Generalized Predictive Control and Neural Generalized Predictive Control
Directory of Open Access Journals (Sweden)
Sadhana CHIDRAWAR
2008-12-01
Full Text Available As Model Predictive Control (MPC relies on the predictive Control using a multilayer feed forward network as the plants linear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. This paper presents a detailed derivation of the Generalized Predictive Control and Neural Generalized Predictive Control with Newton-Raphson as minimization algorithm. Taking three separate systems, performances of the system has been tested. Simulation results show the effect of neural network on Generalized Predictive Control. The performance comparison of this three system configurations has been given in terms of ISE and IAE.
General predictive control using the delta operator
DEFF Research Database (Denmark)
Jensen, Morten Rostgaard; Poulsen, Niels Kjølstad; Ravn, Ole
1993-01-01
This paper deals with two-discrete-time operators, the conventional forward shift-operator and the δ-operator. Both operators are treated in view of construction of suitable solutions to the Diophantine equation for the purpose of prediction. A general step-recursive scheme is presented. Finally...... a general predictive control (GPC) is formulated and applied adaptively to a continuous-time plant...
Neural Generalized Predictive Control of a non-linear Process
DEFF Research Database (Denmark)
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole
1998-01-01
The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability qu...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem.......The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability...... qualities. The controller is a non-linear version of the well-known generalized predictive controller developed in linear control theory. It involves minimization of a cost function which in the present case has to be done numerically. Therefore, we develop the numerical algorithms necessary in substantial...
Generalized Predictive Control for Non-Stationary Systems
DEFF Research Database (Denmark)
Palsson, Olafur Petur; Madsen, Henrik; Søgaard, Henning Tangen
1994-01-01
This paper shows how the generalized predictive control (GPC) can be extended to non-stationary (time-varying) systems. If the time-variation is slow, then the classical GPC can be used in context with an adaptive estimation procedure of a time-invariant ARIMAX model. However, in this paper prior...... knowledge concerning the nature of the parameter variations is assumed available. The GPC is based on the assumption that the prediction of the system output can be expressed as a linear combination of present and future controls. Since the Diophantine equation cannot be used due to the time......-variation of the parameters, the optimal prediction is found as the general conditional expectation of the system output. The underlying model is of an ARMAX-type instead of an ARIMAX-type as in the original version of the GPC (Clarke, D. W., C. Mohtadi and P. S. Tuffs (1987). Automatica, 23, 137-148) and almost all later...
Generalized predictive control in the delta-domain
DEFF Research Database (Denmark)
Lauritsen, Morten Bach; Jensen, Morten Rostgaard; Poulsen, Niels Kjølstad
1995-01-01
This paper describes new approaches to generalized predictive control formulated in the delta (δ) domain. A new δ-domain version of the continuous-time emulator-based predictor is presented. It produces the optimal estimate in the deterministic case whenever the predictor order is chosen greater...... than or equal to the number of future predicted samples, however a “good” estimate is usually obtained in a much longer range of samples. This is particularly advantageous at fast sampling rates where a “conventional” predictor is bound to become very computationally demanding. Two controllers...
Effective and Robust Generalized Predictive Speed Control of Induction Motor
Directory of Open Access Journals (Sweden)
Patxi Alkorta
2013-01-01
Full Text Available This paper presents and validates a new proposal for effective speed vector control of induction motors based on linear Generalized Predictive Control (GPC law. The presented GPC-PI cascade configuration simplifies the design with regard to GPC-GPC cascade configuration, maintaining the advantages of the predictive control algorithm. The robust stability of the closed loop system is demonstrated by the poles placement method for several typical cases of uncertainties in induction motors. The controller has been tested using several simulations and experiments and has been compared with Proportional Integral Derivative (PID and Sliding Mode (SM control schemes, obtaining outstanding results in speed tracking even in the presence of parameter uncertainties, unknown load disturbance, and measurement noise in the loop signals, suggesting its use in industrial applications.
Stability analysis of embedded nonlinear predictor neural generalized predictive controller
Directory of Open Access Journals (Sweden)
Hesham F. Abdel Ghaffar
2014-03-01
Full Text Available Nonlinear Predictor-Neural Generalized Predictive Controller (NGPC is one of the most advanced control techniques that are used with severe nonlinear processes. In this paper, a hybrid solution from NGPC and Internal Model Principle (IMP is implemented to stabilize nonlinear, non-minimum phase, variable dead time processes under high disturbance values over wide range of operation. Also, the superiority of NGPC over linear predictive controllers, like GPC, is proved for severe nonlinear processes over wide range of operation. The necessary conditions required to stabilize NGPC is derived using Lyapunov stability analysis for nonlinear processes. The NGPC stability conditions and improvement in disturbance suppression are verified by both simulation using Duffing’s nonlinear equation and real-time using continuous stirred tank reactor. Up to our knowledge, the paper offers the first hardware embedded Neural GPC which has been utilized to verify NGPC–IMP improvement in realtime.
A Robust Practical Generalized Predictive Control for BoilerSuper Heater Temperature Control
Zaki Maki Mohialdeen
2015-01-01
A practical method of robust generalized predictive controller (GPC) application is developed using a combination of Ziegler-Nichols type functions relating the GPC controller parameters to a first order with time delay process parameters and a model matching controller. The GPC controller and the model matching controller are used in a master/slave configuration, with the GPC as the master controller and the model matching controller as the slave controlle...
Force Control for a Pneumatic Cylinder Using Generalized Predictive Controller Approach
Mohd Faudzi, Ahmad ’Athif; Mustafa, Nu’man Din; Osman, Khairuddin
2014-01-01
Pneumatic cylinder is a well-known device because of its high power to weight ratio, easy use, and environmental safety. Pneumatic cylinder uses air as its power source and converts it to a possible movement such as linear and rotary movement. In order to control the pneumatic cylinder, controller algorithm is needed to control the on-off solenoid valve with encoder and pressure sensor as the feedback inputs. In this paper, generalized predictive controller (GPC) is proposed as the control st...
Comparison of H-infinity control and generalized predictive control for a laser scanner system
DEFF Research Database (Denmark)
Ordys, A.W.; Stoustrup, Jakob; Smillie, I.
2000-01-01
This paper describes tests performed on a laser scanner system to assess the feasibility of H-infinity control and generalized predictive control design techniques in achieving a required performance in a trajectory folowing problem. The two methods are compared with respect to achieved scan times...
Force Control for a Pneumatic Cylinder Using Generalized Predictive Controller Approach
Directory of Open Access Journals (Sweden)
Ahmad ’Athif Mohd Faudzi
2014-01-01
Full Text Available Pneumatic cylinder is a well-known device because of its high power to weight ratio, easy use, and environmental safety. Pneumatic cylinder uses air as its power source and converts it to a possible movement such as linear and rotary movement. In order to control the pneumatic cylinder, controller algorithm is needed to control the on-off solenoid valve with encoder and pressure sensor as the feedback inputs. In this paper, generalized predictive controller (GPC is proposed as the control strategy for the pneumatic cylinder force control. To validate and compare the performance, proportional-integral (PI controller is also presented. Both controllers algorithms GPC and PI are developed using existing linear model of the cylinder from previous research. Results are presented in simulation and experimental approach using MATLAB-Simulink as the platform. The results show that the GPC is capable of fast response with low steady state error and percentage overshoot compared to PI.
Yao, Xuliang; Yang, Guangyi
2016-01-01
This paper presents the design and simulation validation of a multivariable GPC (generalized predictive control) for AUV (autonomous underwater vehicle) in vertical plane. This control approach has been designed in the case of AUV navigating with low speed near water surface, in order to restrain wave disturbance effectively and improve pitch and heave motion stability. The proposed controller guarantees compliance with rudder manipulation, AUV output constraints, and driving energy consumpti...
Directory of Open Access Journals (Sweden)
Hicham El bahja
2018-04-01
Full Text Available The main scope of this paper is the proposal of a new single layer Nonlinear Economic Closed-Loop Generalized Predictive Control (NECLGPC as an efficient advanced control technique for improving economics in the operation of nonlinear plants. Instead of the classic dual-mode MPC (model predictive controller schemes, where the terminal control law defined in the terminal region is obtained offline solving a linear quadratic regulator problem, here the terminal control law in the NECLGPC is determined online by an unconstrained Nonlinear Generalized Predictive Control (NGPC. In order to make the optimization problem more tractable two considerations have been made in the present work. Firstly, the prediction model consisting of a nonlinear phenomenological model of the plant is expressed with linear structure and state dependent matrices. Secondly, instead of including the nonlinear economic cost in the objective function, an approximation of the reduced gradient of the economic function is used. These assumptions allow us to design an economic unconstrained nonlinear GPC analytically and to state the NECLGPC allow for the design of an economic problem as a QP (Quadratic Programing problem each sampling time. Four controllers based on GPC that differ in designs and structures are compared with the proposed control technique in terms of process performance and energy costs. Particularly, the methodology is implemented in the N-Removal process of a Wastewater Treatment Plant (WWTP and the results prove the efficiency of the method and that it can be used profitably in practical cases.
Ouari, Kamel; Rekioua, Toufik; Ouhrouche, Mohand
2014-01-01
In order to make a wind power generation truly cost-effective and reliable, an advanced control techniques must be used. In this paper, we develop a new control strategy, using nonlinear generalized predictive control (NGPC) approach, for DFIG-based wind turbine. The proposed control law is based on two points: NGPC-based torque-current control loop generating the rotor reference voltage and NGPC-based speed control loop that provides the torque reference. In order to enhance the robustness of the controller, a disturbance observer is designed to estimate the aerodynamic torque which is considered as an unknown perturbation. Finally, a real-time simulation is carried out to illustrate the performance of the proposed controller. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
M. Romero
2013-01-01
Full Text Available There is an increasing interest in using fractional calculus applied to control theory generalizing classical control strategies as the PID controller and developing new ones with the intention of taking advantage of characteristics supplied by this mathematical tool for the controller definition. In this work, the fractional generalization of the successful and spread control strategy known as model predictive control is applied to drive autonomously a gasoline-propelled vehicle at low speeds. The vehicle is a Citroën C3 Pluriel that was modified to act over the throttle and brake pedals. Its highly nonlinear dynamics are an excellent test bed for applying beneficial characteristics of fractional predictive formulation to compensate unmodeled dynamics and external disturbances.
Generalized Predictive Control of Dynamic Systems with Rigid-Body Modes
Kvaternik, Raymond G.
2013-01-01
Numerical simulations to assess the effectiveness of Generalized Predictive Control (GPC) for active control of dynamic systems having rigid-body modes are presented. GPC is a linear, time-invariant, multi-input/multi-output predictive control method that uses an ARX model to characterize the system and to design the controller. Although the method can accommodate both embedded (implicit) and explicit feedforward paths for incorporation of disturbance effects, only the case of embedded feedforward in which the disturbances are assumed to be unknown is considered here. Results from numerical simulations using mathematical models of both a free-free three-degree-of-freedom mass-spring-dashpot system and the XV-15 tiltrotor research aircraft are presented. In regulation mode operation, which calls for zero system response in the presence of disturbances, the simulations showed reductions of nearly 100%. In tracking mode operations, where the system is commanded to follow a specified path, the GPC controllers produced the desired responses, even in the presence of disturbances.
An Improved Generalized Predictive Control in a Robust Dynamic Partial Least Square Framework
Directory of Open Access Journals (Sweden)
Jin Xin
2015-01-01
Full Text Available To tackle the sensitivity to outliers in system identification, a new robust dynamic partial least squares (PLS model based on an outliers detection method is proposed in this paper. An improved radial basis function network (RBFN is adopted to construct the predictive model from inputs and outputs dataset, and a hidden Markov model (HMM is applied to detect the outliers. After outliers are removed away, a more robust dynamic PLS model is obtained. In addition, an improved generalized predictive control (GPC with the tuning weights under dynamic PLS framework is proposed to deal with the interaction which is caused by the model mismatch. The results of two simulations demonstrate the effectiveness of proposed method.
Explicit Generalized Predictive Control of Speed and Position of PMSM Drives
Czech Academy of Sciences Publication Activity Database
Belda, Květoslav; Vošmik, D.
2016-01-01
Roč. 63, č. 6 (2016), s. 3889-3896 ISSN 0278-0046 Institutional support: RVO:67985556 Keywords : current limitation * field weakening * motion control * permanent magnet synchronous motors * position control * predictive control * speed control Subject RIV: BC - Control Systems Theory Impact factor: 7.168, year: 2016 http://library.utia.cas.cz/separaty/2016/AS/belda-0457259.pdf
Low speed hybrid generalized predictive control of a gasoline-propelled car.
Romero, M; de Madrid, A P; Mañoso, C; Milanés, V
2015-07-01
Low-speed driving in traffic jams causes significant pollution and wasted time for commuters. Additionally, from the passengers׳ standpoint, this is an uncomfortable, stressful and tedious scene that is suitable to be automated. The highly nonlinear dynamics of car engines at low-speed turn its automation in a complex problem that still remains as unsolved. Considering the hybrid nature of the vehicle longitudinal control at low-speed, constantly switching between throttle and brake pedal actions, hybrid control is a good candidate to solve this problem. This work presents the analytical formulation of a hybrid predictive controller for automated low-speed driving. It takes advantage of valuable characteristics supplied by predictive control strategies both for compensating un-modeled dynamics and for keeping passengers security and comfort analytically by means of the treatment of constraints. The proposed controller was implemented in a gas-propelled vehicle to experimentally validate the adopted solution. To this end, different scenarios were analyzed varying road layouts and vehicle speeds within a private test track. The production vehicle is a commercial Citroën C3 Pluriel which has been modified to automatically act over its throttle and brake pedals. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Jun Yang
2015-03-01
Full Text Available In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G technique, electric vehicles (EVs can act as mobile energy storage units, which could be a solution for load frequency control (LFC in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances.
