The book presents on a research level the most recent and advanced research results, promotes the research of polynomial-fuzzy-model-based control systems, and provides theoretical support and point a research direction to postgraduate students and fellow researchers. Each chapter provides numerical examples to verify the analysis results, demonstrate the effectiveness of the proposed polynomial fuzzy control schemes, and explain the design procedure. The book is comprehensively written enclosing detailed derivation steps and mathematical derivations also for read...
Model-based workflows for optimal long-term reservoir mangement
Leeuwenburgh, O.; Egberts, P.; Chitu, A.; Wilschut, F.
2014-01-01
Life-cycle optimization is the process of finding field operation strategies that aim to optimize recovery or economic value with a long-term (years to decades) horizon. A reservoir simulation model is therefore generally appropriate and sufficient to explore the impact of different recovery
Optimal Control of Electrodynamic Tethers
2008-06-01
left with ( ) ( ) 1 2 1 2 23 3 3 32 1 2 1 2 3 3 ˆ ˆ 2 2 2 ˆ ˆ 6 6 t t t t t t m m m m m T m L m L M M m LM M M MLm M M... Contract RH4-394049, March 1985, p 31. 9 Pelaez, J. and Lorenzini, E. C., “Libration Control of Electrodynamic Tethers in Inclined Orbit,” Journal of...COVERED (From – To) Aug 2006 – Jul 2008 4. TITLE AND SUBTITLE Optimal Control of Electrodynamic Tethers 5a. CONTRACT NUMBER 5b
Directory of Open Access Journals (Sweden)
Haihua Zhu
2016-01-01
Full Text Available To make the optimal design of the multilink transmission mechanism applied in mechanical press, the intelligent optimization techniques are explored in this paper. A preference polyhedron model and new domination relationships evaluation methodology are proposed for the purpose of reaching balance among kinematic performance, dynamic performance, and other performances of the multilink transmission mechanism during the conceptual design phase. Based on the traditional evaluation index of single target of multicriteria design optimization, the robust metrics of the mechanism system and preference metrics of decision-maker are taken into consideration in this preference polyhedron model and reflected by geometrical characteristic of the model. At last, two optimized multilink transmission mechanisms are designed based on the proposed preference polyhedron model with different evolutionary algorithms, and the result verifies the validity of the proposed optimization method.
Institute of Scientific and Technical Information of China (English)
Gao Ning; Sun Wei
2015-01-01
Based on the study of supply chain (SC) and SC optimization in engineering projects, a mixed integer nonlinear programming (MINLP) optimization model is developed to minimize the total SC cost for international petrochemical en-gineering projects. A steam cracking project is selected and analyzed, from which typical SC characteristics in international engineering projects in the area of petrochemical industry are summarized. The MINLP model is therefore developed and applied to projects with detailed data. The optimization results are analyzed and compared by the MINLP model, indicat-ing that they are appropriate to SC management practice in engineering projects, and are consistent with the optimal price-effective strategy in procurement. As a result, the model could provide useful guidance to SC optimization of international engineering projects in petrochemical industry, and improve SC management by selecting more reliable and qualiifed part-ner enterprises in SC for the project.
Multi-attribute decision making model based on optimal membership and relative entropy
Institute of Scientific and Technical Information of China (English)
Rao Congjun; Zhao Yong
2009-01-01
To study the problems of multi-attribute decision making in which the attribute values are given in the form of linguistic fuzzy numbers and the information of attribute weights are incomplete, a new multi-attribute decision making model is presented based on the optimal membership and the relative entropy. Firstly, the definitions of the optimal membership and the relative entropy are given. Secondly, for all alternatives, a set of preference weight vectors are obtained by solving a set of linear programming models whose goals are all to maximize the optimal membership. Thirdly, a relative entropy model is established to aggregate the preference weight vectors, thus an optimal weight vector is determined. Based on this optimal weight vector, the algorithm of deviation degree minimization is proposed to rank all the alternatives. Finally, a decision making example is given to demonstrate the feasibility and rationality of this new model.
Fatigue design of a cellular phone folder using regression model-based multi-objective optimization
Kim, Young Gyun; Lee, Jongsoo
2016-08-01
In a folding cellular phone, the folding device is repeatedly opened and closed by the user, which eventually results in fatigue damage, particularly to the front of the folder. Hence, it is important to improve the safety and endurance of the folder while also reducing its weight. This article presents an optimal design for the folder front that maximizes its fatigue endurance while minimizing its thickness. Design data for analysis and optimization were obtained experimentally using a test jig. Multi-objective optimization was carried out using a nonlinear regression model. Three regression methods were employed: back-propagation neural networks, logistic regression and support vector machines. The AdaBoost ensemble technique was also used to improve the approximation. Two-objective Pareto-optimal solutions were identified using the non-dominated sorting genetic algorithm (NSGA-II). Finally, a numerically optimized solution was validated against experimental product data, in terms of both fatigue endurance and thickness index.
Decomposition method of complex optimization model based on global sensitivity analysis
Qiu, Qingying; Li, Bing; Feng, Peien; Gao, Yu
2014-07-01
The current research of the decomposition methods of complex optimization model is mostly based on the principle of disciplines, problems or components. However, numerous coupling variables will appear among the sub-models decomposed, thereby make the efficiency of decomposed optimization low and the effect poor. Though some collaborative optimization methods are proposed to process the coupling variables, there lacks the original strategy planning to reduce the coupling degree among the decomposed sub-models when we start decomposing a complex optimization model. Therefore, this paper proposes a decomposition method based on the global sensitivity information. In this method, the complex optimization model is decomposed based on the principle of minimizing the sensitivity sum between the design functions and design variables among different sub-models. The design functions and design variables, which are sensitive to each other, will be assigned to the same sub-models as much as possible to reduce the impacts to other sub-models caused by the changing of coupling variables in one sub-model. Two different collaborative optimization models of a gear reducer are built up separately in the multidisciplinary design optimization software iSIGHT, the optimized results turned out that the decomposition method proposed in this paper has less analysis times and increases the computational efficiency by 29.6%. This new decomposition method is also successfully applied in the complex optimization problem of hydraulic excavator working devices, which shows the proposed research can reduce the mutual coupling degree between sub-models. This research proposes a decomposition method based on the global sensitivity information, which makes the linkages least among sub-models after decomposition, and provides reference for decomposing complex optimization models and has practical engineering significance.
Bonne, François; Alamir, Mazen; Bonnay, Patrick; Bradu, Benjamin
2014-01-01
In this paper, a multivariable model-based non-linear controller for Warm Compression Stations (WCS) is proposed. 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 have precise control of every pressure in normal operation or to stabilize and control the cryoplant under high variation of thermal loads (such as a pulsed heat load expected to take place in future fusion reactors such as those 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 how to set the WCS model up to synthesize the Linear Quadratic Optimal feedback gain and how to use it. After preliminary tuning at CEA-Grenoble on the 400W@1.8K helium test facility, the controller has been implemented on a Schneider PLC and fully tested first on the CERN's real-time simulator. Then, it was experimentally validated on a real CERN cryoplant. The efficiency of the solution is experimentally assessed using a reasonable operating scenario of start and stop of compressors and cryogenic turbines. This work is partially supported through the European Fusion Development Agreement (EFDA) Goal Oriented Training Program, task agreement WP10-GOT-GIRO.
An Optimization Model Based on Electric Power Generation in Steel Industry
Directory of Open Access Journals (Sweden)
Jing-yu Liu
2014-01-01
Full Text Available Electric power is an important energy in steel industry. Electricity accounts for roughly 20% to 30% of the gross energy consumption and costs about 10% of the gross cost of energy. In this paper, under the premise of ensuring the stability of energy supply and the normal production safety, the mathematical programming method and the dynamic mathematical optimization model were used to set up the surplus gas in the optimal allocation among the buffer users and steam production dispatching for the production equipment. The application of this optimization model can effectively improve the energy efficiency and the accuracy of power generation, making full use of secondary energy and residual heat. It also can realize the rationalization of the electricity production structure optimization which can effectively reduce the flare of the gas and steam on one hand, and save energy and decrease production cost on the other.
Directory of Open Access Journals (Sweden)
Qing-chun Meng
Full Text Available CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996-2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated.
Meng, Qing-chun; Rong, Xiao-xia; Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi
2016-01-01
CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996-2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated.
Yong, Peng; Huang, Jianping; Li, Zhenchun; Liao, Wenyuan; Qu, Luping; Li, Qingyang; Liu, Peijun
2017-02-01
In finite-difference (FD) method, numerical dispersion is the dominant factor influencing the accuracy of seismic modelling. Various optimized FD schemes for scalar wave modelling have been proposed to reduce grid dispersion, while the optimized time-space domain FD schemes for elastic wave modelling have not been fully investigated yet. In this paper, an optimized FD scheme with Equivalent Staggered Grid (ESG) for elastic modelling has been developed. We start from the constant P- and S-wave speed elastic wave equations and then deduce analytical plane wave solutions in the wavenumber domain with eigenvalue decomposition method. Based on the elastic plane wave solutions, three new time-space domain dispersion relations of ESG elastic modelling are obtained, which are represented by three equations corresponding to P-, S- and converted-wave terms in the elastic equations, respectively. By using these new relations, we can study the dispersion errors of different spatial FD terms independently. The dispersion analysis showed that different spatial FD terms have different errors. It is therefore suggested that different FD coefficients to be used to approximate the three spatial derivative terms. In addition, the relative dispersion error in L2-norm is minimized through optimizing FD coefficients using Newton's method. Synthetic examples have demonstrated that this new optimal FD schemes have superior accuracy for elastic wave modelling compared to Taylor-series expansion and optimized space domain FD schemes.
Model-based optimal design of experiments - semidefinite and nonlinear programming formulations.
Duarte, Belmiro P M; Wong, Weng Kee; Oliveira, Nuno M C
2016-02-15
We use mathematical programming tools, such as Semidefinite Programming (SDP) and Nonlinear Programming (NLP)-based formulations to find optimal designs for models used in chemistry and chemical engineering. In particular, we employ local design-based setups in linear models and a Bayesian setup in nonlinear models to find optimal designs. In the latter case, Gaussian Quadrature Formulas (GQFs) are used to evaluate the optimality criterion averaged over the prior distribution for the model parameters. Mathematical programming techniques are then applied to solve the optimization problems. Because such methods require the design space be discretized, we also evaluate the impact of the discretization scheme on the generated design. We demonstrate the techniques for finding D-, A- and E-optimal designs using design problems in biochemical engineering and show the method can also be directly applied to tackle additional issues, such as heteroscedasticity in the model. Our results show that the NLP formulation produces highly efficient D-optimal designs but is computationally less efficient than that required for the SDP formulation. The efficiencies of the generated designs from the two methods are generally very close and so we recommend the SDP formulation in practice.
Study of Super-Twisting sliding mode control for U model based nonlinear system
Directory of Open Access Journals (Sweden)
Jianhua ZHANG
2016-08-01
Full Text Available The Super-Twisting control algorithm is adopted to analyze the U model based nonlinear control system in order to solve the controller design problems of non-affine nonlinear systems. The non-affine nonlinear systems are studied, the neural network approximation of the nonlinear function is performed, and the Super-Twisting control algorithm is used to control. The convergence of the Super-Twisting algorithm is proved by selecting an appropriate Lyapunov function. The Matlab simulation is carried out to verify the feasibility and effectiveness of the described method. The result shows that the output of the controlled system can be tracked in a very short time by using the designed Super-Twisting controller, and the robustness of the controlled system is significantly improved as well.
Hierarchical model-based predictive control of a power plant portfolio
DEFF Research Database (Denmark)
Edlund, Kristian; Bendtsen, Jan Dimon; Jørgensen, John Bagterp
2011-01-01
control” – becomes increasingly important as the ratio of renewable energy in a power system grows. As a consequence, tomorrow's “smart grids” require highly flexible and scalable control systems compared to conventional power systems. This paper proposes a hierarchical model-based predictive control...... design for power system portfolio control, which aims specifically at meeting these demands.The design involves a two-layer hierarchical structure with clearly defined interfaces that facilitate an object-oriented implementation approach. The same hierarchical structure is reflected in the underlying...
Directory of Open Access Journals (Sweden)
Chandranath R. N. Athaudage
2003-09-01
Full Text Available A dynamic programming-based optimization strategy for a temporal decomposition (TD model of speech and its application to low-rate speech coding in storage and broadcasting is presented. In previous work with the spectral stability-based event localizing (SBEL TD algorithm, the event localization was performed based on a spectral stability criterion. Although this approach gave reasonably good results, there was no assurance on the optimality of the event locations. In the present work, we have optimized the event localizing task using a dynamic programming-based optimization strategy. Simulation results show that an improved TD model accuracy can be achieved. A methodology of incorporating the optimized TD algorithm within the standard MELP speech coder for the efficient compression of speech spectral information is also presented. The performance evaluation results revealed that the proposed speech coding scheme achieves 50%Ã¢Â€Â“60% compression of speech spectral information with negligible degradation in the decoded speech quality.
Inverse hydrograph routing optimization model based on the kinematic wave approach
Saghafian, B.; Jannaty, M. H.; Ezami, N.
2015-08-01
This article presents and validates the inverse flood hydrograph routing optimization model under kinematic wave (KW) approximation in order to produce the upstream (inflow) hydrograph, given the downstream (outflow) hydrograph of a river reach. The cost function involves minimization of the error between the observed outflow hydrograph and the corresponding directly routed outflow hydrograph. Decision variables are the inflow hydrograph ordinates. The KW and genetic algorithm (GA) are coupled, representing the selected methods of direct routing and optimization, respectively. A local search technique is also enforced to achieve better agreement of the routed outflow hydrograph with the observed hydrograph. Computer programs handling the direct flood routing, cost function and local search are linked with the optimization model. The results show that the case study inflow hydrographs obtained by the GA were reconstructed with accuracy. It was also concluded that the coupled KW-GA model framework can perform inverse hydrograph routing with numerical stability.
On the optimal control problem for two regions’ macroeconomic model
Directory of Open Access Journals (Sweden)
Surkov Platon G.
2015-12-01
Full Text Available In this paper we consider a model of joint economic growth of two regions. This model bases on the classical Kobb-Douglas function and is described by a nonlinear system of differential equations. The interaction between regions is carried out by changing the balance of trade. The optimal control problem for this system is posed and the Pontryagin maximum principle is used for analysis the problem. The maximized functional represents the global welfare of regions. The numeric solution of the optimal control problem for particular regions is found. The used parameters was obtained from the basic scenario of the MERGE
Model-based Robotic Dynamic Motion Control for the Robonaut 2 Humanoid Robot
Badger, Julia M.; Hulse, Aaron M.; Taylor, Ross C.; Curtis, Andrew W.; Gooding, Dustin R.; Thackston, Allison
2013-01-01
Robonaut 2 (R2), an upper-body dexterous humanoid robot, has been undergoing experimental trials on board the International Space Station (ISS) for more than a year. R2 will soon be upgraded with two climbing appendages, or legs, as well as a new integrated model-based control system. This control system satisfies two important requirements; first, that the robot can allow humans to enter its workspace during operation and second, that the robot can move its large inertia with enough precision to attach to handrails and seat track while climbing around the ISS. This is achieved by a novel control architecture that features an embedded impedance control law on the motor drivers called Multi-Loop control which is tightly interfaced with a kinematic and dynamic coordinated control system nicknamed RoboDyn that resides on centralized processors. This paper presents the integrated control algorithm as well as several test results that illustrate R2's safety features and performance.
Optimal Power Flow Control by Rotary Power Flow Controller
Directory of Open Access Journals (Sweden)
KAZEMI, A.
2011-05-01
Full Text Available This paper presents a new power flow model for rotary power flow controller (RPFC. RPFC injects a series voltage into the transmission line and provides series compensation and phase shifting simultaneously. Therefore, it is able to control the transmission line impedance and the active power flow through it. An RPFC is composed mainly of two rotary phase shifting transformers (RPST and two conventional (series and shunt transformers. Structurally, an RPST consists of two windings (stator and rotor windings. The rotor windings of the two RPSTs are connected in parallel and their stator windings are in series. The injected voltage is proportional to the vector sum of the stator voltages and so its amplitude and angle are affected by the rotor position of the two RPSTs. This paper, describes the steady state operation and single-phase equivalent circuit of the RPFC. Also in this paper, a new power flow model, based on power injection model of flexible ac transmission system (FACTS controllers, suitable for the power flow analysis is introduced. Proposed model is used to solve optimal power flow (OPF problem in IEEE standard test systems incorporating RPFC and the optimal settings and location of the RPFC is determined.
Model-Based Energy Efficiency Optimization of a Low-Temperature Adsorption Dryer
Atuonwu, J.C.; Straten, G. van; Deventer, H.C. van; Boxtel, A.J.B. van
2011-01-01
Low-temperature drying is important for heat-sensitive products, but at these temperatures conventional convective dryers have low energy efficiencies. To overcome this challenge, an energy efficiency optimization procedure is applied to a zeolite adsorption dryer subject to product quality. The
Innovative model-based flow rate optimization for vanadium redox flow batteries
König, S.; Suriyah, M. R.; Leibfried, T.
2016-11-01
In this paper, an innovative approach is presented to optimize the flow rate of a 6-kW vanadium redox flow battery with realistic stack dimensions. Efficiency is derived using a multi-physics battery model and a newly proposed instantaneous efficiency determination technique. An optimization algorithm is applied to identify optimal flow rates for operation points defined by state-of-charge (SoC) and current. The proposed method is evaluated against the conventional approach of applying Faraday's first law of electrolysis, scaled to the so-called flow factor. To make a fair comparison, the flow factor is also optimized by simulating cycles with different charging/discharging currents. It is shown through the obtained results that the efficiency is increased by up to 1.2% points; in addition, discharge capacity is also increased by up to 1.0 kWh or 5.4%. Detailed loss analysis is carried out for the cycles with maximum and minimum charging/discharging currents. It is shown that the proposed method minimizes the sum of losses caused by concentration over-potential, pumping and diffusion. Furthermore, for the deployed Nafion 115 membrane, it is observed that diffusion losses increase with stack SoC. Therefore, to decrease stack SoC and lower diffusion losses, a higher flow rate during charging than during discharging is reasonable.
Analytical Model-Based Design Optimization of a Transverse Flux Machine
Energy Technology Data Exchange (ETDEWEB)
Hasan, Iftekhar; Husain, Tausif; Sozer, Yilmaz; Husain, Iqbal; Muljadi, Eduard
2017-02-16
This paper proposes an analytical machine design tool using magnetic equivalent circuit (MEC)-based particle swarm optimization (PSO) for a double-sided, flux-concentrating transverse flux machine (TFM). The magnetic equivalent circuit method is applied to analytically establish the relationship between the design objective and the input variables of prospective TFM designs. This is computationally less intensive and more time efficient than finite element solvers. A PSO algorithm is then used to design a machine with the highest torque density within the specified power range along with some geometric design constraints. The stator pole length, magnet length, and rotor thickness are the variables that define the optimization search space. Finite element analysis (FEA) was carried out to verify the performance of the MEC-PSO optimized machine. The proposed analytical design tool helps save computation time by at least 50% when compared to commercial FEA-based optimization programs, with results found to be in agreement with less than 5% error.
HCCI Engine Optimization and Control
Energy Technology Data Exchange (ETDEWEB)
Rolf D. Reitz
2005-09-30
The goal of this project was to develop methods to optimize and control Homogeneous-Charge Compression Ignition (HCCI) engines, with emphasis on diesel-fueled engines. HCCI offers the potential of nearly eliminating IC engine NOx and particulate emissions at reduced cost over Compression Ignition Direct Injection engines (CIDI) by controlling pollutant emissions in-cylinder. The project was initiated in January, 2002, and the present report is the final report for work conducted on the project through December 31, 2004. Periodic progress has also been reported at bi-annual working group meetings held at USCAR, Detroit, MI, and at the Sandia National Laboratories. Copies of these presentation materials are available on CD-ROM, as distributed by the Sandia National Labs. In addition, progress has been documented in DOE Advanced Combustion Engine R&D Annual Progress Reports for FY 2002, 2003 and 2004. These reports are included as the Appendices in this Final report.
Power optimized programmable embedded controller
Kamaraju, M; Tilak, A V N; 10.5121/ijcnc.2010.2409
2010-01-01
Now a days, power has become a primary consideration in hardware design, and is critical in computer systems especially for portable devices with high performance and more functionality. Clock-gating is the most common technique used for reducing processor's power. In this work clock gating technique is applied to optimize the power of fully programmable Embedded Controller (PEC) employing RISC architecture. The CPU designed supports i) smart instruction set, ii) I/O port, UART iii) on-chip clocking to provide a range of frequencies , iv) RISC as well as controller concepts. The whole design is captured using VHDL and is implemented on FPGA chip using Xilinx .The architecture and clock gating technique together is found to reduce the power consumption by 33.33% of total power consumed by this chip.
Nonlinear Dynamic Model-Based Adaptive Control of a Solenoid-Valve System
Directory of Open Access Journals (Sweden)
DongBin Lee
2012-01-01
Full Text Available In this paper, a nonlinear model-based adaptive control approach is proposed for a solenoid-valve system. The challenge is that solenoids and butterfly valves have uncertainties in multiple parameters in the nonlinear model; various kinds of physical appearance such as size and stroke, dynamic parameters including inertia, damping, and torque coefficients, and operational parameters especially, pipe diameters and flow velocities. These uncertainties are making the system not only difficult to adjust to the environment, but also further complicated to develop the appropriate control approach for meeting the system objectives. The main contribution of this research is the application of adaptive control theory and Lyapunov-type stability approach to design a controller for a dynamic model of the solenoid-valve system in the presence of those uncertainties. The control objectives such as set-point regulation, parameter compensation, and stability are supposed to be simultaneously accomplished. The error signals are first formulated based on the nonlinear dynamic models and then the control input is developed using the Lyapunov stability-type analysis to obtain the error bounded while overcoming the uncertainties. The parameter groups are updated by adaptation laws using a projection algorithm. Numerical simulation results are shown to demonstrate good performance of the proposed nonlinear model-based adaptive approach and to compare the performance of the same solenoid-valve system with a non-adaptive method as well.
Directory of Open Access Journals (Sweden)
Dawei Chen
2015-01-01
Full Text Available This paper analyzes the impact factors and principles of siting urban refueling stations and proposes a three-stage method. The main objective of the method is to minimize refueling vehicles’ detour time. The first stage aims at identifying the most frequently traveled road segments for siting refueling stations. The second stage focuses on adding additional refueling stations to serve vehicles whose demands are not directly satisfied by the refueling stations identified in the first stage. The last stage further adjusts and optimizes the refueling station plan generated by the first two stages. A genetic simulated annealing algorithm is proposed to solve the optimization problem in the second stage and the results are compared to those from the genetic algorithm. A case study is also conducted to demonstrate the effectiveness of the proposed method and algorithm. The results indicate the proposed method can provide practical and effective solutions that help planners and government agencies make informed refueling station location decisions.
A musculoskeletal shoulder model based on pseudo-inverse and null-space optimization
Terrier, Alexandre; Aeberhard, Martin; Michellod, Yvan; Müllhaupt, Philippe; Gillet, Denis; Farron, Alain; Pioletti, Dominique P.
2010-01-01
The goal of the present work was assess the feasibility of using a pseudo-inverse and null-space optimization approach in the modeling of the shoulder biomechanics. The method was applied to a simplified musculoskeletal shoulder model. The mechanical system consisted in the arm, and the external forces were the arm weight, 6 scapulohumeral muscles and the reaction at the glenohumeral joint, which was considered as a spherical joint. The muscle wrapping was considered around the humeral head a...
Developing an Inspection Optimization Model Based on the Delay-Time Concept
Directory of Open Access Journals (Sweden)
Ehsan Nazemi
2015-01-01
Full Text Available Infrastructures are considered as important facilities required for every country and society to be able to work properly. Aging and deterioration of such structures during their lifetime are a major concern both for maintenance researchers in the academic world and for the practitioners. This concern is mainly because the deterioration increases the maintenance costs dramatically and lowers the reliability, availability, and safety of the structural system. Preventive maintenance and inspection activities are the most usual means for keeping the structure in a good condition. This paper utilizes the concept of delay-time for developing the optimal inspection policy for deteriorating structures. In the proposed stochastic model, discrete times of inspection activities are taken as the decision variables of an optimization problem, in a way that the obtained aperiodic (nonuniform inspection schedule minimizes the total downtime ratio of the structure. To illustrate the model capabilities, various numerical examples are solved and results are compared with the traditional periodic (uniform inspection policies. The results indicate the substantial reduction in system downtime due to the wisely planned inspection schedule and the appropriate utilization of delay-time concept, which is indeed a powerful framework for inspection optimization problems.
Parameter identification theory of a complex model based on global optimization method
Institute of Scientific and Technical Information of China (English)
2008-01-01
With the development of computer technology and numerical simulation technol- ogy, computer aided engineering (CAE) technology has been widely applied to many fields. One of the main obstacles, which hinder the further application of CAE technology, is how to successfully identify the parameters of the selected model. An elementary framework for parameter identification of a complex model is pro-vided in this paper. The framework includes the construction of objective function, the design of the optimization method and the evaluation of the identified results, etc. The parameter identification process is described in this framework, taking the parameter identification of the superplastic constitutive model considering grain growth for Ti-6Al-4V at 927℃ as an example. The objective function is the weighted quadratic sums of the difference between the experimental and computational data for the stress-strain relationship and the grain growth relationship; the designed optimization method is a hybrid global optimization method, which is based on the feature of the objective function and incorporates the strengths of genetic algo-rithm (GA), the Levenberg-Marquardt algorithm and the augmented Gauss-Newton algorithm. The reliability evaluation of parameter identification result is made through the comparison between the calculated and experimental results and be-tween the theoretical values of the parameters and the identified ones.
Directory of Open Access Journals (Sweden)
Hongying Jin
2013-10-01
Full Text Available This paper aims at effectively predicting the dynamic network traffic flow based on quantum-behaved particle swarm optimization algorithm. Firstly, the dynamic network traffic flow prediction problem is analyzed through formal description. Secondly, the structure of the network traffic flow prediction model is given. In this structure, Users can used a computer to start the traffic flow prediction process, and data collecting module can collect and return the data through the destination device. Thirdly, the dynamic network traffic flow prediction model is implemented based on BP Neural Network. Particularly, in this paper, the BP Neural Network is trained by a modified quantum-behaved particle swarm optimization(QPSO. We modified the QPSO by utilizing chaos signals to implement typical logistic mapping and pursuing the fitness function of a particle by a set of optimal parameters. Afterwards, based on the above process, dynamic network traffic flow prediction model is illustrated. Finally, a series of experiments are conduct to make performance evaluation, and related analyses for experimental results are also given
A musculoskeletal shoulder model based on pseudo-inverse and null-space optimization.
Terrier, Alexandre; Aeberhard, Martin; Michellod, Yvan; Mullhaupt, Philippe; Gillet, Denis; Farron, Alain; Pioletti, Dominique P
2010-11-01
The goal of the present work was assess the feasibility of using a pseudo-inverse and null-space optimization approach in the modeling of the shoulder biomechanics. The method was applied to a simplified musculoskeletal shoulder model. The mechanical system consisted in the arm, and the external forces were the arm weight, 6 scapulo-humeral muscles and the reaction at the glenohumeral joint, which was considered as a spherical joint. The muscle wrapping was considered around the humeral head assumed spherical. The dynamical equations were solved in a Lagrangian approach. The mathematical redundancy of the mechanical system was solved in two steps: a pseudo-inverse optimization to minimize the square of the muscle stress and a null-space optimization to restrict the muscle force to physiological limits. Several movements were simulated. The mathematical and numerical aspects of the constrained redundancy problem were efficiently solved by the proposed method. The prediction of muscle moment arms was consistent with cadaveric measurements and the joint reaction force was consistent with in vivo measurements. This preliminary work demonstrated that the developed algorithm has a great potential for more complex musculoskeletal modeling of the shoulder joint. In particular it could be further applied to a non-spherical joint model, allowing for the natural translation of the humeral head in the glenoid fossa.
Performance metric optimization advocates CPFR in supply chains: A system dynamics model based study
Directory of Open Access Journals (Sweden)
Balaji Janamanchi
2016-12-01
Full Text Available Background: Supply Chain partners often find themselves in rather helpless positions, unable to improve their firm’s performance and profitability because their partners although willing to share production information do not fully collaborate in tackling customer order variations as they don’t seem to appreciate the benefits of such collaboration. Methods: We use a two-player (supplier-manufacturer System Dynamics model to study the dynamics to assess the impact and usefulness of supply chain partner collaboration on the supply chain performance measures. Results: Simulation results of supply chain metrics under varied customer order patterns viz., basecase, random normal, random uniform, random upwardtrend, and random downwardtrend under (a basecase, (b independent optimization by manufacturer, and (c collaborative optimization by manufacturer and supplier, are obtained to contrast them to develop useful insights. Conclusions: Focus on obtaining improved inventory turns with optimization techniques provides some viable options to managers and makes a strong case for increased collaborative planning forecasting and replenishment (CPFR in supply chains. Despite the differences in the inventory management practices that it was contrasted with, CPFR has proven to be beneficial in a supply chain environment for all SC partners.
Optimal control of induction heating processes
Rapoport, Edgar
2006-01-01
This book introduces new approaches to solving optimal control problems in induction heating process applications. Optimal Control of Induction Heating Processes demonstrates how to apply and use new optimization techniques for different types of induction heating installations. Focusing on practical methods for solving real engineering optimization problems, the text features a variety of specific optimization examples for induction heater modes and designs, particularly those used in industrial applications. The book describes basic physical phenomena in induction heating and induction
Design of Takagi-Sugeno fuzzy model based nonlinear sliding model controller
Institute of Scientific and Technical Information of China (English)
Xu Yong; Chen Zengqiang; Yuan Zhuzhi
2005-01-01
A design method is presented for Takagi-Sugeno (T-S) fuzzy model based nonlinear sliding model controller. First, the closed-loop fuzzy system is divided into a set of dominant local linear systems according to operating sub-regions. In each sub-region the fuzzy system consists of nominal linear system and a group of interacting systems. Then the controller composed two parts is designed. One part is designed to control the nominal system, the other is designed to control the interacting systems with sliding mode theory. The proposed controller can improve the robustness and guarantee tracking performance of the fuzzy system. Stability is guaranteed without finding a common positive definite matrix.
Feedback Scheduling of Model-based Networked Control Systems with Flexible Workload
Institute of Scientific and Technical Information of China (English)
Xian-Ming Tang; Jin-Shou Yu
2008-01-01
In this paper, a novel control structure called feedback scheduling of model-based networked control systems is proposed to cope with a flexible network load and resource constraints. The state update time is adjusted according to the real-time network congestion situation. State observer is used under the situation where the state of the controlled plant could not be acquired. The stability criterion of the proposed structure is proved with time-varying state update time. On the basis of the stability of the novel system structure, the compromise between the control performance and the network utilization is realized by using feedback scheduler.Examples are provided to show the advantage of the proposed control structure.