Sunan, Huang; Heng, Lee Tong
2002-01-01
The presence of considerable time delays in the dynamics of many industrial processes, leading to difficult problems in the associated closed-loop control systems, is a well-recognized phenomenon. The performance achievable in conventional feedback control systems can be significantly degraded if an industrial process has a relatively large time delay compared with the dominant time constant. Under these circumstances, advanced predictive control is necessary to improve the performance of the control system significantly. The book is a focused treatment of the subject matter, including the fundamentals and some state-of-the-art developments in the field of predictive control. Three main schemes for advanced predictive control are addressed in this book: • Smith Predictive Control; • Generalised Predictive Control; • a form of predictive control based on Finite Spectrum Assignment. A substantial part of the book addresses application issues in predictive control, providing several interesting case studie...
Differential Prediction Generalization in College Admissions Testing
Aguinis, Herman; Culpepper, Steven A.; Pierce, Charles A.
2016-01-01
We introduce the concept of "differential prediction generalization" in the context of college admissions testing. Specifically, we assess the extent to which predicted first-year college grade point average (GPA) based on high-school grade point average (HSGPA) and SAT scores depends on a student's ethnicity and gender and whether this…
Data-Based Predictive Control with Multirate Prediction Step
Barlow, Jonathan S.
2010-01-01
Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.
Tapping generalized essentialism to predict outgroup prejudices.
Hodson, Gordon; Skorska, Malvina N
2015-06-01
Psychological essentialism, the perception that groups possess inherent properties binding them and differentiating them from others, is theoretically relevant to predicting prejudice. Recent developments isolate two key dimensions: essentialistic entitativity (EE; groups as unitary, whole, entity-like) and essentialistic naturalness (EN; groups as fixed and immutable). We introduce a novel question: does tapping the covariance between EE and EN, rather than pitting them against each other, boost prejudice prediction? In Study 1 (re-analysis of Roets & Van Hiel, 2011b, Samples 1-3, in Belgium) and Study 2 (new Canadian data) their common/shared variance, modelled as generalized essentialism, doubles the predictive power relative to regression-based approaches with regard to racism (but not anti-gay or -schizophrenic prejudices). Theoretical implications are discussed. © 2014 The British Psychological Society.
Adaptive filtering prediction and control
Goodwin, Graham C
2009-01-01
Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o
Ralston, J. N.; Barnhart, B. P.
1984-01-01
The influence of control deflections on the rotational flow aerodynamics and on predicted spin modes is discussed for a 1/6-scale general aviation airplane model. The model was tested for various control settings at both zero and ten degree sideslip angles. Data were measured, using a rotary balance, over an angle-of-attack range of 30 deg to 90 deg, and for clockwise and counter-clockwise rotations covering an omegab/2V range of 0 to 0.5.
Intelligent Predictive Control of Nonlienar Processes Using
DEFF Research Database (Denmark)
Nørgård, Peter Magnus; Sørensen, Paul Haase; Poulsen, Niels Kjølstad
1996-01-01
This paper presents a novel approach to design of generalized predictive controllers (GPC) for nonlinear processes. A neural network is used for modelling the process and a gain-scheduling type of GPC is subsequently designed. The combination of neural network models and predictive control has...... frequently been discussed in the neural network community. This paper proposes an approximate scheme, the approximate predictive control (APC), which facilitates the implementation and gives a substantial reduction in the required amount of computations. The method is based on a technique for extracting...... linear models from a nonlinear neural network and using them in designing the control system. The performance of the controller is demonstrated in a simulation study of a pneumatic servo system...
Model predictive control using fuzzy decision functions
Kaymak, U.; Costa Sousa, da J.M.
2001-01-01
Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the
Link prediction via generalized coupled tensor factorisation
DEFF Research Database (Denmark)
Ermiş, Beyza; Evrim, Acar Ataman; Taylan Cemgil, A.
2012-01-01
and higher-order tensors. We propose to use an approach based on probabilistic interpretation of tensor factorisation models, i.e., Generalised Coupled Tensor Factorisation, which can simultaneously fit a large class of tensor models to higher-order tensors/matrices with com- mon latent factors using...... different loss functions. Numerical experiments demonstrate that joint analysis of data from multiple sources via coupled factorisation improves the link prediction performance and the selection of right loss function and tensor model is crucial for accurately predicting missing links....
Generalized synchronization via impulsive control
International Nuclear Information System (INIS)
Zhang Rong; Xu Zhenyuan; Yang, Simon X.; He Xueming
2008-01-01
This paper demonstrates theoretically that two completely different systems can implement GS via impulsive control, moreover by using impulsive control, for a given manifold y = H(x) we construct a response system to achieve GS with drive system and the synchronization manifold is y = H(x). Our theoretical results are supported by numerical examples
Generalized trust predicts young children's willingness to delay gratification.
Ma, Fengling; Chen, Biyun; Xu, Fen; Lee, Kang; Heyman, Gail D
2018-05-01
Young children's willingness to delay gratification by forgoing an immediate reward to obtain a more desirable one in the future predicts a wide range of positive social, cognitive, and health outcomes. Standard accounts of this phenomenon have focused on individual differences in cognitive control skills that allow children to engage in goal-oriented behavior, but recent findings suggest that person-specific trust is also important, with children showing a stronger tendency to delay gratification if they have reason to trust the individual who is promising the future reward. The current research builds on those findings by examining generalized trust, which refers to the extent to which others are generally viewed as trustworthy. A total of 150 3- to 5-year-olds in China were tested. Participants were given the opportunity to obtain one sticker immediately, or wait for 15 min for two stickers. Results showed that participants with high levels of generalized trust waited longer even after controlling for age and level of executive function. These results suggest that trust plays a role in delaying gratification even when children have no information about the individual who is promising the future reward. More broadly, the findings build on recent evidence that there is more to delay of gratification than cognitive capacity, and they suggest that there are individual differences in whether children consider sacrificing for a future outcome to be worth the risk. Copyright © 2017 Elsevier Inc. All rights reserved.
DEFF Research Database (Denmark)
Nadimi, Esmaeil Sharak; Bak, Thomas; Izadi-Zamanabadi, Roozbeh
2006-01-01
The main objective of this paper is to investigate the erformance and applicability of two GPC (generalized predictive control) based control methods on a complete benchmark model of the Stewart platform made in MATLAB V6.5. The first method involves an LQG controller (Linear Quadratic Gaussian...
Model Prediction Control For Water Management Using Adaptive Prediction Accuracy
Tian, X.; Negenborn, R.R.; Van Overloop, P.J.A.T.M.; Mostert, E.
2014-01-01
In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for
General Pest Control - Industrial. Manual 95.
Missouri Univ., Columbia. Agricultural Experiment Station.
This training manual provides information needed to meet the minimum EPA standards for certification as a commercial applicator of pesticides in the general pest control category. The text discusses general, parasitic and miscellaneous pests such as ants, ticks, and spiders; fabric, wood-destroying, and grain pests such as beetles, termites, and…
Evaluation and comparison of predictive individual-level general surrogates.
Gabriel, Erin E; Sachs, Michael C; Halloran, M Elizabeth
2018-07-01
An intermediate response measure that accurately predicts efficacy in a new setting at the individual level could be used both for prediction and personalized medical decisions. In this article, we define a predictive individual-level general surrogate (PIGS), which is an individual-level intermediate response that can be used to accurately predict individual efficacy in a new setting. While methods for evaluating trial-level general surrogates, which are predictors of trial-level efficacy, have been developed previously, few, if any, methods have been developed to evaluate individual-level general surrogates, and no methods have formalized the use of cross-validation to quantify the expected prediction error. Our proposed method uses existing methods of individual-level surrogate evaluation within a given clinical trial setting in combination with cross-validation over a set of clinical trials to evaluate surrogate quality and to estimate the absolute prediction error that is expected in a new trial setting when using a PIGS. Simulations show that our method performs well across a variety of scenarios. We use our method to evaluate and to compare candidate individual-level general surrogates over a set of multi-national trials of a pentavalent rotavirus vaccine.
Seasonal predictability of Kiremt rainfall in coupled general circulation models
Gleixner, Stephanie; Keenlyside, Noel S.; Demissie, Teferi D.; Counillon, François; Wang, Yiguo; Viste, Ellen
2017-11-01
The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June-September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985-2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.
General distributed control system for fusion experiments
International Nuclear Information System (INIS)
Klingner, P.L.; Levings, S.J.; Wilkins, R.W.
1986-01-01
A general control system using distributed LSI-11 microprocessors is being developed. Common software residues in each LSI-11 and is tailored to an application by control specifications downloaded from a host computer. The microprocessors, their control interfaces, and the micro-to-host communications are CAMAC based. The host computer also supports an operator interface, coordination of multiple microprocessors, and utilities to create and maintain the control specifications. Typical applications include monitoring safety interlocks as well as controlling vacuum systems, high voltage charging systems, and diagnostics
General Theory versus ENA Theory: Comparing Their Predictive Accuracy and Scope.
Ellis, Lee; Hoskin, Anthony; Hartley, Richard; Walsh, Anthony; Widmayer, Alan; Ratnasingam, Malini
2015-12-01
General theory attributes criminal behavior primarily to low self-control, whereas evolutionary neuroandrogenic (ENA) theory envisions criminality as being a crude form of status-striving promoted by high brain exposure to androgens. General theory predicts that self-control will be negatively correlated with risk-taking, while ENA theory implies that these two variables should actually be positively correlated. According to ENA theory, traits such as pain tolerance and muscularity will be positively associated with risk-taking and criminality while general theory makes no predictions concerning these relationships. Data from Malaysia and the United States are used to test 10 hypotheses derived from one or both of these theories. As predicted by both theories, risk-taking was positively correlated with criminality in both countries. However, contrary to general theory and consistent with ENA theory, the correlation between self-control and risk-taking was positive in both countries. General theory's prediction of an inverse correlation between low self-control and criminality was largely supported by the U.S. data but only weakly supported by the Malaysian data. ENA theory's predictions of positive correlations between pain tolerance, muscularity, and offending were largely confirmed. For the 10 hypotheses tested, ENA theory surpassed general theory in predictive scope and accuracy. © The Author(s) 2014.
State-space Generalized Predicitve Control for redundant parallel robots
Czech Academy of Sciences Publication Activity Database
Belda, Květoslav; Böhm, Josef; Valášek, M.
2003-01-01
Roč. 31, č. 3 (2003), s. 413-432 ISSN 1539-7734 R&D Projects: GA ČR GA101/03/0620 Grant - others:CTU(CZ) 0204512 Institutional research plan: CEZ:AV0Z1075907 Keywords : parallel robot construction * generalized predictive control * drive redundancy Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/historie/belda-0411126.pdf
Model predictive Controller for Mobile Robot
Alireza Rezaee
2017-01-01
This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is...
Dynamic Algorithm for LQGPC Predictive Control
DEFF Research Database (Denmark)
Hangstrup, M.; Ordys, A.W.; Grimble, M.J.
1998-01-01
In this paper the optimal control law is derived for a multi-variable state space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady state controller. Knowledge of future reference values is incorporated into the control......In this paper the optimal control law is derived for a multi-variable state space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady state controller. Knowledge of future reference values is incorporated...... into the controller design and the solution is derived using the method of Lagrange multipliers. It is shown how well-known GPC controller can be obtained as a special case of the LQGPC controller design. The important advantage of using the LQGPC framework for designing predictive, e.g. GPS is that LQGPC enables...
Hybrid Predictive Control for Dynamic Transport Problems
Núñez, Alfredo A; Cortés, Cristián E
2013-01-01
Hybrid Predictive Control for Dynamic Transport Problems develops methods for the design of predictive control strategies for nonlinear-dynamic hybrid discrete-/continuous-variable systems. The methodology is designed for real-time applications, particularly the study of dynamic transport systems. Operational and service policies are considered, as well as cost reduction. The control structure is based on a sound definition of the key variables and their evolution. A flexible objective function able to capture the predictive behaviour of the system variables is described. Coupled with efficient algorithms, mainly drawn from the area of computational intelligence, this is shown to optimize performance indices for real-time applications. The framework of the proposed predictive control methodology is generic and, being able to solve nonlinear mixed-integer optimization problems dynamically, is readily extendable to other industrial processes. The main topics of this book are: ●hybrid predictive control (HPC) ...
Model predictive control classical, robust and stochastic
Kouvaritakis, Basil
2016-01-01
For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...
Unreachable Setpoints in Model Predictive Control
DEFF Research Database (Denmark)
Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp
2008-01-01
In this work, a new model predictive controller is developed that handles unreachable setpoints better than traditional model predictive control methods. The new controller induces an interesting fast/slow asymmetry in the tracking response of the system. Nominal asymptotic stability of the optimal...... steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...
Wind turbine control and model predictive control for uncertain systems
DEFF Research Database (Denmark)
Thomsen, Sven Creutz
as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time...
Model Predictive Control for Smart Energy Systems
DEFF Research Database (Denmark)
Halvgaard, Rasmus
pumps, heat tanks, electrical vehicle battery charging/discharging, wind farms, power plants). 2.Embed forecasting methodologies for the weather (e.g. temperature, solar radiation), the electricity consumption, and the electricity price in a predictive control system. 3.Develop optimization algorithms....... Chapter 3 introduces Model Predictive Control (MPC) including state estimation, filtering and prediction for linear models. Chapter 4 simulates the models from Chapter 2 with the certainty equivalent MPC from Chapter 3. An economic MPC minimizes the costs of consumption based on real electricity prices...... that determined the flexibility of the units. A predictive control system easily handles constraints, e.g. limitations in power consumption, and predicts the future behavior of a unit by integrating predictions of electricity prices, consumption, and weather variables. The simulations demonstrate the expected...
Implementation of neural network based non-linear predictive control
DEFF Research Database (Denmark)
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole
1999-01-01
This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control...... of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...... on an efficient quasi-Newton algorithm. The performance is demonstrated on a pneumatic servo system....
Predicting and Controlling Complex Networks
2015-06-22
ubiquitous in nature and fundamental to evolution in ecosystems. However, a significant chal- lenge remains in understanding biodiversity since, by the...networks and control . . . . . . . . . . . . . . . . . . . 7 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically...Ni, Y.-C. Lai, and C. Grebogi, “Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games,” Physical Review E 83
General Purpose Crate (GPC) for control applications
International Nuclear Information System (INIS)
Singh, Kundan; Munda, Deepak K.; Jain, Mamta; Archunan, M.; Barua, P.; Ajith Kumar, B.P.
2011-01-01
A General Purpose Crate (GPC) capable of handling digital and analog Inputs/Outputs signals has been developed at Inter University Accelerator Centre (IUAC), New Delhi, for accelerator control system applications. The system includes back-plane bus with on board plugged-in single board computer with PC104 and Ethernet interface, running Linux operating system. The bus control logic is designed on the back-plane pcb itself, making the system more rugged. The various types of digital and analog input/output modules can be plugged into the back plane bus randomly with standard euro connectors, which provides highly reliable and dust free contacts. Maximum eight modules can be inserted into the crate. The total power consumption for various types of modules and back-plane controller is approximately 50 watts. The multi-output DC power supply from COSEL has been used in the crate. The general purpose crate is software compatible with the CAMAC crates used in the accelerator control system. (author)
Model complexity control for hydrologic prediction
Schoups, G.; Van de Giesen, N.C.; Savenije, H.H.G.
2008-01-01
A common concern in hydrologic modeling is overparameterization of complex models given limited and noisy data. This leads to problems of parameter nonuniqueness and equifinality, which may negatively affect prediction uncertainties. A systematic way of controlling model complexity is therefore
An adaptive predictive controller and its applications in power stations
Energy Technology Data Exchange (ETDEWEB)
Yang Zhiyuan; Lu Huiming; Zhang Xinggao [North China Electric Power University, Beijing (China); Song Chunping [Tsinghua University, Beijing (China). Dept. of Thermal Energy Engineering
1999-07-01
Based on the objective function in the form of integration of generalized model error, a globally convergent model reference adaptive predictive control algorithm (MRAPC) containing inertia-time compensators is presented in this paper. MRAPC has been successfully applied to control important thermal process of more than 20 units in many Chinese power stations. In this paper three representative examples are described. Continual operation results for years demonstrate that MRAPC is a successful attempt for the practical applications of adaptive control techniques. (author)
Delta-Domain Predictive Control and Identification for Control
DEFF Research Database (Denmark)
Lauritsen, Morten Bach
1997-01-01
The present thesis is concerned with different aspects of modelling, control and identification of linear systems. Traditionally, discrete-time sampled-data systems are represented using shift-operator parametrizations. Such parametrizations are not suitable at fast sampling rates. An alternative...... minimum-variance predictor as a special case and to have a well-defined continuous-time limit. By means of this new prediction method a unified framework for discrete-time and continuous-time predictive control algorithms is developed. This contains a continuous-time like discrete-time predictive...... controller which is insensitive to the choice of sampling period and has a well-defined limit in the continuous-time case. Also more conventional discrete-time predictive control methods may be described within the unified approach. The predictive control algorithms are extended to frequency weighted...
Improved fuzzy PID controller design using predictive functional control structure.
Wang, Yuzhong; Jin, Qibing; Zhang, Ridong
2017-11-01
In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Nonlinear Model Predictive Control for Cooperative Control and Estimation
Ru, Pengkai
Recent advances in computational power have made it possible to do expensive online computations for control systems. It is becoming more realistic to perform computationally intensive optimization schemes online on systems that are not intrinsically stable and/or have very small time constants. Being one of the most important optimization based control approaches, model predictive control (MPC) has attracted a lot of interest from the research community due to its natural ability to incorporate constraints into its control formulation. Linear MPC has been well researched and its stability can be guaranteed in the majority of its application scenarios. However, one issue that still remains with linear MPC is that it completely ignores the system's inherent nonlinearities thus giving a sub-optimal solution. On the other hand, if achievable, nonlinear MPC, would naturally yield a globally optimal solution and take into account all the innate nonlinear characteristics. While an exact solution to a nonlinear MPC problem remains extremely computationally intensive, if not impossible, one might wonder if there is a middle ground between the two. We tried to strike a balance in this dissertation by employing a state representation technique, namely, the state dependent coefficient (SDC) representation. This new technique would render an improved performance in terms of optimality compared to linear MPC while still keeping the problem tractable. In fact, the computational power required is bounded only by a constant factor of the completely linearized MPC. The purpose of this research is to provide a theoretical framework for the design of a specific kind of nonlinear MPC controller and its extension into a general cooperative scheme. The controller is designed and implemented on quadcopter systems.
Model predictive control for a thermostatic controlled system
DEFF Research Database (Denmark)
Shafiei, Seyed Ehsan; Rasmussen, Henrik; Stoustrup, Jakob
2013-01-01
This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff temperat......This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff...
Nonparametric predictive inference in statistical process control
Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.
2000-01-01
New methods for statistical process control are presented, where the inferences have a nonparametric predictive nature. We consider several problems in process control in terms of uncertainties about future observable random quantities, and we develop inferences for these random quantities hased on
Nonparametric predictive inference in statistical process control
Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.
2004-01-01
Statistical process control (SPC) is used to decide when to stop a process as confidence in the quality of the next item(s) is low. Information to specify a parametric model is not always available, and as SPC is of a predictive nature, we present a control chart developed using nonparametric
Predicting dyadic adjustment from general and relationship-specific beliefs.
DeBord, J; Romans, J S; Krieshok, T
1996-05-01
The cognitive mediation model of human psychological functioning has received increasing attention by researchers studying the role of cognitive variables in relationship distress. This study is an examination of the role of general irrational beliefs, as measured by the Irrational Beliefs Test (IBT; Jones, 1968), and relationship-specific irrational beliefs, as measured by the Relationship Belief Questionnaire (RBQ; Romans & DeBord, 1994), in predicting the perceived quality of relationships by married or cohabiting couples. Results indicated that respondents who reported higher levels of relationship-specific irrational beliefs also reported higher levels of dyadic adjustment; but contrary to expectation, higher levels of general irrational beliefs correlated with lower levels of dyadic adjustment. Implications of these findings are discussed in relation to the depressive realism hypothesis.
Tube Model Predictive Control with an Auxiliary Sliding Mode Controller
Directory of Open Access Journals (Sweden)
Miodrag Spasic
2016-07-01
Full Text Available This paper studies Tube Model Predictive Control (MPC with a Sliding Mode Controller (SMC as an auxiliary controller. It is shown how to calculate the tube widths under SMC control, and thus how much the constraints of the nominal MPC have to be tightened in order to achieve robust stability and constraint fulfillment. The analysis avoids the assumption of infinitely fast switching in the SMC controller.
[Predictive ocular motor control in Parkinson's disease].
Ying, Li; Liu, Zhen-Guo; Chen, Wei; Gan, Jing; Wang, Wen-An
2008-02-19
To investigate the changes of predictive ocular motor function in the patients with Parkinson's disease (PD), and to discuss its clinical value. Videonystagmography (VNG) was used to examine 24 patients with idiopathic Parkinson's disease, 15 males and 9 females, aged 61 +/- 6 (50-69), and 24 sex and age-matched healthy control subjects on random ocular saccade (with the target moving at random intervals to random positions) and predictive ocular saccade (with the 1.25-second light target moving 10 degrees right or left from the center). In the random ocular saccade program, the latency of saccade of the PD patients was 284 ms +/- 58 ms, significantly longer than that of the healthy controls (236 ms +/- 37 ms, P = 0.003). In the predictive ocular saccade pattern, the latency of saccades the PD patients was 150 ms +/- 138 ms, significantly longer than that of the healthy controls (59 ms +/- 102 ms, P = 0.002). The appearance rate of predictive saccades (with the latency of saccade <80 ms) in the PD group was 21%, significantly lower than that in the control group (31%, P = 0.003). There is dysfunction of predictive ocular motor control in the PD patients, and the cognitive function may be impaired at the early stage of PD.
Nonlinear predictive control in the LHC accelerator
Blanco, E; Cristea, S; Casas, J
2009-01-01
This paper describes the application of a nonlinear model-based control strategy in a real challenging process. A predictive controller based on a nonlinear model derived from physical relationships, mainly heat and mass balances, has been developed and commissioned in the inner triplet heat exchanger unit (IT-HXTU) of the large hadron collider (LHC) particle accelerator at European Center for Nuclear Research (CERN). The advanced regulation\\ maintains the magnets temperature at about 1.9 K. The development includes a constrained nonlinear state estimator with a receding horizon estimation procedure to improve the regulator predictions.
Applying model predictive control to power system frequency control
Ersdal, AM; Imsland, L; Cecilio, IM; Fabozzi, D; Thornhill, NF
2013-01-01
16.07.14 KB Ok to add accepted version to Spiral Model predictive control (MPC) is investigated as a control method which may offer advantages in frequency control of power systems than the control methods applied today, especially in presence of increased renewable energy penetration. The MPC includes constraints on both generation amount and generation rate of change, and it is tested on a one-area system. The proposed MPC is tested against a conventional proportional-integral (PI) cont...
Distributed model predictive control made easy
Negenborn, Rudy
2014-01-01
The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems. This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...
Model predictive controller design of hydrocracker reactors
GÖKÇE, Dila
2011-01-01
This study summarizes the design of a Model Predictive Controller (MPC) in Tüpraş, İzmit Refinery Hydrocracker Unit Reactors. Hydrocracking process, in which heavy vacuum gasoil is converted into lighter and valuable products at high temperature and pressure is described briefly. Controller design description, identification and modeling studies are examined and the model variables are presented. WABT (Weighted Average Bed Temperature) equalization and conversion increase are simulate...
Distributed Model Predictive Control via Dual Decomposition
DEFF Research Database (Denmark)
Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle
2014-01-01
This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...
Quantized Predictive Control over Erasure Channels
DEFF Research Database (Denmark)
E. Quevedo, Daniel; Østergaard, Jan
2009-01-01
.i.d. dropouts, the controller transmits data packets containing quantized plant input predictions. These minimize a finite horizon cost function and are provided by an appropriate optimal entropy coded dithered lattice vector quantizer. Within this context, we derive an equivalent noise-shaping model...
Predictive Feedback and Feedforward Control for Systems with Unknown Disturbances
Juang, Jer-Nan; Eure, Kenneth W.
1998-01-01
Predictive feedback control has been successfully used in the regulation of plate vibrations when no reference signal is available for feedforward control. However, if a reference signal is available it may be used to enhance regulation by incorporating a feedforward path in the feedback controller. Such a controller is known as a hybrid controller. This paper presents the theory and implementation of the hybrid controller for general linear systems, in particular for structural vibration induced by acoustic noise. The generalized predictive control is extended to include a feedforward path in the multi-input multi-output case and implemented on a single-input single-output test plant to achieve plate vibration regulation. There are cases in acoustic-induce vibration where the disturbance signal is not available to be used by the hybrid controller, but a disturbance model is available. In this case the disturbance model may be used in the feedback controller to enhance performance. In practice, however, neither the disturbance signal nor the disturbance model is available. This paper presents the theory of identifying and incorporating the noise model into the feedback controller. Implementations are performed on a test plant and regulation improvements over the case where no noise model is used are demonstrated.
Nonlinear model predictive control theory and algorithms
Grüne, Lars
2017-01-01
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...
Fault Tolerant Control Using Gaussian Processes and Model Predictive Control
Directory of Open Access Journals (Sweden)
Yang Xiaoke
2015-03-01
Full Text Available Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control.
Plant control using embedded predictive models
International Nuclear Information System (INIS)
Godbole, S.S.; Gabler, W.E.; Eschbach, S.L.
1990-01-01
B and W recently undertook the design of an advanced light water reactor control system. A concept new to nuclear steam system (NSS) control was developed. The concept, which is called the Predictor-Corrector, uses mathematical models of portions of the controlled NSS to calculate, at various levels within the system, demand and control element position signals necessary to satisfy electrical demand. The models give the control system the ability to reduce overcooling and undercooling of the reactor coolant system during transients and upsets. Two types of mathematical models were developed for use in designing and testing the control system. One model was a conventional, comprehensive NSS model that responds to control system outputs and calculates the resultant changes in plant variables that are then used as inputs to the control system. Two other models, embedded in the control system, were less conventional, inverse models. These models accept as inputs plant variables, equipment states, and demand signals and predict plant operating conditions and control element states that will satisfy the demands. This paper reports preliminary results of closed-loop Reactor Coolant (RC) pump trip and normal load reduction testing of the advanced concept. Results of additional transient testing, and of open and closed loop stability analyses will be reported as they are available
Multi-year predictability in a coupled general circulation model
Energy Technology Data Exchange (ETDEWEB)
Power, Scott; Colman, Rob [Bureau of Meteorology Research Centre, Melbourne, VIC (Australia)
2006-02-01
Multi-year to decadal variability in a 100-year integration of a BMRC coupled atmosphere-ocean general circulation model (CGCM) is examined. The fractional contribution made by the decadal component generally increases with depth and latitude away from surface waters in the equatorial Indo-Pacific Ocean. The relative importance of decadal variability is enhanced in off-equatorial ''wings'' in the subtropical eastern Pacific. The model and observations exhibit ''ENSO-like'' decadal patterns. Analytic results are derived, which show that the patterns can, in theory, occur in the absence of any predictability beyond ENSO time-scales. In practice, however, modification to this stochastic view is needed to account for robust differences between ENSO-like decadal patterns and their interannual counterparts. An analysis of variability in the CGCM, a wind-forced shallow water model, and a simple mixed layer model together with existing and new theoretical results are used to improve upon this stochastic paradigm and to provide a new theory for the origin of decadal ENSO-like patterns like the Interdecadal Pacific Oscillation and Pacific Decadal Oscillation. In this theory, ENSO-driven wind-stress variability forces internal equatorially-trapped Kelvin waves that propagate towards the eastern boundary. Kelvin waves can excite reflected internal westward propagating equatorially-trapped Rossby waves (RWs) and coastally-trapped waves (CTWs). CTWs have no impact on the off-equatorial sub-surface ocean outside the coastal wave guide, whereas the RWs do. If the frequency of the incident wave is too high, then only CTWs are excited. At lower frequencies, both CTWs and RWs can be excited. The lower the frequency, the greater the fraction of energy transmitted to RWs. This lowers the characteristic frequency of variability off the equator relative to its equatorial counterpart. Both the eastern boundary interactions and the accumulation of
Predictive access control for distributed computation
DEFF Research Database (Denmark)
Yang, Fan; Hankin, Chris; Nielson, Flemming
2013-01-01
We show how to use aspect-oriented programming to separate security and trust issues from the logical design of mobile, distributed systems. The main challenge is how to enforce various types of security policies, in particular predictive access control policies — policies based on the future beh...... behavior of a program. A novel feature of our approach is that we can define policies concerning secondary use of data....