Galanis, George; Famelis, Ioannis; Kalogeri, Christina
2014-10-01
The last years a new highly demanding framework has been set for environmental sciences and applied mathematics as a result of the needs posed by issues that are of interest not only of the scientific community but of today's society in general: global warming, renewable resources of energy, natural hazards can be listed among them. Two are the main directions that the research community follows today in order to address the above problems: The utilization of environmental observations obtained from in situ or remote sensing sources and the meteorological-oceanographic simulations based on physical-mathematical models. In particular, trying to reach credible local forecasts the two previous data sources are combined by algorithms that are essentially based on optimization processes. The conventional approaches in this framework usually neglect the topological-geometrical properties of the space of the data under study by adopting least square methods based on classical Euclidean geometry tools. In the present work new optimization techniques are discussed making use of methodologies from a rapidly advancing branch of applied Mathematics, the Information Geometry. The latter prove that the distributions of data sets are elements of non-Euclidean structures in which the underlying geometry may differ significantly from the classical one. Geometrical entities like Riemannian metrics, distances, curvature and affine connections are utilized in order to define the optimum distributions fitting to the environmental data at specific areas and to form differential systems that describes the optimization procedures. The methodology proposed is clarified by an application for wind speed forecasts in the Kefaloniaisland, Greece.
Institute of Scientific and Technical Information of China (English)
Ali Darvishi; Razieh Davand; Farhad Khorasheh; Moslem Fattahi
2016-01-01
An industrial scale propylene production via oxidative dehydrogenation of propane (ODHP) in multi-tubular re-actors was modeled. Multi-tubular fixed-bed reactor used for ODHP process, employing 10000 of smal diameter tubes immersed in a shel through a proper coolant flows. Herein, a theory-based pseudo-homogeneous model to describe the operation of a fixed bed reactor for the ODHP to correspondence olefin over V2O5/γ-Al2O3 catalyst was presented. Steady state one dimensional model has been developed to identify the operation parameters and to describe the propane and oxygen conversions, gas process and coolant temperatures, as well as other pa-rameters affecting the reactor performance such as pressure. Furthermore, the applied model showed that a double-bed multitubular reactor with intermediate air injection scheme was superior to a single-bed design due to the increasing of propylene selectivity while operating under lower oxygen partial pressures resulting in propane conversion of about 37.3%. The optimized length of the reactor needed to reach 100%conversion of the oxygen was theoretically determined. For the single-bed reactor the optimized length of 11.96 m including 0.5 m of inert section at the entrance region and for the double-bed reactor design the optimized lengths of 5.72 m for the first and 7.32 m for the second reactor were calculated. Ultimately, the use of a distributed oxygen feed with limited number of injection points indicated a significant improvement on the reactor performance in terms of propane conversion and propylene selectivity. Besides, this concept could overcome the reactor run-away temperature problem and enabled operations at the wider range of conditions to obtain enhanced propyl-ene production in an industrial scale reactor.
Numerical Methods for a Kohn-Sham Density Functional Model Based on Optimal Transport.
Chen, Huajie; Friesecke, Gero; Mendl, Christian B
2014-10-14
In this paper, we study numerical discretizations to solve density functional models in the "strictly correlated electrons" (SCE) framework. Unlike previous studies, our work is not restricted to radially symmetric densities. In the SCE framework, the exchange-correlation functional encodes the effects of the strong correlation regime by minimizing the pairwise Coulomb repulsion, resulting in an optimal transport problem. We give a mathematical derivation of the self-consistent Kohn-Sham-SCE equations, construct an efficient numerical discretization for this type of problem for N = 2 electrons, and apply it to the H2 molecule in its dissociating limit.
Toward Model-Based Control of Non-linear Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat; Jensen, Tom Nørgaard; Kallesøe, Carsten
2013-01-01
consumption. To have a better understanding of leakage in WSSs, to control pressure and leakage effectively, and for optimal design of WSSs, suitable modeling is an important prerequisite. In this paper a model with the main objective of pressure control and consequently leakage reduction is presented...
State Estimation and Model-Based Control of a Pilot Anaerobic Digestion Reactor
Directory of Open Access Journals (Sweden)
Finn Haugen
2014-01-01
Full Text Available A state estimator and various model-based control systems have been designed for a real anaerobic digestion (AD pilot reactor fed with dairy manure. The model used is a modified Hill model which is a relatively simple dynamical AD process model. The state estimator is an Unscented Kalman Filter (UKF which uses only methane gas flow measurement to update its states. The model and the state estimates are used in different control systems. One of the control systems aims at controlling the methane gas flow to a setpoint. Simulations indicate that the setpoint tracking performance of a predictive control system is considerably better comparing with PI control, while disturbance compensation is not much better. Consequently, assuming the setpoint is constant, the PI controller competes well with the predictive controller. A successful application of predictive control of the real reactor is presented. Also, three different control systems aiming at retaining the reactor at an operating point where the volatile fatty acids (VFA concentration has a maximum, safe value are designed. A simulation study indicates that the best control solution among the three alternatives is PI control based on feedback from estimated VFA.
Shen, Chengcheng; Shi, Honghua; Liu, Yongzhi; Li, Fen; Ding, Dewen
2016-07-01
Marine ecosystem dynamic models (MEDMs) are important tools for the simulation and prediction of marine ecosystems. This article summarizes the methods and strategies used for the improvement and assessment of MEDM skill, and it attempts to establish a technical framework to inspire further ideas concerning MEDM skill improvement. The skill of MEDMs can be improved by parameter optimization (PO), which is an important step in model calibration. An efficient approach to solve the problem of PO constrained by MEDMs is the global treatment of both sensitivity analysis and PO. Model validation is an essential step following PO, which validates the efficiency of model calibration by analyzing and estimating the goodness-of-fit of the optimized model. Additionally, by focusing on the degree of impact of various factors on model skill, model uncertainty analysis can supply model users with a quantitative assessment of model confidence. Research on MEDMs is ongoing; however, improvement in model skill still lacks global treatments and its assessment is not integrated. Thus, the predictive performance of MEDMs is not strong and model uncertainties lack quantitative descriptions, limiting their application. Therefore, a large number of case studies concerning model skill should be performed to promote the development of a scientific and normative technical framework for the improvement of MEDM skill.
Optimal control of sun tracking solar concentrators
Hughes, R. O.
1979-01-01
Application of the modern control theory to derive an optimal sun tracking control for a point focusing solar concentrator is presented. A standard tracking problem converted to regulator problem using a sun rate input achieves an almost zero steady state tracking error with the optimal control formulation. However, these control techniques are costly because optimal type algorithms require large computing systems, thus they will be used mainly as comparison standards for other types of control algorithms and help in their development.
Temperature controller optimization by computational intelligence
Directory of Open Access Journals (Sweden)
Ćojbašić Žarko M.
2016-01-01
Full Text Available In this paper a temperature control system for an automated educational classroom is optimized with several advanced computationally intelligent methods. Controller development and optimization has been based on developed and extensively tested mathematical and simulation model of the observed object. For the observed object cascade P-PI temperature controller has been designed and conventionally tuned. To improve performance and energy efficiency of the system, several metaheuristic optimizations of the controller have been attempted, namely genetic algorithm optimization, simulated annealing optimization, particle swarm optimization and ant colony optimization. Efficiency of the best results obtained with proposed computationally intelligent optimization methods has been compared with conventional controller tuning. Results presented in this paper demonstrate that heuristic optimization of advanced temperature controller can provide improved energy efficiency along with other performance improvements and improvements regarding equipment wear. Not only that presented methodology provides for determination and tuning of the core controller, but it also allows that advanced control concepts such as anti-windup controller gain are optimized simultaneously, which is of significant importance since interrelation of all control system parameters has important influence on the stability and performance of the system as a whole. Based on the results obtained, general conclusions are presented indicating that meta-heuristic computationally intelligent optimization of heating, ventilation, and air conditioning control systems is a feasible concept with strong potential in providing improved performance, comfort and energy efficiency. [Projekat Ministarstva nauke Republike Srbije, br. TR 33047 i br. TR 35016
Model-Based Development and Evaluation of Control for Complex Multi-Domain Systems
DEFF Research Database (Denmark)
Grujic, Ivan; Nilsson, Rene
A Cyber-Physical System (CPS) incorporates sensing, actuating, computing and communicative capabilities, which are often combined to control the system. The development of CPSs poses a challenge, since the complexity of the physical system dynamics must be taken into account when designing...... the control application. The physical system dynamics are often defined within mechanical and electrical engineering domains, with the control application residing in software and control engineering domains. Therefore, such a system can be considered multi-domain. With the constant increase in the complexity...... of such systems, caused by technological advances in all domains, new ways of approaching multi- domain system development are needed. One methodology, which excels in complexity management, is model-based development. Multidomain systems require collaborative modeling, where the physical system dynamics...
Model-based control of vortex shedding at low Reynolds numbers
Illingworth, Simon J.
2016-10-01
Model-based feedback control of vortex shedding at low Reynolds numbers is considered. The feedback signal is provided by velocity measurements in the wake, and actuation is achieved using blowing and suction on the cylinder's surface. Using two-dimensional direct numerical simulations and reduced-order modelling techniques, linear models of the wake are formed at Reynolds numbers between 45 and 110. These models are used to design feedback controllers using {H}_∞ loop-shaping. Complete suppression of shedding is demonstrated up to Re = 110—both for a single-sensor arrangement and for a three-sensor arrangement. The robustness of the feedback controllers is also investigated by applying them over a range of off-design Reynolds numbers, and good robustness properties are seen. It is also observed that it becomes increasingly difficult to achieve acceptable control performance—measured in a suitable way—as Reynolds number increases.
Modeling-based optimization study for an EDXRD system in a portable configuration
Energy Technology Data Exchange (ETDEWEB)
Peterzol, Angela, E-mail: angela_peterzol@yahoo.it [CNDRI (Nondestructive Testing using Ionizing Radiation) Laboratory, INSA-Lyon, 69621 Villeurbanne (France); Duvauchelle, Philippe; Kaftandjian, Valerie [CNDRI (Nondestructive Testing using Ionizing Radiation) Laboratory, INSA-Lyon, 69621 Villeurbanne (France); Ponard, Pascal [THALES Components and Subsystems, 2 rue Marcel Dassault 78941 Velizy cedex (France)
2011-10-21
Energy-Dispersive X-ray Diffraction (EDXRD) is well suited for the detection of narcotics and a wide range of explosives. This technique, combined with the dual-energy tomosynthesis, has been used for verification of a novel portable imaging system, the aim of which is characterization of dangerous/illicit materials inside objects. We present the design methodology and optimization study using EDXRD modality. In order to evaluate the experimental conditions best suited for system purposes, kinematic theory of diffraction has been exploited to model the height and shape of diffraction patterns. From the simulation-based analysis a diffraction angle of 2.75{sup o}{+-}0.10{sup o} and an X-ray tube voltage {<=}160 kV have been selected.
Constrained Optimization and Optimal Control for Partial Differential Equations
Leugering, Günter; Griewank, Andreas
2012-01-01
This special volume focuses on optimization and control of processes governed by partial differential equations. The contributors are mostly participants of the DFG-priority program 1253: Optimization with PDE-constraints which is active since 2006. The book is organized in sections which cover almost the entire spectrum of modern research in this emerging field. Indeed, even though the field of optimal control and optimization for PDE-constrained problems has undergone a dramatic increase of interest during the last four decades, a full theory for nonlinear problems is still lacking. The cont
Fuzzy logic control and optimization system
Lou, Xinsheng [West Hartford, CT
2012-04-17
A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
AN APPLICATION OF OPTIMAL CONTROL THEORY.
The purpose of this article is to show that optimal control theory can be used to develop a control strategy for a practical system, namely a distillation column. The approach will be to model the complex system with a simple model, use optimal control theory to determine a control strategy for the simple model, and then apply the results to the original system. (Author)
Multi-Agent Model-Based Optimization for Future Electrical Grids
Bajracharya, G.
2014-01-01
The electricity grid is one of the most complex systems created by human beings. It consists of an intricate network of components such as generators, transmission and distribution lines, transformers, breakers, various controllers, and various measurement and monitoring systems. The grid has been g
Design of a dynamic positioning system using model-based control
Directory of Open Access Journals (Sweden)
Asgeir J. Sørensen
1996-04-01
Full Text Available A dynamic positioning (DP system includes different control functions for automatic positioning and guidance of marine vessels by means of thruster and propeller actions. This paper describes the control functions which provide station-keeping and tracking. The DP controller is designed using model-based control, where a new modified LQG feedback controller and a model reference feedforward controller are applied. A reference model calculates appropriate reference trajectories. Since it is not desirable nor even possible to counteract the wave-frequency movement caused by first-order wave loads, the control action of the propulsion system should be produced by the low frequency part of the vessel movement caused by current, wind and second-order mean and slowly varying wave loads. A Kalman filter based state estimator and a Luenberger observer are used to compute the low-frequency feedback and feedforward control signals. Full-scale experiments with a supply vessel demonstrate the performance of the proposed controller.
A Quasi Time Optimal Receding Horizon Control
Bania, Piotr
2007-01-01
This paper presents a quasi time optimal receding horizon control algorithm. The proposed algorithm generates near time optimal control when the state of the system is far from the target. When the state attains a certain neighbourhood of the aim, it begins the adaptation of the cost function. The purpose of this adaptation is to move from the time optimal control to the stabilizing control. Sufficient conditions for the stability of the closed loop system and the manner of the adaptation of ...
A novel model-based control strategy for aerobic filamentous fungal fed-batch fermentation processes
DEFF Research Database (Denmark)
Mears, Lisa; Stocks, Stuart M.; Albaek, Mads O.
2017-01-01
A novel model-based control strategy has been developed for filamentous fungal fed-batch fermentation processes. The system of interest is a pilot scale (550 L) filamentous fungus process operating at Novozymes A/S. In such processes, it is desirable to maximize the total product achieved...... in a batch in a defined process time. In order to achieve this goal, it is important to maximize both the product concentration, and also the total final mass in the fed-batch system. To this end, we describe the development of a control strategy which aims to achieve maximum tank fill, while avoiding oxygen...... limited conditions. This requires a two stage approach: (i) calculation of the tank start fill; and (ii) on-line control in order to maximize fill subject to oxygen transfer limitations. First, a mechanistic model was applied off-line in order to determine the appropriate start fill for processes...
Reference model based consensus control of second-order multi-agent systems
Institute of Scientific and Technical Information of China (English)
Li Jian-Zhen
2011-01-01
This paper deals with the consensus problem of multi-agent systems with second-order dynamics. The objective is to design algorithms such that all agents will have same positions and velocities. First, a reference model based consensus algorithm is proposed. It is proved that the consensus can be achieved if the communication graph has a spanning tree. Different from most of the consensus algorithms proposed in the literature, the parameters of the control laws are different among agents. Therefore, each agent can design its control law independently. Secondly, it gives a consensus algorithm for the case that the velocities of the agents are not available. Thirdly, the effectiveness of the input delay and the communication delay is considered. It shows that consensus can be achieved if the input delay of every agent is smaller than a bound related to parameters in its control law. Finally, some numerical examples are given to illustrate the proposed results.
Model Based Control System Design Using SysML, Simulink, and Computer Algebra System
Directory of Open Access Journals (Sweden)
Takashi Sakairi
2013-01-01
Full Text Available The Systems Modeling Language (SysML is a standard, general-purpose, modeling language for model-based systems engineering (MBSE. SysML supports the specification, analysis, and design of a broad range of complex systems such as control systems. The authors demonstrate how they can integrate a SysML modeling tool (IBM Rational Rhapsody with a proprietary simulation tool (MathWorks Simulink and a Computer Algebra System (CAS to validate system specification. The integration with Simulink enables users to perform systems engineering process in a SysML model, while designing continuous control algorithms and plant behavior in Simulink, and to validate the behavior by simulating the overall composition in Simulink. The integration with a CAS enables the evaluation of mathematical constraints defined in SysML parametric diagrams. The authors also show the overall approach using a Dual Clutch Transmission (DCT and a Cruise Control System as examples.
Optimal control of CPR procedure using hemodynamic circulation model
Lenhart, Suzanne M.; Protopopescu, Vladimir A.; Jung, Eunok
2007-12-25
A method for determining a chest pressure profile for cardiopulmonary resuscitation (CPR) includes the steps of representing a hemodynamic circulation model based on a plurality of difference equations for a patient, applying an optimal control (OC) algorithm to the circulation model, and determining a chest pressure profile. The chest pressure profile defines a timing pattern of externally applied pressure to a chest of the patient to maximize blood flow through the patient. A CPR device includes a chest compressor, a controller communicably connected to the chest compressor, and a computer communicably connected to the controller. The computer determines the chest pressure profile by applying an OC algorithm to a hemodynamic circulation model based on the plurality of difference equations.
On optimal temperature control in hothouses
Astashova, I. V.; Filinovskiy, A. V.; Lashin, D. A.
2017-07-01
We study the problem of control over the temperature conditions in industrial hothouses. We consider a model based on the one-dimensional heat equation on a bounded interval with quadratic cost functional, examine the existence and uniqueness of a control function from a prescribed set, and study the structure of the set of accessible temperature functions.
Directory of Open Access Journals (Sweden)
Baofeng Shi
2016-01-01
Full Text Available This paper introduces a novel decision assessment method which is suitable for customers’ credit risk evaluation and credit decision. First of all, the paper creates an optimal credit rating model, and it consisted of an objective function and two constraint conditions. The first constraint condition of the strictly increasing LGDs eliminates the unreasonable phenomenon that the higher the credit rating is, the higher the LGD (loss given default is. Secondly, on the basis of the credit rating results, a credit decision-making assessment model based on measuring the acceptable maximum LGD of commercial banks is established. Thirdly, empirical results using the data on 2817 farmers’ microfinance of a Chinese commercial bank suggest that the proposed approach can accurately find out the good customers from all the loan applications. Moreover, our approach contributes to providing a reference for decision assessment of customers in other commercial banks in the world.
Shapiro, Carl R.; Meyers, Johan; Meneveau, Charles; Gayme, Dennice F.
2016-09-01
We investigate the use of wind farms to provide secondary frequency regulation for a power grid. Our approach uses model-based receding horizon control of a wind farm that is tested using a large eddy simulation (LES) framework. In order to enable real-time implementation, the control actions are computed based on a time-varying one-dimensional wake model. This model describes wake advection and interactions, both of which play an important role in wind farm power production. This controller is implemented in an LES model of an 84-turbine wind farm represented by actuator disk turbine models. Differences between the velocities at each turbine predicted by the wake model and measured in LES are used for closed-loop feedback. The controller is tested on two types of regulation signals, “RegA” and “RegD”, obtained from PJM, an independent system operator in the eastern United States. Composite performance scores, which are used by PJM to qualify plants for regulation, are used to evaluate the performance of the controlled wind farm. Our results demonstrate that the controlled wind farm consistently performs well, passing the qualification threshold for all fastacting RegD signals. For the RegA signal, which changes over slower time scales, the controlled wind farm's average performance surpasses the threshold, but further work is needed to enable the controlled system to achieve qualifying performance all of the time.
Energy Technology Data Exchange (ETDEWEB)
Aditya Kumar
2010-12-30
This report summarizes the achievements and final results of this program. The objective of this program is to develop a comprehensive systems approach to integrated design of sensing and control systems for an Integrated Gasification Combined Cycle (IGCC) plant, using advanced model-based techniques. In particular, this program is focused on the model-based sensing and control system design for the core gasification section of an IGCC plant. The overall approach consists of (i) developing a first-principles physics-based dynamic model of the gasification section, (ii) performing model-reduction where needed to derive low-order models suitable for controls analysis and design, (iii) developing a sensing system solution combining online sensors with model-based estimation for important process variables not measured directly, and (iv) optimizing the steady-state and transient operation of the plant for normal operation as well as for startup using model predictive controls (MPC). Initially, available process unit models were implemented in a common platform using Matlab/Simulink{reg_sign}, and appropriate model reduction and model updates were performed to obtain the overall gasification section dynamic model. Also, a set of sensor packages were developed through extensive lab testing and implemented in the Tampa Electric Company IGCC plant at Polk power station in 2009, to measure temperature and strain in the radiant syngas cooler (RSC). Plant operation data was also used to validate the overall gasification section model. The overall dynamic model was then used to develop a sensing solution including a set of online sensors coupled with model-based estimation using nonlinear extended Kalman filter (EKF). Its performance in terms of estimating key unmeasured variables like gasifier temperature, carbon conversion, etc., was studied through extensive simulations in the presence sensing errors (noise and bias) and modeling errors (e.g. unknown gasifier kinetics, RSC
Davda, Jasmine P; Dodds, Michael G; Gibbs, Megan A; Wisdom, Wendy; Gibbs, John
2014-01-01
The objectives of this retrospective analysis were (1) to characterize the population pharmacokinetics (popPK) of four different monoclonal antibodies (mAbs) in a combined analysis of individual data collected during first-in-human (FIH) studies and (2) to provide a scientific rationale for prospective design of FIH studies with mAbs. The data set was composed of 171 subjects contributing a total of 2716 mAb serum concentrations, following intravenous (IV) and subcutaneous (SC) doses. mAb PK was described by an open 2-compartment model with first-order elimination from the central compartment and a depot compartment with first-order absorption. Parameter values obtained from the popPK model were further used to generate optimal sampling times for a single dose study. A robust fit to the combined data from four mAbs was obtained using the 2-compartment model. Population parameter estimates for systemic clearance and central volume of distribution were 0.20 L/day and 3.6 L with intersubject variability of 31% and 34%, respectively. The random residual error was 14%. Differences (> 2-fold) in PK parameters were not apparent across mAbs. Rich designs (22 samples/subject), minimal designs for popPK (5 samples/subject), and optimal designs for non-compartmental analysis (NCA) and popPK (10 samples/subject) were examined by stochastic simulation and estimation. Single-dose PK studies for linear mAbs executed using the optimal designs are expected to yield high-quality model estimates, and accurate capture of NCA estimations. This model-based meta-analysis has determined typical popPK values for four mAbs with linear elimination and enabled prospective optimization of FIH study designs, potentially improving the efficiency of FIH studies for this class of therapeutics.
Kuizenga, Merel H.; Vereecke, Hugo E. M.; Struys, Michel M. R. F.
2016-01-01
Purpose of review Drug administration might be optimized by incorporating pharmacokinetic-dynamic (PK/PD) principles and control engineering theories. This review gives an update of the actual status of target-controlled infusion (TCI) and closed-loop computer-controlled drug administration and the
Energy Technology Data Exchange (ETDEWEB)
Kohler, Christian [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2012-04-01
Complex glazing systems such as venetian blinds, fritted glass and woven shades require more detailed optical and thermal input data for their components than specular non light-redirecting glazing systems. Various methods for measuring these data sets are described in this paper. These data sets are used in multiple simulation tools to model the thermal and optical properties of complex glazing systems. The output from these tools can be used to generate simplified rating values or as an input to other simulation tools such as whole building annual energy programs, or lighting analysis tools. I also describe some of the challenges of creating a rating system for these products and which factors affect this rating. A potential future direction of simulation and building operations is model based predictive controls, where detailed computer models are run in real-time, receiving data for an actual building and providing control input to building elements such as shades.
A Controlled Particle Filter for Global Optimization
Zhang, Chi; Taghvaei, Amirhossein; Mehta, Prashant G.
2017-01-01
A particle filter is introduced to numerically approximate a solution of the global optimization problem. The theoretical significance of this work comes from its variational aspects: (i) the proposed particle filter is a controlled interacting particle system where the control input represents the solution of a mean-field type optimal control problem; and (ii) the associated density transport is shown to be a gradient flow (steepest descent) for the optimal value function, with respect to th...
Efficiency optimized control of medium-size induction motor drives
DEFF Research Database (Denmark)
Abrahamsen, F.; Blaabjerg, Frede; Pedersen, John Kim
2000-01-01
The efficiency of a variable speed induction motor drive can be optimized by adaption of the motor flux level to the load torque. In small drives (... not be disregarded without further analysis. The importance of the converter losses on efficiency optimization in medium-size drives is analyzed in this paper. Based on the experiments with a 90 kW drive it is found that it is not critical if the converter losses are neglected in the control, except...... that the robustness towards load disturbances may unnecessarily be reduced. Both displacement power factor and model-based efficiency optimizing control methods perform well in medium-size drives. The last strategy is also tested on a 22 kW drive with good results....
Efficiency optimized control of medium-size induction motor drives
DEFF Research Database (Denmark)
Abrahamsen, F.; Blaabjerg, Frede; Pedersen, John Kim;
2000-01-01
The efficiency of a variable speed induction motor drive can be optimized by adaption of the motor flux level to the load torque. In small drives (... not be disregarded without further analysis. The importance of the converter losses on efficiency optimization in medium-size drives is analyzed in this paper. Based on the experiments with a 90 kW drive it is found that it is not critical if the converter losses are neglected in the control, except...... that the robustness towards load disturbances may unnecessarily be reduced. Both displacement power factor and model-based efficiency optimizing control methods perform well in medium-size drives. The last strategy is also tested on a 22 kW drive with good results....
Bilinear Approximate Model-Based Robust Lyapunov Control for Parabolic Distributed Collectors
Elmetennani, Shahrazed
2016-11-09
This brief addresses the control problem of distributed parabolic solar collectors in order to maintain the field outlet temperature around a desired level. The objective is to design an efficient controller to force the outlet fluid temperature to track a set reference despite the unpredictable varying working conditions. In this brief, a bilinear model-based robust Lyapunov control is proposed to achieve the control objectives with robustness to the environmental changes. The bilinear model is a reduced order approximate representation of the solar collector, which is derived from the hyperbolic distributed equation describing the heat transport dynamics by means of a dynamical Gaussian interpolation. Using the bilinear approximate model, a robust control strategy is designed applying Lyapunov stability theory combined with a phenomenological representation of the system in order to stabilize the tracking error. On the basis of the error analysis, simulation results show good performance of the proposed controller, in terms of tracking accuracy and convergence time, with limited measurement even under unfavorable working conditions. Furthermore, the presented work is of interest for a large category of dynamical systems knowing that the solar collector is representative of physical systems involving transport phenomena constrained by unknown external disturbances.
Hormiga, José A; Almansa, Eduardo; Sykes, António V; Torres, Néstor V
2010-09-01
The culture of common octopus (Octopus vulgaris), one important candidate to the aquaculture diversification, faces significant difficulties, mainly related with an inadequate first development stages diet. A mathematical model integrating disperse information on the nutrient composition throughout the species ontogenic development as well as on the effects of broodstock feeding and diet composition data of O. vulgaris, allowed us to predict the time evolution of paralarvae nutritional composition in terms of protein and lipid fractions and to design an optimal diet composition with the objective to ensure the maximal survival. The optimization routine showed that a diet based on the spider crab (Maja squinado) zoea composition is the most suitable for reaching the best survival rates. Results are verified by comparison with available experimental data. The obtained results and the prospective developments are a good example of how the systemic, quantitative model based approach can be used to analyse and contribute to the understanding of complex biological systems. Copyright 2009 Elsevier B.V. All rights reserved.
Optimizing Dynamical Network Structure for Pinning Control
Orouskhani, Yasin; Jalili, Mahdi; Yu, Xinghuo
2016-04-01
Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i) optimizing the locations of driver nodes, ii) optimizing the feedback gains, iii) optimizing simultaneously the locations of driver nodes and feedback gains, and iv) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks, and model scale-free and small-world networks. Extensive simulation results show that the optimal placement of driver nodes significantly outperforms heuristic methods including placing drivers based on various centrality measures (degree, betweenness, closeness and clustering coefficient). The pinning controllability is further improved by optimizing the feedback gains. We also show that one can significantly improve the controllability by optimizing the connection weights.
Adaptive optimization and control using neural networks
Energy Technology Data Exchange (ETDEWEB)
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
Neubert, M.; Winkler, J.
2012-12-01
This contribution continues an article series [1,2] about the nonlinear model-based control of the Czochralski crystal growth process. The key idea of the presented approach is to use a sophisticated combination of nonlinear model-based and conventional (linear) PI controllers for tracking of both, crystal radius and growth rate. Using heater power and pulling speed as manipulated variables several controller structures are possible. The present part tries to systematize the properties of the materials to be grown in order to get unambiguous decision criteria for a most profitable choice of the controller structure. For this purpose a material specific constant M called interface mobility and a more process specific constant S called system response number are introduced. While the first one summarizes important material properties like thermal conductivity and latent heat the latter one characterizes the process by evaluating the average axial thermal gradients at the phase boundary and the actual growth rate at which the crystal is grown. Furthermore these characteristic numbers are useful for establishing a scheduling strategy for the PI controller parameters in order to improve the controller performance. Finally, both numbers give a better understanding of the general thermal system dynamics of the Czochralski technique.
Optimal Control of Switched Systems based on Bezier Control Points
FatemeGhomanjani; Mohammad HadiFarahi
2012-01-01
This paper presents a new approach for solving optimal control problems for switched systems. We focus on problems in which a pre-specified sequence of active subsystems is given. For such problems, we need to seek both the optimal switching instants and the optimal continuous inputs. A Bezier control points method is applied for solving an optimal control problem which is supervised by a switched dynamic system. Two steps of approximation exist here. First, the time interval is divided into ...
DEFF Research Database (Denmark)
Christiansen, Søren; Tabatabaeipour, Seyed Mojtaba; Bak, Thomas;
2013-01-01
Floating wind turbines are considered as a new and promising solution for reaching higher wind resources beyond the water depth restriction of monopile wind turbines. But on a floating structure, the wave-induced loads significantly increase the oscillations of the structure. Furthermore, using...... a controller designed for an onshore wind turbine yields instability in the fore-aft rotation. In this paper, we propose a general framework, where a reference model models the desired closed-loop behavior of the system. Model predictive control combined with a state estimator finds the optimal rotor blade...... compared to a baseline floating wind turbine controller at the cost of more pitch action....
Nonlinear model-based control algorithm for a distillation column using software sensor.
Jana, Amiya Kumar; Samanta, Amar Nath; Ganguly, Saibal
2005-04-01
This paper presents the design of model-based globally linearizing control (GLC) structure for a distillation process within the differential geometric framework. The model of a nonideal binary distillation column, whose characteristics were highly nonlinear and strongly interactive, is used as a real process. The classical GLC law is comprised of a transformer (input-output linearizing state feedback), a nonlinear state observer, and an external PI controller. The tray temperature based short-cut observer (TTBSCO) has been used as a state estimator within the control structure, in which all tray temperatures were considered to be measured. Accordingly, the liquid phase composition of each tray was calculated online using the derived temperature-composition correlation. In the simulation experiment, the proposed GLC coupled with TTBSCO (GLC-TTBSCO) outperformed a conventional PI controller based on servo performances with and without measurement noise as well as on regulatory behaviors. In the subsequent part, the GLC law has been synthesized in conjunction with tray temperature based reduced-order observer (GLC-TTBROO) where the distillate and bottom compositions of the distillation process have been inferred from top and bottom product temperatures respectively, which were measured online. Finally, the comparative performance of the GLC-TTBSCO and the GLC-TTBROO has been addressed under parametric uncertainty and the GLC-TTBSCO algorithm provided slightly better performance than the GLC-TTBROO. The resulting control laws are rather general and can be easily adopted for other binary distillation columns.