Model Predictive Control of Sewer Networks
DEFF Research Database (Denmark)
Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik
2016-01-01
The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....
NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES
Directory of Open Access Journals (Sweden)
SILVA R. G.
1999-01-01
Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.
Hanna-Pladdy, Brenda; Gajewski, Byron
2012-01-01
Studies evaluating the impact of modifiable lifestyle factors on cognition offer potential insights into sources of cognitive aging variability. Recently, we reported an association between extent of musical instrumental practice throughout the life span (greater than 10 years) on preserved cognitive functioning in advanced age. These findings raise the question of whether there are training-induced brain changes in musicians that can transfer to non-musical cognitive abilities to allow for compensation of age-related cognitive declines. However, because of the relationship between engagement in general lifestyle activities and preserved cognition, it remains unclear whether these findings are specifically driven by musical training or the types of individuals likely to engage in greater activities in general. The current study controlled for general activity level in evaluating cognition between musicians and nomusicians. Also, the timing of engagement (age of acquisition, past versus recent) was assessed in predictive models of successful cognitive aging. Seventy age and education matched older musicians (>10 years) and non-musicians (ages 59-80) were evaluated on neuropsychological tests and general lifestyle activities. Musicians scored higher on tests of phonemic fluency, verbal working memory, verbal immediate recall, visuospatial judgment, and motor dexterity, but did not differ in other general leisure activities. Partition analyses were conducted on significant cognitive measures to determine aspects of musical training predictive of enhanced cognition. The first partition analysis revealed education best predicted visuospatial functions in musicians, followed by recent musical engagement which offset low education. In the second partition analysis, early age of musical acquisition (memory in musicians, while analyses for other measures were not predictive. Recent and past musical activity, but not general lifestyle activities, predicted variability
General unifying features of controlled quantum phenomena
International Nuclear Information System (INIS)
Pechen, Alexander; Brif, Constantin; Wu, Rebing; Chakrabarti, Raj; Rabitz, Herschel
2010-01-01
Many proposals have been put forth for controlling quantum phenomena, including open-loop, adaptive feedback, and real-time feedback control. Each of these approaches has been viewed as operationally, and even physically, distinct from the others. This work shows that all such scenarios inherently share the same fundamental control features residing in the topology of the landscape relating the target physical observable to the applied controls. This unified foundation may provide a basis for development of hybrid control schemes that would combine the advantages of the existing approaches to achieve the best overall performance.
Wind farms production: Control and prediction
El-Fouly, Tarek Hussein Mostafa
Wind energy resources, unlike dispatchable central station generation, produce power dependable on external irregular source and that is the incident wind speed which does not always blow when electricity is needed. This results in the variability, unpredictability, and uncertainty of wind resources. Therefore, the integration of wind facilities to utility electrical grid presents a major challenge to power system operator. Such integration has significant impact on the optimum power flow, transmission congestion, power quality issues, system stability, load dispatch, and economic analysis. Due to the irregular nature of wind power production, accurate prediction represents the major challenge to power system operators. Therefore, in this thesis two novel models are proposed for wind speed and wind power prediction. One proposed model is dedicated to short-term prediction (one-hour ahead) and the other involves medium term prediction (one-day ahead). The accuracy of the proposed models is revealed by comparing their results with the corresponding values of a reference prediction model referred to as the persistent model. Utility grid operation is not only impacted by the uncertainty of the future production of wind farms, but also by the variability of their current production and how the active and reactive power exchange with the grid is controlled. To address this particular task, a control technique for wind turbines, driven by doubly-fed induction generators (DFIGs), is developed to regulate the terminal voltage by equally sharing the generated/absorbed reactive power between the rotor-side and the gridside converters. To highlight the impact of the new developed technique in reducing the power loss in the generator set, an economic analysis is carried out. Moreover, a new aggregated model for wind farms is proposed that accounts for the irregularity of the incident wind distribution throughout the farm layout. Specifically, this model includes the wake effect
Explicit prediction of ice clouds in general circulation models
Kohler, Martin
1999-11-01
Although clouds play extremely important roles in the radiation budget and hydrological cycle of the Earth, there are large quantitative uncertainties in our understanding of their generation, maintenance and decay mechanisms, representing major obstacles in the development of reliable prognostic cloud water schemes for General Circulation Models (GCMs). Recognizing their relative neglect in the past, both observationally and theoretically, this work places special focus on ice clouds. A recent version of the UCLA - University of Utah Cloud Resolving Model (CRM) that includes interactive radiation is used to perform idealized experiments to study ice cloud maintenance and decay mechanisms under various conditions in term of: (1) background static stability, (2) background relative humidity, (3) rate of cloud ice addition over a fixed initial time-period and (4) radiation: daytime, nighttime and no-radiation. Radiation is found to have major effects on the life-time of layer-clouds. Optically thick ice clouds decay significantly slower than expected from pure microphysical crystal fall-out (taucld = 0.9--1.4 h as opposed to no-motion taumicro = 0.5--0.7 h). This is explained by the upward turbulent fluxes of water induced by IR destabilization, which partially balance the downward transport of water by snowfall. Solar radiation further slows the ice-water decay by destruction of the inversion above cloud-top and the resulting upward transport of water. Optically thin ice clouds, on the other hand, may exhibit even longer life-times (>1 day) in the presence of radiational cooling. The resulting saturation mixing ratio reduction provides for a constant cloud ice source. These CRM results are used to develop a prognostic cloud water scheme for the UCLA-GCM. The framework is based on the bulk water phase model of Ose (1993). The model predicts cloud liquid water and cloud ice separately, and which is extended to split the ice phase into suspended cloud ice (predicted
Hierarchical Model Predictive Control for Resource Distribution
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob
2010-01-01
units. The approach is inspired by smart-grid electric power production and consumption systems, where the flexibility of a large number of power producing and/or power consuming units can be exploited in a smart-grid solution. The objective is to accommodate the load variation on the grid, arising......This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...... on one hand from varying consumption, on the other hand by natural variations in power production e.g. from wind turbines. The approach presented is based on quadratic optimization and possess the properties of low algorithmic complexity and of scalability. In particular, the proposed design methodology...
Economic model predictive control theory, formulations and chemical process applications
Ellis, Matthew; Christofides, Panagiotis D
2017-01-01
This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes: Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples. The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes. In addition to being...
Endogenous Generalized Weights under DEA Control
DEFF Research Database (Denmark)
Agrell, Per J.; Bogetoft, Peter
Non-parametric efficiency analysis, such as Data Envelopment Analysis (DEA) relies so far on endogenous local or exogenous general weights, based on revealed preferences or market prices. However, as DEA is gaining popularity in regulation and normative budgeting, the strategic interest...... of the evaluated industry calls for attention. We offer endogenous general prices based on a reformulation of DEA where the units collectively propose the set of weights that maximize their efficiency. Thus, the sector-wide efficiency is then a result of compromising the scores of more specialized smaller units...
Optimization control of LNG regasification plant using Model Predictive Control
Wahid, A.; Adicandra, F. F.
2018-03-01
Optimization of liquified natural gas (LNG) regasification plant is important to minimize costs, especially operational costs. Therefore, it is important to choose optimum LNG regasification plant design and maintaining the optimum operating conditions through the implementation of model predictive control (MPC). Optimal tuning parameter for MPC such as P (prediction horizon), M (control of the horizon) and T (sampling time) are achieved by using fine-tuning method. The optimal criterion for design is the minimum amount of energy used and for control is integral of square error (ISE). As a result, the optimum design is scheme 2 which is developed by Devold with an energy savings of 40%. To maintain the optimum conditions, required MPC with P, M and T as follows: tank storage pressure: 90, 2, 1; product pressure: 95, 2, 1; temperature vaporizer: 65, 2, 2; and temperature heater: 35, 6, 5, with ISE value at set point tracking respectively 0.99, 1792.78, 34.89 and 7.54, or improvement of control performance respectively 4.6%, 63.5%, 3.1% and 58.2% compared to PI controller performance. The energy savings that MPC controllers can make when there is a disturbance in temperature rise 1°C of sea water is 0.02 MW.
Model predictive control of room temperature with disturbance compensation
Kurilla, Jozef; Hubinský, Peter
2017-08-01
This paper deals with temperature control of multivariable system of office building. The system is simplified to several single input-single output systems by decoupling their mutual linkages, which are separately controlled by regulator based on generalized model predictive control. Main part of this paper focuses on the accuracy of the office temperature with respect to occupancy profile and effect of disturbance. Shifting of desired temperature and changing of weighting coefficients are used to achieve the desired accuracy of regulation. The final structure of regulation joins advantages of distributed computing power and possibility to use network communication between individual controllers to consider the constraints. The advantage of using decoupled MPC controllers compared to conventional PID regulators is demonstrated in a simulation study.
Output Feedback Stabilization with Nonlinear Predictive Control: Asymptotic properties
Directory of Open Access Journals (Sweden)
Lars Imsland
2003-07-01
Full Text Available State space based nonlinear model predictive control (NM PC needs the state for the prediction of the system behaviour. Unfortunately, for most applications, not all states are directly measurable. To recover the unmeasured states, typically a stable state observer is used. However, this implies that the stability of the closed-loop should be examined carefully, since no general nonlinear separation principle exists. Recently semi-global practical stability results for output feedback NMPC using a high-gain observer for state estimation have been established. One drawback of this result is that (in general the observer gain must be increased, if the desired set the state should converge to is made smaller. We show that under slightly stronger assumptions, not only practical stability, but also convergence of the system states and observer error to the origin for a sufficiently large but bounded observer gain can be achieved.
Predictive torque and flux control of an induction machine drive ...
Indian Academy of Sciences (India)
Finite-state model predictive control; fuzzy decision making; multi-objective optimization; predictive torque control. Abstract. Among the numerous direct torque control techniques, the finite-state predictive torque control (FS-PTC) has emerged as a powerful alternative as it offers the fast dynamic response and the flexibility to ...
Model Predictive Control for Load Frequency Control with Wind Turbines
Directory of Open Access Journals (Sweden)
Yi Zhang
2015-01-01
Full Text Available Reliable load frequency (LFC control is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control (DMPC based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The scheme incorporates the two critical nonlinear constraints, for example, the generation rate constraint (GRC and the valve limit, into convex optimization problems. Furthermore, the algorithm reduces the impact on the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and that without the participation of the wind turbines is carried out. Good performance is obtained in the presence of power system nonlinearities due to the governors and turbines constraints and load change disturbances.
A controllability test for general first-order representations
U. Helmke; J. Rosenthal; J.M. Schumacher (Hans)
1995-01-01
textabstractIn this paper we derive a new controllability rank test for general first-order representations. The criterion generalizes the well-known controllability rank test for linear input-state systems as well as a controllability rank test by Mertzios et al. for descriptor systems.
49 CFR 192.475 - Internal corrosion control: General.
2010-10-01
... 49 Transportation 3 2010-10-01 2010-10-01 false Internal corrosion control: General. 192.475... Control § 192.475 Internal corrosion control: General. (a) Corrosive gas may not be transported by... taken to minimize internal corrosion. (b) Whenever any pipe is removed from a pipeline for any reason...
Water hammer prediction and control: the Green's function method
Xuan, Li-Jun; Mao, Feng; Wu, Jie-Zhi
2012-04-01
By Green's function method we show that the water hammer (WH) can be analytically predicted for both laminar and turbulent flows (for the latter, with an eddy viscosity depending solely on the space coordinates), and thus its hazardous effect can be rationally controlled and minimized. To this end, we generalize a laminar water hammer equation of Wang et al. (J. Hydrodynamics, B2, 51, 1995) to include arbitrary initial condition and variable viscosity, and obtain its solution by Green's function method. The predicted characteristic WH behaviors by the solutions are in excellent agreement with both direct numerical simulation of the original governing equations and, by adjusting the eddy viscosity coefficient, experimentally measured turbulent flow data. Optimal WH control principle is thereby constructed and demonstrated.