Model-based control of the temporal patterns of intracellular signaling in silico
Murakami, Yohei; Koyama, Masanori; Oba, Shigeyuki; Kuroda, Shinya; Ishii, Shin
2017-01-01
The functions of intracellular signal transduction systems are determined by the temporal behavior of intracellular molecules and their interactions. Of the many dynamical properties of the system, the relationship between the dynamics of upstream molecules and downstream molecules is particularly important. A useful tool in understanding this relationship is a methodology to control the dynamics of intracellular molecules with an extracellular stimulus. However, this is a difficult task because the relationship between the levels of upstream molecules and those of downstream molecules is often not only stochastic, but also time-inhomogeneous, nonlinear, and not one-to-one. In this paper, we present an easy-to-implement model-based control method that makes the target downstream molecule to trace a desired time course by changing the concentration of a controllable upstream molecule. Our method uses predictions from Monte Carlo simulations of the model to decide the strength of the stimulus, while using a particle-based approach to make inferences regarding unobservable states. We applied our method to in silico control problems of insulin-dependent AKT pathway model and EGF-dependent Akt pathway model with system noise. We show that our method can robustly control the dynamics of the intracellular molecules against unknown system noise of various strengths, even in the absence of complete knowledge of the true model of the target system. PMID:28275530
Model-Based Integrated Process Design and Controller Design of Chemical Processes
DEFF Research Database (Denmark)
Abd Hamid, Mohd Kamaruddin Bin
and verification. Using thermodynamic and process insights, a bounded search space is first identified. This feasible solution space is further reduced to satisfy the process design and controller design constraints in sub-problems 2 and 3, respectively, until in the final sub-problem all feasible candidates...... may or may not be able to find the optimal solution, depending on the performance of their search algorithms and computational demand, this method using the attainable region and driving force concepts is simple and able to find at least near-optimal designs (if not optimal) to IPDC problems...... tested using a series of case studies that represents three different systems in chemical processes: a single reactor system, a single separator system and a reactor-separator-recycle system....
Optimal Control and Optimization of Stochastic Supply Chain Systems
Song, Dong-Ping
2013-01-01
Optimal Control and Optimization of Stochastic Supply Chain Systems examines its subject in the context of the presence of a variety of uncertainties. Numerous examples with intuitive illustrations and tables are provided, to demonstrate the structural characteristics of the optimal control policies in various stochastic supply chains and to show how to make use of these characteristics to construct easy-to-operate sub-optimal policies. In Part I, a general introduction to stochastic supply chain systems is provided. Analytical models for various stochastic supply chain systems are formulated and analysed in Part II. In Part III the structural knowledge of the optimal control policies obtained in Part II is utilized to construct easy-to-operate sub-optimal control policies for various stochastic supply chain systems accordingly. Finally, Part IV discusses the optimisation of threshold-type control policies and their robustness. A key feature of the book is its tying together of ...
Synthesis of Model Based Robust Stabilizing Reactor Power Controller for Nuclear Power Plant
Directory of Open Access Journals (Sweden)
Arshad Habib Malik
2011-04-01
Full Text Available In this paper, a nominal SISO (Single Input Single Output model of PHWR (Pressurized Heavy Water Reactor type nuclear power plant is developed based on normal moderator pump-up rate capturing the moderator level dynamics using system identification technique. As the plant model is not exact, therefore additive and multiplicative uncertainty modeling is required. A robust perturbed plant model is derived based on worst case model capturing slowest moderator pump-up rate dynamics and moderator control valve opening delay. Both nominal and worst case models of PHWR-type nuclear power plant have ARX (An Autoregressive Exogenous structures and the parameters of both models are estimated using recursive LMS (Least Mean Square optimization algorithm. Nominal and worst case discrete plant models are transformed into frequency domain for robust controller design purpose. The closed loop system is configured into two port model form and H? robust controller is synthesized. The H?controller is designed based on singular value loop shaping and desired magnitude of control input. The selection of desired disturbance attenuation factor and size of the largest anticipated multiplicative plant perturbation for loop shaping of H? robust controller form a constrained multi-objective optimization problem. The performance and robustness of the proposed controller is tested under transient condition of a nuclear power plant in Pakistan and found satisfactory.
A semiparametric Wald statistic for testing logistic regression models based on case-control data
Institute of Scientific and Technical Information of China (English)
2008-01-01
We propose a semiparametric Wald statistic to test the validity of logistic regression models based on case-control data. The test statistic is constructed using a semiparametric ROC curve estimator and a nonparametric ROC curve estimator. The statistic has an asymptotic chi-squared distribution and is an alternative to the Kolmogorov-Smirnov-type statistic proposed by Qin and Zhang in 1997, the chi-squared-type statistic proposed by Zhang in 1999 and the information matrix test statistic proposed by Zhang in 2001. The statistic is easy to compute in the sense that it requires none of the following methods: using a bootstrap method to find its critical values, partitioning the sample data or inverting a high-dimensional matrix. We present some results on simulation and on analysis of two real examples. Moreover, we discuss how to extend our statistic to a family of statistics and how to construct its Kolmogorov-Smirnov counterpart.
Adaptive and model-based control theory applied to convectively unstable flows
Fabbiane, N; Bagheri, S; Henningson, D S
2014-01-01
Research on active control for the delay of laminar-turbulent transition in boundary layers has made a significant progress in the last two decades, but the employed strategies have been many and dispersed. Using one framework, we review model-based techniques, such as linear-quadratic regulators, and model-free adaptive methods, such as least-mean square filters. The former are supported by a elegant and powerful theoretical basis, whereas the latter may provide a more practical approach in the presence of complex disturbance environments, that are difficult to model. We compare the methods with a particular focus on efficiency, practicability and robustness to uncertainties. Each step is exemplified on the one-dimensional linearized Kuramoto-Sivashinsky equation, that shows many similarities with the initial linear stages of the transition process of the flow over a flat plate. Also, the source code for the examples are provided.
Optimization of Temperature Controller for Electric Furnace
Institute of Scientific and Technical Information of China (English)
2000-01-01
Genetic algorithms are based on the principle of natural selection and the optimization of natural generation. We can select the number of the bit strings and mutation rate reasonably, the global optimal solution can be obtained. GAs adopt the binary code as optimizing parameter and this binary code can be used in computer controller easily. This paper studies the application of the GAs to the electric furnace temperature control. When the electric furnace mathematics model varies with the working condition, the parameter of controller can be optimized on line. So the system performance can be improved effectively.
Characteristic model based control of the X-34 reusable launch vehicle in its climbing phase
Institute of Scientific and Technical Information of China (English)
MENG Bin; WU HongXin; LIN ZongLi; LI Guo
2009-01-01
In this paper,a characteristic model based longitudinal control design for the trans-aerosphere vehicle X-34 In its transonic and hypersonic climbing phase is proposed.The design is based on the dynamic characteristics of the vehicle and the curves it is to track in this climbing phase.Through a detailed analysis of the aerodynamics and vehicle dynamics during this climbing phase,an explicit description of the tracking curve for the flight path angle is derived.On the basis of this tracking curve,the tracking curves for the two short-period variables,the angle of attack and the pitch rate,are designed.An all-coefficient adaptive controller is then designed,based on the characteristic modeling,to cause these two short-period variables to follow their respective tracking curves.The proposed design does not require multiple working points,making the design procedure simple.Numerical simulation is performed to validate the performance of the controller.The simulation results Indicate that the resulting control law ensures that the vehicle climbs up successfully under the restrictions on the pitch angle and overloading.
A Model-based Framework for Risk Assessment in Human-Computer Controlled Systems
Hatanaka, Iwao
2000-01-01
The rapid growth of computer technology and innovation has played a significant role in the rise of computer automation of human tasks in modem production systems across all industries. Although the rationale for automation has been to eliminate "human error" or to relieve humans from manual repetitive tasks, various computer-related hazards and accidents have emerged as a direct result of increased system complexity attributed to computer automation. The risk assessment techniques utilized for electromechanical systems are not suitable for today's software-intensive systems or complex human-computer controlled systems. This thesis will propose a new systemic model-based framework for analyzing risk in safety-critical systems where both computers and humans are controlling safety-critical functions. A new systems accident model will be developed based upon modem systems theory and human cognitive processes to better characterize system accidents, the role of human operators, and the influence of software in its direct control of significant system functions. Better risk assessments will then be achievable through the application of this new framework to complex human-computer controlled systems.
Coelho, Antonio Augusto Rodrigues
2016-01-01
This paper introduces the Fuzzy Logic Hypercube Interpolator (FLHI) and demonstrates applications in control of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) processes with Hammerstein nonlinearities. FLHI consists of a Takagi-Sugeno fuzzy inference system where membership functions act as kernel functions of an interpolator. Conjunction of membership functions in an unitary hypercube space enables multivariable interpolation of N-dimensions. Membership functions act as interpolation kernels, such that choice of membership functions determines interpolation characteristics, allowing FLHI to behave as a nearest-neighbor, linear, cubic, spline or Lanczos interpolator, to name a few. The proposed interpolator is presented as a solution to the modeling problem of static nonlinearities since it is capable of modeling both a function and its inverse function. Three study cases from literature are presented, a single-input single-output (SISO) system, a MISO and a MIMO system. Good results are obtained regarding performance metrics such as set-point tracking, control variation and robustness. Results demonstrate applicability of the proposed method in modeling Hammerstein nonlinearities and their inverse functions for implementation of an output compensator with Model Based Predictive Control (MBPC), in particular Dynamic Matrix Control (DMC). PMID:27657723
The applications of model-based geostatistics in helminth epidemiology and control.
Magalhães, Ricardo J Soares; Clements, Archie C A; Patil, Anand P; Gething, Peter W; Brooker, Simon
2011-01-01
Funding agencies are dedicating substantial resources to tackle helminth infections. Reliable maps of the distribution of helminth infection can assist these efforts by targeting control resources to areas of greatest need. The ability to define the distribution of infection at regional, national and subnational levels has been enhanced greatly by the increased availability of good quality survey data and the use of model-based geostatistics (MBG), enabling spatial prediction in unsampled locations. A major advantage of MBG risk mapping approaches is that they provide a flexible statistical platform for handling and representing different sources of uncertainty, providing plausible and robust information on the spatial distribution of infections to inform the design and implementation of control programmes. Focussing on schistosomiasis and soil-transmitted helminthiasis, with additional examples for lymphatic filariasis and onchocerciasis, we review the progress made to date with the application of MBG tools in large-scale, real-world control programmes and propose a general framework for their application to inform integrative spatial planning of helminth disease control programmes.
Energy Technology Data Exchange (ETDEWEB)
Uchida, Hiroaki [Kisarazu National College of Technology, Kisarazu, Chiba (Japan); Nonami, Kenzo; Yanai, Takaaki [Chiba Univ. (Japan). Faculty of Engineering; Iguchi, Yoshihiko; Huang Qing Jiu [Chiba Univ. (Japan)
2000-06-01
It is considered that locomotion robots are aggressive under the circumstances where human hardly work, for example, in the nuclear power plant, in the bottom of the sea and on a planet. The injury and the fault of the robot might occur frequently under those circumstances. It is very important problem that the robot can realize the walking with the fault. This is very difficult problem for biped and quadruped robot to realize a stable walking in the case that actuator or sensor is broken. And, in walking of mammal, gait pattern is generated by neural oscillator existing in the spinal cord. In the case that a lower neural system is injured, mammal realize a walking by a higher neural system. Thus, mammal has a self renovation function. In this study, in order to realize the stable walking of the quadruped robot with fault, we discuss the control method with self renovation function for the fault of the decentralized controller and the angular sensor. First, we design the centralized controller of one leg by sliding mode control for the fault of decentralized controller. Second, Sky Hook Suspension Control is applied for the fault of the angular sensor. The proposed methods are verified by 3D simulations by CAD and experiments. (author)
Model-Based Self-Tuning Multiscale Method for Combustion Control
Le, Dzu, K.; DeLaat, John C.; Chang, Clarence T.; Vrnak, Daniel R.
2006-01-01
A multi-scale representation of the combustor dynamics was used to create a self-tuning, scalable controller to suppress multiple instability modes in a liquid-fueled aero engine-derived combustor operating at engine-like conditions. Its self-tuning features designed to handle the uncertainties in the combustor dynamics and time-delays are essential for control performance and robustness. The controller was implemented to modulate a high-frequency fuel valve with feedback from dynamic pressure sensors. This scalable algorithm suppressed pressure oscillations of different instability modes by as much as 90 percent without the peak-splitting effect. The self-tuning logic guided the adjustment of controller parameters and converged quickly toward phase-lock for optimal suppression of the instabilities. The forced-response characteristics of the control model compare well with those of the test rig on both the frequency-domain and the time-domain.
OPTIMAL CONTROL PROBLEM FOR PARABOLIC VARIATIONAL INEQUALITIES
Institute of Scientific and Technical Information of China (English)
汪更生
2001-01-01
This paper deals with the optimal control problems of systems governed by a parabolic variational inequality coupled with a semilinear parabolic differential equations.The maximum principle and some kind of approximate controllability are studied.
A PSO-PID quaternion model based trajectory control of a hexarotor UAV
Artale, Valeria; Milazzo, Cristina L. R.; Orlando, Calogero; Ricciardello, Angela
2015-12-01
A quaternion based trajectory controller for a prototype of an Unmanned Aerial Vehicle (UAV) is discussed in this paper. The dynamics of the UAV, a hexarotor in details, is described in terms of quaternion instead of the usual Euler angle parameterization. As UAV flight management concerns, the method here implemented consists of two main steps: trajectory and attitude control via Proportional-Integrative-Derivative (PID) and Proportional-Derivative (PD) technique respectively and the application of Particle Swarm Optimization (PSO) method in order to tune the PID and PD parameters. The optimization is the consequence of the minimization of a objective function related to the error with the respect to a proper trajectory. Numerical simulations support and validate the proposed method.
Fast Solvers of Fredholm Optimal Control Problems
Institute of Scientific and Technical Information of China (English)
Mario; Borzì
2010-01-01
The formulation of optimal control problems governed by Fredholm integral equations of second kind and an efficient computational framework for solving these control problems is presented. Existence and uniqueness of optimal solutions is proved.A collective Gauss-Seidel scheme and a multigrid scheme are discussed. Optimal computational performance of these iterative schemes is proved by local Fourier analysis and demonstrated by results of numerical experiments.
Almost optimal adaptive LQ control: SISO case
Polderman, Jan W.; Daams, Jasper
2002-01-01
In this paper an almost optimal indirect adaptive controller for input/output dynamical systems is proposed. The control part of the adaptive control scheme is based on a modified LQ control law: by adding a time-varying gain to the certainty equivalent control law the conflict between
Efficient evolutionary algorithms for optimal control
López Cruz, I.L.
2002-01-01
If optimal control problems are solved by means of gradient based local search methods, convergence to local solutions is likely. Recently, there has been an increasing interest in the use of global optimisation algorithms to solve optimal control problems, wh
A general U-block model-based design procedure for nonlinear polynomial control systems
Zhu, Q. M.; Zhao, D. Y.; Zhang, Jianhua
2016-10-01
The proposition of U-model concept (in terms of 'providing concise and applicable solutions for complex problems') and a corresponding basic U-control design algorithm was originated in the first author's PhD thesis. The term of U-model appeared (not rigorously defined) for the first time in the first author's other journal paper, which established a framework for using linear polynomial control system design approaches to design nonlinear polynomial control systems (in brief, linear polynomial approaches → nonlinear polynomial plants). This paper represents the next milestone work - using linear state-space approaches to design nonlinear polynomial control systems (in brief, linear state-space approaches → nonlinear polynomial plants). The overall aim of the study is to establish a framework, defined as the U-block model, which provides a generic prototype for using linear state-space-based approaches to design the control systems with smooth nonlinear plants/processes described by polynomial models. For analysing the feasibility and effectiveness, sliding mode control design approach is selected as an exemplary case study. Numerical simulation studies provide a user-friendly step-by-step procedure for the readers/users with interest in their ad hoc applications. In formality, this is the first paper to present the U-model-oriented control system design in a formal way and to study the associated properties and theorems. The previous publications, in the main, have been algorithm-based studies and simulation demonstrations. In some sense, this paper can be treated as a landmark for the U-model-based research from intuitive/heuristic stage to rigour/formal/comprehensive studies.
Connections Between Singular Control and Optimal Switching
Guo, Xin; Tomecek, Pascal
2007-01-01
This paper builds a new theoretical connection between singular control of finite variation and optimal switching problems. This correspondence provides a novel method for solving high-dimensional singular control problems, and enables us to extend the theory of reversible investment: sufficient conditions are derived for the existence of optimal controls and for the regularity of value functions. Consequently, our regularity result links singular controls and Dynkin games through sequential ...
Neural Networks for Optimal Control
DEFF Research Database (Denmark)
Sørensen, O.
1995-01-01
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....
Neural Networks for Optimal Control
DEFF Research Database (Denmark)
Sørensen, O.
1995-01-01
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....
Optimal switching using coherent control
DEFF Research Database (Denmark)
Kristensen, Philip Trøst; Heuck, Mikkel; Mørk, Jesper
2013-01-01
that the switching time, in general, is not limited by the cavity lifetime. Therefore, the total energy required for switching is a more relevant figure of merit than the switching speed, and for a particular two-pulse switching scheme we use calculus of variations to optimize the switching in terms of input energy....
Optimal control, optimization and asymptotic analysis of Purcell's microswimmer model
Wiezel, Oren; Or, Yizhar
2016-11-01
Purcell's swimmer (1977) is a classic model of a three-link microswimmer that moves by performing periodic shape changes. Becker et al. (2003) showed that the swimmer's direction of net motion is reversed upon increasing the stroke amplitude of joint angles. Tam and Hosoi (2007) used numerical optimization in order to find optimal gaits for maximizing either net displacement or Lighthill's energetic efficiency. In our work, we analytically derive leading-order expressions as well as next-order corrections for both net displacement and energetic efficiency of Purcell's microswimmer. Using these expressions enables us to explicitly show the reversal in direction of motion, as well as obtaining an estimate for the optimal stroke amplitude. We also find the optimal swimmer's geometry for maximizing either displacement or energetic efficiency. Additionally, the gait optimization problem is revisited and analytically formulated as an optimal control system with only two state variables, which can be solved using Pontryagin's maximum principle. It can be shown that the optimal solution must follow a "singular arc". Numerical solution of the boundary value problem is obtained, which exactly reproduces Tam and Hosoi's optimal gait.
USING OPTIMAL FEEDBACK CONTROL FOR CHAOS TARGETING
Institute of Scientific and Technical Information of China (English)
PENG ZHAO-WANG; ZHONG TING-XIU
2000-01-01
Since the conventional open-loop optimal targeting of chaos is very sensitive to noise, a close-loop optimal targeting method is proposed to improve the targeting performance under noise. The present optimal targeting model takes into consideration both precision and speed of the targeting procedure. The parameters, rather than the output, of the targeting controller, are directly optimized to obtain optimal chaos targeting. Analysis regarding the mechanism is given from physics aspect and numerical experiment on the Hénon map is carried out to compare the targeting performance under noise between the close-loop and the open-loop methods.
Directory of Open Access Journals (Sweden)
Yulong Ying
2015-01-01
Full Text Available In the lifespan of a gas turbine engine, abrupt faults and performance degradation of its gas-path components may happen; however the performance degradation is not easily foreseeable when the level of degradation is small. Gas path analysis (GPA method has been widely applied to monitor gas turbine engine health status as it can easily obtain the magnitudes of the detected component faults. However, when the number of components within engine is large or/and the measurement noise level is high, the smearing effect may be strong and the degraded components may not be recognized. In order to improve diagnostic effect, a nonlinear steady-state model based gas turbine health status estimation approach with improved particle swarm optimization algorithm (PSO-GPA has been proposed in this study. The proposed approach has been tested in ten test cases where the degradation of a model three-shaft marine engine has been analyzed. These case studies have shown that the approach can accurately search and isolate the degraded components and further quantify the degradation for major gas-path components. Compared with the typical GPA method, the approach has shown better measurement noise immunity and diagnostic accuracy.
Linear optimal control of tokamak fusion devices
Energy Technology Data Exchange (ETDEWEB)
Kessel, C.E.; Firestone, M.A.; Conn, R.W.
1989-05-01
The control of plasma position, shape and current in a tokamak fusion reactor is examined using linear optimal control. These advanced tokamaks are characterized by non up-down symmetric coils and structure, thick structure surrounding the plasma, eddy currents, shaped plasmas, superconducting coils, vertically unstable plasmas, and hybrid function coils providing ohmic heating, vertical field, radial field, and shaping field. Models of the electromagnetic environment in a tokamak are derived and used to construct control gains that are tested in nonlinear simulations with initial perturbations. The issues of applying linear optimal control to advanced tokamaks are addressed, including complex equilibrium control, choice of cost functional weights, the coil voltage limit, discrete control, and order reduction. Results indicate that the linear optimal control is a feasible technique for controlling advanced tokamaks where the more common classical control will be severely strained or will not work. 28 refs., 13 figs.
Gunn, Cameron A; Dickson, Jennifer L; Hewett, James N; Lynn, Adrienne; Rose, Hamish J; Clarkson, Sooji H; Shaw, Geoffrey M; Chase, J Geoffrey
2013-05-01
STAR (stochastic targeted) is a glycemic control model-based framework for critically ill neonates that has shown benefits in reducing hypoglycemia and hyperglycemia. STAR uses a stochastic matrix method to forecast future changes in a patient's insulin sensitivity and then applies this result to a physiological model to select an optimal insulin treatment. Nasogastric aspiration may be used as an indicator to suggest periods of care when enteral feed absorption is compromised, improving the performance of glycemic control. An analysis has been carried out to investigate the effect of poorly absorbed feeds on glycemic control. Clinical data were collected from eight patients on insulin therapy and enteral feed, which included large or significantly milky aspirates. Patients had a median gestational age of 25 weeks and postnatal age of 5.5 days. Virtual patients were created using the NICING model, and insulin sensitivity (SI) profiles were fit. Alternative feed profiles were generated whereby enteral feed absorption was redistributed with time to account for poor feed absorption. The effect of poor feed absorption, as indicated by aspirates, is investigated. The average percentage change of SI 4 h before a significant aspirate was 1.16%, and 1.49% in the 4 h following the aspirate. No distinct relationship was found between the fractional change in SI and the volume of the aspirate. Accounting for aspirates had a clinically negligible impact on glycemic control in virtual trials. Accounting for aspirates by manipulating enteral feed profiles had a minimal influence on both modeling and controlling glycemia in neonates. The impact of this method is clinically insignificant, suggesting that a population constant for the rate of glucose absorption in the gut adequately models feed absorption within the STAR framework. © 2013 Diabetes Technology Society.
Optimal Control of Switched Systems based on Bezier Control Points
Directory of Open Access Journals (Sweden)
FatemeGhomanjani
2012-06-01
Full Text Available This paper presents a new approach for solving optimal control problems for switched systems. We focus on problems in which a pre-specified sequence of active subsystems is given. For such problems, we need to seek both the optimal switching instants and the optimal continuous inputs. A Bezier control points method is applied for solving an optimal control problem which is supervised by a switched dynamic system. Two steps of approximation exist here. First, the time interval is divided into k sub-intervals. Second, the trajectory and control functions are approximatedby Bezier curves in each subinterval. Bezier curves have been considered as piecewise polynomials of degree n, then they will be determined by n+1 control points on any subinterval. The optimal control problem is there by converted into a nonlinear programming problem (NLP, which can be solved by known algorithms. However in this paper the MATLAB optimization routine FMINCON is used for solving resulting NLP.
System Optimization by Periodic Control.
1979-09-30
extended re- sults are now contained in a single report [3] which will appear as a regular paper in the December, 1979 issue of the IEEE Transactions on Automatic Control . The...Test Revisited, " to appear in the IEEE Transactions on Automatic Control . 4. D. J. Lyons, "Improved Aircraft Cruise by Periodic Control," Ph. D
Integration of supervisory control synthesis in model-based systems engineering
J.C.M. Baeten (Jos); J.M. van de Mortel-Fronczak; J.E. Rooda
2011-01-01
htmlabstractDue to increasing system complexity, time-to-market and development costs reduction, there are higher demands on engineering processes. Model-based engineering processes can play a role here because they support system development by enabling the use of various model-based analysis
Toward Model-Based Control of Non-linear Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat; Jensen, Tom Nørgaard; Kallesøe, Carsten;
2013-01-01
consumption. To have a better understanding of leakage in WSSs, to control pressure and leakage effectively, and for optimal design of WSSs, suitable modeling is an important prerequisite. In this paper a model with the main objective of pressure control and consequently leakage reduction is presented......Water leakage is an important component of water loss. Many methods have emerged from urban water supply systems (WSSs) for leakage control, but it still remains a challenge in many countries. Pressure management is an effective way to reduce the leakage in a system. It can also reduce the power....... Following an analogy to electric circuits, first the mathematical expression for pressure drop over each component of the pipe network (WSS) such as pipes, pumps, valves and water towers is presented. Then the network model is derived based on the circuit theory and subsequently used for pressure management...
Model-Based Integrated Process Design and Controller Design of Chemical Processes
DEFF Research Database (Denmark)
Abd Hamid, Mohd Kamaruddin Bin
This thesis describes the development and application of a new systematic modelbased methodology for performing integrated process design and controller design (IPDC) of chemical processes. The new methodology is simple to apply, easy to visualize and efficient to solve. Here, the IPDC problem...... and verification. Using thermodynamic and process insights, a bounded search space is first identified. This feasible solution space is further reduced to satisfy the process design and controller design constraints in sub-problems 2 and 3, respectively, until in the final sub-problem all feasible candidates...... are ordered according to the defined performance criteria (objective function). The final selected design is then verified through rigorous simulation. In the pre-analysis sub-problem, the concepts of attainable region and driving force are used to locate the optimal process-controller design solution...
A Model-Based Methodology for Simultaneous Design and Control of a Bioethanol Production Process
DEFF Research Database (Denmark)
Alvarado-Morales, Merlin; Abd.Hamid, Mohd-Kamaruddin; Sin, Gürkan
2010-01-01
In this work, a framework for the simultaneous solution of design and control problems is presented. Within this framework, two methodologies are presented, the integrated process design and controller design (IPDC) methodology and the process-group contribution (PGC) methodology. The concepts...... of attainable region (AR), driving force (DF), process-group (PG) and reverse simulation are used within these methodologies. The IPDC methodology is used to find the optimal design-control strategy of a process by locating the maximum point in the AR and DF diagrams for reactor and separator, respectively....... The PGC methodology is used to generate more efficient separation designs in terms of energy consumption by targeting the separation task at the largest DF. Both methodologies are highlighted through the application of two case studies, a bioethanol production process and a succinic acid production...
Optimal Control Development System for Electrical Drives
Directory of Open Access Journals (Sweden)
Marian GAICEANU
2008-08-01
Full Text Available In this paper the optimal electrical drive development system is presented. It consists of both electrical drive types: DC and AC. In order to implement the optimal control for AC drive system an Altivar 71 inverter, a Frato magnetic particle brake (as load, three-phase induction machine, and dSpace 1104 controller have been used. The on-line solution of the matrix Riccati differential equation (MRDE is computed by dSpace 1104 controller, based on the corresponding feedback signals, generating the optimal speed reference for the AC drive system. The optimal speed reference is tracked by Altivar 71 inverter, conducting to energy reduction in AC drive. The classical control (consisting of rotor field oriented control with PI controllers and the optimal one have been implemented by designing an adequate ControlDesk interface. The three-phase induction machine (IM is controlled at constant flux. Therefore, the linear dynamic mathematical model of the IM has been obtained. The optimal control law provides transient regimes with minimal energy consumption. The obtained solution by integration of the MRDE is orientated towards the numerical implementation-by using a zero order hold. The development system is very useful for researchers, doctoral students or experts training in electrical drive. The experimental results are shown.
Dynamic optimization and adaptive controller design
Inamdar, S. R.
2010-10-01
In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.
Optimal control of renewable economic resources
Energy Technology Data Exchange (ETDEWEB)
Adelani, L.A.
1987-01-01
Two main problems are studied: economic optimization, and determination of the optimal age of harvest for an initially immature population which follows a Bertalanffy-type growth law. Conditions are derived on the economic parameters that make maximization of economic rent biologically superior to maximization of sustainable yield. A general equation is derived for the optimal equilibrium biomass size when maximization of present value is the control objective. Also, it is shown that under perfectly elastic demand for the resource, a critical price level exists beyond which economic optimization has to be sacrificed in order to enhance conservation of the resource. An equation is derived whose solution represents the optimal age of harvest for an initially immature population stock. In certain circumstances, analytic forms are obtained for the optimal age of harvest. Some properties of the optimal age of harvest are also investigated.
Optimal Control of Isometric Muscle Dynamics
Directory of Open Access Journals (Sweden)
Robert Rockenfeller
2015-03-01
Full Text Available We use an indirect optimal control approach to calculate the optimal neural stimulation needed to obtain measured isometric muscle forces. The neural stimulation of the nerve system is hereby considered to be a control function (input of the system ’muscle’ that solely determines the muscle force (output. We use a well-established muscle model and experimental data of isometric contractions. The model consists of coupled activation and contraction dynamics described by ordinary differential equations. To validate our results, we perform a comparison with commercial optimal control software.
Optimal Control of Evolution Mixed Variational Inclusions
Energy Technology Data Exchange (ETDEWEB)
Alduncin, Gonzalo, E-mail: alduncin@geofisica.unam.mx [Universidad Nacional Autónoma de México, Departamento de Recursos Naturales, Instituto de Geofísica (Mexico)
2013-12-15
Optimal control problems of primal and dual evolution mixed variational inclusions, in reflexive Banach spaces, are studied. The solvability analysis of the mixed state systems is established via duality principles. The optimality analysis is performed in terms of perturbation conjugate duality methods, and proximation penalty-duality algorithms to mixed optimality conditions are further presented. Applications to nonlinear diffusion constrained problems as well as quasistatic elastoviscoplastic bilateral contact problems exemplify the theory.
DEFF Research Database (Denmark)
Henriksen, Lars Christian; Bergami, Leonardo; Andersen, Peter Bjørn
2013-01-01
This work investigates how adaptive trailing edge flaps and classical blade pitch can work in concert using a model-based state space control formulation. The trade-off between load reduction and actuator activity is decided by setting different weights in the objective function used by the model......-based controller. The combined control approach allow to achieve higher load alleviations, furthermore, in the presence of e.g. deterioration of an actuator, it enables an online re-tuning of the workload distribution of blade pitch and trailing edge flaps, thus potentially increasing the smart rotor reliability....
DEFF Research Database (Denmark)
This work investigates how adaptive trailing edge flaps and classical blade pitch can work in concert using a model-based state space control formulation. The trade-off between load reduction and actuator activity is decided by setting different weights in the objective function used by the model......-based controller. The combined control approach allow to achieve higher load alleviations, furthermore, in the presence of e.g. deterioration of an actuator, it enables an online re-tuning of the workload distribution of blade pitch and trailing edge flaps, thus potentially increasing the smart rotor reliability....