Prediction of cloud droplet number in a general circulation model
Energy Technology Data Exchange (ETDEWEB)
Ghan, S.J.; Leung, L.R. [Pacific Northwest National Lab., Richland, WA (United States)
1996-04-01
We have applied the Colorado State University Regional Atmospheric Modeling System (RAMS) bulk cloud microphysics parameterization to the treatment of stratiform clouds in the National Center for Atmospheric Research Community Climate Model (CCM2). The RAMS predicts mass concentrations of cloud water, cloud ice, rain and snow, and number concnetration of ice. We have introduced the droplet number conservation equation to predict droplet number and it`s dependence on aerosols.
General Intelligence Predicts Reasoning Ability Even for Evolutionarily Familiar Content
Kaufman, Scott Barry; DeYoung, Colin G.; Reis, Deidre L.; Gray, Jeremy R.
2011-01-01
The existence of general-purpose cognitive mechanisms related to intelligence, which appear to facilitate all forms of problem solving, conflicts with the strong modularity view of the mind espoused by some evolutionary psychologists. The current study assessed the contribution of general intelligence ("g") to explaining variation in…
Prediction Governors for Input-Affine Nonlinear Systems and Application to Automatic Driving Control
Directory of Open Access Journals (Sweden)
Yuki Minami
2018-04-01
Full Text Available In recent years, automatic driving control has attracted attention. To achieve a satisfactory driving control performance, the prediction accuracy of the traveling route is important. If a highly accurate prediction method can be used, an accurate traveling route can be obtained. Despite the considerable efforts that have been invested in improving prediction methods, prediction errors do occur in general. Thus, a method to minimize the influence of prediction errors on automatic driving control systems is required. This need motivated us to focus on the design of a mechanism for shaping prediction signals, which is called a prediction governor. In this study, we first extended our previous study to the input-affine nonlinear system case. Then, we analytically derived a solution to an optimal design problem of prediction governors. Finally, we applied the solution to an automatic driving control system, and demonstrated its usefulness through a numerical example and an experiment using a radio controlled car.
Predictive IP controller for robust position control of linear servo system.
Lu, Shaowu; Zhou, Fengxing; Ma, Yajie; Tang, Xiaoqi
2016-07-01
Position control is a typical application of linear servo system. In this paper, to reduce the system overshoot, an integral plus proportional (IP) controller is used in the position control implementation. To further improve the control performance, a gain-tuning IP controller based on a generalized predictive control (GPC) law is proposed. Firstly, to represent the dynamics of the position loop, a second-order linear model is used and its model parameters are estimated on-line by using a recursive least squares method. Secondly, based on the GPC law, an optimal control sequence is obtained by using receding horizon, then directly supplies the IP controller with the corresponding control parameters in the real operations. Finally, simulation and experimental results are presented to show the efficiency of proposed scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Prediction of RNA secondary structure using generalized centroid estimators.
Hamada, Michiaki; Kiryu, Hisanori; Sato, Kengo; Mituyama, Toutai; Asai, Kiyoshi
2009-02-15
Recent studies have shown that the methods for predicting secondary structures of RNAs on the basis of posterior decoding of the base-pairing probabilities has an advantage with respect to prediction accuracy over the conventionally utilized minimum free energy methods. However, there is room for improvement in the objective functions presented in previous studies, which are maximized in the posterior decoding with respect to the accuracy measures for secondary structures. We propose novel estimators which improve the accuracy of secondary structure prediction of RNAs. The proposed estimators maximize an objective function which is the weighted sum of the expected number of the true positives and that of the true negatives of the base pairs. The proposed estimators are also improved versions of the ones used in previous works, namely CONTRAfold for secondary structure prediction from a single RNA sequence and McCaskill-MEA for common secondary structure prediction from multiple alignments of RNA sequences. We clarify the relations between the proposed estimators and the estimators presented in previous works, and theoretically show that the previous estimators include additional unnecessary terms in the evaluation measures with respect to the accuracy. Furthermore, computational experiments confirm the theoretical analysis by indicating improvement in the empirical accuracy. The proposed estimators represent extensions of the centroid estimators proposed in Ding et al. and Carvalho and Lawrence, and are applicable to a wide variety of problems in bioinformatics. Supporting information and the CentroidFold software are available online at: http://www.ncrna.org/software/centroidfold/.
Validation of the mortality prediction equation for damage control ...
African Journals Online (AJOL)
, preoperative lowest pH and lowest core body temperature to derive an equation for the purpose of predicting mortality in damage control surgery. It was shown to reliably predict death despite damage control surgery. The equation derivation ...
Data Driven Economic Model Predictive Control
Directory of Open Access Journals (Sweden)
Masoud Kheradmandi
2018-04-01
Full Text Available This manuscript addresses the problem of data driven model based economic model predictive control (MPC design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example.
A General Role for Medial Prefrontal Cortex in Event Prediction
2014-07-11
al., 2001; Nee and Brown, 2013; Mian et al., 2014) and known to project reciprocally to mPFC (Barbas and Pandya, 1989). Another possible substrate of...A. J., and Li , C. R. (2013). Bayesian prediction and evaluation in the anterior cingulate cortex. J. Neurosci. 33, 2039–2047. doi: 10. 1523/JNEUROSCI...prediction in mPFC Mian , M. K., Sheth, S. A., Patel, S. R., Spiliopoulos, K., Eskandar, E. N., and Williams, Z. M. (2014). Encoding of rules by neurons in the
Distributed predictive control of spiral wave in cardiac excitable media
International Nuclear Information System (INIS)
Zheng-Ning, Gan; Xin-Ming, Cheng
2010-01-01
In this paper, we propose the distributed predictive control strategies of spiral wave in cardiac excitable media. The modified FitzHugh–Nagumo model was used to express the cardiac excitable media approximately. Based on the control-Lyapunov theory, we obtained the distributed control equation, which consists of a positive control-Lyapunov function and a positive cost function. Using the equation, we investigate two kinds of robust control strategies: the time-dependent distributed control strategy and the space-time dependent distributed control strategy. The feasibility of the strategies was demonstrated via an illustrative example, in which the spiral wave was prevented to occur, and the possibility for inducing ventricular fibrillation was eliminated. The strategies are helpful in designing various cardiac devices. Since the second strategy is more efficient and robust than the first one, and the response time in the second strategy is far less than that in the first one, the former is suitable for the quick-response control systems. In addition, our spatiotemporal control strategies, especially the second strategy, can be applied to other cardiac models, even to other reaction-diffusion systems. (general)
Nonconvex model predictive control for commercial refrigeration
Gybel Hovgaard, Tobias; Boyd, Stephen; Larsen, Lars F. S.; Bagterp Jørgensen, John
2013-08-01
We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time period we choose cooling capacity to each unit and a common evaporation temperature. The goal is to minimise the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost function, however, is nonconvex due to the temperature dependence of thermodynamic efficiency. To handle this nonconvexity we propose a sequential convex optimisation method, which typically converges in fewer than 5 or so iterations. We employ a fast convex quadratic programming solver to carry out the iterations, which is more than fast enough to run in real time. We demonstrate our method on a realistic model, with a full year simulation and 15-minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost savings, on the order of 30%, compared to a standard thermostat-based control system. Perhaps more important, we see that the method exhibits sophisticated response to real-time variations in electricity prices. This demand response is critical to help balance real-time uncertainties in generation capacity associated with large penetration of intermittent renewable energy sources in a future smart grid.
Predictive Control of Networked Multiagent Systems via Cloud Computing.
Liu, Guo-Ping
2017-01-18
This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.
Predicting Loss-of-Control Boundaries Toward a Piloting Aid
Barlow, Jonathan; Stepanyan, Vahram; Krishnakumar, Kalmanje
2012-01-01
This work presents an approach to predicting loss-of-control with the goal of providing the pilot a decision aid focused on maintaining the pilot's control action within predicted loss-of-control boundaries. The predictive architecture combines quantitative loss-of-control boundaries, a data-based predictive control boundary estimation algorithm and an adaptive prediction method to estimate Markov model parameters in real-time. The data-based loss-of-control boundary estimation algorithm estimates the boundary of a safe set of control inputs that will keep the aircraft within the loss-of-control boundaries for a specified time horizon. The adaptive prediction model generates estimates of the system Markov Parameters, which are used by the data-based loss-of-control boundary estimation algorithm. The combined algorithm is applied to a nonlinear generic transport aircraft to illustrate the features of the architecture.
DEEP: a general computational framework for predicting enhancers
Kleftogiannis, Dimitrios A.
2014-11-05
Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer\\'s properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.
DEEP: a general computational framework for predicting enhancers
Kleftogiannis, Dimitrios A.; Kalnis, Panos; Bajic, Vladimir B.
2014-01-01
Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer's properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.
Generalized Mutual Synchronization between Two Controlled Interdependent Networks
Xu, Quan; Zhuang, Shengxian; Hu, Dan; Zeng, Yingfeng; Xiao, Jian
2014-01-01
This paper mainly focuses on the generalized mutual synchronization between two controlled interdependent networks. First, we propose the general model of controlled interdependent networks $A$ and $B$ with time-varying internetwork delays coupling. Then, by constructing Lyapunov functions and utilizing adaptive control technique, some sufficient conditions are established to ensure that the mutual synchronization errors between the state variables of networks $A$ and $B$ can asymptotically c...
Fuzzy Logic Decoupled Lateral Control for General Aviation Airplanes
Duerksen, Noel
1997-01-01
It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control different airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control aileron or roll spoiler position. This controller was used to control bank angle for both a piston powered single engine aileron equipped airplane simulation and a business jet simulation which used spoilers for primary roll control. Overspeed, stall and overbank protection were incorporated in the form of expert systems supervisors and weighted fuzzy rules. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic lateral controller could be successfully used on two general aviation aircraft types that have very different characteristics. These controllers worked for both airplanes over their entire flight envelopes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle ]ever travel, etc.). This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.
Lai, Ying-Cheng; Harrison, Mary Ann F; Frei, Mark G; Osorio, Ivan
2004-09-01
Lyapunov exponents are a set of fundamental dynamical invariants characterizing a system's sensitive dependence on initial conditions. For more than a decade, it has been claimed that the exponents computed from electroencephalogram (EEG) or electrocorticogram (ECoG) signals can be used for prediction of epileptic seizures minutes or even tens of minutes in advance. The purpose of this paper is to examine the predictive power of Lyapunov exponents. Three approaches are employed. (1) We present qualitative arguments suggesting that the Lyapunov exponents generally are not useful for seizure prediction. (2) We construct a two-dimensional, nonstationary chaotic map with a parameter slowly varying in a range containing a crisis, and test whether this critical event can be predicted by monitoring the evolution of finite-time Lyapunov exponents. This can thus be regarded as a "control test" for the claimed predictive power of the exponents for seizure. We find that two major obstacles arise in this application: statistical fluctuations of the Lyapunov exponents due to finite time computation and noise from the time series. We show that increasing the amount of data in a moving window will not improve the exponents' detective power for characteristic system changes, and that the presence of small noise can ruin completely the predictive power of the exponents. (3) We report negative results obtained from ECoG signals recorded from patients with epilepsy. All these indicate firmly that, the use of Lyapunov exponents for seizure prediction is practically impossible as the brain dynamical system generating the ECoG signals is more complicated than low-dimensional chaotic systems, and is noisy. Copyright 2004 American Institute of Physics
Model Predictive Control for Connected Hybrid Electric Vehicles
Directory of Open Access Journals (Sweden)
Kaijiang Yu
2015-01-01
Full Text Available This paper presents a new model predictive control system for connected hybrid electric vehicles to improve fuel economy. The new features of this study are as follows. First, the battery charge and discharge profile and the driving velocity profile are simultaneously optimized. One is energy management for HEV for Pbatt; the other is for the energy consumption minimizing problem of acc control of two vehicles. Second, a system for connected hybrid electric vehicles has been developed considering varying drag coefficients and the road gradients. Third, the fuel model of a typical hybrid electric vehicle is developed using the maps of the engine efficiency characteristics. Fourth, simulations and analysis (under different parameters, i.e., road conditions, vehicle state of charge, etc. are conducted to verify the effectiveness of the method to achieve higher fuel efficiency. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results reveal improvements in fuel economy using the proposed control method.
Dark Radiation predictions from general Large Volume Scenarios
Hebecker, Arthur; Mangat, Patrick; Rompineve, Fabrizio; Witkowski, Lukas T.
2014-09-01
Recent observations constrain the amount of Dark Radiation (Δ N eff ) and may even hint towards a non-zero value of Δ N eff . It is by now well-known that this puts stringent constraints on the sequestered Large Volume Scenario (LVS), i.e. on LVS realisations with the Standard Model at a singularity. We go beyond this setting by considering LVS models where SM fields are realised on 7-branes in the geometric regime. As we argue, this naturally goes together with high-scale supersymmetry. The abundance of Dark Radiation is determined by the competition between the decay of the lightest modulus to axions, to the SM Higgs and to gauge fields, and leads to strict constraints on these models. Nevertheless, these constructions can in principle meet current DR bounds due to decays into gauge bosons alone. Further, a rather robust prediction for a substantial amount of Dark Radiation can be made. This applies both to cases where the SM 4-cycles are stabilised by D-terms and are small `by accident', i.e. tuning, as well as to fibred models with the small cycles stabilised by loops. In these constructions the DR axion and the QCD axion are the same field and we require a tuning of the initial misalignment to avoid Dark Matter overproduction. Furthermore, we analyse a closely related setting where the SM lives at a singularity but couples to the volume modulus through flavour branes. We conclude that some of the most natural LVS settings with natural values of model parameters lead to Dark Radiation predictions just below the present observational limits. Barring a discovery, rather modest improvements of present Dark Radiation bounds can rule out many of these most simple and generic variants of the LVS.
Client-controlled case information: a general system theory perspective.