MDP Optimal Control under Temporal Logic Constraints
Ding, Xu Chu; Belta, Calin; Rus, Daniela
2011-01-01
In this paper, we develop a method to automatically generate a control policy for a dynamical system modeled as a Markov Decision Process (MDP). The control specification is given as a Linear Temporal Logic (LTL) formula over a set of propositions defined on the states of the MDP. We synthesize a control policy such that the MDP satisfies the given specification almost surely, if such a policy exists. In addition, we designate an "optimizing proposition" to be repeatedly satisfied, and we formulate a novel optimization criterion in terms of minimizing the expected cost in between satisfactions of this proposition. We propose a sufficient condition for a policy to be optimal, and develop a dynamic programming algorithm that synthesizes a policy that is optimal under some conditions, and sub-optimal otherwise. This problem is motivated by robotic applications requiring persistent tasks, such as environmental monitoring or data gathering, to be performed.
Optimal Speed Control for Cruising
DEFF Research Database (Denmark)
Blanke, M.
1994-01-01
With small profit margins in merchant shipping and more than eighty percent of sailing time being cross ocean voyages, speed control is crucial for vessel profitability......With small profit margins in merchant shipping and more than eighty percent of sailing time being cross ocean voyages, speed control is crucial for vessel profitability...
Optimal Speed Control for Cruising
DEFF Research Database (Denmark)
Blanke, M.
1994-01-01
With small profit margins in merchant shipping and more than eighty percent of sailing time being cross ocean voyages, speed control is crucial for vessel profitability......With small profit margins in merchant shipping and more than eighty percent of sailing time being cross ocean voyages, speed control is crucial for vessel profitability...
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
to check if the network is controllable. Afterward the pressure control problem in water supply systems is formulated as an optimal control problem. The goal is to minimize the power consumption in pumps and also to regulate the pressure drop at the end-users to a desired value. The formulated optimal...... in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply...... systems. To have better understanding of water leakage, to control pressure and leakage effectively and for optimal design of water supply system, suitable modeling is an important prerequisite. Therefore a model with the main objective of pressure control and consequently leakage reduction is presented...
Greenhouse climate management : an optimal control approach
Henten, van E.J.
1994-01-01
In this thesis a methodology is developed for the construction and analysis of an optimal greenhouse climate control system.
In chapter 1, the results of a literature survey are presented and the research objectives are defined. In the literature, optimal greenhouse climate
Optimal control and the calculus of variations
Pinch, Enid R
1993-01-01
This introduction to optimal control theory is intended for undergraduate mathematicians and for engineers and scientists with some knowledge of classical analysis. It includes sections on classical optimization and the calculus of variations. All the important theorems are carefully proved. There are many worked examples and exercises for the reader to attempt.
Optimization and control of metal forming processes
Havinga, Gosse Tjipke
2016-01-01
Inevitable variations in process and material properties limit the accuracy of metal forming processes. Robust optimization methods or control systems can be used to improve the production accuracy. Robust optimization methods are used to design production processes with low sensitivity to the distu
Greenhouse climate management: an optimal control approach.
Henten, van E.J.
1994-01-01
In this thesis a methodology is developed for the construction and analysis of an optimal greenhouse climate control system.In chapter 1, the results of a literature survey are presented and the research objectives are defined. In the literature, optimal greenhouse climate management systems have be
Directory of Open Access Journals (Sweden)
Danilo Donato
2014-01-01
Full Text Available Radial flow perfusion of cell-seeded hollow cylindrical porous scaffolds may overcome the transport limitations of pure diffusion and direct axial perfusion in the realization of bioengineered substitutes of failing or missing tissues. Little has been reported on the optimization criteria of such bioreactors. A steady-state model was developed, combining convective and dispersive transport of dissolved oxygen with Michaelis-Menten cellular consumption kinetics. Dimensional analysis was used to combine more effectively geometric and operational variables in the dimensionless groups determining bioreactor performance. The effectiveness of cell oxygenation was expressed in terms of non-hypoxic fractional construct volume. The model permits the optimization of the geometry of hollow cylindrical constructs, and direction and magnitude of perfusion flow, to ensure cell oxygenation and culture at controlled oxygen concentration profiles. This may help engineer tissues suitable for therapeutic and drug screening purposes.
Pack, Robert C.; Standiford, Keith; Lukanc, Todd; Ning, Guo Xiang; Verma, Piyush; Batarseh, Fadi; Chua, Gek Soon; Fujimura, Akira; Pang, Linyong
2014-10-01
A methodology is described wherein a calibrated model-based `Virtual' Variable Shaped Beam (VSB) mask writer process simulator is used to accurately verify complex Optical Proximity Correction (OPC) and Inverse Lithography Technology (ILT) mask designs prior to Mask Data Preparation (MDP) and mask fabrication. This type of verification addresses physical effects which occur in mask writing that may impact lithographic printing fidelity and variability. The work described here is motivated by requirements for extreme accuracy and control of variations for today's most demanding IC products. These extreme demands necessitate careful and detailed analysis of all potential sources of uncompensated error or variation and extreme control of these at each stage of the integrated OPC/ MDP/ Mask/ silicon lithography flow. The important potential sources of variation we focus on here originate on the basis of VSB mask writer physics and other errors inherent in the mask writing process. The deposited electron beam dose distribution may be examined in a manner similar to optical lithography aerial image analysis and image edge log-slope analysis. This approach enables one to catch, grade, and mitigate problems early and thus reduce the likelihood for costly long-loop iterations between OPC, MDP, and wafer fabrication flows. It moreover describes how to detect regions of a layout or mask where hotspots may occur or where the robustness to intrinsic variations may be improved by modification to the OPC, choice of mask technology, or by judicious design of VSB shots and dose assignment.
A semiparametric Wald statistic for testing logistic regression models based on case-control data
Institute of Scientific and Technical Information of China (English)
WAN ShuWen
2008-01-01
We propose a semiparametric Wald statistic to test the validity of logistic regression models based on case-control data.The test statistic is constructed using a semiparametric ROC curve estimator and a nonparametric ROC curve estimator.The statistic has an asymptotic chi-squared distribution and is an alternative to the Kolmogorov-Smirnov-type statistic proposed by Qin and Zhang in 1997,the chi-squared-type statistic proposed by Zhang in 1999 and the information matrix test statistic proposed by Zhang in 2001.The statistic is easy to compute in the sense that it requires none of the following methods:using a bootstrap method to find its critical values,partitioning the sample data or inverting a high-dimensional matrix.We present some results on simulation and on analysis of two real examples.Moreover,we discuss how to extend our statistic to a family of statistics and how to construct its Kolmogorov-Smirnov counterpart.
Optimal control problems with switching points
Seywald, Hans
1991-09-01
An overview is presented of the problems and difficulties that arise in solving optimal control problems with switching points. A brief discussion of existing optimality conditions is given and a numerical approach for solving the multipoint boundary value problems associated with the first-order necessary conditions of optimal control is presented. Two real-life aerospace optimization problems are treated explicitly. These are altitude maximization for a sounding rocket (Goddard Problem) in the presence of a dynamic pressure limit, and range maximization for a supersonic aircraft flying in the vertical, also in the presence of a dynamic pressure limit. In the second problem singular control appears along arcs with active dynamic pressure limit, which in the context of optimal control, represents a first-order state inequality constraint. An extension of the Generalized Legendre-Clebsch Condition to the case of singular control along state/control constrained arcs is presented and is applied to the aircraft range maximization problem stated above. A contribution to the field of Jacobi Necessary Conditions is made by giving a new proof for the non-optimality of conjugate paths in the Accessory Minimum Problem. Because of its simple and explicit character, the new proof may provide the basis for an extension of Jacobi's Necessary Condition to the case of the trajectories with interior point constraints. Finally, the result that touch points cannot occur for first-order state inequality constraints is extended to the case of vector valued control functions.
A Feedback Optimal Control Algorithm with Optimal Measurement Time Points
Directory of Open Access Journals (Sweden)
Felix Jost
2017-02-01
Full Text Available Nonlinear model predictive control has been established as a powerful methodology to provide feedback for dynamic processes over the last decades. In practice it is usually combined with parameter and state estimation techniques, which allows to cope with uncertainty on many levels. To reduce the uncertainty it has also been suggested to include optimal experimental design into the sequential process of estimation and control calculation. Most of the focus so far was on dual control approaches, i.e., on using the controls to simultaneously excite the system dynamics (learning as well as minimizing a given objective (performing. We propose a new algorithm, which sequentially solves robust optimal control, optimal experimental design, state and parameter estimation problems. Thus, we decouple the control and the experimental design problems. This has the advantages that we can analyze the impact of measurement timing (sampling independently, and is practically relevant for applications with either an ethical limitation on system excitation (e.g., chemotherapy treatment or the need for fast feedback. The algorithm shows promising results with a 36% reduction of parameter uncertainties for the Lotka-Volterra fishing benchmark example.
Optimized chaos control with simple limiters.
Wagner, C; Stoop, R
2001-01-01
We present an elementary derivation of chaos control with simple limiters using the logistic map and the Henon map as examples. This derivation provides conditions for optimal stabilization of unstable periodic orbits of a chaotic attractor.
Energy efficient model based algorithm for control of building HVAC systems.
Kirubakaran, V; Sahu, Chinmay; Radhakrishnan, T K; Sivakumaran, N
2015-11-01
Energy efficient designs are receiving increasing attention in various fields of engineering. Heating ventilation and air conditioning (HVAC) control system designs involve improved energy usage with an acceptable relaxation in thermal comfort. In this paper, real time data from a building HVAC system provided by BuildingLAB is considered. A resistor-capacitor (RC) framework for representing thermal dynamics of the building is estimated using particle swarm optimization (PSO) algorithm. With objective costs as thermal comfort (deviation of room temperature from required temperature) and energy measure (Ecm) explicit MPC design for this building model is executed based on its state space representation of the supply water temperature (input)/room temperature (output) dynamics. The controllers are subjected to servo tracking and external disturbance (ambient temperature) is provided from the real time data during closed loop control. The control strategies are ported on a PIC32mx series microcontroller platform. The building model is implemented in MATLAB and hardware in loop (HIL) testing of the strategies is executed over a USB port. Results indicate that compared to traditional proportional integral (PI) controllers, the explicit MPC's improve both energy efficiency and thermal comfort significantly.
The optimal control and its multiple applications
2009-01-01
In this work we refer to motivations, applications, and relations of control theory with other areas of mathematics. We present a brief historical review of optimal control theory, from its roots in the calculus of variations and the classical theory of control to the present time, giving particular emphasis to the Pontryagin maximum principle.
Multiple Objective Optimization and Optimal Control of Fermentation Processes
Directory of Open Access Journals (Sweden)
Mitko Petrov
2008-10-01
Full Text Available A multiple objective optimization is applied for finding an optimum policy of fed-batch processes of whey fermentation and L-lysine production. The multiple objective optimization problems are transformed to a standard problem of optimization with single objective function by a general utility function with weight coefficients for each single utility coefficient criteria. A combined algorithm is applied when solving the maximizing decision problem. The algorithm includes a method for random search of finding an initial point and a method based on the fuzzy sets theory, combined in order to find the best solution of the optimization problem. The application of the combined algorithm eliminates the main disadvantage of the used fuzzy optimization method, namely it decreases the number of discrete values of the control variables. Thus, the algorithm allows problems with larger scale to be solved. After this multiple optimization, the useful product quality rises and the residual substrate concentration at the end of the process decreases. In this way, the process productivity is increased.
Optimal impulse control problems and linear programming.
Bauso, D.
2009-01-01
Optimal impulse control problems are, in general, difficult to solve. A current research goal is to isolate those problems that lead to tractable solutions. In this paper, we identify a special class of optimal impulse control problems which are easy to solve. Easy to solve means that solution algorithms are polynomial in time and therefore suitable to the on-line implementation in real-time problems. We do this by using a paradigm borrowed from the Operations Research field. As main result, ...
Neuro-optimal control of helicopter UAVs
Nodland, David; Ghosh, Arpita; Zargarzadeh, H.; Jagannathan, S.
2011-05-01
Helicopter UAVs can be extensively used for military missions as well as in civil operations, ranging from multirole combat support and search and rescue, to border surveillance and forest fire monitoring. Helicopter UAVs are underactuated nonlinear mechanical systems with correspondingly challenging controller designs. This paper presents an optimal controller design for the regulation and vertical tracking of an underactuated helicopter using an adaptive critic neural network framework. The online approximator-based controller learns the infinite-horizon continuous-time Hamilton-Jacobi-Bellman (HJB) equation and then calculates the corresponding optimal control input that minimizes the HJB equation forward-in-time. In the proposed technique, optimal regulation and vertical tracking is accomplished by a single neural network (NN) with a second NN necessary for the virtual controller. Both of the NNs are tuned online using novel weight update laws. Simulation results are included to demonstrate the effectiveness of the proposed control design in hovering applications.
The effects of redundant control inputs in optimal control
Institute of Scientific and Technical Information of China (English)
DUAN ZhiSheng; HUANG Lin; YANG Ying
2009-01-01
For a stabillzable system,the extension of the control inputs has no use for stabllizability,but it is important for optimal control.In this paper,a necessary and sufficient condition is presented to strictly decrease the quadratic optimal performance index after control input extensions.A similar result is also provided for H_2 optimal control problem.These results show an essential difference between single-input and multi-input control systems.Several examples are taken to illustrate related problems.
Hybrid optimization schemes for quantum control
Energy Technology Data Exchange (ETDEWEB)
Goerz, Michael H.; Koch, Christiane P. [Universitaet Kassel, Theoretische Physik, Kassel (Germany); Whaley, K. Birgitta [University of California, Department of Chemistry, Berkeley, CA (United States)
2015-12-15
Optimal control theory is a powerful tool for solving control problems in quantum mechanics, ranging from the control of chemical reactions to the implementation of gates in a quantum computer. Gradient-based optimization methods are able to find high fidelity controls, but require considerable numerical effort and often yield highly complex solutions. We propose here to employ a two-stage optimization scheme to significantly speed up convergence and achieve simpler controls. The control is initially parametrized using only a few free parameters, such that optimization in this pruned search space can be performed with a simplex method. The result, considered now simply as an arbitrary function on a time grid, is the starting point for further optimization with a gradient-based method that can quickly converge to high fidelities. We illustrate the success of this hybrid technique by optimizing a geometric phase gate for two superconducting transmon qubits coupled with a shared transmission line resonator, showing that a combination of Nelder-Mead simplex and Krotov's method yields considerably better results than either one of the two methods alone. (orig.)
Chemical optimization algorithm for fuzzy controller design
Astudillo, Leslie; Castillo, Oscar
2014-01-01
In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application
Directory of Open Access Journals (Sweden)
Sie Long Kek
2015-01-01
Full Text Available A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation are integrated interactively. On the other hand, the output is measured from the real plant and is fed back into the parameter estimation problem to establish a matching scheme. During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed.
OPTIMAL OPERATIONAL CONTROL OF INTERCEPTOR SEWER SYSTEM
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
In this paper, a mathematical model was built up to solve the problem of optimal operational control by analysing the factors on an interceptor sewer system and a Fortran program was produced for this model. This paper shows that the optimal control states can be determined by working out the optimal flow rates by means of Linear Programming (LP). The result is very sensitive to interception points and the concentration weight coefficients over time. The result further highlights some practical applications for the existing sewer systems or the sewer systems under design.
Investigation on evolutionary optimization of chaos control
Energy Technology Data Exchange (ETDEWEB)
Zelinka, Ivan [Faculty of Applied Informatics, Tomas Bata University in Zli' n, Nad Stranemi 4511, 762 72 Zli' n (Czech Republic)], E-mail: zelinka@fai.utb.cz; Senkerik, Roman [Faculty of Applied Informatics, Tomas Bata University in Zli' n, Nad Stranemi 4511, 762 72 Zli' n (Czech Republic)], E-mail: senkerik@fai.utb.cz; Navratil, Eduard [Faculty of Applied Informatics, Tomas Bata University in Zli' n, Nad Stranemi 4511, 762 72 Zli' n (Czech Republic)], E-mail: enavratil@fai.utb.cz
2009-04-15
This work deals with an investigation on optimization of the feedback control of chaos based on the use of evolutionary algorithms. The main objective is to show that evolutionary algorithms are capable of optimization of chaos control. As models of deterministic chaotic systems, one-dimensional Logistic equation and two-dimensional Henon map were used. The optimizations were realized in several ways, each one for another set of parameters of evolution algorithms or separate cost functions. The evolutionary algorithm SOMA (self-organizing migrating algorithm) was used in four versions. For each version simulations were repeated several times to show and check for robustness of the applied method.
A model-based production planning and control method supporting delivery of cast-in-place concrete
Norberg, Håkan; Jongeling, Rogier
2008-01-01
This paper combines model-based design and construction techniques with principles from location based planning methods. Both concepts are applied in a practical study in which a model-based method is developed for the planning and control of cast in place concrete deliveries to the building site. The overall aim of this paper is to show a method that can be used to make the planning and control of the delivery process for cast in place concrete more efficient. The paper shows examples of cha...
Optimal Wentzell Boundary Control of Parabolic Equations
Energy Technology Data Exchange (ETDEWEB)
Luo, Yousong, E-mail: yousong.luo@rmit.edu.au [RMIT University, School of Mathematical and Geospatial Sciences (Australia)
2017-04-15
This paper deals with a class of optimal control problems governed by an initial-boundary value problem of a parabolic equation. The case of semi-linear boundary control is studied where the control is applied to the system via the Wentzell boundary condition. The differentiability of the state variable with respect to the control is established and hence a necessary condition is derived for the optimal solution in the case of both unconstrained and constrained problems. The condition is also sufficient for the unconstrained convex problems. A second order condition is also derived.
Optimal control problem for the extended Fisher–Kolmogorov equation
Indian Academy of Sciences (India)
Ning Duan
2016-02-01
In this paper, the optimal control problem for the extended Fisher–Kolmogorov equation is studied. The optimal control under boundary condition is given, the existence of optimal solution to the equation is proved and the optimality system is established.
Optimal control novel directions and applications
Aronna, Maria; Kalise, Dante
2017-01-01
Focusing on applications to science and engineering, this book presents the results of the ITN-FP7 SADCO network’s innovative research in optimization and control in the following interconnected topics: optimality conditions in optimal control, dynamic programming approaches to optimal feedback synthesis and reachability analysis, and computational developments in model predictive control. The novelty of the book resides in the fact that it has been developed by early career researchers, providing a good balance between clarity and scientific rigor. Each chapter features an introduction addressed to PhD students and some original contributions aimed at specialist researchers. Requiring only a graduate mathematical background, the book is self-contained. It will be of particular interest to graduate and advanced undergraduate students, industrial practitioners and to senior scientists wishing to update their knowledge.
OPTIMAL CONTROL FOR ELECTRIC VEHICLE STABILIZATION
Directory of Open Access Journals (Sweden)
MARIAN GAICEANU
2016-01-01
Full Text Available This main objective of the paper is to stabilize an electric vehicle in optimal manner to a step lane change maneuver. To define the mathematical model of the vehicle, the rigid body moving on a plane is taken into account. An optimal lane keeping controller delivers the adequate angles in order to stabilize the vehicle’s trajectory in an optimal way. Two degree of freedom linear bicycle model is adopted as vehicle model, consisting of lateral and yaw motion equations. The proposed control maintains the lateral stability by taking the feedback information from the vehicle transducers. In this way only the lateral vehicle’s dynamics are enough to considerate. Based on the obtained linear mathematical model the quadratic optimal control is designed in order to maintain the lateral stability of the electric vehicle. The numerical simulation results demonstrate the feasibility of the proposed solution.
Optimal control of a CSTR process
Directory of Open Access Journals (Sweden)
A. Soukkou
2008-12-01
Full Text Available Designing an effective criterion and learning algorithm for find the best structure is a major problem in the control design process. In this paper, the fuzzy optimal control methodology is applied to the design of the feedback loops of an Exothermic Continuous Stirred Tank Reactor system. The objective of design process is to find an optimal structure/gains of the Robust and Optimal Takagi Sugeno Fuzzy Controller (ROFLC. The control signal thus obtained will minimize a performance index, which is a function of the tracking/regulating errors, the quantity of the energy of the control signal applied to the system, and the number of fuzzy rules. The genetic learning is proposed for constructing the ROFLC. The chromosome genes are arranged into two parts, the binary-coded part contains the control genes and the real-coded part contains the genes parameters representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the ROFLC are compared to these found by the traditional PD controller with Genetic Optimization (PD_GO. Simulations demonstrate that the proposed ROFLC and PD_GO has successfully met the design specifications.
Energy Technology Data Exchange (ETDEWEB)
Sayyar-Rodsari, Bijan; Schweiger, Carl; /SLAC /Pavilion Technologies, Inc., Austin, TX
2010-08-25
parameters of the beam lifetime model) are physically meaningful. (3) Numerical Efficiency of the Training - We investigated the numerical efficiency of the SVM training. More specifically, for the primal formulation of the training, we have developed a problem formulation that avoids the linear increase in the number of the constraints as a function of the number of data points. (4) Flexibility of Software Architecture - The software framework for the training of the support vector machines was designed to enable experimentation with different solvers. We experimented with two commonly used nonlinear solvers for our simulations. The primary application of interest for this project has been the sustained optimal operation of particle accelerators at the Stanford Linear Accelerator Center (SLAC). Particle storage rings are used for a variety of applications ranging from 'colliding beam' systems for high-energy physics research to highly collimated x-ray generators for synchrotron radiation science. Linear accelerators are also used for collider research such as International Linear Collider (ILC), as well as for free electron lasers, such as the Linear Coherent Light Source (LCLS) at SLAC. One common theme in the operation of storage rings and linear accelerators is the need to precisely control the particle beams over long periods of time with minimum beam loss and stable, yet challenging, beam parameters. We strongly believe that beyond applications in particle accelerators, the high fidelity and cost benefits of a combined model-based fault estimation/correction system will attract customers from a wide variety of commercial and scientific industries. Even though the acquisition of Pavilion Technologies, Inc. by Rockwell Automation Inc. in 2007 has altered the small business status of the Pavilion and it no longer qualifies for a Phase II funding, our findings in the course of the Phase I research have convinced us that further research will render a workable
Energy Technology Data Exchange (ETDEWEB)
Sayyar-Rodsari, Bijan; Schweiger, Carl; /SLAC /Pavilion Technologies, Inc., Austin, TX
2010-08-25
parameters of the beam lifetime model) are physically meaningful. (3) Numerical Efficiency of the Training - We investigated the numerical efficiency of the SVM training. More specifically, for the primal formulation of the training, we have developed a problem formulation that avoids the linear increase in the number of the constraints as a function of the number of data points. (4) Flexibility of Software Architecture - The software framework for the training of the support vector machines was designed to enable experimentation with different solvers. We experimented with two commonly used nonlinear solvers for our simulations. The primary application of interest for this project has been the sustained optimal operation of particle accelerators at the Stanford Linear Accelerator Center (SLAC). Particle storage rings are used for a variety of applications ranging from 'colliding beam' systems for high-energy physics research to highly collimated x-ray generators for synchrotron radiation science. Linear accelerators are also used for collider research such as International Linear Collider (ILC), as well as for free electron lasers, such as the Linear Coherent Light Source (LCLS) at SLAC. One common theme in the operation of storage rings and linear accelerators is the need to precisely control the particle beams over long periods of time with minimum beam loss and stable, yet challenging, beam parameters. We strongly believe that beyond applications in particle accelerators, the high fidelity and cost benefits of a combined model-based fault estimation/correction system will attract customers from a wide variety of commercial and scientific industries. Even though the acquisition of Pavilion Technologies, Inc. by Rockwell Automation Inc. in 2007 has altered the small business status of the Pavilion and it no longer qualifies for a Phase II funding, our findings in the course of the Phase I research have convinced us that further research will render a workable
Model based adaptive control of a continuous capture process for monoclonal antibodies production.
Steinebach, Fabian; Angarita, Monica; Karst, Daniel J; Müller-Späth, Thomas; Morbidelli, Massimo
2016-04-29
A two-column capture process for continuous processing of cell-culture supernatant is presented. Similar to other multicolumn processes, this process uses sequential countercurrent loading of the target compound in order maximize resin utilization and productivity for a given product yield. The process was designed using a novel mechanistic model for affinity capture, which takes both specific adsorption as well as transport through the resin beads into account. Simulations as well as experimental results for the capture of an IgG antibody are discussed. The model was able to predict the process performance in terms of yield, productivity and capacity utilization. Compared to continuous capture with two columns operated batch wise in parallel, a 2.5-fold higher capacity utilization was obtained for the same productivity and yield. This results in an equal improvement in product concentration and reduction of buffer consumption. The developed model was used not only for the process design and optimization but also for its online control. In particular, the unit operating conditions are changed in order to maintain high product yield while optimizing the process performance in terms of capacity utilization and buffer consumption also in the presence of changing upstream conditions and resin aging.
Optimal Fuzzy Controller Tuned by TV-PSO for Induction Motor Speed Control
Directory of Open Access Journals (Sweden)
KULIC, F.
2011-02-01
Full Text Available This paper reports an automated procedure for the design of an optimal fuzzy logic controller to be used as an induction motor speed controller. The procedure consists of selection of a suitable well known fuzzy logic controller and tuning via particle swarm optimization optimal for the selected criteria. In this way the time required for tuning of the controller is significantly reduced in comparison with trial and error methods. As a benchmark a proportional-integral (PI controller is used. The PI controller is tuned via the symmetrical optimum procedure, the standard procedure for tuning a speed controller of an induction motor. Simulation results are obtained via a mathematical model developed in Matlab/Simulink. Experimental verification is carried out with a laboratory model based on the DS1104 digital control card. To minimize iron losses and to provide better motor performance for low loads, flux is reduced from nominal and speed is kept below nominal. Results are presented in tables and graphics. The optimal fuzzy logic controller provides a slight practical advantage.
Optimal Control of Active Recoil Mechanisms
1977-02-01
pressures in different chambers, rod pull are available and can be plotted. A linear state feedback control system is proposed to adapt this...desirable. A linear state feedback control system with variable gains is proposed in the report. A separate control law is designed for each...optimization algorithm to choose a feasible solution. 27 3.3 Results for M-37 Recoil Mechanism The linear state feedback control system and
Ward, Logan; Steel, James; Le Compte, Aaron; Evans, Alicia; Tan, Chia-Siong; Penning, Sophie; Shaw, Geoffrey M; Desaive, Thomas; Chase, J Geoffrey
2012-01-01
Tight glycemic control (TGC) has shown benefits but has been difficult to implement. Model-based methods and computerized protocols offer the opportunity to improve TGC quality and compliance. This research presents an interface design to maximize compliance, minimize real and perceived clinical effort, and minimize error based on simple human factors and end user input. The graphical user interface (GUI) design is presented by construction based on a series of simple, short design criteria based on fundamental human factors engineering and includes the use of user feedback and focus groups comprising nursing staff at Christchurch Hospital. The overall design maximizes ease of use and minimizes (unnecessary) interaction and use. It is coupled to a protocol that allows nurse staff to select measurement intervals and thus self-manage workload. The overall GUI design is presented and requires only one data entry point per intervention cycle. The design and main interface are heavily focused on the nurse end users who are the predominant users, while additional detailed and longitudinal data, which are of interest to doctors guiding overall patient care, are available via tabs. This dichotomy of needs and interests based on the end user's immediate focus and goals shows how interfaces must adapt to offer different information to multiple types of users. The interface is designed to minimize real and perceived clinical effort, and ongoing pilot trials have reported high levels of acceptance. The overall design principles, approach, and testing methods are based on fundamental human factors principles designed to reduce user effort and error and are readily generalizable. © 2012 Diabetes Technology Society.
Optimal control of radiator systems; Optimal reglering av radiatorsystem
Energy Technology Data Exchange (ETDEWEB)
Wollerstrand, J.; Ljunggren, P.; Johansson, P.O.
2007-07-01
This report presents results from a study aiming to considerably improve the development towards minimizing the primary return temperature from a district heating (DH) substation by optimizing the control algorithm for the space heating system. The investigation of this research field started about 20 years ago in Sweden when low flow operation of space heating systems was introduced. Following a couple of years of partly confused discussions, the method was accepted by many, but was rejected by others. Our thesis is that further improvement of cooling of DH water is possible when advanced, but robust, control algorithms are used for the space heating system. A space heating system is traditionally designed for a specific constant circulation flow combined with a suitable control curve for the space heating supply temperature as a function of the outdoor temperature. Optimal choice of the control curve varies from case to case and is an issue both we and others have dealt with in previous work. A large step was to derive theoretical control curves for optimal control of the space heating system, with an analysis of how temperature and circulation flow varies with heat load. The estimated gain varies strongly depending on the conditions, however, with realistic conditions it can be as much as 5 deg C decreased DH return temperature on yearly average. To be able to work properly under varying physical circumstances, a control algorithm must be able to combine variation of space heating supply temperature and circulation flow as a function of the heat load. By regulating the rotation speed of the circulation pump this can be achieved. Such regulation can be adjusted for each and every building by regulating a few parameters in a regulator. The results from this work are, that important theoretical knowledge has been completed, to show results systematically and to find support from practical experiments. A hands-on description of the method for optimizing DH water
Nonlinear Burn Control and Operating Point Optimization in ITER
Boyer, Mark; Schuster, Eugenio
2013-10-01
Control of the fusion power through regulation of the plasma density and temperature will be essential for achieving and maintaining desired operating points in fusion reactors and burning plasma experiments like ITER. In this work, a volume averaged model for the evolution of the density of energy, deuterium and tritium fuel ions, alpha-particles, and impurity ions is used to synthesize a multi-input multi-output nonlinear feedback controller for stabilizing and modulating the burn condition. Adaptive control techniques are used to account for uncertainty in model parameters, including particle confinement times and recycling rates. The control approach makes use of the different possible methods for altering the fusion power, including adjusting the temperature through auxiliary heating, modulating the density and isotopic mix through fueling, and altering the impurity density through impurity injection. Furthermore, a model-based optimization scheme is proposed to drive the system as close as possible to desired fusion power and temperature references. Constraints are considered in the optimization scheme to ensure that, for example, density and beta limits are avoided, and that optimal operation is achieved even when actuators reach saturation. Supported by the NSF CAREER award program (ECCS-0645086).
Optimization problems for switched systems with impulsive control
Institute of Scientific and Technical Information of China (English)
Junhao HU; Huayou WANG; Xinzhi LIU; Bin LIU
2005-01-01
By using Impulsive Maximum Principal and three stage optimization method,this paper discusses optimization problems for linear impulsive switched systems with hybrid controls,which includes continuous control and impulsive control.The linear quadratic optimization problems without constraints such as optimal hybrid control,optimal stability and optimal switching instants are addressed in detail.These results are applicable to optimal control problems in economics,mechanics,and management.
Centralized Stochastic Optimal Control of Complex Systems
Energy Technology Data Exchange (ETDEWEB)
Malikopoulos, Andreas [ORNL
2015-01-01
In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain using the long-run expected average cost per unit time criterion, and we show that the control policy yielding the Pareto optimal solution minimizes the average cost criterion online. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion.