Fitch, Dale
2004-07-01
The author proposes a model for client control of case information via the World Wide Web built on principles of general system theory. It incorporates the client into the design, resulting in an information structure that differs from traditional human services information-sharing practices. Referencing general system theory, the concepts of controller and controlled system, as well as entropy and negentropy, are applied to the information flow and autopoietic behavior as they relate to the boundary-maintaining functions of today's organizations. The author's conclusions synthesize general system theory and human services values to lay the foundation for an information-sharing framework for human services in the 21st century.
Testing General Relativistic Predictions with the LAGEOS Satellites
International Nuclear Information System (INIS)
Peron, Roberto
2014-01-01
The spacetime around Earth is a good environment in order to perform tests of gravitational theories. According to Einstein’s view of gravitational phenomena, the Earth mass-energy content curves the surrounding spacetime in a peculiar way. This (relatively) quiet dynamical environment enables a good reconstruction of geodetic satellites (test masses) orbit, provided that high-quality tracking data are available. This is the case of the LAGEOS satellites, built and launched mainly for geodetic and geodynamical purposes, but equally good for fundamental physics studies. A review of these studies is presented, focusing on data, models, and analysis strategies. Some recent and less recent results are presented. All of them indicate general relativity theory as a very good description of gravitational phenomena, at least in the studied environment.
Model Predictive Control with Constraints of a Wind Turbine
DEFF Research Database (Denmark)
Henriksen, Lars Christian; Poulsen, Niels Kjølstad
2007-01-01
Model predictive control of wind turbines offer a more systematic approach of constructing controllers that handle constraints while focusing on the main control objective. In this article several controllers are designed for different wind conditions and appropriate switching conditions ensure a...... an efficient control of the wind turbine over the entire range of wind speeds. Both onshore and floating offshore wind turbines are tested with the controllers.......Model predictive control of wind turbines offer a more systematic approach of constructing controllers that handle constraints while focusing on the main control objective. In this article several controllers are designed for different wind conditions and appropriate switching conditions ensure...
Characterizing and predicting rates of delirium across general hospital settings.
McCoy, Thomas H; Hart, Kamber L; Perlis, Roy H
2017-05-01
To better understand variation in reported rates of delirium, this study characterized delirium occurrence rate by department of service and primary admitting diagnosis. Nine consecutive years (2005-2013) of general hospital admissions (N=831,348) were identified across two academic medical centers using electronic health records. The primary admitting diagnosis and the treating clinical department were used to calculate occurrence rates of a previously published delirium definition composed of billing codes and natural language processing of discharge summaries. Delirium rates varied significantly across both admitting diagnosis group (X 2 10 =12786, pdelirium (86/109764; 0.08%) and neurological admissions the greatest (2851/25450; 11.2%). Although the rate of delirium varied across the two hospitals the relative rates within departments (r=0.96, pdelirium varies significantly across admitting diagnosis and hospital department. Both admitting diagnosis and department of care are even stronger predictors of risk than age; as such, simple risk stratification may offer avenues for targeted prevention and treatment efforts. Copyright © 2017 Elsevier Inc. All rights reserved.
Client-Controlled Case Information: A General System Theory Perspective
Fitch, Dale
2004-01-01
The author proposes a model for client control of case information via the World Wide Web built on principles of general system theory. It incorporates the client into the design, resulting in an information structure that differs from traditional human services information-sharing practices. Referencing general system theory, the concepts of…
Power Admission Control with Predictive Thermal Management in Smart Buildings
DEFF Research Database (Denmark)
Yao, Jianguo; Costanzo, Giuseppe Tommaso; Zhu, Guchuan
2015-01-01
This paper presents a control scheme for thermal management in smart buildings based on predictive power admission control. This approach combines model predictive control with budget-schedulability analysis in order to reduce peak power consumption as well as ensure thermal comfort. First...
Predictive Variable Gain Iterative Learning Control for PMSM
Directory of Open Access Journals (Sweden)
Huimin Xu
2015-01-01
Full Text Available A predictive variable gain strategy in iterative learning control (ILC is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains.
Traffic Predictive Control: Case Study and Evaluation
2017-06-26
This project developed a quantile regression method for predicting future traffic flow at a signalized intersection by combining both historical and real-time data. The algorithm exploits nonlinear correlations in historical measurements and efficien...
Optimal control of large space structures via generalized inverse matrix
Nguyen, Charles C.; Fang, Xiaowen
1987-01-01
Independent Modal Space Control (IMSC) is a control scheme that decouples the space structure into n independent second-order subsystems according to n controlled modes and controls each mode independently. It is well-known that the IMSC eliminates control and observation spillover caused when the conventional coupled modal control scheme is employed. The independent control of each mode requires that the number of actuators be equal to the number of modelled modes, which is very high for a faithful modeling of large space structures. A control scheme is proposed that allows one to use a reduced number of actuators to control all modeled modes suboptimally. In particular, the method of generalized inverse matrices is employed to implement the actuators such that the eigenvalues of the closed-loop system are as closed as possible to those specified by the optimal IMSC. Computer simulation of the proposed control scheme on a simply supported beam is given.
Synchronization of generalized Henon map by using adaptive fuzzy controller
Energy Technology Data Exchange (ETDEWEB)
Xue Yueju E-mail: xueyj@mail.tsinghua.edu.cn; Yang Shiyuan E-mail: ysy-dau@tsinghua.edu.cn
2003-08-01
In this paper, an adaptive fuzzy control method is presented to synchronize model-unknown discrete-time generalized Henon map. The proposed method is robust to approximate errors and disturbances, because it integrates the merits of adaptive fuzzy and the variable structure control. Moreover, it can realize the synchronizations of non-identical chaotic systems. The simulation results of synchronization of generalized Henon map show that it not only can synchronize model-unknown generalized Henon map but also is robust against the noise of the systems. These merits are advantageous for engineering realization.
Synchronization of generalized Henon map by using adaptive fuzzy controller
International Nuclear Information System (INIS)
Xue Yueju; Yang Shiyuan
2003-01-01
In this paper, an adaptive fuzzy control method is presented to synchronize model-unknown discrete-time generalized Henon map. The proposed method is robust to approximate errors and disturbances, because it integrates the merits of adaptive fuzzy and the variable structure control. Moreover, it can realize the synchronizations of non-identical chaotic systems. The simulation results of synchronization of generalized Henon map show that it not only can synchronize model-unknown generalized Henon map but also is robust against the noise of the systems. These merits are advantageous for engineering realization
Generalized projective synchronization of chaotic systems via adaptive learning control
International Nuclear Information System (INIS)
Yun-Ping, Sun; Jun-Min, Li; Hui-Lin, Wang; Jiang-An, Wang
2010-01-01
In this paper, a learning control approach is applied to the generalized projective synchronisation (GPS) of different chaotic systems with unknown periodically time-varying parameters. Using the Lyapunov–Krasovskii functional stability theory, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to make the states of two different chaotic systems asymptotically synchronised. The scheme is successfully applied to the generalized projective synchronisation between the Lorenz system and Chen system. Moreover, numerical simulations results are used to verify the effectiveness of the proposed scheme. (general)
Cheung, Nicole W. T.; Cheung, Yuet W.
2008-01-01
The objectives of this study were to test the predictive power of self-control theory for delinquency in a Chinese context, and to explore if social factors as predicted in social bonding theory, differential association theory, general strain theory, and labeling theory have effects on delinquency in the presence of self-control. Self-report data…
Generic Model Predictive Control Framework for Advanced Driver Assistance Systems
Wang, M.
2014-01-01
This thesis deals with a model predictive control framework for control design of Advanced Driver Assistance Systems, where car-following tasks are under control. The framework is applied to design several autonomous and cooperative controllers and to examine the controller properties at the
Speed Control of General Purpose Engine with Electronic Governor
Sawut, Umerujan; Tohti, Gheyret; Takigawa, Buso; Tsuji, Teruo
This paper presents a general purpose engine speed control system with an electronic governor in order to improve the current system with a mechanical governor which shows unstable characteristics by change of mecanical friction or A/F ratio (Air/Fuel ratio). For the control system above, there are problems that the feedback signal is only a crank angle because of cost and the controlled object is a general purpose engine which is strongly nonlinear. In order to overcome these problems, the system model is shown for the dynamic estimation of the amount of air flow and the robust controller is designed. That is, the proposed system includes the robust sliding-mode controller by the feedback signal of only a crank angle where Genetic Algorithm is applied for the controller design. The simulation and the experiments by MATLAB/Simulink are performed to show the effectiveness of our proposal.
Proposed general amendments to the atomic energy control regulations
International Nuclear Information System (INIS)
1986-01-01
Canada's Atomic Energy Control Act defines the powers and responsibilities of the Atomic Energy Control Board (AECB). Among these is to make regulations to control the development, application and use of atomic energy. In these proposed general amendments to the Atomic Energy Control Regulations substantial changes are proposed in the designation of the authority of AECB staff, exemptions from licensing, international safeguards, duties of licensees and atomic radiation workers, security of information, and provision for hearings. The scope of the control of atomic energy has been redefined as relating to matters of health, safety, security, international safeguards, and the protection of the environment
Generalized internal model robust control for active front steering intervention
Wu, Jian; Zhao, Youqun; Ji, Xuewu; Liu, Yahui; Zhang, Lipeng
2015-03-01
Because of the tire nonlinearity and vehicle's parameters' uncertainties, robust control methods based on the worst cases, such as H ∞, µ synthesis, have been widely used in active front steering control, however, in order to guarantee the stability of active front steering system (AFS) controller, the robust control is at the cost of performance so that the robust controller is a little conservative and has low performance for AFS control. In this paper, a generalized internal model robust control (GIMC) that can overcome the contradiction between performance and stability is used in the AFS control. In GIMC, the Youla parameterization is used in an improved way. And GIMC controller includes two sections: a high performance controller designed for the nominal vehicle model and a robust controller compensating the vehicle parameters' uncertainties and some external disturbances. Simulations of double lane change (DLC) maneuver and that of braking on split- µ road are conducted to compare the performance and stability of the GIMC control, the nominal performance PID controller and the H ∞ controller. Simulation results show that the high nominal performance PID controller will be unstable under some extreme situations because of large vehicle's parameters variations, H ∞ controller is conservative so that the performance is a little low, and only the GIMC controller overcomes the contradiction between performance and robustness, which can both ensure the stability of the AFS controller and guarantee the high performance of the AFS controller. Therefore, the GIMC method proposed for AFS can overcome some disadvantages of control methods used by current AFS system, that is, can solve the instability of PID or LQP control methods and the low performance of the standard H ∞ controller.
Directory of Open Access Journals (Sweden)
José-Manuel de Haro
2014-05-01
Full Text Available Although the study of factors affecting career success has shown connections between biographical and other aspects related to ability, knowledge and personality, few studies have examined the relationship between emotional intelligence and professional success at the initial career stage. When these studies were carried out, the results showed significant relationships between the dimensions of emotional intelligence (emotional self-awareness, self-regulation, social awareness or social skills and the level of professional competence. In this paper, we analyze the relationship between perceived emotional intelligence, measured by the Trait Meta-Mood Scale (TMMS-24 questionnaire, general intelligence assessed by the Cattell factor "g" test, scale 3, and extrinsic indicators of career success, in a sample of 130 graduates at the beginning of their careers. Results from hierarchical regression analysis indicate that emotional intelligence makes a specific contribution to the prediction of salary, after controlling the general intelligence effect. The perceived emotional intelligence dimensions of TMMS repair, TMMS attention and sex show a higher correlation and make a greater contribution to professional success than general intelligence. The implications of these results for the development of socio-emotional skills among University graduates are discussed.
SCADA system with predictive controller applied to irrigation canals
Figueiredo, João; Botto, Miguel; Rijo, Manuel
2013-01-01
This paper applies a model predictive controller (MPC) to an automatic water canal with sensors and actuators controlled by a network (programmable logic controller), and supervised by a SCADA system (supervisory control and a data acquisition). This canal is composed by a set of distributed sub-systems that control the water level in each canal pool, constrained by discharge gates (control variables) and water off-takes (disturbances). All local controllers are available through an industria...
Why are predictions of general relativity theory for gravitational effects non-unique?
International Nuclear Information System (INIS)
Loskutov, Yu.M.
1990-01-01
Reasons of non-uniqueness of predictions of the general relativity theory (GRT) for gravitational effects are analyzed in detail. To authors' opinion, the absence of comparison mechanism of curved and plane metrics is the reason of non-uniqueness
Climate control loads prediction of electric vehicles
International Nuclear Information System (INIS)
Zhang, Ziqi; Li, Wanyong; Zhang, Chengquan; Chen, Jiangping
2017-01-01
Highlights: • A model of vehicle climate control loads is proposed based on experiments. • Main climate control loads of the modeled vehicle are quantitatively analyzed. • Range reductions of the modeled vehicle under different conditions are simulated. - Abstract: A new model of electric vehicle climate control loads is provided in this paper. The mathematical formulations of the major climate control loads are developed, and the coefficients of the formulations are experimentally determined. Then, the detailed climate control loads are analyzed, and the New European Driving Cycle (NEDC) range reductions due to these loads are calculated under different conditions. It is found that in an electric vehicle, the total climate control loads vary with the vehicle speed, HVAC mode and blower level. The ventilation load is the largest climate control load, followed by the solar radiation load. These two add up to more than 80% of total climate control load in summer. The ventilation load accounts for 70.7–83.9% of total heating load under the winter condition. The climate control loads will cause a 17.2–37.1% reduction of NEDC range in summer, and a 17.1–54.1% reduction in winter, compared to the AC off condition. The heat pump system has an advantage in range extension. A heat pump system with an average heating COP of 1.7 will extend the range by 7.6–21.1% based on the simulation conditions.
Prediction based chaos control via a new neural network
International Nuclear Information System (INIS)
Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui
2008-01-01
In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network
Modeling, robust and distributed model predictive control for freeway networks
Liu, S.