Cyclic Control Optimization for a Smart Rotor
DEFF Research Database (Denmark)
Bergami, Leonardo; Henriksen, Lars Christian
2012-01-01
The paper presents a method to determine cyclic control trajectories for a smart rotor undergoing periodic-deterministic load variations. The control trajectories result from a constrained optimization problem, where the cost function to minimize is given by the variation of the blade root flapwise...... bending moment within a rotor revolution. The method is applied to a rotor equipped with trailing edge flaps, and capable of individual blade pitching. Results show that the optimized cyclic control significantly alleviates the load variations from periodic disturbances; the combination of both cyclic...
An example in linear quadratic optimal control
Weiss, George; Zwart, Heiko J.
1998-01-01
We construct a simple example of a quadratic optimal control problem for an infinite-dimensional linear system based on a shift semigroup. This system has an unbounded control operator. The cost is quadratic in the input and the state, and the weighting operators are bounded. Despite its extreme
An example in linear quadratic optimal control
Weiss, George; Zwart, Heiko J.
1998-01-01
We construct a simple example of a quadratic optimal control problem for an infinite-dimensional linear system based on a shift semigroup. This system has an unbounded control operator. The cost is quadratic in the input and the state, and the weighting operators are bounded. Despite its extreme sim
Optimal control of nonsmooth distributed parameter systems
Tiba, Dan
1990-01-01
The book is devoted to the study of distributed control problems governed by various nonsmooth state systems. The main questions investigated include: existence of optimal pairs, first order optimality conditions, state-constrained systems, approximation and discretization, bang-bang and regularity properties for optimal control. In order to give the reader a better overview of the domain, several sections deal with topics that do not enter directly into the announced subject: boundary control, delay differential equations. In a subject still actively developing, the methods can be more important than the results and these include: adapted penalization techniques, the singular control systems approach, the variational inequality method, the Ekeland variational principle. Some prerequisites relating to convex analysis, nonlinear operators and partial differential equations are collected in the first chapter or are supplied appropriately in the text. The monograph is intended for graduate students and for resea...
DEFF Research Database (Denmark)
Yang, Z.; Izadi-Zamanabadi, Roozbeh; Blanke, M.
2000-01-01
Based on the model-matching strategy, an adaptive control reconfiguration method for a class of nonlinear control systems is proposed by using the multiple-model scheme. Instead of requiring the nominal and faulty nonlinear systems to match each other directly in some proper sense, three sets of ...... corresponding to the updating of local LTI models, which validations are determined by the model approximation errors and the optimal index of local design. The test on a nonlinear ship propulsion system shows the promising potential of this method for system reconfiguration...
Optimal performance of constrained control systems
Harvey, P. Scott, Jr.; Gavin, Henri P.; Scruggs, Jeffrey T.
2012-08-01
This paper presents a method to compute optimal open-loop trajectories for systems subject to state and control inequality constraints in which the cost function is quadratic and the state dynamics are linear. For the case in which inequality constraints are decentralized with respect to the controls, optimal Lagrange multipliers enforcing the inequality constraints may be found at any time through Pontryagin’s minimum principle. In so doing, the set of differential algebraic Euler-Lagrange equations is transformed into a nonlinear two-point boundary-value problem for states and costates whose solution meets the necessary conditions for optimality. The optimal performance of inequality constrained control systems is calculable, allowing for comparison to previous, sub-optimal solutions. The method is applied to the control of damping forces in a vibration isolation system subjected to constraints imposed by the physical implementation of a particular controllable damper. An outcome of this study is the best performance achievable given a particular objective, isolation system, and semi-active damper constraints.
Computational Methods for Design, Control and Optimization
2007-10-01
34scenario" that applies to channel flows ( Poiseuille flows , Couette flow ) and pipe flows . Over the past 75 years many complex "transition theories" have...other areas of flow control, optimization and aerodynamic design. approximate sensitivity calculations and optimization codes. The effort was built on a...for fluid flow problems. The improved robustness and computational efficiency of this approach makes it practical for a wide class of problems. The
FEEDBACK CONTROL OPTIMIZATION FOR SEISMICALLY EXCITED BUILDINGS
Institute of Scientific and Technical Information of China (English)
Xueping Li; Zuguang Ying
2007-01-01
A feedback control optimization method of partially observable linear structures via stationary response is proposed and analyzed with linear building structures equipped with control devices and sensors. First, the partially observable control problem of the structure under horizontal ground acceleration excitation is converted into a completely observable control problem. Then the It(o) stochastic differential equations of the system are derived based on the stochastic averaging method for quasi-integrable Hamiltonian systems and the stationary solution to the Fokker-Plank-Kolmogorov (FPK) equation associated with the It(o) equations is obtained.The performance index in terms of the mean system energy and mean square control force is established and the optimal control force is obtained by minimizing the performance index. Finally, the numerical results for a three-story building structure model under El Centro, Hachinohe,Northridge and Kobe earthquake excitations are given to illustrate the application and the effectiveness of the proposed method.
Multimodel methods for optimal control of aeroacoustics.
Energy Technology Data Exchange (ETDEWEB)
Chen, Guoquan (Rice University, Houston, TX); Collis, Samuel Scott
2005-01-01
A new multidomain/multiphysics computational framework for optimal control of aeroacoustic noise has been developed based on a near-field compressible Navier-Stokes solver coupled with a far-field linearized Euler solver both based on a discontinuous Galerkin formulation. In this approach, the coupling of near- and far-field domains is achieved by weakly enforcing continuity of normal fluxes across a coupling surface that encloses all nonlinearities and noise sources. For optimal control, gradient information is obtained by the solution of an appropriate adjoint problem that involves the propagation of adjoint information from the far-field to the near-field. This computational framework has been successfully applied to study optimal boundary-control of blade-vortex interaction, which is a significant noise source for helicopters on approach to landing. In the model-problem presented here, the noise propagated toward the ground is reduced by 12dB.
Optimal control applications in electric power systems
Christensen, G S; Soliman, S A
1987-01-01
Significant advances in the field of optimal control have been made over the past few decades. These advances have been well documented in numerous fine publications, and have motivated a number of innovations in electric power system engineering, but they have not yet been collected in book form. Our purpose in writing this book is to provide a description of some of the applications of optimal control techniques to practical power system problems. The book is designed for advanced undergraduate courses in electric power systems, as well as graduate courses in electrical engineering, applied mathematics, and industrial engineering. It is also intended as a self-study aid for practicing personnel involved in the planning and operation of electric power systems for utilities, manufacturers, and consulting and government regulatory agencies. The book consists of seven chapters. It begins with an introductory chapter that briefly reviews the history of optimal control and its power system applications and also p...
2016 Network Games, Control, and Optimization Conference
Jimenez, Tania; Solan, Eilon
2017-01-01
This contributed volume offers a collection of papers presented at the 2016 Network Games, Control, and Optimization conference (NETGCOOP), held at the University of Avignon in France, November 23-25, 2016. These papers highlight the increasing importance of network control and optimization in many networking application domains, such as mobile and fixed access networks, computer networks, social networks, transportation networks, and, more recently, electricity grids and biological networks. Covering a wide variety of both theoretical and applied topics in the areas listed above, the authors explore several conceptual and algorithmic tools that are needed for efficient and robust control operation, performance optimization, and better understanding the relationships between entities that may be acting cooperatively or selfishly in uncertain and possibly adversarial environments. As such, this volume will be of interest to applied mathematicians, computer scientists, engineers, and researchers in other relate...
Rhoads, Lloyd A.
This thesis builds upon recent studies focusing on modeling, operation, and control of high temperature gas cooled reactors. A computer model was developed, based on mass, energy, and momentum balances of control volumes throughout the plant. Several simulations of the plant behavior were conducted and their results were compared with those from the literature. Proportional control was combined with optimal control to form a time varying, adjustable gain predictive controller which adjusts the proportional gains during transients. The controller was designed to utilize control rod motions and bypass control valves to maintain desired plant conditions. An optimization scheme was introduced to efficiently solve the optimization problem formulated as part of the predictive controller operation. Several additional transients were run to examine the full plant controller performance. Multiple predictive controllers were designed and their performance was compared with a proportional controller throughout each transient. The predictive controller results confirmed the importance of proper selection of the optimal controller parameters, in particular the controller time step size and the horizon time. The well-designed proportional controllers clearly demonstrated improvements in plant performance during short time scale transients, namely a loss of secondary heat transfer transient and a step change in desired power transient. Results from long time scale transients demonstrated the capabilities of the proposed bypass control system to control electrical power production without the need for storage vessels.
Stochastic Optimal Control Models for Online Stores
Bradonjić, Milan
2011-01-01
We present a model for the optimal design of an online auction/store by a seller. The framework we use is a stochastic optimal control problem. In our setting, the seller wishes to maximize her average wealth level, where she can control her price per unit via her reputation level. The corresponding Hamilton-Jacobi-Bellmann equation is analyzed for an introductory case. We then turn to an empirically justified model, and present introductory analysis. In both cases, {\\em pulsing} advertising strategies are recovered for resource allocation. Further numerical and functional analysis will appear shortly.
Optimal control application to an Ebola model
Institute of Scientific and Technical Information of China (English)
Ebenezer Bonyah; Kingsley Badu; Samuel Kwesi Asiedu-Addo
2016-01-01
Ebola virus is a severe,frequently fatal illness,with a case fatality rate up to 90%.The outbreak of the disease has been acknowledged by World Health Organization as Public Health Emergency of International Concern.The threat of Ebola in West Africa is still a major setback to the socioeconomic development.Optimal control theory is applied to a system of ordinary differential equations which is modeling Ebola infection through three different routes including contact between humans and a dead body.In an attempt to reduce infection in susceptible population,a preventive control is put in the form of education and campaign and two treatment controls are applied to infected and late-stage infected(super) human population.The Pontryagins maximum principle is employed to characterize optimality control,which is then solved numerically.It is observed that time optimal control is existed in the model.The activation of each control showed a positive reduction of infection.The overall effect of activation of all the controls simultaneously reduced the effort required for the reduction of the infection quickly.The obtained results present a good framework for planning and designing cost-effective strategies for good interventions in dealing with Ebola disease.It is established that in order to reduce Ebola threat all the three controls must be taken into consideration concurrently.
OPTIMAL CONTROL ALGORITHMS FOR SECOND ORDER SYSTEMS
Directory of Open Access Journals (Sweden)
Danilo Pelusi
2013-01-01
Full Text Available Proportional Integral Derivative (PID controllers are widely used in industrial processes for their simplicity and robustness. The main application problems are the tuning of PID parameters to obtain good settling time, rise time and overshoot. The challenge is to improve the timing parameters to achieve optimal control performances. Remarkable findings are obtained through the use of Artificial Intelligence techniques as Fuzzy Logic, Genetic Algorithms and Neural Networks. The combination of these theories can give good results in terms of settling time, rise time and overshoot. In this study, suitable controllers able of improving timing performance of second order plants are proposed. The results show that the PID controller has good overshoot values and shows optimal robustness. The genetic-fuzzy controller gives a good value of settling time and a very good overshoot value. The neural-fuzzy controller gives the best timing parameters improving the control performances of the others two approaches. Further improvements are achieved designing a real-time optimization algorithm which works on a genetic-neuro-fuzzy controller.
Directory of Open Access Journals (Sweden)
Alireza Khosravi
2012-03-01
Full Text Available This paper deals with the design of optimal backstepping controller, by using the chaotic particle swarm optimization (CPSO algorithm to control of chaos in Lure like chaotic system. The backstepping method consists of parameters which could have positive values. The parameters are usually chosen optional by trial and error method. The controlled system provides different behaviors for different values of the parameters. It is necessary to select proper parameters to obtain a good response, because the improper selection of the parameters leads to inappropriate responses or even may lead to instability of the system. The proposed optimal backstepping controller without trial and error determines the parameters of backstepping controller automatically and intelligently by minimizing the Integral of Time multiplied Absolute Error (ITAE and squared controller output. Finally, the efficiency of the proposed optimal backstepping controller (OBSC is illustrated by implementing the method on the Lure like chaotic system.
Directory of Open Access Journals (Sweden)
Mohammad Aghaei
2013-10-01
Full Text Available To maintain and achieve optimal growth, development and to be more competitive, organizations need a comprehensive and coherent plan compatible with their objectives and goals which is called strategic planning. This research aims to analyse strategically “Etka Chain Stores” and to propose optimal strategies by using SWOT model and based on fuzzy logic. The scope of this research is limited to “Etka Chain stores in Tehran”. As instrumentation, a questioner, consisting of 138 questions, was used. The data have been extracted from interviews to managers and the expert in Etka Chain stores and from studying the available reports in the organization. Validity and reliability of research have been measured. The research consists of two main and four subsidiary questions and lacks hypothesis and regarding type of the objective, this research is an applied one and regarding the data gathering, it is experimental-descriptive and a case study. Analysing the data consists of five steps. In the first step, all the documents, interviews to organization experts and “Etka Chain stores” reports were analysed by tests and a list of environmental opportunities and threats together with strengths and weaknesses was prepared. In the second step, all the above-mentioned factors were screened and opportunities, threats, strengths and weaknesses were identified. In the third stage, by using key factors and SWOT model, the most suitable strategies for the company have been proposed. In the fifth step, an operational program is proposed. The findings of the research indicate that to be more competitive in key axis which includes customers, supply chain, expanses control, competitive smartness, human resources and operational productivity, the company should adopt suitable strategies. In this regard, the suitable strategies were identified, codified and proposed. In this research, planning a strategic management model, analysing value chain for spotting
Optimal Investment Control of Macroeconomic Systems
Institute of Scientific and Technical Information of China (English)
ZHAO Ke-jie; LIU Chuan-zhe
2006-01-01
Economic growth is always accompanied by economic fluctuation. The target of macroeconomic control is to keep a basic balance of economic growth, accelerate the optimization of economic structures and to lead a rapid, sustainable and healthy development of national economies, in order to propel society forward. In order to realize the above goal, investment control must be regarded as the most important policy for economic stability. Readjustment and control of investment includes not only control of aggregate investment, but also structural control which depends on economic-technology relationships between various industries of a national economy. On the basis of the theory of a generalized system, an optimal investment control model for government has been developed. In order to provide a scientific basis for government to formulate a macroeconomic control policy, the model investigates the balance of total supply and aggregate demand through an adjustment in investment decisions realizes a sustainable and stable growth of the national economy. The optimal investment decision function proposed by this study has a unique and specific expression, high regulating precision and computable characteristics.
Optimal control of anthracnose using mixed strategies.
Fotsa Mbogne, David Jaures; Thron, Christopher
2015-11-01
In this paper we propose and study a spatial diffusion model for the control of anthracnose disease in a bounded domain. The model is a generalization of the one previously developed in [15]. We use the model to simulate two different types of control strategies against anthracnose disease. Strategies that employ chemical fungicides are modeled using a continuous control function; while strategies that rely on cultivational practices (such as pruning and removal of mummified fruits) are modeled with a control function which is discrete in time (though not in space). For comparative purposes, we perform our analyses for a spatially-averaged model as well as the space-dependent diffusion model. Under weak smoothness conditions on parameters we demonstrate the well-posedness of both models by verifying existence and uniqueness of the solution for the growth inhibition rate for given initial conditions. We also show that the set [0, 1] is positively invariant. We first study control by impulsive strategies, then analyze the simultaneous use of mixed continuous and pulse strategies. In each case we specify a cost functional to be minimized, and we demonstrate the existence of optimal control strategies. In the case of pulse-only strategies, we provide explicit algorithms for finding the optimal control strategies for both the spatially-averaged model and the space-dependent model. We verify the algorithms for both models via simulation, and discuss properties of the optimal solutions. Copyright © 2015 Elsevier Inc. All rights reserved.
Optimal Control Design with Limited Model Information
Farokhi, F; Johansson, K H
2011-01-01
We introduce the family of limited model information control design methods, which construct controllers by accessing the plant's model in a constrained way, according to a given design graph. We investigate the achievable closed-loop performance of discrete-time linear time-invariant plants under a separable quadratic cost performance measure with structured static state-feedback controllers. We find the optimal control design strategy (in terms of the competitive ratio and domination metrics) when the control designer has access to the local model information and the global interconnection structure of the plant-to-be-controlled. At last, we study the trade-off between the amount of model information exploited by a control design method and the best closed-loop performance (in terms of the competitive ratio) of controllers it can produce.
Optimally Controlled Flexible Fuel Powertrain System
Energy Technology Data Exchange (ETDEWEB)
Hakan Yilmaz; Mark Christie; Anna Stefanopoulou
2010-12-31
The primary objective of this project was to develop a true Flex Fuel Vehicle capable of running on any blend of ethanol from 0 to 85% with reduced penalty in usable vehicle range. A research and development program, targeting 10% improvement in fuel economy using a direct injection (DI) turbocharged spark ignition engine was conducted. In this project a gasoline-optimized high-technology engine was considered and the hardware and configuration modifications were defined for the engine, fueling system, and air path. Combined with a novel engine control strategy, control software, and calibration this resulted in a highly efficient and clean FFV concept. It was also intended to develop robust detection schemes of the ethanol content in the fuel integrated with adaptive control algorithms for optimized turbocharged direct injection engine combustion. The approach relies heavily on software-based adaptation and optimization striving for minimal modifications to the gasoline-optimized engine hardware system. Our ultimate objective was to develop a compact control methodology that takes advantage of any ethanol-based fuel mixture and not compromise the engine performance under gasoline operation.
An asymptotically optimal nonparametric adaptive controller
Institute of Scientific and Technical Information of China (English)
郭雷; 谢亮亮
2000-01-01
For discrete-time nonlinear stochastic systems with unknown nonparametric structure, a kernel estimation-based nonparametric adaptive controller is constructed based on truncated certainty equivalence principle. Global stability and asymptotic optimality of the closed-loop systems are established without resorting to any external excitations.
On Optimal Control of a Brownian Motion.
1982-06-01
barriers. Puterman [9] uses diffusion processes to model production and inventory processes. In both cases they assume the existence of a stationary... Puterman , A diffusion model for a storage system, Logistic, M. Geisler ed., North-Holland 197S. [101 J. Rath, The optimal policy for a controlled
Optimizing discrete control systems with phase limitations
Energy Technology Data Exchange (ETDEWEB)
Shakhverdian, S.B.; Abramian, A.K.
1981-01-01
A new method is proposed for solving discrete problems of optimizing control systems with limitations on the phase coordinates. Results are given from experimental research which demonstrate the need to introduce tangential limitations independent of the method of accounting for the phase limitations.
Efficient evolutionary algorithms for optimal control
López Cruz, I.L.
2002-01-01
If optimal control problems are solved by means of gradient based local search methods, convergence to local solutions is likely. Recently, there has been an increasing interest in the use
Determination of optimal gains for constrained controllers
Energy Technology Data Exchange (ETDEWEB)
Kwan, C.M.; Mestha, L.K.
1993-08-01
In this report, we consider the determination of optimal gains, with respect to a certain performance index, for state feedback controllers where some elements in the gain matrix are constrained to be zero. Two iterative schemes for systematically finding the constrained gain matrix are presented. An example is included to demonstrate the procedures.
2009-01-01
This paper describes the application of model-based predictive control (MPC) techniques to the flow management in large-scale drinking water networks including a telemetry/telecontrol system. MPC technique is used to generate flow control strategies from the sources to the consumer areas to meet future demands, optimizing performance indexes associated to operational goals such as economic cost, network safety volumes and flow control stability. The designed management strategies are...
Optimization-based controller design for rotorcraft
Tsing, N.-K.; Fan, M. K. H.; Barlow, J.; Tits, A. L.; Tischler, M. B.
1993-01-01
An optimization-based methodology for linear control system design is outlined by considering the design of a controller for a UH-60 rotorcraft in hover. A wide range of design specifications is taken into account: internal stability, decoupling between longitudinal and lateral motions, handling qualities, and rejection of windgusts. These specifications are investigated while taking into account physical limitations in the swashplate displacements and rates of displacement. The methodology crucially relies on user-machine interaction for tradeoff exploration.
Energy Technology Data Exchange (ETDEWEB)
Ureba, A. [Dpto. Fisiología Médica y Biofísica. Facultad de Medicina, Universidad de Sevilla, E-41009 Sevilla (Spain); Salguero, F. J. [Nederlands Kanker Instituut, Antoni van Leeuwenhoek Ziekenhuis, 1066 CX Ámsterdam, The Nederlands (Netherlands); Barbeiro, A. R.; Jimenez-Ortega, E.; Baeza, J. A.; Leal, A., E-mail: alplaza@us.es [Dpto. Fisiología Médica y Biofísica, Facultad de Medicina, Universidad de Sevilla, E-41009 Sevilla (Spain); Miras, H. [Servicio de Radiofísica, Hospital Universitario Virgen Macarena, E-41009 Sevilla (Spain); Linares, R.; Perucha, M. [Servicio de Radiofísica, Hospital Infanta Luisa, E-41010 Sevilla (Spain)
2014-08-15
Purpose: The authors present a hybrid direct multileaf collimator (MLC) aperture optimization model exclusively based on sequencing of patient imaging data to be implemented on a Monte Carlo treatment planning system (MC-TPS) to allow the explicit radiation transport simulation of advanced radiotherapy treatments with optimal results in efficient times for clinical practice. Methods: The planning system (called CARMEN) is a full MC-TPS, controlled through aMATLAB interface, which is based on the sequencing of a novel map, called “biophysical” map, which is generated from enhanced image data of patients to achieve a set of segments actually deliverable. In order to reduce the required computation time, the conventional fluence map has been replaced by the biophysical map which is sequenced to provide direct apertures that will later be weighted by means of an optimization algorithm based on linear programming. A ray-casting algorithm throughout the patient CT assembles information about the found structures, the mass thickness crossed, as well as PET values. Data are recorded to generate a biophysical map for each gantry angle. These maps are the input files for a home-made sequencer developed to take into account the interactions of photons and electrons with the MLC. For each linac (Axesse of Elekta and Primus of Siemens) and energy beam studied (6, 9, 12, 15 MeV and 6 MV), phase space files were simulated with the EGSnrc/BEAMnrc code. The dose calculation in patient was carried out with the BEAMDOSE code. This code is a modified version of EGSnrc/DOSXYZnrc able to calculate the beamlet dose in order to combine them with different weights during the optimization process. Results: Three complex radiotherapy treatments were selected to check the reliability of CARMEN in situations where the MC calculation can offer an added value: A head-and-neck case (Case I) with three targets delineated on PET/CT images and a demanding dose-escalation; a partial breast
Meng, Deyuan; Tao, Guoliang; Liu, Hao; Zhu, Xiaocong
2014-07-01
Friction compensation is particularly important for motion trajectory tracking control of pneumatic cylinders at low speed movement. However, most of the existing model-based friction compensation schemes use simple classical models, which are not enough to address applications with high-accuracy position requirements. Furthermore, the friction force in the cylinder is time-varying, and there exist rather severe unmodelled dynamics and unknown disturbances in the pneumatic system. To deal with these problems effectively, an adaptive robust controller with LuGre model-based dynamic friction compensation is constructed. The proposed controller employs on-line recursive least squares estimation (RLSE) to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. In addition, in order to realize LuGre model-based friction compensation, the modified dual-observer structure for estimating immeasurable friction internal state is developed. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology is applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping is used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Finally, the proposed controller is tested for tracking sinusoidal trajectories and smooth square trajectory under different loads and sudden disturbance. The testing results demonstrate that the achievable performance of the proposed controller is excellent and is much better than most other studies in literature. Especially when a 0.5 Hz sinusoidal trajectory is tracked, the maximum tracking error is 0.96 mm and the average tracking error is 0.45 mm. This
Helicopter trajectory planning using optimal control theory
Menon, P. K. A.; Cheng, V. H. L.; Kim, E.
1988-01-01
A methodology for optimal trajectory planning, useful in the nap-of-the-earth guidance of helicopters, is presented. This approach uses an adjoint-control transformation along with a one-dimensional search scheme for generating the optimal trajectories. In addition to being useful for helicopter nap-of-the-earth guidance, the trajectory planning solution is of interest in several other contexts, such as robotic vehicle guidance and terrain-following guidance for cruise missiles and aircraft. A distinguishing feature of the present research is that the terrain constraint and the threat envelopes are incorporated in the equations of motion. Second-order necessary conditions are examined.
A model based message passing approach for flexible and scalable home automation controllers
Energy Technology Data Exchange (ETDEWEB)
Bienhaus, D. [INNIAS GmbH und Co. KG, Frankenberg (Germany); David, K.; Klein, N.; Kroll, D. [ComTec Kassel Univ., SE Kassel Univ. (Germany); Heerdegen, F.; Jubeh, R.; Zuendorf, A. [Kassel Univ. (Germany). FG Software Engineering; Hofmann, J. [BSC Computer GmbH, Allendorf (Germany)
2012-07-01
There is a large variety of home automation systems that are largely proprietary systems from different vendors. In addition, the configuration and administration of home automation systems is frequently a very complex task especially, if more complex functionality shall be achieved. Therefore, an open model for home automation was developed that is especially designed for easy integration of various home automation systems. This solution also provides a simple modeling approach that is inspired by typical home automation components like switches, timers, etc. In addition, a model based technology to achieve rich functionality and usability was implemented. (orig.)
Optimization for efficient structure-control systems
Oz, Hayrani; Khot, Narendra S.
1993-01-01
The efficiency of a structure-control system is a nondimensional parameter which indicates the fraction of the total control power expended usefully in controlling a finite-dimensional system. The balance of control power is wasted on the truncated dynamics serving no useful purpose towards the control objectives. Recently, it has been demonstrated that the concept of efficiency can be used to address a number of control issues encountered in the control of dynamic systems such as the spillover effects, selection of a good input configuration and obtaining reduced order control models. Reference (1) introduced the concept and presented analyses of several Linear Quadratic Regulator designs on the basis of their efficiencies. Encouraged by the results of Ref. (1), Ref. (2) introduces an efficiency modal analysis of a structure-control system which gives an internal characterization of the controller design and establishes the link between the control design and the initial disturbances to affect efficient structure-control system designs. The efficiency modal analysis leads to identification of principal controller directions (or controller modes) distinct from the structural natural modes. Thus ultimately, many issues of the structure-control system revolve around the idea of insuring compatibility of the structural modes and the controller modes with each other, the better the match the higher the efficiency. A key feature in controlling a reduced order model of a high dimensional (or infinity-dimensional distributed parameter system) structural dynamic system must be to achieve high efficiency of the control system while satisfying the control objectives and/or constraints. Formally, this can be achieved by designing the control system and structural parameters simultaneously within an optimization framework. The subject of this paper is to present such a design procedure.
Optimal control of vibrational transitions of HCl
Indian Academy of Sciences (India)
KRISHNA REDDY NANDIPATI; ARUN KUMAR KANAKATI
2016-10-01
Control of fundamental and overtone transitions of a vibration are studied for the diatomic molecule, HCl. Specifically, the results of the effect of variation of the penalty factor on the physical attributes of the system (i.e., probabilities) and pulse (i.e., amplitudes) considering three different pulse durations for each value of the penalty factor are shown and discussed. We have employed the optimal control theory to obtain infrared pulses for selective vibrational transitions. The optimization of initial guess field with Gaussian envelope, phrased as maximization of cost functional, is done using the conjugate gradient method. The interaction of the field with the molecule is treated within the semiclassical dipole approximation. The potential and the dipole moment functions used in the calculations of control dynamics are obtained from high level ab-initio calculations.
Recent developments in cooperative control and optimization
Murphey, Robert; Pardalos, Panos
2004-01-01
Over the past several years, cooperative control and optimization has un questionably been established as one of the most important areas of research in the military sciences. Even so, cooperative control and optimization tran scends the military in its scope -having become quite relevant to a broad class of systems with many exciting, commercial, applications. One reason for all the excitement is that research has been so incredibly diverse -spanning many scientific and engineering disciplines. This latest volume in the Cooperative Systems book series clearly illustrates this trend towards diversity and creative thought. And no wonder, cooperative systems are among the hardest systems control science has endeavored to study, hence creative approaches to model ing, analysis, and synthesis are a must! The definition of cooperation itself is a slippery issue. As you will see in this and previous volumes, cooperation has been cast into many different roles and therefore has assumed many diverse meanings. P...
Maximum process problems in optimal control theory
Directory of Open Access Journals (Sweden)
Goran Peskir
2005-01-01
Full Text Available Given a standard Brownian motion (Btt≥0 and the equation of motion dXt=vtdt+2dBt, we set St=max0≤s≤tXs and consider the optimal control problem supvE(Sτ−Cτ, where c>0 and the supremum is taken over all admissible controls v satisfying vt∈[μ0,μ1] for all t up to τ=inf{t>0|Xt∉(ℓ0,ℓ1} with μ0g∗(St, where s↦g∗(s is a switching curve that is determined explicitly (as the unique solution to a nonlinear differential equation. The solution found demonstrates that the problem formulations based on a maximum functional can be successfully included in optimal control theory (calculus of variations in addition to the classic problem formulations due to Lagrange, Mayer, and Bolza.
On the Optimal Controller for LTV Measurement Feedback Control Problem
Institute of Scientific and Technical Information of China (English)
Ting GONG; Yu Feng LU
2011-01-01
In this paper, we consider the measurement feedback control problem for discrete linear time-varying systems within the framework of nest algebra consisting of causal and bounded linear operators. Based on the inner-outer factorization of operators, we reduce the control problem to a distance from a certain operator to a special subspace of a nest algebra and show the existence of the optimal LTV controller in two different ways: one via the characteristic of the subspace in question directly, the other via the duality theory. The latter also gives a new formula for computing the optimal cost.
Optimization Algorithms for Nuclear Reactor Power Control
Energy Technology Data Exchange (ETDEWEB)
Kim, Yeong Min; Oh, Won Jong; Oh, Seung Jin; Chun, Won Gee; Lee, Yoon Joon [Jeju National University, Jeju (Korea, Republic of)
2010-10-15
One of the control techniques that could replace the present conventional PID controllers in nuclear plants is the linear quadratic regulator (LQR) method. The most attractive feature of the LQR method is that it can provide the systematic environments for the control design. However, the LQR approach heavily depends on the selection of cost function and the determination of the suitable weighting matrices of cost function is not an easy task, particularly when the system order is high. The purpose of this paper is to develop an efficient and reliable algorithm that could optimize the weighting matrices of the LQR system
Robust Structured Control Design via LMI Optimization
DEFF Research Database (Denmark)
Adegas, Fabiano Daher; Stoustrup, Jakob
2011-01-01
This paper presents a new procedure for discrete-time robust structured control design. Parameter-dependent nonconvex conditions for stabilizable and induced L2-norm performance controllers are solved by an iterative linear matrix inequalities (LMI) optimization. A wide class of controller...... structures including decentralized of any order, ﬁxed-order dynamic output feedback, static output feedback can be designed robust to polytopic uncertainties. Stability is proven by a parameter-dependent Lyapunov function. Numerical examples on robust stability margins shows that the proposed procedure can...