2016-01-01
In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of
Reducing prediction uncertainty of weather controlled systems
Doeswijk, T.G.
2007-01-01
In closed agricultural systems the weather acts both as a disturbance and as a resource. By using weather forecasts in control strategies the effects of disturbances can be minimized whereas the resources can be utilized. In this situation weather forecast uncertainty and model based control are
Predicting glycated hemoglobin levels in the non-diabetic general population
DEFF Research Database (Denmark)
Rauh, Simone P; Heymans, Martijn W; Koopman, Anitra D M
2017-01-01
AIMS/HYPOTHESIS: To develop a prediction model that can predict HbA1c levels after six years in the non-diabetic general population, including previously used readily available predictors. METHODS: Data from 5,762 initially non-diabetic subjects from three population-based cohorts (Hoorn Study, I...
Model predictive control of a crude oil distillation column
Directory of Open Access Journals (Sweden)
Morten Hovd
1999-04-01
Full Text Available The project of designing and implementing model based predictive control on the vacuum distillation column at the Nynäshamn Refinery of Nynäs AB is described in this paper. The paper describes in detail the modeling for the model based control, covers the controller implementation, and documents the benefits gained from the model based controller.
Model Predictive Control of Wind Turbines
DEFF Research Database (Denmark)
Henriksen, Lars Christian
Wind turbines play a major role in the transformation from a fossil fuel based energy production to a more sustainable production of energy. Total-cost-of-ownership is an important parameter when investors decide in which energy technology they should place their capital. Modern wind turbines...... the need for maintenance of the wind turbine. Either way, better total-cost-of-ownership for wind turbine operators can be achieved by improved control of the wind turbines. Wind turbine control can be improved in two ways, by improving the model on which the controller bases its design or by improving...
Directory of Open Access Journals (Sweden)
V. Balaji
2016-12-01
Full Text Available pH control plays a important role in any chemical plant and process industries. For the past four decades the classical PID controller has been occupied by the industries. Due to the faster computing technology in the industry demands a tighter advanced control strategy. To fulfill the needs and requirements Model Predictive Control (MPC is the best among all the advanced control algorithms available in the present scenario. The study and analysis has been done for First Order plus Delay Time (FOPDT model controlled by Proportional Integral Derivative (PID and MPC using the Matlab software. This paper explores the capability of the MPC strategy, analyze and compare the control effects with conventional control strategy in pH control. A comparison results between the PID and MPC is plotted using the software. The results clearly show that MPC provide better performance than the classical controller.
Takács, Gergely
2012-01-01
Real-time model predictive controller (MPC) implementation in active vibration control (AVC) is often rendered difficult by fast sampling speeds and extensive actuator-deformation asymmetry. If the control of lightly damped mechanical structures is assumed, the region of attraction containing the set of allowable initial conditions requires a large prediction horizon, making the already computationally demanding on-line process even more complex. Model Predictive Vibration Control provides insight into the predictive control of lightly damped vibrating structures by exploring computationally efficient algorithms which are capable of low frequency vibration control with guaranteed stability and constraint feasibility. In addition to a theoretical primer on active vibration damping and model predictive control, Model Predictive Vibration Control provides a guide through the necessary steps in understanding the founding ideas of predictive control applied in AVC such as: · the implementation of ...
Predictive Control Based upon State Space Models
Directory of Open Access Journals (Sweden)
Jens G. Balchen
1989-04-01
Full Text Available Repetitive online computation of the control vector by solving the optimal control problem of a non-linear multivariable process with arbitrary performance indices is investigated. Two different methods are considered in the search for an optimal, parameterized control vector: Pontryagin's Maximum Principle and optimization by using the performance index and its gradient directly. Unfortunately, solving this optimization problem has turned out to be a rather time-consuming task which has resulted in a time delay that cannot be accepted when the actual process is exposed to rapidly-varying disturbances. However, an instantaneous feedback strategy operating in parallel with the original control aogorithm was found to be able to cope with this problem.
On generalized fixed sequence procedures for controlling the FWER.
Qiu, Zhiying; Guo, Wenge; Lynch, Gavin
2015-12-30
Testing a sequence of pre-ordered hypotheses to decide which of these can be rejected or accepted while controlling the familywise error rate (FWER) is of importance in many scientific studies such as clinical trials. In this paper, we first introduce a generalized fixed sequence procedure whose critical values are defined by using a function of the numbers of rejections and acceptances, and which allows follow-up hypotheses to be tested even if some earlier hypotheses are not rejected. We then construct the least favorable configuration for this generalized fixed sequence procedure and present a sufficient condition for the FWER control under arbitrary dependence. Based on the condition, we develop three new generalized fixed sequence procedures controlling the FWER under arbitrary dependence. We also prove that each generalized fixed sequence procedure can be described as a specific closed testing procedure. Through simulation studies and a clinical trial example, we compare the power performance of these proposed procedures with those of the existing FWER controlling procedures. Finally, when the pairwise joint distributions of the true null p-values are known, we further improve these procedures by incorporating pairwise correlation information while maintaining the control of the FWER. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.
Stability Constraints for Robust Model Predictive Control
Directory of Open Access Journals (Sweden)
Amanda G. S. Ottoni
2015-01-01
Full Text Available This paper proposes an approach for the robust stabilization of systems controlled by MPC strategies. Uncertain SISO linear systems with box-bounded parametric uncertainties are considered. The proposed approach delivers some constraints on the control inputs which impose sufficient conditions for the convergence of the system output. These stability constraints can be included in the set of constraints dealt with by existing MPC design strategies, in this way leading to the “robustification” of the MPC.
Dynamic optimization and robust explicit model predictive control of hydrogen storage tank
Panos, C.
2010-09-01
We present a general framework for the optimal design and control of a metal-hydride bed under hydrogen desorption operation. The framework features: (i) a detailed two-dimension dynamic process model, (ii) a design and operational dynamic optimization step, and (iii) an explicit/multi-parametric model predictive controller design step. For the controller design, a reduced order approximate model is obtained, based on which nominal and robust multi-parametric controllers are designed. © 2010 Elsevier Ltd.
Dynamic optimization and robust explicit model predictive control of hydrogen storage tank
Panos, C.; Kouramas, K.I.; Georgiadis, M.C.; Pistikopoulos, E.N.
2010-01-01
We present a general framework for the optimal design and control of a metal-hydride bed under hydrogen desorption operation. The framework features: (i) a detailed two-dimension dynamic process model, (ii) a design and operational dynamic optimization step, and (iii) an explicit/multi-parametric model predictive controller design step. For the controller design, a reduced order approximate model is obtained, based on which nominal and robust multi-parametric controllers are designed. © 2010 Elsevier Ltd.
A General Model for Repeated Audit Controls Using Monotone Subsampling
Raats, V.M.; van der Genugten, B.B.; Moors, J.J.A.
2002-01-01
In categorical repeated audit controls, fallible auditors classify sample elements in order to estimate the population fraction of elements in certain categories.To take possible misclassifications into account, subsequent checks are performed with a decreasing number of observations.In this paper a model is presented for a general repeated audit control system, where k subsequent auditors classify elements into r categories.Two different sub-sampling procedures will be discussed, named 'stra...
Model predictive control for spacecraft rendezvous in elliptical orbit
Li, Peng; Zhu, Zheng H.
2018-05-01
This paper studies the control of spacecraft rendezvous with attitude stable or spinning targets in an elliptical orbit. The linearized Tschauner-Hempel equation is used to describe the motion of spacecraft and the problem is formulated by model predictive control. The control objective is to maximize control accuracy and smoothness simultaneously to avoid unexpected change or overshoot of trajectory for safe rendezvous. It is achieved by minimizing the weighted summations of control errors and increments. The effects of two sets of horizons (control and predictive horizons) in the model predictive control are examined in terms of fuel consumption, rendezvous time and computational effort. The numerical results show the proposed control strategy is effective.
Multiple descriptions for packetized predictive control
DEFF Research Database (Denmark)
Østergaard, Jan; Quevedo, Daniel
2016-01-01
be reliably reconstructed at the plant side. For the particular case of LTI plant models and i.i.d. channels, we show that the overall system forms a Markov jump linear system. We provide conditions for mean square stability and derive upper bounds on the operational bit rate of the quantizer to guarantee......In this paper, we propose to use multiple descriptions (MDs) to achieve a high degree of robustness towards random packet delays and erasures in networked control systems. In particular, we consider the scenario, where a data-rate limited channel is located between the controller and the plant...
Predicting Cost/Reliability/Maintainability of Advanced General Aviation Avionics Equipment
Davis, M. R.; Kamins, M.; Mooz, W. E.
1978-01-01
A methodology is provided for assisting NASA in estimating the cost, reliability, and maintenance (CRM) requirements for general avionics equipment operating in the 1980's. Practical problems of predicting these factors are examined. The usefulness and short comings of different approaches for modeling coast and reliability estimates are discussed together with special problems caused by the lack of historical data on the cost of maintaining general aviation avionics. Suggestions are offered on how NASA might proceed in assessing cost reliability CRM implications in the absence of reliable generalized predictive models.
General problems of dynamics and control of vibratory gyroscopes
CSIR Research Space (South Africa)
Shatalov, MY
2008-05-01
Full Text Available A general model of operation of vibratory gyroscopes, which is applicable to a broad class of instruments, including cylindrical, disc and micro-machined gyros, is formulated on the basis of analysis of dynamics and control of a hemispherical...
Controlling general projective synchronization of fractional order Rossler systems
International Nuclear Information System (INIS)
Shao Shiquan
2009-01-01
This paper proposed a method to achieve general projective synchronization of two fractional order Rossler systems. First, we construct the fractional order Rossler system's corresponding approximation integer order system. Then, a control method based on a partially linear decomposition and negative feedback of state errors was utilized on the integer order system. Numerical simulations show the effectiveness of the proposed method.
Fault Predictive Control of Compact Disk Players
DEFF Research Database (Denmark)
Odgaard, Peter Fogh; Wickerhauser, Mladen Victor
2006-01-01
Optical disc players such as CD-players have problems playing certain discs with surface faults like scratches and fingerprints. The problem is to be found in the servo controller which positions the optical pick-up, such that the laser beam is focused on the information track. A scheme handling...
Mapping automotive like controls to a general aviation aircraft
Carvalho, Christopher G.
The purpose of this thesis was to develop fly-by-wire control laws enabling a general aviation aircraft to be flown with automotive controls, i.e. a steering wheel and gas/brake pedals. There was a six speed shifter used to change the flight mode of the aircraft. This essentially allows the pilot to have control over different aspects of the flight profile such as climb/descend or cruise. A highway in the sky was used to aid in the navigation since it is not intuitive to people without flight experience how to navigate from the sky or when to climb and descend. Many believe that general aviation could become as widespread as the automobile. Every person could have a personal aircraft at their disposal and it would be as easy to operate as driving an automobile. The goal of this thesis is to fuse the ease of drivability of a car with flight of a small general aviation aircraft. A standard automotive control hardware setup coupled with variably autonomous control laws will allow new pilots to fly a plane as easily as driving a car. The idea is that new pilots will require very little training to become proficient with these controls. Pilots with little time to stay current can maintain their skills simply by driving a car which is typically a daily activity. A human factors study was conducted to determine the feasibility of the applied control techniques. Pilot performance metrics were developed to compare candidates with no aviation background and experienced pilots. After analyzing the relative performance between pilots and non-pilots, it has been determined that the control system is robust and easy to learn. Candidates with no aviation experience whatsoever can learn to fly an aircraft as safely and efficiently as someone with hundreds of hours of flight experience using these controls.
Finite Control Set Model Predictive Control for Multiple Distributed Generators Microgrids
Babqi, Abdulrahman Jamal
This dissertation proposes two control strategies for AC microgrids that consist of multiple distributed generators (DGs). The control strategies are valid for both grid-connected and islanded modes of operation. In general, microgrid can operate as a stand-alone system (i.e., islanded mode) or while it is connected to the utility grid (i.e., grid connected mode). To enhance the performance of a micrgorid, a sophisticated control scheme should be employed. The control strategies of microgrids can be divided into primary and secondary controls. The primary control regulates the output active and reactive powers of each DG in grid-connected mode as well as the output voltage and frequency of each DG in islanded mode. The secondary control is responsible for regulating the microgrid voltage and frequency in the islanded mode. Moreover, it provides power sharing schemes among the DGs. In other words, the secondary control specifies the set points (i.e. reference values) for the primary controllers. In this dissertation, Finite Control Set Model Predictive Control (FCS-MPC) was proposed for controlling microgrids. FCS-MPC was used as the primary controller to regulate the output power of each DG (in the grid-connected mode) or the voltage of the point of DG coupling (in the islanded mode of operation). In the grid-connected mode, Direct Power Model Predictive Control (DPMPC) was implemented to manage the power flow between each DG and the utility grid. In the islanded mode, Voltage Model Predictive Control (VMPC), as the primary control, and droop control, as the secondary control, were employed to control the output voltage of each DG and system frequency. The controller was equipped with a supplementary current limiting technique in order to limit the output current of each DG in abnormal incidents. The control approach also enabled smooth transition between the two modes. The performance of the control strategy was investigated and verified using PSCAD/EMTDC software
Directory of Open Access Journals (Sweden)
Kaijiang YU
2015-10-01
Full Text Available As the conventional control method for hybrid electric vehicle doesn’t consider the effect of known traffic light information on the vehicle energy management, this paper proposes a model predictive control intelligent optimization strategies based on traffic light information for hybrid electric vehicles. By building the simplified model of the hybrid electric vehicle and adopting the continuation/generalized minimum residual method, the model prediction problem is solved. The simulation is conducted by using MATLAB/Simulink platform. The simulation results show the effectiveness of the proposed model of the traffic light information, and that the proposed model predictive control method can improve fuel economy and the real-time control performance significantly. The research conclusions show that the proposed control strategy can achieve optimal control of the vehicle trajectory, significantly improving fuel economy of the vehicle, and meet the system requirements for the real-time optimal control.