Indian Academy of Sciences (India)
J Satya Eswari; M Anand; C Venkateswarlu
2016-01-01
Central composite rotatable design (CCRD) of experiments was used to obtain data for Lipopeptide and Biomass concentrations from fermentation medium containing the following five components: glucose,monosodium glutamate, yeast extract,MgSO4·7H2O, and K2HPO4. Data was used to develop a second order regression response surface model (RSM) which was coupled with ant colony optimization (ACO) to optimize the media compositions so as to enhance the productivity of lipopeptide. The optimized media by ACO was found to yield 1.501 g/L of lipopeptide concentration which was much higher compared to 1.387 g/L predicted by Nelder–Mead optimization (NMO). The optimum from ACO was validated experimentally. RSM-based ACO is thus shown to be an effective tool for medium optimization of biosurfactant production.
DEFF Research Database (Denmark)
Linker, Raphael; Ioslovich, Ilya; Sylaios, Georgios
2016-01-01
variables are the irrigation amounts for each day of the season. The objective function is the expected yield calculated with the use of a model. In the present work we solved this optimization problem for three crops modeled by the model AquaCrop. This optimization problem is non-trivial due to the non......-smooth behavior of the objective function and the fact that it involves multiple integer variables. We developed an optimization scheme for generating sub-optimal irrigation schedules that take implicitly into account the response of the crop to water stress, and used these as initial guesses for a full...... should use an irrigation schedule that maximizes the yield and abides to the quota constraints. In contrast to the widespread use of irrigation scheduling based on agronomy practices, irrigation scheduling may be considered as a constrained optimization problem. When drip irrigation is used, the decision...
Optimal coordinated voltage control of power systems
Institute of Scientific and Technical Information of China (English)
LI Yan-jun; HILL David J.; WU Tie-jun
2006-01-01
An immune algorithm solution is proposed in this paper to deal with the problem of optimal coordination of local physically based controllers in order to preserve or retain mid and long term voltage stability. This problem is in fact a global coordination control problem which involves not only sequencing and timing different control devices but also tuning the parameters of controllers. A multi-stage coordinated control scheme is presented, aiming at retaining good voltage levels with minimal control efforts and costs after severe disturbances in power systems. A self-pattern-recognized vaccination procedure is developed to transfer effective heuristic information into the new generation of solution candidates to speed up the convergence of the search procedure to global optima. An example of four bus power system case study is investigated to show the effectiveness and efficiency of the proposed algorithm, compared with several existing approaches such as differential dynamic programming and tree-search.
Computer-Aided Design Methods for Model-Based Nonlinear Engine Control Systems Project
National Aeronautics and Space Administration — Traditional design methods for aircraft turbine engine control systems have relied on the use of linearized models and linear control theory. While these controllers...
Aerodynamic shape optimization using control theory
Reuther, James
1996-01-01
Aerodynamic shape design has long persisted as a difficult scientific challenge due its highly nonlinear flow physics and daunting geometric complexity. However, with the emergence of Computational Fluid Dynamics (CFD) it has become possible to make accurate predictions of flows which are not dominated by viscous effects. It is thus worthwhile to explore the extension of CFD methods for flow analysis to the treatment of aerodynamic shape design. Two new aerodynamic shape design methods are developed which combine existing CFD technology, optimal control theory, and numerical optimization techniques. Flow analysis methods for the potential flow equation and the Euler equations form the basis of the two respective design methods. In each case, optimal control theory is used to derive the adjoint differential equations, the solution of which provides the necessary gradient information to a numerical optimization method much more efficiently then by conventional finite differencing. Each technique uses a quasi-Newton numerical optimization algorithm to drive an aerodynamic objective function toward a minimum. An analytic grid perturbation method is developed to modify body fitted meshes to accommodate shape changes during the design process. Both Hicks-Henne perturbation functions and B-spline control points are explored as suitable design variables. The new methods prove to be computationally efficient and robust, and can be used for practical airfoil design including geometric and aerodynamic constraints. Objective functions are chosen to allow both inverse design to a target pressure distribution and wave drag minimization. Several design cases are presented for each method illustrating its practicality and efficiency. These include non-lifting and lifting airfoils operating at both subsonic and transonic conditions.
Bulgakov, V. K.; Strigunov, V. V.
2009-05-01
The Pontryagin maximum principle is used to prove a theorem concerning optimal control in regional macroeconomics. A boundary value problem for optimal trajectories of the state and adjoint variables is formulated, and optimal curves are analyzed. An algorithm is proposed for solving the boundary value problem of optimal control. The performance of the algorithm is demonstrated by computing an optimal control and the corresponding optimal trajectories.
Linear systems optimal and robust control
Sinha, Alok
2007-01-01
Introduction Overview Contents of the Book State Space Description of a Linear System Transfer Function of a Single Input/Single Output (SISO) System State Space Realizations of a SISO System SISO Transfer Function from a State Space Realization Solution of State Space Equations Observability and Controllability of a SISO System Some Important Similarity Transformations Simultaneous Controllability and Observability Multiinput/Multioutput (MIMO) Systems State Space Realizations of a Transfer Function Matrix Controllability and Observability of a MIMO System Matrix-Fraction Description (MFD) MFD of a Transfer Function Matrix for the Minimal Order of a State Space Realization Controller Form Realization from a Right MFD Poles and Zeros of a MIMO Transfer Function Matrix Stability Analysis State Feedback Control and Optimization State Variable Feedback for a Single Input System Computation of State Feedback Gain Matrix for a Multiinput System State Feedback Gain Matrix for a Multi...
Iterative learning control an optimization paradigm
Owens, David H
2016-01-01
This book develops a coherent theoretical approach to algorithm design for iterative learning control based on the use of optimization concepts. Concentrating initially on linear, discrete-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately because their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates that there are new algorithms that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference signals and also to support new algorithms for local convergence of nonlinear iterative control. Simulation and application studies are used to illustrate algorithm properties and performance in systems like gantry robots and other elect...
Dynamics of Dengue epidemics using optimal control
Rodrigues, Helena Sofia; Torres, Delfim F M
2010-01-01
We present an application of optimal control theory to Dengue epidemics. This epidemiologic disease is an important theme in tropical countries due to the growing number of infected individuals. The dynamic model is described by a set of nonlinear ordinary differential equations, that depend on the dynamic of the Dengue mosquito, the number of infected individuals, and the people's motivation to combat the mosquito. The cost functional depends not only on the costs of medical treatment of the infected people but also on the costs related to educational and sanitary campaigns. Two approaches to solve the problem are considered: one using optimal control theory, another one by discretizing first the problem and then solving it with nonlinear programming. The results obtained with OC-ODE and IPOPT solvers are given and discussed. We observe that with current computational tools it is easy to obtain, in an efficient way, better solutions to Dengue problems, leading to a decrease of infected mosquitoes and individ...
Model-Based Development and Evaluation of Control for Complex Multi-Domain Systems
DEFF Research Database (Denmark)
Grujic, Ivan; Nilsson, Rene
Unmanned Aerial Vehicle (UAV) has been constructed and used to develop an attitude controller based on Model Predictive Control (MPC). The MPC controller has been compared to an existing open source Proportional Integral Derivative (PID) attitude controller. This thesis contributes to the discipline...
Model-Based Control using Model and Mechanization Fusion Techniques for Image-Aided Navigation
2009-03-01
sized helicopter, LQG control has also been utilized in controlling the same type of vehicle. Zhe Jiang, Jianda Han, Yuechao Wang , and Qi Song, from the...Han J. Wang Y., Z. and Q. Song. “Enhanced LQR Control for Unmanned Helicopter in Hover”. Proceedings of Systems and Control in Aerospace and As
Mesh refinement strategy for optimal control problems
Paiva, Luis Tiago; Fontes, Fernando,
2013-01-01
International audience; Direct methods are becoming the most used technique to solve nonlinear optimal control problems. Regular time meshes having equidistant spacing are frequently used. However, in some cases these meshes cannot cope accurately with nonlinear behavior. One way to improve the solution is to select a new mesh with a greater number of nodes. Another way, involves adaptive mesh refinement. In this case, the mesh nodes have non equidistant spacing which allow a non uniform node...
Optimal control of complex atomic quantum systems
van Frank, S.; Bonneau, M.; Schmiedmayer, J.; Hild, S.; Gross, C.; Cheneau, M.; Bloch, I.; Pichler, T.; Negretti, A.; Calarco, T.; Montangero, S.
2016-10-01
Quantum technologies will ultimately require manipulating many-body quantum systems with high precision. Cold atom experiments represent a stepping stone in that direction: a high degree of control has been achieved on systems of increasing complexity. However, this control is still sub-optimal. In many scenarios, achieving a fast transformation is crucial to fight against decoherence and imperfection effects. Optimal control theory is believed to be the ideal candidate to bridge the gap between early stage proof-of-principle demonstrations and experimental protocols suitable for practical applications. Indeed, it can engineer protocols at the quantum speed limit – the fastest achievable timescale of the transformation. Here, we demonstrate such potential by computing theoretically and verifying experimentally the optimal transformations in two very different interacting systems: the coherent manipulation of motional states of an atomic Bose-Einstein condensate and the crossing of a quantum phase transition in small systems of cold atoms in optical lattices. We also show that such processes are robust with respect to perturbations, including temperature and atom number fluctuations.
Active control of transient rotordynamic vibration by optimal control methods
Palazzolo, A. B.; Lin, R. R.; Alexander, R. M.; Kascak, A. F.
1988-01-01
Although considerable effort has been put into the study of steady state vibration control, there are few methods applicable to transient vibration control of rotorbearing systems. In this paper optimal control theory has been adopted to minimize rotor vibration due to sudden imbalance, e.g., blade loss. The system gain matrix is obtained by choosing the weighting matrices and solving the Riccati equation. Control forces are applied to the system via a feedback loop. A seven mass rotor system is simulated for illustration. A relationship between the number of sensors and the number of modes used in the optimal control model is investigated. Comparisons of responses are made for various configurations of modes, sensors, and actuators. Furthermore, spillover effect is examined by comparing results from collocated and noncollocated sensor configurations. Results show that shaft vibration is significantly attenuated in the closed loop system.
An optimal promotion cost control model for a markovian manpower ...
African Journals Online (AJOL)
An optimal promotion cost control model for a markovian manpower system. ... Log in or Register to get access to full text downloads. ... A theory concerning the existence of an optimal promotion control strategy for controlling a Markovian ...
Automatic Synthesis of Robust and Optimal Controllers
DEFF Research Database (Denmark)
Cassez, Franck; Jessen, Jan Jacob; Larsen, Kim Guldstrand;
2009-01-01
In this paper, we show how to apply recent tools for the automatic synthesis of robust and near-optimal controllers for a real industrial case study. We show how to use three different classes of models and their supporting existing tools, Uppaal-TiGA for synthesis, phaver for verification......, and Simulink for simulation, in a complementary way. We believe that this case study shows that our tools have reached a level of maturity that allows us to tackle interesting and relevant industrial control problems....
Tractable problems in optimal decentralized control
Rotkowitz, Michael Charles
2005-07-01
This thesis considers the problem of constructing optimal decentralized controllers. The problem is formulated as one of minimizing the closed-loop norm of a feedback system subject to constraints on the controller structure. The notion of quadratic invariance of a constraint set with respect to a system is defined. It is shown that quadratic invariance is necessary and sufficient for the constraint set to be preserved under feedback. It is further shown that if the constraint set has this property, this allows the constrained minimum-norm problem to be solved via convex programming. These results are developed in a very general framework, and are shown to hold for continuous-time systems, discrete-time systems, or operators on Banach spaces, for stable or unstable plants, and for the minimization of any norm. The utility of these results is then demonstrated on some specific constraint classes. An explicit test is derived for sparsity constraints on a controller to be quadratically invariant, and thus amenable to convex synthesis. Symmetric synthesis is also shown to be quadratically invariant. The problem of control over networks with delays is then addressed as another constraint class. Multiple subsystems are considered, each with its own controller, such that the dynamics of each subsystem may affect those of other subsystems with some propagation delays, and the controllers may communicate with each other with some transmission delays. It is shown that if the communication delays are less than the propagation delays, then the associated constraints are quadratically invariant, and thus optimal controllers can be synthesized. We further show that this result still holds in the presence of computational delays. This thesis unifies the few previous results on specific tractable decentralized control problems, identifies broad and useful classes of new solvable problems, and delineates the largest known class of convex problems in decentralized control.
On necessary optimality conditions in discrete control systems
Mardanov, M. J.; Melikov, T. K.; Mahmudov, N. I.
2015-10-01
The paper deals with a nonlinear discrete-time optimal control problem with a cost functional of terminal type. Using a new variation of the control and new properties of optimal controls, we prove the linearised optimality conditions extending such classical optimality conditions. Along with this, various optimality conditions of quasi-singular controls are obtained. Finally, the examples illustrating the rich content of the obtained results are illustrated.
Energy Technology Data Exchange (ETDEWEB)
Queipo, Nestor V.; Pintos, Salvador; Rincon, Nestor; Contreras, Nemrod; Colmenares, Juan [Applied Computing Institute, Faculty of Engineering, University of Zulia, Zulia (Venezuela)
2002-08-01
This paper presents a solution methodology for the inverse problem of estimating the distributions of permeability and porosity in heterogeneous and multiphase petroleum reservoirs by matching the static and dynamic data available. The solution methodology includes, the construction of a 'fast surrogate' of an objective function whose evaluation involves the execution of a time-consuming mathematical model (i.e., reservoir numerical simulator) based on neural networks, DACE (design and analysis of computer experiment) modeling, and adaptive sampling. Using adaptive sampling, promising areas are searched considering the information provided by the surrogate model and the expected value of the errors. The proposed methodology provides a global optimization method, hence avoiding the potential problem of convergence to a local minimum in the objective function exhibited by the commonly Gauss-Newton methods. Furthermore, it exhibits an affordable computational cost, is amenable to parallel processing, and is expected to outperform other general-purpose global optimization methods such as, simulated annealing, and genetic algorithms.The methodology is evaluated using two case studies of increasing complexity (from 6 to 23 independent parameters). From the results, it is concluded that the methodology can be used effectively and efficiently for reservoir characterization purposes. In addition, the optimization approach holds promise to be useful in the optimization of objective functions involving the execution of computationally expensive reservoir numerical simulators, such as those found, not only in reservoir characterization, but also in other areas of petroleum engineering (e.g., EOR optimization)
Optimal feedback scheduling of model predictive controllers
Institute of Scientific and Technical Information of China (English)
Pingfang ZHOU; Jianying XIE; Xiaolong DENG
2006-01-01
Model predictive control (MPC) could not be reliably applied to real-time control systems because its computation time is not well defined. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance, thus obtaining the predictability in time. Optimal feedback scheduling (FS-CBS) of a set of MPC tasks is presented to maximize the global control performance subject to limited processor time. Each MPC task is assigned with a constant bandwidth server (CBS), whose reserved processor time is adjusted dynamically. The constraints in the FSCBS guarantee scheduler of the total task set and stability of each component. The FS-CBS is shown robust against the variation of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.
Ulusoy, Mehmet Alphan
2009-01-01
Owing to their distributed architecture, networked control systems are proven to be feasible in scenarios where a spatially distributed control system is required. Traditionally, such networked control systems operate over real-time wired networks over which sensors, controllers and actuators interact with each other. Recently, in order to achieve the utmost flexibility, scalability, ease of deployment and maintainability, wireless networks such as IEEE 802.11 LANs are being preferred over d...
Multiple Model Adaptive Estimation Techniques for Adaptive Model-Based Robot Control
1989-12-01
Proportional Derivative (PD) or Propor- tional Integral Derivative (PID) feedback controller [6]. 1-1 The PD or PID controllers feedback the measured...Unfortunately, as the speed of the trajectory increases or the con- figuration of the robot changes, the PD or PID controllers cannot maintain track along the...desired trajectory. The main reason for poor tracking is that the PD and PID controllers were developed based on a simplified linear dynamics model
Beyond the CP-curve in Model-based Control of Wind Turbines
DEFF Research Database (Denmark)
Henriksen, Lars Christian; Hansen, Morten Hartvig; Poulsen, Niels Kjølstad
2012-01-01
The importance of including dynamic inflow in the model used by the control algorithm is investigated in this contribution. A control setup consisting of a model predictive controller and an extended Kalman filter in conjunction with mechanisms to switch smoothly between partial and full load ope...
Applied optimal control theory of distributed systems
Lurie, K A
1993-01-01
This book represents an extended and substantially revised version of my earlierbook, Optimal Control in Problems ofMathematical Physics,originally published in Russian in 1975. About 60% of the text has been completely revised and major additions have been included which have produced a practically new text. My aim was to modernize the presentation but also to preserve the original results, some of which are little known to a Western reader. The idea of composites, which is the core of the modern theory of optimization, was initiated in the early seventies. The reader will find here its implementation in the problem of optimal conductivity distribution in an MHD-generatorchannel flow.Sincethen it has emergedinto an extensive theory which is undergoing a continuous development. The book does not pretend to be a textbook, neither does it offer a systematic presentation of the theory. Rather, it reflects a concept which I consider as fundamental in the modern approach to optimization of dis tributed systems. ...
Optimal control of circular cylinder wakes using long control horizons
Flinois, Thibault L B
2015-01-01
The classical problem of minimizing the drag of a circular cylinder by using body rotation is revisited in an adjoint-based optimal control framework. The cylinder's unsteady and fully unconstrained rotation rate is optimized at Reynolds numbers of 100 and 200 and over horizons that are longer than in previous studies, where they are typically of the order of a vortex shedding period or shorter. In the best configuration, the drag is reduced by $19\\%$, the vortex shedding is effectively suppressed, and this low drag state is maintained with minimal cylinder rotation after transients. Without closed-loop control, which maintains a specific phase relationship between the actuation and the shedding, the wake is not stabilized. A comparison is also given between the performance of optimizations for different horizon lengths and cost functions. It is shown that the long horizons used are necessary in order to stabilize the vortex shedding efficiently.
Tracking Control of A Balancing Robot – A Model-Based Approach
Directory of Open Access Journals (Sweden)
Zaiczek Tobias
2014-08-01
Full Text Available This paper presents a control concept for a single-axle mobile robot moving on the horizontal plane. A mathematical model of the nonholonomic mechanical system is derived using Hamel's equations of motion. Subsequently, a concept for a tracking controller is described in detail. This controller keeps the mobile robot on a given reference trajectory while maintaining it in an upright position. The control objective is reached by a cascade control structure. By an appropriate input transformation, we are able to utilize an input-output linearization of a subsystem. For the remaining dynamics a linear set-point control law is presented. Finally, the performance of the implemented control law is illustrated by simulation results.
Institute of Scientific and Technical Information of China (English)
Yuanyuan ZOU; Shaoyuan LI
2007-01-01
In this paper,a linear programming method is proposed to solve model predictive control for a class of hybrid systems.Firstly,using the(max,+)algebra,a typical subclass of hybrid systems called max-plus-linear(MPL)systems is obtained.And then,model predictive control(MPC)framework is extended to MPL systems.In general,the nonlinear optimization approach or extended linear complementarity problem(ELCP)were applied to solve the MPL-MPC optimization problem.A new optimization method based on canonical forms for max-min-plus-scaling(MMPS)functions (using the operations maximization,minimization,addition and scalar multiplication)with linear constraints on the inputs is presented.The proposed approach consists in solving several linear programming problems and is more efficient than nonlinear optimization.The validity of the algorithm is illustrated by an example.
Model-based control structure design of a full-scale WWTP under the retrofitting process.
Machado, V C; Lafuente, J; Baeza, J A
2015-01-01
The anoxic-oxic (A/O) municipal wastewater treatment plant (WWTP) of Manresa (Catalonia, Spain) was studied for a possible conversion to an anaerobic/anoxic/oxic (A2/O) configuration to promote enhanced biological phosphorus removal. The control structure had to be redesigned to satisfy the new necessity to control phosphorus concentration, besides ammonium and nitrate concentrations (main pollutant concentrations). Thereby, decentralized control structures with proportional-integral-derivative (PID) controllers and centralized control structures with model-predictive controllers (MPC) were designed and tested. All the designed control structures had their performance systematically tested regarding effluent quality and operating costs. The centralized control structure, A2/O-3-MPC, achieved the lowest operating costs with the best effluent quality using the A2/O plant configuration for the Manresa WWTP. The controlled variables used in this control structure were ammonium in the effluent, nitrate at the end of the anoxic zone and phosphate at the end of the anaerobic zone, while the manipulated variables were the internal and external recycle flow rates and the dissolved oxygen setpoint in the aerobic reactors.
Blasting neuroblastoma using optimal control of chemotherapy.
Collins, Craig; Fister, K Renee; Key, Bethany; Williams, Mary
2009-07-01
A mathematical model is used to investigate the effectiveness of the chemotherapy drug Topotecan against neuroblastoma. Optimal control theory is applied to minimize the tumor volume and the amount of drug utilized. The model incorporates a state constraint that requires the level of circulating neutrophils (white blood cells that form an integral part of the immune system) to remain above an acceptable value. The treatment schedule is designed to simultaneously satisfy this constraint and achieve the best results in fighting the tumor. Existence and uniqueness of the solution of the optimality system, which is the state system coupled with the adjoint system, is established. Numerical simulations are given to demonstrate the behavior of the tumor and the immune system components represented in the model.
Optimization-Based Approaches to Control of Probabilistic Boolean Networks
Directory of Open Access Journals (Sweden)
Koichi Kobayashi
2017-02-01
Full Text Available Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs, which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.
Lattice hydrodynamic model based traffic control: A transportation cyber-physical system approach
Liu, Hui; Sun, Dihua; Liu, Weining
2016-11-01
Lattice hydrodynamic model is a typical continuum traffic flow model, which describes the jamming transition of traffic flow properly. Previous studies in lattice hydrodynamic model have shown that the use of control method has the potential to improve traffic conditions. In this paper, a new control method is applied in lattice hydrodynamic model from a transportation cyber-physical system approach, in which only one lattice site needs to be controlled in this control scheme. The simulation verifies the feasibility and validity of this method, which can ensure the efficient and smooth operation of the traffic flow.
Development of a Robust Model-Based Water Level Controller for U-Tube Steam Generator
Energy Technology Data Exchange (ETDEWEB)
Basher, A.M.H.
2001-09-04
Poor control of steam generator water level of a nuclear power plant may lead to frequent nuclear reactor shutdowns. These shutdowns are more common at low power where the plant exhibits strong non-minimum phase characteristics and flow measurements at low power are unreliable in many instances. There is need to investigate this problem and systematically design a controller for water level regulation. This work is concerned with the study and the design of a suitable controller for a U-Tube Steam Generator (UTSG) of a Pressurized Water Reactor (PWR) which has time varying dynamics. The controller should be suitable for the water level control of UTSG without manual operation from start-up to full load transient condition. Some preliminary simulation results are presented that demonstrate the effectiveness of the proposed controller. The development of the complete control algorithm includes components such as robust output tracking, and adaptively estimating both the system parameters and state variables simultaneously. At the present time all these components are not completed due to time constraints. A robust tracking component of the controller for water level control is developed and its effectiveness on the parameter variations is demonstrated in this study. The results appear encouraging and they are only preliminary. Additional work is warranted to resolve other issues such as robust adaptive estimation.
Model-based Fuel Flow Control for Fossil-fired Power Plants
DEFF Research Database (Denmark)
Niemczyk, Piotr
2010-01-01
such sources may vary unpredictably meaning that the desired level of generation cannot always be achieved upon request. On-demand production from controllable units, such as thermal power plants, must change quickly in order to ensure balance between consumer demands and electricity generation. Coal......-fired power plants represent the largest reserve of such controllable power sources in several countries. However, their production take-up rates are limited, mainly due to poor fuel flow control. The thesis presents analysis of difficulties and potential improvements in the control of the coal grinding...
A fuzzy model based adaptive PID controller design for nonlinear and uncertain processes.
Savran, Aydogan; Kahraman, Gokalp
2014-03-01
We develop a novel adaptive tuning method for classical proportional-integral-derivative (PID) controller to control nonlinear processes to adjust PID gains, a problem which is very difficult to overcome in the classical PID controllers. By incorporating classical PID control, which is well-known in industry, to the control of nonlinear processes, we introduce a method which can readily be used by the industry. In this method, controller design does not require a first principal model of the process which is usually very difficult to obtain. Instead, it depends on a fuzzy process model which is constructed from the measured input-output data of the process. A soft limiter is used to impose industrial limits on the control input. The performance of the system is successfully tested on the bioreactor, a highly nonlinear process involving instabilities. Several tests showed the method's success in tracking, robustness to noise, and adaptation properties. We as well compared our system's performance to those of a plant with altered parameters with measurement noise, and obtained less ringing and better tracking. To conclude, we present a novel adaptive control method that is built upon the well-known PID architecture that successfully controls highly nonlinear industrial processes, even under conditions such as strong parameter variations, noise, and instabilities.
Model-Based State Feedback Controller Design for a Turbocharged Diesel Engine with an EGR System
Directory of Open Access Journals (Sweden)
Tianpu Dong
2015-05-01
Full Text Available This paper describes a method for the control of transient exhaust gas recirculation (EGR systems. Firstly, a state space model of the air system is developed by simplifying a mean value model. The state space model is linearized by using linearization theory and validated by the GT-Power data with an operating point of the diesel engine. Secondly, a state feedback controller based on the intake oxygen mass fraction is designed for EGR control. Since direct measurement of the intake oxygen mass fraction is unavailable on the engine, the estimation method for intake oxygen mass fraction has been proposed in this paper. The control strategy is analyzed by using co-simulation with the Matlab/Simulink and GT-Powers software. Finally, the whole control system is experimentally validated against experimental data of a turbocharged diesel engine. The control effect of the state feedback controller compared with PID controller proved to be further verify the feasibility and advantages of the proposed state feedback controller.
Energy Technology Data Exchange (ETDEWEB)
Appelgren, A.; Bergstrom, U. [Studsvik Eco and AB, Nykoping (Sweden); Brittain, J. [Oslo Univ. (Norway). LFI Zoological Museum; Gallego Diaz, E. [Madrid Universidad Politecnica (Spain). Dept. de Ingenieria Nuclear; Hakanson, L. [KEMA Nuclear, Arnhem (Niger); Monte, L. [ENEA, Centro Ricerche Casaccia, Rome (Italy). Dip. Ambiente
1996-10-01
The present report describes the fundamental principles of the research programme MOIRA (a model based computerized system for management support to Identify optimal remedial strategies for Restoring radionuclide contaminated Aquatic ecosystems and drainage areas) financed by the EC (European Community) (Contract N F14P-CT96-0036). The interventions to restore radionuclides contaminated aquatic systems may result in detrimental ecological, social and economical effects. Decision makers must carefully evaluate these impacts. The main aim of the MOIRA project is the development of an expert system based on validated models predicting the evolution of the radioactive contamination of fresh water systems following countermeasure applications and their relevant ecological, social and economical impacts. The expert system will help decision makers, that are not necessarily gifted with experience in environmental modeling, to identify optimal remedial strategies for restoring contaminated fresh water systems.
Optimal control of HIV/AIDS dynamic: Education and treatment
Sule, Amiru; Abdullah, Farah Aini
2014-07-01
A mathematical model which describes the transmission dynamics of HIV/AIDS is developed. The optimal control representing education and treatment for this model is explored. The existence of optimal Control is established analytically by the use of optimal control theory. Numerical simulations suggest that education and treatment for the infected has a positive impact on HIV/AIDS control.
A Generic Model Based Tracking Controller for Hydraulic Valve-Cylinder Drives
DEFF Research Database (Denmark)
Hansen, Anders Hedegaard; Schmidt, Lasse; Pedersen, Henrik Clemmensen
2016-01-01
, and are therefore constrained to utilize only measurements of the piston position, the valve spool position, the transmission line pressures and the supply pressure, as feedbacks. The control structures are generally targeting a high bandwidth of the controlled cylinder drive and accurate tracking ability...
Nonlinear Model-Based Predictive Control applied to Large Scale Cryogenic Facilities
Blanco Vinuela, Enrique; de Prada Moraga, Cesar
2001-01-01
The thesis addresses the study, analysis, development, and finally the real implementation of an advanced control system for the 1.8 K Cooling Loop of the LHC (Large Hadron Collider) accelerator. The LHC is the next accelerator being built at CERN (European Center for Nuclear Research), it will use superconducting magnets operating below a temperature of 1.9 K along a circumference of 27 kilometers. The temperature of these magnets is a control parameter with strict operating constraints. The first control implementations applied a procedure that included linear identification, modelling and regulation using a linear predictive controller. It did improve largely the overall performance of the plant with respect to a classical PID regulator, but the nature of the cryogenic processes pointed out the need of a more adequate technique, such as a nonlinear methodology. This thesis is a first step to develop a global regulation strategy for the overall control of the LHC cells when they will operate simultaneously....
Comparative Results on 3D Navigation of Quadrotor using two Nonlinear Model based Controllers
Bouzid, Y.; Siguerdidjane, H.; Bestaoui, Y.
2017-01-01
Recently the quadrotors are being increasingly employed in both military and civilian areas where a broad range of nonlinear flight control techniques are successfully implemented. With this advancement, it has become necessary to investigate the efficiency of these flight controllers by studying theirs features and compare their performance. In this paper, the control of Unmanned Aerial Vehicle (UAV) quadrotor, using two different approaches, is presented. The first controller is Nonlinear PID (NLPID) whilst the second one is Nonlinear Internal Model Control (NLIMC) that are used for the stabilization as well as for the 3D trajectory tracking. The numerical simulations have shown satisfactory results using nominal system model or disturbed model for both of them. The obtained results are analyzed with respect to several criteria for the sake of comparison.
Neural model-based adaptive control for systems with unknown Preisach-type hysteresis
Institute of Scientific and Technical Information of China (English)
Chuntao LI; Yonghong TAN
2004-01-01
An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks (NN). The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The laws for model updating and the control laws for the neural adaptive controller are derived from Lyapunov stability theorem, therefore the semi- global stability of the closed-loop system is guaranteed. At last, the simulation results are illustrated.
Visual Trajectory-Tracking Model-Based Control for Mobile Robots
Directory of Open Access Journals (Sweden)
Andrej Zdešar
2013-09-01
Full Text Available In this paper we present a visual-control algorithm for driving a mobile robot along the reference trajectory. The configuration of the system consists of a two-wheeled differentially driven mobile robot that is observed by an overhead camera, which can be placed at arbitrary, but reasonable, inclination with respect to the ground plane. The controller must be capable of generating appropriate tangential and angular control velocities for the trajectory-tracking problem, based on the information received about the robot position obtained in the image. To be able to track the position of the robot through a sequence of images in real-time, the robot is marked with an artificial marker that can be distinguishably recognized by the image recognition subsystem. Using the property of differential flatness, a dynamic feedback compensator can be designed for the system, thereby extending the system into a linear form. The presented control algorithm for reference tracking combines a feedforward and a feedback loop, the structure also known as a two DOF control scheme. The feedforward part should drive the system to the vicinity of the reference trajectory and the feedback part should eliminate any errors that occur due to noise and other disturbances etc. The feedforward control can never achieve accurate reference following, but this deficiency can be eliminated with the introduction of the feedback loop. The design of the model predictive control is based on the linear error model. The model predictive control is given in analytical form, so the computational burden is kept at a reasonable level for real-time implementation. The control algorithm requires that a reference trajectory is at least twice differentiable function. A suitable approach to design such a trajectory is by exploiting some useful properties of the Bernstein-Bézier parametric curves. The simulation experiments as well as real system experiments on a robot normally used in the
Directory of Open Access Journals (Sweden)
Ahmed R. Abas
2012-07-01
Full Text Available In this paper, a new algorithm is presented for unsupervised learning of finite mixture models (FMMs using data set with missing values. This algorithm overcomes the local optima problem of the Expectation-Maximization (EM algorithm via integrating the EM algorithm with Particle Swarm Optimization (PSO. In addition, the proposed algorithm overcomes the problem of biased estimation due to overlapping clusters in estimating missing values in the input data set by integrating locally-tuned general regression neural networks with Optimal Completion Strategy (OCS. A comparison study shows the superiority of the proposed algorithm over other algorithms commonly used in the literature in unsupervised learning of FMM parameters that result in minimum mis-classification errors when used in clustering incomplete data set that is generated from overlapping clusters and these clusters are largely different in their sizes.
Wang, Zhongqi; Yang, Bo; Kang, Yonggang; Yang, Yuan
2016-01-01
Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method.
Intelligent control using multiple models based on on-line learning
Institute of Scientific and Technical Information of China (English)
Junyong ZHAI; Shumin FEI; Feipeng DA
2006-01-01
In this paper we deal with the problem of plants with large parameter variations under different operating modes. A novel intelligent control algorithm based on multiple models is proposed to improve the dynamical response performance. At the same time adaptive model bank is applied to establish models without prior system information.Multiple models and corresponding controllers are automatically established on-line by a conventionally adaptive model and a re-initialized one. A best controller is chosen by the performance function at every instant. The closed-loop system's stability and asymptotical convergence of tracking error can be guaranteed. Simulation results have confirmed the validity of the proposed method.
Directory of Open Access Journals (Sweden)
Farzin Piltan
2013-08-01
Full Text Available This research involved developing a surgical robot assistant using an articulated PUMA robot running on a linear or nonlinear axis. The research concentrated on studying the artificial intelligence based switching computed torque controller to localization of an endoscopic tool. Results show that the switching artificial nonlinear control algorithm is capable to design a stable controller. For this system, error was used as the performance metric. Positioning of the endoscopic manipulator relative to the world coordinate frame was possible to within 0.05 inch. Error in maintaining a constant point in space is evident during repositioning however this was caused by limitations in the robot arm.
Directory of Open Access Journals (Sweden)
Ping Jiang
2015-01-01
Full Text Available The establishment of electrical power system cannot only benefit the reasonable distribution and management in energy resources, but also satisfy the increasing demand for electricity. The electrical power system construction is often a pivotal part in the national and regional economic development plan. This paper constructs a hybrid model, known as the E-MFA-BP model, that can forecast indices in the electrical power system, including wind speed, electrical load, and electricity price. Firstly, the ensemble empirical mode decomposition can be applied to eliminate the noise of original time series data. After data preprocessing, the back propagation neural network model is applied to carry out the forecasting. Owing to the instability of its structure, the modified firefly algorithm is employed to optimize the weight and threshold values of back propagation to obtain a hybrid model with higher forecasting quality. Three experiments are carried out to verify the effectiveness of the model. Through comparison with other traditional well-known forecasting models, and models optimized by other optimization algorithms, the experimental results demonstrate that the hybrid model has the best forecasting performance.
Directory of Open Access Journals (Sweden)
Zongxi Qu
2016-01-01
Full Text Available As a type of clean and renewable energy, the superiority of wind power has increasingly captured the world’s attention. Reliable and precise wind speed prediction is vital for wind power generation systems. Thus, a more effective and precise prediction model is essentially needed in the field of wind speed forecasting. Most previous forecasting models could adapt to various wind speed series data; however, these models ignored the importance of the data preprocessing and model parameter optimization. In view of its importance, a novel hybrid ensemble learning paradigm is proposed. In this model, the original wind speed data is firstly divided into a finite set of signal components by ensemble empirical mode decomposition, and then each signal is predicted by several artificial intelligence models with optimized parameters by using the fruit fly optimization algorithm and the final prediction values were obtained by reconstructing the refined series. To estimate the forecasting ability of the proposed model, 15 min wind speed data for wind farms in the coastal areas of China was performed to forecast as a case study. The empirical results show that the proposed hybrid model is superior to some existing traditional forecasting models regarding forecast performance.
Feedback control for car following model based on two-lane traffic flow
Ge, Hong-xia; Meng, Xiang-pei; Zhu, Hui-bing; Li, Zhi-Peng
2014-08-01
In the paper, two-lane traffic flow considering lane changing behaviors has been discussed based on the control theory, and the friction interference which is from the neighbor lane has been taken into account. By using the control method, the stability condition is derived. The feedback signals, which include vehicular information from both lanes, acting on the two-lane traffic system have been introduced into the Full Velocity Difference car-following model. In the end, simulations are conducted to examine the validity and reasonability of the control method. It is proven that lane changing behaviors can aggravate the traffic perturbation. The traffic flow congestion could be suppressed by using the control method and the simulation results are in good agreement with the theoretical analysis.
Neuro-fuzzy and model-based motion control for mobile manipulator among dynamic obstacles
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive Fuzzy Neural Controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.
Fractional conservation laws in optimal control theory
Frederico, Gastao S F
2007-01-01
Using the recent formulation of Noether's theorem for the problems of the calculus of variations with fractional derivatives, the Lagrange multiplier technique, and the fractional Euler-Lagrange equations, we prove a Noether-like theorem to the more general context of the fractional optimal control. As a corollary, it follows that in the fractional case the autonomous Hamiltonian does not define anymore a conservation law. Instead, it is proved that the fractional conservation law adds to the Hamiltonian a new term which depends on the fractional-order of differentiation, the generalized momentum, and the fractional derivative of the state variable.
Optimal control of Rydberg lattice gases
Cui, Jian; van Bijnen, Rick; Pohl, Thomas; Montangero, Simone; Calarco, Tommaso
2017-09-01
We present optimal control protocols to prepare different many-body quantum states of Rydberg atoms in optical lattices. Specifically, we show how to prepare highly ordered many-body ground states, GHZ states as well as some superposition of symmetric excitation number Fock states, that inherit the translational symmetry from the Hamiltonian, within sufficiently short excitation times minimising detrimental decoherence effects. For the GHZ states, we propose a two-step detection protocol to experimentally verify the optimised preparation of the target state based only on standard measurement techniques. Realistic experimental constraints and imperfections are taken into account by our optimisation procedure making it applicable to ongoing experiments.
Optimization of Urban Wastewater Systems using Model Based Design and Control
Velez Quintero, C.A.
2012-01-01
In this research a considerable amount of scientific evidence had been collected which leads to the conclusion that the urban wastewater components should be designed as one integrated system, if the protection of the receiving waters is to be achieved cost-effectively. Even more, there is a need t
Smolders, K.; Volckaert, M.; Swevers, J.
2008-11-01
This paper presents a nonlinear model-based iterative learning control procedure to achieve accurate tracking control for nonlinear lumped mechanical continuous-time systems. The model structure used in this iterative learning control procedure is new and combines a linear state space model and a nonlinear feature space transformation. An intuitive two-step iterative algorithm to identify the model parameters is presented. It alternates between the estimation of the linear and the nonlinear model part. It is assumed that besides the input and output signals also the full state vector of the system is available for identification. A measurement and signal processing procedure to estimate these signals for lumped mechanical systems is presented. The iterative learning control procedure relies on the calculation of the input that generates a given model output, so-called offline model inversion. A new offline nonlinear model inversion method for continuous-time, nonlinear time-invariant, state space models based on Newton's method is presented and applied to the new model structure. This model inversion method is not restricted to minimum phase models. It requires only calculation of the first order derivatives of the state space model and is applicable to multivariable models. For periodic reference signals the method yields a compact implementation in the frequency domain. Moreover it is shown that a bandwidth can be specified up to which learning is allowed when using this inversion method in the iterative learning control procedure. Experimental results for a nonlinear single-input-single-output system corresponding to a quarter car on a hydraulic test rig are presented. It is shown that the new nonlinear approach outperforms the linear iterative learning control approach which is currently used in the automotive industry on durability test rigs.
Robust optimal control of material flows in demand-driven supply networks
Laumanns, Marco; Lefeber, Erjen
2006-04-01
We develop a model based on stochastic discrete-time controlled dynamical systems in order to derive optimal policies for controlling the material flow in supply networks. Each node in the network is described as a transducer such that the dynamics of the material and information flows within the entire network can be expressed by a system of first-order difference equations, where some inputs to the system act as external disturbances. We apply methods from constrained robust optimal control to compute the explicit control law as a function of the current state. For the numerical examples considered, these control laws correspond to certain classes of optimal ordering policies from inventory management while avoiding, however, any a priori assumptions about the general form of the policy.
Optimal Control of Non-well-posed Heat Equations
Institute of Scientific and Technical Information of China (English)
Geng Sheng WANG
2005-01-01
This work is concerned with Pontryagin's maximum principle of optimal control problems governed by some non-well-posed semilinear heat equations. A type of approach to the non-well-posed optimal control problem is given.
Optimal Control of Pseudoparabolic Variational Inequalities Involving State Constraint
Directory of Open Access Journals (Sweden)
Youjun Xu
2014-01-01
Full Text Available We establish the necessary condition of optimality for optimal control problem governed by some pseudoparabolic differential equations involving monotone graphs. Some approximating control process and examples are given.
Community control of scabies: a model based on use of permethrin cream.
Taplin, D; Porcelain, S L; Meinking, T L; Athey, R L; Chen, J A; Castillero, P M; Sanchez, R
1991-04-27
For 18 years treatment with lindane or crotamiton products has failed to stem the epidemic of scabies among the Kuna Indians in the San Blas islands of the Republic of Panama. Permethrin 5% cream was introduced as the only treatment in a programme to control scabies on an island of 756 inhabitants and involving workers recruited locally. Prevalence fell from 33% to less than 1% after every person was treated. As long as continued surveillance and treatment of newly introduced cases was maintained, prevalence of scabies remained below 1.5% for over 3 years. When supply of medication was interrupted for 3 weeks, prevalence rose to 3.6%. When control was lost after the US invasion of Panama, prevalence rose to 12% within 3 months. Bacterial skin infections decreased dramatically when scabies was controlled. Permethrin is safe and effective even in areas where this disease has become resistant to lindane.
The Public Opinion Control Model Based on the Connecting Multi-Small-World-Network
Directory of Open Access Journals (Sweden)
Wen-Qi Zhong
2013-09-01
Full Text Available Based on the propagation mechanism of the rumor control, this study proposes a mode of propagation found on the information content to describe the dissemination of two opposite rumors on the same subject among crowds and sets up public opinion control model on the basis of this mode. Two opposite rumors on the same subject in our mode of propagation can respectively represent rumor and truth, so we investigate their interactions during the dissemination among crowd and simulate it in the connecting multi-small-world-network. Finally, by adjusting the factors which can affect the control effect of the model, we propose a corresponding rumor immunization strategy. Based on that, we conduct the analogy analysis of interactions of many opposite rumors on the same subject when they spread among crowds.
SVM regression model based on f ruit fly optimization%基于果蝇优化的支持向量机回归模型
Institute of Scientific and Technical Information of China (English)
赵伟
2015-01-01
给出一种基于果蝇优化的支持向量机回归模型。将支持向量机惩罚因子和核函数参数初始化为果蝇群体，根据果蝇优化算法原理，依据适应度最优原则进行迭代觅食，搜索最优参数，建立模型。将该模型用于分析有机化合物熔点预测问题，结果显示，该模型预测均方误差为3．02％，相关系数达到89．39％。%A support vector machine (SVM ) regression model based on fruit fly optimization algorithm is proposed .Initialize the SVM penalty factor and kernel function parameter as a fruit fly group .By the rule of fruit fly optimization ,execute iterative optimal foraging according to the fitness principle until the optimal parameters are sought out ,thus ,the model can be set up .Use this model to analyze the melting point prediction of organic compounds ,it turns out that ,the prediction error is 3 .02% , and the correlation coefficient is 89 .39% .
Controlling automobile thermal comfort using optimized fuzzy controller
Energy Technology Data Exchange (ETDEWEB)
Farzaneh, Yadollah; Tootoonchi, Ali A. [Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad (Iran)
2008-10-15
Providing thermal comfort and saving energy are two main goals of heating, ventilation and air conditioning (HVAC) systems. A controller with temperature feedback cannot best achieve the thermal comfort. This is because thermal comfort is influenced by many variables such as, temperature, relative humidity, air velocity, environment radiation, activity level and cloths insulation. In this study Fanger's predicted mean value (PMV) index is used as controller feedback. It is simplified without introducing significant error. Thermal models of the cabin and HVAC system are developed. Evaporator cooling capacity is selected as a criterion for energy consumption. Two fuzzy controllers one with temperature as its feedback and the other PMV index as its feedback are designed. Results show that the PMV feedback controller better controls the thermal comfort and energy consumption than the system with temperature feedback. Next, the parameters of the fuzzy controller are optimized by genetic algorithm. Results indicate that thermal comfort level is further increased while energy consumption is decreased. Finally, robustness analysis is performed which shows the robustness of optimized controller to variables variations. (author)
Crouch, Dustin L.; (Helen Huang, He
2017-06-01
Objective. We investigated the feasibility of a novel, customizable, simplified EMG-driven musculoskeletal model for estimating coordinated hand and wrist motions during a real-time path tracing task. Approach. A two-degree-of-freedom computational musculoskeletal model was implemented for real-time EMG-driven control of a stick figure hand displayed on a computer screen. After 5-10 minutes of undirected practice, subjects were given three attempts to trace 10 straight paths, one at a time, with the fingertip of the virtual hand. Able-bodied subjects completed the task on two separate test days. Main results. Across subjects and test days, there was a significant linear relationship between log-transformed measures of accuracy and speed (Pearson’s r = 0.25, p motor control patterns were not accustomed to the multi-joint dynamics of the wrist and hand, possibly as a result of post-amputation cortical plasticity, disuse, or sensory deficits. Significance. To our knowledge, our study is one of very few that have demonstrated the real-time simultaneous control of multi-joint movements, especially wrist and finger movements, using an EMG-driven musculoskeletal model, which differs from the many data-driven algorithms that dominate the literature on EMG-driven prosthesis control. Real-time control was achieved with very little training and simple, quick (~15 s) calibration. Thus, our model is potentially a practical and effective control platform for multifunctional myoelectric prostheses that could restore more life-like hand function for individuals with upper limb amputation.
Directory of Open Access Journals (Sweden)
Cihan Turhan
2017-01-01
Full Text Available The paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinear model of a thermal unit. A data-driven grey-box identification approach provided the physically–meaningful nonlinear continuous-time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important, since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte Carlo (MC analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application of the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model-based and data-driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems.
Precise Modeling Based on Dynamic Phasors for Droop-Controlled Parallel-Connected Inverters
DEFF Research Database (Denmark)
Wang, L.; Guo, X.Q.; Gu, H.R.;
2012-01-01
This paper deals with the precise modeling of droop controlled parallel inverters. This is very attractive since that is a common structure that can be found in a stand-alone droopcontrolled MicroGrid. The conventional small-signal dynamic is not able to predict instabilities of the system, so th....... In addition, the virtual ω-E frame power control method, which deals with the power coupling caused by the line impedance X/R characteristic, has been chosen as an application example of this modeling technique....
Wang, Tianbo; Zhou, Wuneng; Zhao, Shouwei; Yu, Weiqin
2014-03-01
In this paper, the robust exponential synchronization problem for a class of uncertain delayed master-slave dynamical system is investigated by using the adaptive control method. Different from some existing master-slave models, the considered master-slave system includes bounded unmodeled dynamics. In order to compensate the effect of unmodeled dynamics and effectively achieve synchronization, a novel adaptive controller with simple updated laws is proposed. Moreover, the results are given in terms of LMIs, which can be easily solved by LMI Toolbox in Matlab. A numerical example is given to illustrate the effectiveness of the method.
1991-12-01
Proc. IEEE Conf. on Robotics and Automation, pages 1520-1531, 1986. Vol. 3. 12. P. Khosla and T. Kanade. Parameters Identification of Robot Dynamics . In...Manipulator Control: A Case Study. In Proc. of IEEE Int. Conf. on Robotics and Automation, pages 1392-1400, 1987. 26. Mark W. Spong and M. Vidyasagar. Robot ... Dynamics and Control. John Wiley and Sons, 1989. 27. T.J. Tan and A.K. Beiczy. Dynamic Equations for PUMA-560 Robot Arm. Technical Report SSM-RL-85-02
Model-based computer-aided design for controlled release of pesticides
DEFF Research Database (Denmark)
Muro Sunè, Nuria; Gani, Rafiqul; Bell, G.;
2005-01-01
In the field of controlled release technology for pesticides or active ingredients (AI), models that can predict its delivery during application are important for purposes of design and marketing of the pesticide product. Appropriate models for the controlled release of pesticides, if available, ...... extended models have been developed and implemented into a computer-aided system. The total model consisting of the property models embedded into the release models are then employed to study the release of different combinations of AIs and polymer-based microcapsules....
Optimal Control of Finite Dimensional Quantum Systems
Mendonca, Paulo E M F
2009-01-01
This thesis addresses the problem of developing a quantum counter-part of the well established classical theory of control. We dwell on the fundamental fact that quantum states are generally not perfectly distinguishable, and quantum measurements typically introduce noise in the system being measured. Because of these, it is generally not clear whether the central concept of the classical control theory -- that of observing the system and then applying feedback -- is always useful in the quantum setting. We center our investigations around the problem of transforming the state of a quantum system into a given target state, when the system can be prepared in different ways, and the target state depends on the choice of preparation. We call this the "quantum tracking problem" and show how it can be formulated as an optimization problem that can be approached both numerically and analytically. This problem provides a simple route to the characterization of the quantum trade-off between information gain and distu...
Reproducibility, controllability, and optimization of LENR experiments
Energy Technology Data Exchange (ETDEWEB)
Nagel, David J. [The George Washington University, Washington DC 20052 (United States)
2006-07-01
Low-energy nuclear reaction (LENR) measurements are significantly, and increasingly reproducible. Practical control of the production of energy or materials by LENR has yet to be demonstrated. Minimization of costly inputs and maximization of desired outputs of LENR remain for future developments. The paper concludes by underlying that it is now clearly that demands for reproducible experiments in the early years of LENR experiments were premature. In fact, one can argue that irreproducibility should be expected for early experiments in a complex new field. As emphasized in the paper and as often happened in the history of science, experimental and theoretical progress can take even decades. It is likely to be many years before investments in LENR experiments will yield significant returns, even for successful research programs. However, it is clearly that a fundamental understanding of the anomalous effects observed in numerous experiments will significantly increase reproducibility, improve controllability, enable optimization of processes, and accelerate the economic viability of LENR.
Optimal second order sliding mode control for nonlinear uncertain systems.
Das, Madhulika; Mahanta, Chitralekha
2014-07-01
In this paper, a chattering free optimal second order sliding mode control (OSOSMC) method is proposed to stabilize nonlinear systems affected by uncertainties. The nonlinear optimal control strategy is based on the control Lyapunov function (CLF). For ensuring robustness of the optimal controller in the presence of parametric uncertainty and external disturbances, a sliding mode control scheme is realized by combining an integral and a terminal sliding surface. The resulting second order sliding mode can effectively reduce chattering in the control input. Simulation results confirm the supremacy of the proposed optimal second order sliding mode control over some existing sliding mode controllers in controlling nonlinear systems affected by uncertainty.
Directory of Open Access Journals (Sweden)
DANIEL ROJAS
2011-01-01
Full Text Available En flotación de minerales, pequeños incrementos en la recuperación resultan económicamente muy signifi cativos, por lo que el control automático del proceso ha pasado a ser indispensable. En esta área se han desarrollado varios trabajos en los que usualmente no se consideran leyes de concentrado en celdas intermedias. Por otra parte, se han realizado varios trabajos para caracterizar la espuma de concentrado por medio de procesamiento de imágenes para extraer color, velocidad y tamaño entre otros y unas pocas investigaciones han mostrado la factibilidad de estimar la ley de concentrado. Este trabajo dos estrategias de control predictivo multivariable La primera estrategia está basada sólo en la información entregada por los sensores de cola y concentrado generales mientras que la segunda incluye, además de esta información, estimación de leyes de concentrado en celdas intermedias. Ambas estrategias son comparadas con una estrategia que considera controles fi jos. Los resultados por simulación muestran que la recuperación puede ser incrementada hasta en un 1,7% con respecto a la estrategia con controles fi jos.
Achieving control and interoperability through unified model-based systems and software engineering
Rasmussen, Robert; Ingham, Michel; Dvorak, Daniel
2005-01-01
Control and interoperation of complex systems is one of the most difficult challenges facing NASA's Exploration Systems Mission Directorate. An integrated but diverse array of vehicles, habitats, and supporting facilities, evolving over the long course of the enterprise, must perform ever more complex tasks while moving steadily away from the sphere of ground support and intervention.
The Pugh Controlled Convergence method: model-based evaluation and implications for design theory
Frey, D.D.; Herder, P.M.; Wijnia, Y.; Saubrahmanian, E.; Katsikopoulos, K.; Clausing, D.P.
2008-01-01
This paper evaluates the Pugh Controlled Convergence method and its relationship to recent developments in design theory. Computer executable models are proposed simulating a team of people involved in iterated cycles of evaluation, ideation, and investigation. The models suggest that: (1) convergen
Predictive Model Based Battery Constraints for Electric Motor Control within EV Powertrains
Roşca, B.; Wilkins, S.; Jacob, J.; Hoedemaekers, E.R.G.; Hoek, S.P. van den
2014-01-01
This paper presents a method of predicting the maximum power capability of a Li-Ion battery, to be used for electric motor control within automotive powertrains. As maximum power is highly dependent on battery state, the method consists of a pack level state observer coupled with a predictive batter
Modular model-based supervisory controller design for wafer logistics in lithography machines
Van Der Sanden, B.; Reniers, M.; Geilen, M.; Basten, T.; Jacobs, J.; Voeten, J.; Schiffelers, R.
2015-01-01
Development of high-level supervisory controllers is an important challenge in the design of high-tech systems. It has become a significant issue due to increased complexity, combined with demands for verified quality, time to market, ease of development, and integration of new functionality. To
Real-Time, Model-Based Spray-Cooling Control System for Steel Continuous Casting
Petrus, Bryan; Zheng, Kai; Zhou, X.; Thomas, Brian G.; Bentsman, Joseph
2011-02-01
This article presents a new system to control secondary cooling water sprays in continuous casting of thin steel slabs (CONONLINE). It uses real-time numerical simulation of heat transfer and solidification within the strand as a software sensor in place of unreliable temperature measurements. The one-dimensional finite-difference model, CON1D, is adapted to create the real-time predictor of the slab temperature and solidification state. During operation, the model is updated with data collected by the caster automation systems. A decentralized controller configuration based on a bank of proportional-integral controllers with antiwindup is developed to maintain the shell surface-temperature profile at a desired set point. A new method of set-point generation is proposed to account for measured mold heat flux variations. A user-friendly monitor visualizes the results and accepts set-point changes from the caster operator. Example simulations demonstrate how a significantly better shell surface-temperature control is achieved.
Predictive Model Based Battery Constraints for Electric Motor Control within EV Powertrains
Roşca, B.; Wilkins, S.; Jacob, J.; Hoedemaekers, E.R.G.; Hoek, S.P. van den
2014-01-01
This paper presents a method of predicting the maximum power capability of a Li-Ion battery, to be used for electric motor control within automotive powertrains. As maximum power is highly dependent on battery state, the method consists of a pack level state observer coupled with a predictive
Fall, D.; Duquennoy, M.; Ouaftouh, M.; Piwakowski, B.; Jenot, F.
This study deals with modelling SAW-IDT transducers for their optimization. These sensors are specifically developed to characterize properties of thin layers, coatings and functional surfaces. Among the methods of characterization, the ultrasonic methods using Rayleigh surface waves are particularly interesting because the propagation of these waves is close to the surface of material and the energy is concentrated within a layer under the surface of about one wavelength thick. In order to characterize these coatings and structures, it is necessary to work in high frequencies, this is why in this study, SAW-IDT sensors are realized for surface acoustic wave generation. For optimization of these SAW-IDT sensors, particularly their band-width, it is necessary to study various IDT configurations by varying the number of electrodes, dimensions of the electrodes, their shapes and spacings. Thus it is necessary to implement effective and rapid technique for modelling. The originality of this study is to develop simulation tools based on Spatial Impulse Response model. Therefore it will be possible to reduce considerably computing time and results are obtained in a few seconds, instead of several hours (or days) by using finite element method. In order to validate this method, theoretical and experimental results are compared with finite element method and Interferometric measurements. The results obtained show a good overall concordance and confirm effectiveness of suggested method.
Optimal control of quantum systems by chirped pulses
DEFF Research Database (Denmark)
Amstrup, Bjarne; Doll, J. D.; Sauerbrey, R. A.
1993-01-01
Research on optimal control of quantum systems has been severely restricted by the lack of experimentally feasible control pulses. Here, to overcome this obstacle, optimal control is considered with the help of chirped pulses. Simulated annealing is used as the optimizing procedure. The examples ...
Optimal Control Problems for Nonlinear Variational Evolution Inequalities
Directory of Open Access Journals (Sweden)
Eun-Young Ju
2013-01-01
Full Text Available We deal with optimal control problems governed by semilinear parabolic type equations and in particular described by variational inequalities. We will also characterize the optimal controls by giving necessary conditions for optimality by proving the Gâteaux differentiability of solution mapping on control variables.
Runge-Kutta model-based nonlinear observer for synchronization and control of chaotic systems.
Beyhan, Selami
2013-07-01
This paper proposes a novel nonlinear gradient-based observer for synchronization and observer-based control of chaotic systems. The model is based on a Runge-Kutta model of the chaotic system where the evolution of the states or parameters is derived based on the error-square minimization. The stability and convergence conditions of observer and control methods are analyzed using a Lyapunov stability approach. In numerical simulations, the proposed observer and well-known sliding-mode observer are compared for the synchronization of a Lü chaotic system and observer-based stabilization of a Chen chaotic system. The noisy case for synchronization and parameter uncertainty case for stabilization are also considered for both observer-based methods. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Harinath, Eranda; Mann, George K I
2008-06-01
This paper describes a design and two-level tuning method for fuzzy proportional-integral derivative (FPID) controllers for a multivariable process where the fuzzy inference uses the inference of standard additive model. The proposed method can be used for any n x n multi-input-multi-output process and guarantees closed-loop stability. In the two-level tuning scheme, the tuning follows two steps: low-level tuning followed by high-level tuning. The low-level tuning adjusts apparent linear gains, whereas the high-level tuning changes the nonlinearity in the normalized fuzzy output. In this paper, two types of FPID configurations are considered, and their performances are evaluated by using a real-time multizone temperature control problem having a 3 x 3 process system.
An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network
Directory of Open Access Journals (Sweden)
Wei He
2013-01-01
Full Text Available Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.
Quasilinear Extreme Learning Machine Model Based Internal Model Control for Nonlinear Process
Directory of Open Access Journals (Sweden)
Dazi Li
2015-01-01
Full Text Available A new strategy for internal model control (IMC is proposed using a regression algorithm of quasilinear model with extreme learning machine (QL-ELM. Aimed at the chemical process with nonlinearity, the learning process of the internal model and inverse model is derived. The proposed QL-ELM is constructed as a linear ARX model with a complicated nonlinear coefficient. It shows some good approximation ability and fast convergence. The complicated coefficients are separated into two parts. The linear part is determined by recursive least square (RLS, while the nonlinear part is identified through extreme learning machine. The parameters of linear part and the output weights of ELM are estimated iteratively. The proposed internal model control is applied to CSTR process. The effectiveness and accuracy of the proposed method are extensively verified through numerical results.
DEFF Research Database (Denmark)
Pierart Vásquez, Fabián Gonzalo
Gas journal bearings have been increasingly adopted in modern turbo-machinery due to their numerous indisputable advantages. They can operate at higher speed than most bearing designs, almost without noise or heat generation and in most cases, as in this work, the gas used is air which is cheap...... work, the control signal design is based on a theoretical model. This approach enables easy modifications of any of the numerous physical parameters in the system if needed. The theoretical model used is based on a modifed version of Reynolds equation where an extra term is added in order to include...... frequencies and damping ratios of the rotor-bearing system) is performed and finally to design controllers that allows improvement of the dynamic properties of the rotor-active gas bearings system and lets the systemto safely cross the critical speeds, using the theoretical model as a design tool. The results...
Chaos and Control of Game Model Based on Heterogeneous Expectations in Electric Power Triopoly
Directory of Open Access Journals (Sweden)
Weizhuo Ji
2009-01-01
Full Text Available A dynamic repeated game model has been established based on heterogeneous expectations in electric power triopoly. Theoretical analysis and numerical simulation show the complexity of this model; suppose that the producers make decisions with naive expectation and bounded rationality. The straight-line stabilization chaos control method was successfully applied to the dynamic repeated game model. The results have important practical value for the producers in the electric power oligopoly.
A PID Positioning Controller with a Curve Fitting Model Based on RFID Technology
Directory of Open Access Journals (Sweden)
Young-Long Chen
2013-04-01
Full Text Available The global positioning system (GPS is an important research topic to solve outdoor positioning problems, but GPS is unable to locate objects accurately and precisely indoors. Some available systems apply ultrasound or optical tracking. This paper presents an efficient proportional-integral-derivative (PID controller with curve fitting model for mobile robot localization and position estimation which adopts passive radio frequency identification (RFID tags in a space. This scheme is based on a mobile robot carries an RFID reader module which reads the installed low-cost passive tags under the floor in a grid-like pattern. The PID controllers increase the efficiency of captured RFID tags and the curve fitting model is used to systematically identify the revolutions per minute (RPM of the motor. We control and monitor the position of the robot from a remote location through a mobile phone via Wi-Fi and Bluetooth network. Experiment results present that the number of captured RFID tags of our proposed scheme outperforms that of the previous scheme.
Narimani, Mohammand; Lam, H K; Dilmaghani, R; Wolfe, Charles
2011-06-01
Relaxed linear-matrix-inequality-based stability conditions for fuzzy-model-based control systems with imperfect premise matching are proposed. First, the derivative of the Lyapunov function, containing the product terms of the fuzzy model and fuzzy controller membership functions, is derived. Then, in the partitioned operating domain of the membership functions, the relations between the state variables and the mentioned product terms are represented by approximated polynomials in each subregion. Next, the stability conditions containing the information of all subsystems and the approximated polynomials are derived. In addition, the concept of the S-procedure is utilized to release the conservativeness caused by considering the whole operating region for approximated polynomials. It is shown that the well-known stability conditions can be special cases of the proposed stability conditions. Simulation examples are given to illustrate the validity of the proposed approach.
DEFF Research Database (Denmark)
Abdul Samad, Noor Asma Fazli Bin
A generic and systematic model-based framework for the design of a process monitoring and control system to achieve the desired crystal size distribution (CSD) and crystal shape for a wide range of crystallization processes has been developed. This framework combines a generic multi......-dimensional to a twodimensional description; b) the systematic framework is used in a case study to design a monitoring and control (PAT) system for a potassium dichromate and KDP crystallization processes to achieve the desired target CSD respectively; and c) Based on the PAT system design in b), the application of uncertainty......-dimensional modelling framework, tools for design of set point profiles, for design of PAT (Process Analytical Technology) systems as well as option to perform the uncertainty and sensitivity analysis of the PAT system design. Through this framework, it is possible for a wide range of crystallization processes...
Combustion Model and Control Parameter Optimization Methods for Single Cylinder Diesel Engine
Directory of Open Access Journals (Sweden)
Bambang Wahono
2014-01-01
Full Text Available This research presents a method to construct a combustion model and a method to optimize some control parameters of diesel engine in order to develop a model-based control system. The construction purpose of the model is to appropriately manage some control parameters to obtain the values of fuel consumption and emission as the engine output objectives. Stepwise method considering multicollinearity was applied to construct combustion model with the polynomial model. Using the experimental data of a single cylinder diesel engine, the model of power, BSFC, NOx, and soot on multiple injection diesel engines was built. The proposed method succesfully developed the model that describes control parameters in relation to the engine outputs. Although many control devices can be mounted to diesel engine, optimization technique is required to utilize this method in finding optimal engine operating conditions efficiently beside the existing development of individual emission control methods. Particle swarm optimization (PSO was used to calculate control parameters to optimize fuel consumption and emission based on the model. The proposed method is able to calculate control parameters efficiently to optimize evaluation item based on the model. Finally, the model which added PSO then was compiled in a microcontroller.
Optimal Sliding Mode Controllers for Attitude Stabilization of Flexible Spacecraft
Directory of Open Access Journals (Sweden)
Chutiphon Pukdeboon
2011-01-01
Full Text Available The robust optimal attitude control problem for a flexible spacecraft is considered. Two optimal sliding mode control laws that ensure the exponential convergence of the attitude control system are developed. Integral sliding mode control (ISMC is applied to combine the first-order sliding mode with optimal control and is used to control quaternion-based spacecraft attitude manoeuvres with external disturbances and an uncertainty inertia matrix. For the optimal control part the state-dependent Riccati equation (SDRE and optimal Lyapunov techniques are employed to solve the infinite-time nonlinear optimal control problem. The second method of Lyapunov is used to guarantee the stability of the attitude control system under the action of the proposed control laws. An example of multiaxial attitude manoeuvres is presented and simulation results are included to verify the usefulness of the developed controllers.
Control Optimization of Solar Thermally Driven Chillers
Directory of Open Access Journals (Sweden)
Antoine Dalibard
2016-10-01
Full Text Available Many installed solar thermally driven cooling systems suffer from high auxiliary electric energy consumption which makes them not more efficient than conventional compression cooling systems. A main reason for this is the use of non-efficient controls with constant set points that do not allow a chiller power modulation at partial-load and therefore lead to unnecessary high power consumption of the parasitics. The aims of this paper are to present a method to control efficiently solar thermally driven chillers, to demonstrate experimentally its applicability and to quantify the benefits. It has been shown that the cooling capacity of a diffusion absorption chiller can be modulated very effectively by adjusting both the temperature and the flow rate of the cooling water. With the developed approach and the use of optimization algorithms, both the temperature and the flow rate can be controlled simultaneously in a way that the cooling load is matched and the electricity consumption is minimized. Depending on the weather and operating conditions, electricity savings between 20% and 60% can be achieved compared to other tested control approaches. The highest savings are obtained when the chiller is operated at partial load. The presented method is not restricted to solar cooling systems and can also be applied to other conventional heating ventilation and air conditioning (HVAC systems.
Silicon controlled rectifier (SCR) compact modeling based on VBIC and Gummel-Poon models
Lou, Lifang; Liou, Juin J.; Dong, Shurong; Han, Yan
2009-02-01
Silicon controlled rectifier (SCR) is frequently used for electrostatic discharge (ESD) protection applications. For computer-aided design purposes, a macromodel can be constructed for such a device, but a model for the NPN and PNP bipolar transistors imbedded in the SCR is required in the macromodel development. In the paper, we use both the Vertical Bipolar Inter-Company (VBIC) and SPICE Gummel-Poon (SGP) models for these bipolar transistors and compare the perspective macromodel results. Measurements obtained from the transmission line pulsing (TLP) tester are also included to assess the suitability and pros and cons of the VBIC and SGP models for the SCR ESD modeling.
The Stability Analysis for an Extended Car Following Model Based on Control Theory
Ge, Hong-Xia; Meng, Xiang-Pei; Zhu, Ke-Qiang; Cheng, Rong-Jun
2014-08-01
A new method is proposed to study the stability of the car-following model considering traffic interruption probability. The stability condition for the extended car-following model is obtained by using the Lyapunov function and the condition for no traffic jam is also given based on the control theory. Numerical simulations are conducted to demonstrate and verify the analytical results. Moreover, numerical simulations show that the traffic interruption probability has an influence on driving behavior and confirm the effectiveness of the method on the stability of traffic flow.
Continuous Control Artificial Potential Function Methods and Optimal Control
2014-03-27
Method, namely r̈VDSVAPF = −K̇SKR∇φ−KSK̇R∇φ−KSKRH(φ)ṙ −KD (KSKR∇φ+ ṙ) . The above dynamics are very nonlinear due to the trigonometric functions (inside...constraints (on KS and θ) and the deletion of trigonometric functions . The suspected reasons for the larger computa- tional expense are twofold. First, this...Continuous Control Artificial Potential Function Methods and Optimal Control THESIS R. Andrew Fields, Civ, USAF AFIT-ENY-14-M-20 DEPARTMENT OF THE
Model-Based Evaluation of Strategies to Control Brucellosis in China
Li, Ming-Tao; Sun, Gui-Quan; Zhang, Wen-Yi; Jin, Zhen
2017-01-01
Brucellosis, the most common zoonotic disease worldwide, represents a great threat to animal husbandry with the potential to cause enormous economic losses. Brucellosis has become a major public health problem in China, and the number of human brucellosis cases has increased dramatically in recent years. In order to evaluate different intervention strategies to curb brucellosis transmission in China, a novel mathematical model with a general indirect transmission incidence rate was presented. By comparing the results of three models using national human disease data and 11 provinces with high case numbers, the best fitted model with standard incidence was used to investigate the potential for future outbreaks. Estimated basic reproduction numbers were highly heterogeneous, varying widely among provinces. The local basic reproduction numbers of provinces with an obvious increase in incidence were much larger than the average for the country as a whole, suggesting that environment-to-individual transmission was more common than individual-to-individual transmission. We concluded that brucellosis can be controlled through increasing animal vaccination rates, environment disinfection frequency, or elimination rates of infected animals. Our finding suggests that a combination of animal vaccination, environment disinfection, and elimination of infected animals will be necessary to ensure cost-effective control for brucellosis. PMID:28287496
A PID Positioning Controller with a Curve Fitting Model Based on RFID Technology
Directory of Open Access Journals (Sweden)
Young-Long Chen
2013-03-01
Full Text Available The global positioning system (GPS is an important research topic to solve outdoor positioning problems, but GPSis unable to locate objects accurately and precisely indoors. Some available systems apply ultrasound or opticaltracking. This paper presents an efficient proportional-integral-derivative (PID controller with curve fitting model formobile robot localization and position estimation which adopts passive radio frequency identification (RFID tags ina space. This scheme is based on a mobile robot carries an RFID reader module which reads the installed low-costpassive tags under the floor in a grid-like pattern. The PID controllers increase the efficiency of captured RFID tagsand the curve fitting model is used to systematically identify the revolutions per minute (RPM of the motor. Wecontrol and monitor the position of the robot from a remote location through a mobile phone via Wi-Fi and Bluetoothnetwork. Experiment results present that the number of captured RFID tags of our proposed scheme outperformsthat of the previous scheme.
Cos, Oriol; Ramon, Ramon; Montesinos, José Luis; Valero, Francisco
2006-09-05
A predictive control algorithm coupled with a PI feedback controller has been satisfactorily implemented in the heterologous Rhizopus oryzae lipase production by Pichia pastoris methanol utilization slow (Mut(s)) phenotype. This control algorithm has allowed the study of the effect of methanol concentration, ranging from 0.5 to 1.75 g/L, on heterologous protein production. The maximal lipolytic activity (490 UA/mL), specific yield (11,236 UA/g(biomass)), productivity (4,901 UA/L . h), and specific productivity (112 UA/g(biomass)h were reached for a methanol concentration of 1 g/L. These parameters are almost double than those obtained with a manual control at a similar methanol set-point. The study of the specific growth, consumption, and production rates showed different patterns for these rates depending on the methanol concentration set-point. Results obtained have shown the need of implementing a robust control scheme when reproducible quality and productivity are sought. It has been demonstrated that the model-based control proposed here is a very efficient, robust, and easy-to-implement strategy from an industrial application point of view.
Directory of Open Access Journals (Sweden)
Georgios E. Stavroulakis
2013-10-01
Full Text Available This paper presents a numerical study on optimal voltages and optimal placement of piezoelectric actuators for shape control of beam structures. A finite element model, based on Timoshenko beam theory, is developed to characterize the behavior of the structure and the actuators. This model accounted for the electromechanical coupling in the entire beam structure, due to the fact that the piezoelectric layers are treated as constituent parts of the entire structural system. A hybrid scheme is presented based on great deluge and genetic algorithm. The hybrid algorithm is implemented to calculate the optimal locations and optimal values of voltages, applied to the piezoelectric actuators glued in the structure, which minimize the error between the achieved and the desired shape. Results from numerical simulations demonstrate the capabilities and efficiency of the developed optimization algorithm in both clamped−free and clamped−clamped beam problems are presented.
Model-based beam control for illumination of remote objects, part II: laboratory testbed
Basu, Santasri; Voelz, David; Chandler, Susan M.; Lukesh, Gordon W.; Sjogren, Jon
2004-10-01
When a laser beam propagates through the atmosphere, it is subject to corrupting influences including mechanical vibrations, turbulence and tracker limitations. As a result, pointing errors can occur, causing loss of energy or signal at the target. Nukove Scientific Consulting has developed algorithms to estimate these pointing errors from the statistics of the return photons from the target. To prove the feasibility of this approach for real-time estimation, an analysis tool called RHINO was developed by Nukove. Associated with this effort, New Mexico State University developed a laboratory testbed, the ultimate objective being to test the estimation algorithms under controlled conditions and to stream data into RHINO to prove the feasibility of real-time operation. The present paper outlines the description of this testbed and the results obtained through RHINO when the testbed was used to test the estimation approach.
Takagi-Sugeno fuzzy-model-based fault detection for networked control systems with Markov delays.
Zheng, Ying; Fang, Huajing; Wang, Hua O
2006-08-01
A Takagi-Sugeno (T-S) model is employed to represent a networked control system (NCS) with different network-induced delays. Comparing with existing NCS modeling methods, this approach does not require the knowledge of exact values of network-induced delays. Instead, it addresses situations involving all possible network-induced delays. Moreover, this approach also handles data-packet loss. As an application of the T-S-based modeling method, a parity-equation approach and a fuzzy-observer-based approach for fault detection of an NCS were developed. An example of a two-link inverted pendulum is used to illustrate the utility and viability of the proposed approaches.
Directory of Open Access Journals (Sweden)
Mathieu Legros
Full Text Available Suppression of dengue and malaria through releases of genetically engineered mosquitoes might soon become feasible. Aedes aegypti mosquitoes carrying a conditionally lethal transgene have recently been used to suppress local vector populations in small-scale field releases. Prior to releases of transgenic insects on a wider scale, however, most regulatory authorities will require additional evidence that suppression will be effective in natural heterogeneous habitats. We use a spatially explicit stochastic model of an Ae. aegypti population in Iquitos, Peru, along with an uncertainty analysis of its predictions, to quantitatively assess the outcome of varied operational approaches for releases of transgenic strains with conditional death of females. We show that population elimination might be an unrealistic objective in heterogeneous populations. We demonstrate that substantial suppression can nonetheless be achieved if releases are deployed in a uniform spatial pattern using strains combining multiple lethal elements, illustrating the importance of detailed spatial models for guiding genetic mosquito control strategies.
Tandem Strip Mill’s Multi-parameter Coupling Dynamic Modeling Based on the Thickness Control
Institute of Scientific and Technical Information of China (English)
PENG Yan; ZHANG Yang; SUN Jianliang; ZANG Yong
2015-01-01
The rolling process is determined by the interaction of a number of different movements, during which the relative movement occurs between the vibrating roll system and the rolled piece, and the roll system’s vibration interacts with the strip’s deformation and rigid movement. So many parameters being involved leads to a complex mechanism of this coupling effect. Through testing and analyzing the vibration signals of the mill in the rolling process, the rolling mill’s coupled model is established with comprehensive consideration of the coupling interaction between the mill’s vertical vibration, its torsional vibration and the working roll’s horizontal vibration, and vibration characteristics of different forms of rolling mill’s vibration are analyzed under the coupling effect. With comprehensive attention to the relationship between the roll system, the moving strip and the rolling parameters’ dynamic properties, and also from the strip thickness control point of view, further research is done on the coupling mechanism between the roll system’s movement and the moving strip’s characteristics in the rolling process. As a result, the law of inertial coupling and the stiffness coupling effect caused by different forms of the roll system’s vibration is determined and the existence of nonlinear characteristics caused by the elastic deformation of moving strip is also found. Furthermore, a multi-parameter coupling-dynamic model is established which takes the tandem strip mill as its research object by making a detailed kinematics analysis of the roll system and using the principle of virtual work. The coupling-dynamic model proposes the instruction to describe the roll system’s movement, and analyzes its dynamic response and working stability, and provides a theoretical basis for the realization of the strip thickness’ dynamic control.
Tandem strip mill's multi-parameter coupling dynamic modeling based on the thickness control
Peng, Yan; Zhang, Yang; Sun, Jianliang; Zang, Yong
2015-03-01
The rolling process is determined by the interaction of a number of different movements, during which the relative movement occurs between the vibrating roll system and the rolled piece, and the roll system's vibration interacts with the strip's deformation and rigid movement. So many parameters being involved leads to a complex mechanism of this coupling effect. Through testing and analyzing the vibration signals of the mill in the rolling process, the rolling mill's coupled model is established with comprehensive consideration of the coupling interaction between the mill's vertical vibration, its torsional vibration and the working roll's horizontal vibration, and vibration characteristics of different forms of rolling mill's vibration are analyzed under the coupling effect. With comprehensive attention to the relationship between the roll system, the moving strip and the rolling parameters' dynamic properties, and also from the strip thickness control point of view, further research is done on the coupling mechanism between the roll system's movement and the moving strip's characteristics in the rolling process. As a result, the law of inertial coupling and the stiffness coupling effect caused by different forms of the roll system's vibration is determined and the existence of nonlinear characteristics caused by the elastic deformation of moving strip is also found. Furthermore, a multi-parameter coupling-dynamic model is established which takes the tandem strip mill as its research object by making a detailed kinematics analysis of the roll system and using the principle of virtual work. The coupling-dynamic model proposes the instruction to describe the roll system's movement, and analyzes its dynamic response and working stability, and provides a theoretical basis for the realization of the strip thickness' dynamic control.
Directory of Open Access Journals (Sweden)
Andrei V. Sazonov
2007-01-01
Full Text Available The mapping of brain sources into the scalp electroencephalogram (EEG depends on volume conduction properties of the head and on an electrode montage involving a reference. Mathematically, this source mapping (SM is fully determined by an observation function (OF matrix. This paper analyses the OF-matrix for a generation model for the desynchronized spontaneous EEG. The model involves a four-shell spherical volume conductor containing dipolar sources that are mutually uncorrelated so as to reflect the desynchronized EEG. The reference is optimized in order to minimize the impact in the SM of the sources located distant from the electrodes. The resulting reference is called the localized reference (LR. The OF-matrix is analyzed in terms of the relative power contribution of the sources and the cross-channel correlation coefficient for five existing references as well as for the LR. It is found that the Hjorth Laplacian reference is a fair approximation of the LR, and thus is close to optimum for practical intents and purposes. The other references have a significantly poorer performance. Furthermore, the OF-matrix is analyzed for limits to the spatial resolution for the EEG. These are estimated to be around 2 cm.
Directory of Open Access Journals (Sweden)
Xuejiao Ma
2016-08-01
Full Text Available Big data mining, analysis, and forecasting play vital roles in modern economic and industrial fields, especially in the energy system. Inaccurate forecasting may cause wastes of scarce energy or electricity shortages. However, forecasting in the energy system has proven to be a challenging task due to various unstable factors, such as high fluctuations, autocorrelation and stochastic volatility. To forecast time series data by using hybrid models is a feasible alternative of conventional single forecasting modelling approaches. This paper develops a group of hybrid models to solve the problems above by eliminating the noise in the original data sequence and optimizing the parameters in a back propagation neural network. One of contributions of this paper is to integrate the existing algorithms and models, which jointly show advances over the present state of the art. The results of comparative studies demonstrate that the hybrid models proposed not only satisfactorily approximate the actual value but also can be an effective tool in the planning and dispatching of smart grids.
Optimal control theory for sustainable environmental management.
Shastri, Yogendra; Diwekar, Urmila; Cabezas, Heriberto
2008-07-15
Sustainable ecosystem management aims to promote the structure and operation of the human components of the system while simultaneously ensuring the persistence of the structures and operation of the natural component. Given the complexity of this task owing to the diverse temporal and spatial scales and multidisciplinary interactions, a systems theory approach based on sound mathematical techniques is essential. Two important aspects of this approach are formulation of sustainability-based objectives and development of the management strategies. Fisher information can be used as the basis of a sustainability hypothesis to formulate relevant mathematical objectives for disparate systems, and optimal control theory provides the means to derive time-dependent management strategies. Partial correlation coefficient analysis is an efficient technique to identify the appropriate control variables for policy development. This paper represents a proof of concept for this approach using a model system that includes an ecosystem, humans, a very rudimentary industrial process, and a very simple agricultural system. Formulation and solution of the control problems help in identifying the effective management options which offer guidelines for policies in real systems. The results also emphasize that management using multiple parameters of different nature can be distinctly effective.
A New Flatness Pattern Recognition Model Based on Cerebellar Model Articulation Controllers Network
Institute of Scientific and Technical Information of China (English)
HE Hai-tao; LI Yan
2008-01-01
In the traditional flatness pattern recognition neural network,the topologic configurations need to be rebuilt with a changing width of cold strip.Furthermore,the large learning assignment,slow convergence,and local minimum in the network are observed.Moreover,going by the structure of the tradtional neural network,according to experience,the model is time-consuming and complex.Thus,a new approach of flatness pattern recognition is proposed based on the CMAC (cerebellar model articulation controllers) neural network.The difference in fuzzy distances between samples and the basic patterns is introduced as the input of the CMAC network.Simultaneously,the adequate learning rate is improved in the error correction algorithm of this neural network.The new approach with advantages,such as high learning speed,good generalization,and easy implementation,is efficient and intelligent.The simulation results show that the speed and accuracy of the flatness pattern recognition model are obviously improved.
Numerical methods for control optimization in linear systems
Tyatyushkin, A. I.
2015-05-01
Numerical methods are considered for solving optimal control problems in linear systems, namely, terminal control problems with control and phase constraints and time-optimal control problems. Several algorithms with various computer storage requirements are proposed for solving these problems. The algorithms are intended for finding an optimal control in linear systems having certain features, for example, when the reachable set of a system has flat faces.
2009-09-01
Systems List CSNL Common System Node List DNC Digital Nautical Chart DoDAF Department of Defense Architecture Framework DOE Design of...Satellite (GPS) receiver and a radar system. The GPS receiver can interface with a Digital Nautical Chart ( DNC ) that provides information on...motion, as well as correlating information from the DNC to enhance positional location. Sensitive actuators are required to control the USV’s
Pariota, Luigi; Bifulco, Gennaro Nicola; Galante, Francesco; Montella, Alfonso; Brackstone, Mark
2016-04-01
This paper analyses driving behaviour in car-following conditions, based on extensive individual vehicle data collected during experimental field surveys carried out in Italy and the UK. The aim is to contribute to identify simple evidence to be exploited in the ongoing process of driving assistance and automation which, in turn, would reduce rear-end crashes. In particular, identification of differences and similarities in observed car-following behaviours for different samples of drivers could justify common tuning, at a European or worldwide level, of a technological solution aimed at active safety, or, in the event of differences, could suggest the most critical aspects to be taken into account for localisation or customisation of driving assistance solutions. Without intending to be exhaustive, this paper moves one step in this direction. Indeed, driving behaviour and human errors are considered to be among the main crash contributory factors, and a promising approach for safety improvement is the progressive introduction of increasing levels of driving automation in next-generation vehicles, according to the active/preventive safety approach. However, the more advanced the system, the more complex will be the integration in the vehicle, and the interaction with the driver may sometimes become unproductive, or risky, should the driver be removed from the driving control loop. Thus, implementation of these systems will require the interaction of human driving logics with automation logics and then an enhanced ability in modelling drivers' behaviour. This will allow both higher active-safety levels and higher user acceptance to be achieved, thus ensuring that the driver is always in the control loop, even if his/her role is limited to supervising the automatic logic. Currently, the driving mode most targeted by driving assistance systems is longitudinal driving. This is required in various driving conditions, among which car-following assumes key importance
Institute of Scientific and Technical Information of China (English)
Long DI; Zongli LIN
2014-01-01
Active magnetic bearings (AMBs) have found a wide range of applications in high-speed rotating machinery industry. The instability and nonlinearity of AMBs make controller designs difficult, and when AMBs are coupled with a flexible rotor, the resulting complex dynamics make the problems of stabilization and disturbance rejection, which are critical for a stable and smooth operation of the rotor AMB system, even more difficult. Proportional-integral-derivative (PID) control dominates the current AMB applications in the field. Even though PID controllers are easy to implement, there are critical performance limitations associated with them that prevent the more advanced applications of AMBs, which usually require stronger robustness and performance offered by modern control methods such as H-infinity control andμ-synthesis. However, these advanced control designs rely heavily on the relatively accurate plant models and uncertainty characterizations, which are sometimes difficult to obtain. In this paper, we explore and report on the use of the characteristic model based all-coefficient adaptive control method to stabilize a flexible rotor AMB test rig. In spite of the simple structure of such a characteristic model based all-coefficient adaptive controller, both simulation and experimental results show its strong performance.
Model-Based Integrated High Penetration Renewables Planning and Control Analysis
Energy Technology Data Exchange (ETDEWEB)
Bank, Jason [Electrical Distribution Design, Blacksburg, VA (United States); Broadwater, Robert [Electrical Distribution Design, Blacksburg, VA (United States); Cheng, Danling [Electrical Distribution Design, Blacksburg, VA (United States); Costyk, David [Electrical Distribution Design, Blacksburg, VA (United States); Leyo, Mark [Electrical Distribution Design, Blacksburg, VA (United States); Seguin, Richard [Electrical Distribution Design, Blacksburg, VA (United States); Woyak, Jeremy [Electrical Distribution Design, Blacksburg, VA (United States); Acharya-Menon, Amrita [Pepco Holdings, Inc. (PHI), Washington, DC (United States); Steffel, Steve [Pepco Holdings, Inc. (PHI), Washington, DC (United States); Dise, John [Clean Power Research, Napa, CA (United States); Athawale, Rasika [Rutgers Univ., New Brunswick, NJ (United States); Felder, Frank [Rutgers Univ., New Brunswick, NJ (United States)
2015-12-14
Increasing adoption of Solar Photovoltaic (PV) generation at the distribution level poses several changes for the reliable operation of electrical power distribution systems. The addition of a significant amount of PV to a distribution network can introduce a variety of operational problems, including steady-state overvoltages, reverse flows, voltage flicker and excessive controller movement among others. These adverse impacts can be mitigated through a variety of equipment upgrades which represent a cost to either the electric utility or the owner of the PV site. The study performed here aims to quantify the levels of PV generation which present operational problems on a distribution circuit and how those problems might be alleviated. The study performed here included 20 distribution feeders selected from Pepco Holdings, Inc. (PHI) service territory. These feeders are located in the states of Delaware, Maryland and New Jersey. A hosting capacity study was performed on each feeder to determine how much additional PV it could support in its current configuration. Several improvements were then performed on these circuits including phase balancing, capacitor redesign, reducing the voltage regulator set points, fixed power factor operation on the PV inverters and the installation of battery storage. After each of these improvements the hosting capacity of the circuit was reevaluated in order to determine how that particular improvement impacted the amount of PV that could be hosted by the circuit. Each of these improvements represents a real cost in terms of labor and equipment in order to be implemented. They are expected to provide a benefit in terms of the amount of additional PV generation which can be safely interconnected to the distribution feeder. A cost benefit analysis was performed in order to evaluate the expected costs of each feeder improvement and how each one was able to increase the PV hosting capacity of each feeder. It is hoped that these results
A Model-Based Study of Ecohydrological Controls in the Mojave Desert
Ng, G. C.; Bedford, D.; Miller, D. M.
2010-12-01
Desert ecosystems represent extreme conditions near the limits of viability for vegetation. Their dependence on scarce resources make them vulnerable to climate and land use change. Understanding how ecohydrological conditions impact plants in such regions is critical for ecological sustainability. Various relationships have been observed in the field between vegetation growth and meteorology, terrain, and plant physiology. Quantifying the complex interactions of those influences on vegetation dynamics can be facilitated with a physically-based ecohydrological model. To assess ecohydrological controls in the Mojave Desert, we employ the CLM4.0 land-surface model with the Carbon-Nitrogen model component to simulate vegetation dynamics [Olesen et al., 2010]. Using an ecohydrological model with fully prognostic vegetation variables is essential for representing the coupled dynamics between plants and soil moisture. We apply the CLM4.0-CN model to a study basin in the Mojave National Preserve that covers a variety of conditions. Soils range from coarse-textured wash sediments to low-permeability desert pavements. Higher elevations in the basin experience cooler and moister conditions than the lower wash areas. The dominant vegetation types in the basin include the evergreen shrub Larrea tridentata (creosote) and the drought-deciduous shrub Ambrosia dumosa. Simulations are conducted over a 50 year period to investigate both seasonal and interannual dynamics. Sensitivity tests indicate that high temporal resolution rainfall inputs (at least hourly) are important for properly resolving ecohydrological dynamics at the study site. As expected, preliminary results show that both coarser soils and milder climate facilitate vegetation growth in this moisture-limited region. However, results indicate that effects of soil texture variations become subordinate with milder climate. The model also reveals how drought-deciduous and evergreen shrub types respond differently to
Institute of Scientific and Technical Information of China (English)
Chen Shan; Pan Tianhong; Li Zhengming; Jang Shi-Shang
2012-01-01
This paper proposes to develop a data-driven via's depth estimator of the deep reactive ion etching process based on statistical identification of key variables.Several feature extraction algorithms are presented to reduce the high-dimensional data and effectively undertake the subsequent virtual metrology (VM) model building process.With the available on-line VM model,the model-based controller is hence readily applicable to improve the quality ofa via's depth.Real operational data taken from a industrial manufacturing process are used to verify the effectiveness of the proposed method.The results demonstrate that the proposed method can decrease the MSE from 2.2 × 10-2 to 9 × 10-4 and has great potential in improving the existing DRIE process.
Process Identification in On-line Optimizing Control, an Application to a Heat Pump
Directory of Open Access Journals (Sweden)
Morten C. Svensson
1996-10-01
Full Text Available The objective of this paper is to focus on on-line state and parameter estimation in connection with on-line model-based optimizing control of continuous processes. A nonlinear programming approach is used to estimate unmeasured state variables and parameters in systems modelled by nonlinear differential-algebraic equations. The nonlinear dynamic model is discretized by orthogonal collocation on finite elements, and the moving-horizon approach is used to reduce the dimension of the final optimization problem.
Directory of Open Access Journals (Sweden)
Zhilong Wang
2014-01-01
Full Text Available In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO, the backpropagation artificial neural network (BPANN, and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.
On a Highly Nonlinear Self-Obstacle Optimal Control Problem
Energy Technology Data Exchange (ETDEWEB)
Di Donato, Daniela, E-mail: daniela.didonato@unitn.it [University of Trento, Department of Mathematics (Italy); Mugnai, Dimitri, E-mail: dimitri.mugnai@unipg.it [Università di Perugia, Dipartimento di Matematica e Informatica (Italy)
2015-10-15
We consider a non-quadratic optimal control problem associated to a nonlinear elliptic variational inequality, where the obstacle is the control itself. We show that, fixed a desired profile, there exists an optimal solution which is not far from it. Detailed characterizations of the optimal solution are given, also in terms of approximating problems.
T-S Fuzzy Model Based Control Strategy for the Networked Suspension Control System of Maglev Train
2015-01-01
The control problem for the networked suspension control system of maglev train with random induced time delay and packet dropouts is investigated. First, Takagi-Sugeno (T-S) fuzzy models are utilized to represent the discrete-time nonlinear networked suspension control system, and the parameters uncertainties of the nonlinear model have also been taken into account. The controllers take the form of parallel distributed compensation. Then, a sufficient condition for the stability of the netwo...
Comprehensive Optimization of Emergency Evacuation Route and Departure Time under Traffic Control
Directory of Open Access Journals (Sweden)
Guo Li
2014-01-01
Full Text Available With the frequent occurrence of major emergencies, emergency management gets high attention from all around the world. This paper investigates the comprehensive optimization of major emergency evacuation route and departure time, in which case the evacuation propagation mechanism is considered under traffic control. Given the practical assumptions, we first establish a comprehensive optimization model based on the simulation of evacuation route and departure time. Furthermore, we explore the reasonable description method of evacuation traffic flow propagation under traffic control, including the establishment of traffic flow propagation model and the design of the simulation mudule that can simulate the evacuation traffic flow. Finally, we propose a heuristic algorithm for the optimization of this comprehensive model. In case analysis, we take some areas in Beijing as the evaluation sources to verify the reliability of our model. A series of constructive suggestions for Beijing's emergency evacuation are proposed, which can be applied to the actual situation under traffic control.
Skinner Rusk unified formalism for optimal control systems and applications
Barbero-Liñán, María; Echeverría-Enríquez, Arturo; Martín de Diego, David; Muñoz-Lecanda, Miguel C.; Román-Roy, Narciso
2007-10-01
A geometric approach to time-dependent optimal control problems is proposed. This formulation is based on the Skinner and Rusk formalism for Lagrangian and Hamiltonian systems. The corresponding unified formalism developed for optimal control systems allows us to formulate geometrically the necessary conditions given by a weak form of Pontryagin's maximum principle, provided that the differentiability with respect to controls is assumed and the space of controls is open. Furthermore, our method is also valid for implicit optimal control systems and, in particular, for the so-called descriptor systems (optimal control problems including both differential and algebraic equations).