Robot trajectory tracking with self-tuning predicted control
Cui, Xianzhong; Shin, Kang G.
1988-01-01
A controller that combines self-tuning prediction and control is proposed for robot trajectory tracking. The controller has two feedback loops: one is used to minimize the prediction error, and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated online to account for the model uncertainty and the time-varying property of the system. The authors describe the principle of STPC, analyze the system performance, and discuss the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.
Fuzzy model predictive control algorithm applied in nuclear power plant
International Nuclear Information System (INIS)
Zuheir, Ahmad
2006-01-01
The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)
Solution of generalized control system equations at steady state
International Nuclear Information System (INIS)
Vilim, R.B.
1987-01-01
Although a number of reactor systems codes feature generalized control system models, none of the models offer a steady-state solution finder. Indeed, if a transient is to begin from steady-state conditions, the user must provide estimates for the control system initial conditions and run a null transient until the plant converges to steady state. Several such transients may have to be run before values for control system demand signals are found that produce the desired plant steady state. The intent of this paper is (a) to present the control system equations assumed in the SASSYS reactor systems code and to identify the appropriate set of initial conditions, (b) to describe the generalized block diagram approach used to represent these equations, and (c) to describe a solution method and algorithm for computing these initial conditions from the block diagram. The algorithm has been installed in the SASSYS code for use with the code's generalized control system model. The solution finder greatly enhances the effectiveness of the code and the efficiency of the user in running it
Model predictive control for wind power gradients
DEFF Research Database (Denmark)
Hovgaard, Tobias Gybel; Boyd, Stephen; Jørgensen, John Bagterp
2015-01-01
We consider the operation of a wind turbine and a connected local battery or other electrical storage device, taking into account varying wind speed, with the goal of maximizing the total energy generated while respecting limits on the time derivative (gradient) of power delivered to the grid. We...... ranges. The system dynamics are quite non-linear, and the constraints and objectives are not convex functions of the control inputs, so the resulting optimal control problem is difficult to solve globally. In this paper, we show that by a novel change of variables, which focuses on power flows, we can...... wind data and modern wind forecasting methods. The simulation results using real wind data demonstrate the ability to reject the disturbances from fast changes in wind speed, ensuring certain power gradients, with an insignificant loss in energy production....
Generalized Mutual Synchronization between Two Controlled Interdependent Networks
Directory of Open Access Journals (Sweden)
Quan Xu
2014-01-01
Full Text Available This paper mainly focuses on the generalized mutual synchronization between two controlled interdependent networks. First, we propose the general model of controlled interdependent networks A and B with time-varying internetwork delays coupling. Then, by constructing Lyapunov functions and utilizing adaptive control technique, some sufficient conditions are established to ensure that the mutual synchronization errors between the state variables of networks A and B can asymptotically converge to zero. Finally, two numerical examples are given to illustrate the effectiveness of the theoretical results and to explore potential application in future smart grid. The simulation results also show how interdependent topologies and internetwork coupling delays influence the mutual synchronizability, which help to design interdependent networks with optimal mutual synchronizability.
Predicting stem borer density in maize using RapidEye data and generalized linear models
Abdel-Rahman, Elfatih M.; Landmann, Tobias; Kyalo, Richard; Ong'amo, George; Mwalusepo, Sizah; Sulieman, Saad; Ru, Bruno Le
2017-05-01
Average maize yield in eastern Africa is 2.03 t ha-1 as compared to global average of 6.06 t ha-1 due to biotic and abiotic constraints. Amongst the biotic production constraints in Africa, stem borers are the most injurious. In eastern Africa, maize yield losses due to stem borers are currently estimated between 12% and 21% of the total production. The objective of the present study was to explore the possibility of RapidEye spectral data to assess stem borer larva densities in maize fields in two study sites in Kenya. RapidEye images were acquired for the Bomet (western Kenya) test site on the 9th of December 2014 and on 27th of January 2015, and for Machakos (eastern Kenya) a RapidEye image was acquired on the 3rd of January 2015. Five RapidEye spectral bands as well as 30 spectral vegetation indices (SVIs) were utilized to predict per field maize stem borer larva densities using generalized linear models (GLMs), assuming Poisson ('Po') and negative binomial ('NB') distributions. Root mean square error (RMSE) and ratio prediction to deviation (RPD) statistics were used to assess the models performance using a leave-one-out cross-validation approach. The Zero-inflated NB ('ZINB') models outperformed the 'NB' models and stem borer larva densities could only be predicted during the mid growing season in December and early January in both study sites, respectively (RMSE = 0.69-1.06 and RPD = 8.25-19.57). Overall, all models performed similar when all the 30 SVIs (non-nested) and only the significant (nested) SVIs were used. The models developed could improve decision making regarding controlling maize stem borers within integrated pest management (IPM) interventions.
Model Predictive Control Algorithms for Pen and Pump Insulin Administration
DEFF Research Database (Denmark)
Boiroux, Dimitri
at mealtime, and the case where the insulin sensitivity increases during the night. This thesis consists of a summary report, glucose and insulin proles of the clinical studies and research papers submitted, peer-reviewed and/or published in the period September 2009 - September 2012....... of current closed-loop controllers. In this thesis, we present different control strategies based on Model Predictive Control (MPC) for an artificial pancreas. We use Nonlinear Model Predictive Control (NMPC) in order to determine the optimal insulin and blood glucose profiles. The optimal control problem...
Musite, a tool for global prediction of general and kinase-specific phosphorylation sites.
Gao, Jianjiong; Thelen, Jay J; Dunker, A Keith; Xu, Dong
2010-12-01
Reversible protein phosphorylation is one of the most pervasive post-translational modifications, regulating diverse cellular processes in various organisms. High throughput experimental studies using mass spectrometry have identified many phosphorylation sites, primarily from eukaryotes. However, the vast majority of phosphorylation sites remain undiscovered, even in well studied systems. Because mass spectrometry-based experimental approaches for identifying phosphorylation events are costly, time-consuming, and biased toward abundant proteins and proteotypic peptides, in silico prediction of phosphorylation sites is potentially a useful alternative strategy for whole proteome annotation. Because of various limitations, current phosphorylation site prediction tools were not well designed for comprehensive assessment of proteomes. Here, we present a novel software tool, Musite, specifically designed for large scale predictions of both general and kinase-specific phosphorylation sites. We collected phosphoproteomics data in multiple organisms from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates local sequence similarities to known phosphorylation sites, protein disorder scores, and amino acid frequencies. Application of Musite on several proteomes yielded tens of thousands of phosphorylation site predictions at a high stringency level. Cross-validation tests show that Musite achieves some improvement over existing tools in predicting general phosphorylation sites, and it is at least comparable with those for predicting kinase-specific phosphorylation sites. In Musite V1.0, we have trained general prediction models for six organisms and kinase-specific prediction models for 13 kinases or kinase families. Although the current pretrained models were not correlated with any particular cellular conditions, Musite provides a unique functionality for training customized prediction models
Najmi, Sadia; Amir, Nader; Frosio, Kristen E.; Ayers, Catherine
2014-01-01
Poor regulation of emotions may involve impaired attention control. In the current paper, we report the results of two studies examining the interaction of anxiety, attention control, and cognitive load. In Study I, using a performance-based task to assess attention control, we examined whether anxiety is associated with impaired attention control, and whether these effects are influenced by working memory load. In Study II we examined these effects in patients with a diagnosis of Generalized Anxiety Disorder (GAD) compared to non-anxious control (NAC) participants. Results of Study I showed that high anxiety was associated with increased attention control, that is decreased interference from distractors, but only under high cognitive load. These results were replicated in Study II such that individuals with GAD showed increased attention control relative to NACs, but only under high cognitive load. These results help clarify previous predictions regarding the effect of anxiety on attention control. PMID:25355423
Distributed Model Predictive Control for Smart Energy Systems
DEFF Research Database (Denmark)
Halvgaard, Rasmus Fogtmann; Vandenberghe, Lieven; Poulsen, Niels Kjølstad
2016-01-01
Integration of a large number of flexible consumers in a smart grid requires a scalable power balancing strategy. We formulate the control problem as an optimization problem to be solved repeatedly by the aggregator in a model predictive control framework. To solve the large-scale control problem...
Introducing Model Predictive Control for Improving Power Plant Portfolio Performance
DEFF Research Database (Denmark)
Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon
2008-01-01
This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...
Model predictive control for Z-source power converter
DEFF Research Database (Denmark)
Mo, W.; Loh, P.C.; Blaabjerg, Frede
2011-01-01
This paper presents Model Predictive Control (MPC) of impedance-source (commonly known as Z-source) power converter. Output voltage control and current control for Z-source inverter are analyzed and simulated. With MPC's ability of multi- system variables regulation, load current and voltage...
Fourier transform wavefront control with adaptive prediction of the atmosphere.
Poyneer, Lisa A; Macintosh, Bruce A; Véran, Jean-Pierre
2007-09-01
Predictive Fourier control is a temporal power spectral density-based adaptive method for adaptive optics that predicts the atmosphere under the assumption of frozen flow. The predictive controller is based on Kalman filtering and a Fourier decomposition of atmospheric turbulence using the Fourier transform reconstructor. It provides a stable way to compensate for arbitrary numbers of atmospheric layers. For each Fourier mode, efficient and accurate algorithms estimate the necessary atmospheric parameters from closed-loop telemetry and determine the predictive filter, adjusting as conditions change. This prediction improves atmospheric rejection, leading to significant improvements in system performance. For a 48x48 actuator system operating at 2 kHz, five-layer prediction for all modes is achievable in under 2x10(9) floating-point operations/s.
Ionescu, Clara A.; De Keyser, Robin; Torrico, Bismark Claure; De Smet, Tom; Struys, Michel M. R. F.; Normey-Rico, Julio E.
This paper presents the application of predictive control to drug dosing during anesthesia in patients undergoing surgery. The performance of a generic predictive control strategy in drug dosing control, with a previously reported anesthesia-specific control algorithm, has been evaluated. The
Meir Drexler, Shira; Hamacher-Dang, Tanja C; Wolf, Oliver T
2017-05-01
In extinction learning, the individual learns that a previously acquired association (e.g. between a threat and its predictor) is no longer valid. This learning is the principle underlying many cognitive-behavioral psychotherapeutic treatments, e.g. 'exposure therapy'. However, extinction is often highly-context dependent, leading to renewal (relapse of extinguished conditioned response following context change). We have previously shown that post-extinction stress leads to a more context-dependent extinction memory in a predictive learning task. Yet as stress prior to learning can impair the integration of contextual cues, here we aim to create a more generalized extinction memory by inducing stress prior to extinction. Forty-nine men and women learned the associations between stimuli and outcomes in a predictive learning task (day 1), extinguished them shortly after an exposure to a stress/control condition (day 2), and were tested for renewal (day 3). No group differences were seen in acquisition and extinction learning, and a renewal effect was present in both groups. However, the groups differed in the strength and context-dependency of the extinction memory. Compared to the control group, the stress group showed an overall reduced recovery of responding to the extinguished stimuli, in particular in the acquisition context. These results, together with our previous findings, demonstrate that the effects of stress exposure on extinction memory depend on its timing. While post-extinction stress makes the memory more context-bound, pre-extinction stress strengthens its consolidation for the acquisition context as well, making it potentially more resistant to relapse. These results have implications for the use of glucocorticoids as extinction-enhancers in exposure therapy. Copyright © 2017 Elsevier Inc. All rights reserved.
Towards a general neural controller for quadrupedal locomotion.
Maufroy, Christophe; Kimura, Hiroshi; Takase, Kunikatsu
2008-05-01
Our study aims at the design and implementation of a general controller for quadruped locomotion, allowing the robot to use the whole range of quadrupedal gaits (i.e. from low speed walking to fast running). A general legged locomotion controller must integrate both posture control and rhythmic motion control and have the ability to shift continuously from one control method to the other according to locomotion speed. We are developing such a general quadrupedal locomotion controller by using a neural model involving a CPG (Central Pattern Generator) utilizing ground reaction force sensory feedback. We used a biologically faithful musculoskeletal model with a spine and hind legs, and computationally simulated stable stepping motion at various speeds using the neuro-mechanical system combining the neural controller and the musculoskeletal model. We compared the changes of the most important locomotion characteristics (stepping period, duty ratio and support length) according to speed in our simulations with the data on real cat walking. We found similar tendencies for all of them. In particular, the swing period was approximately constant while the stance period decreased with speed, resulting in a decreasing stepping period and duty ratio. Moreover, the support length increased with speed due to the posterior extreme position that shifted progressively caudally, while the anterior extreme position was approximately constant. This indicates that we succeeded in reproducing to some extent the motion of a cat from the kinematical point of view, even though we used a 2D bipedal model. We expect that such computational models will become essential tools for legged locomotion neuroscience in the future.
Generalized space vector control for current source inverters and rectifiers
Directory of Open Access Journals (Sweden)
Roseline J. Anitha
2016-06-01
Full Text Available Current source inverters (CSI is one of the widely used converter topology in medium voltage drive applications due to its simplicity, motor friendly waveforms and reliable short circuit protection. The current source inverters are usually fed by controlled current source rectifiers (CSR with a large inductor to provide a constant supply current. A generalized control applicable for both CSI and CSR and their extension namely current source multilevel inverters (CSMLI are dealt in this paper. As space vector pulse width modulation (SVPWM features the advantages of flexible control, faster dynamic response, better DC utilization and easy digital implementation it is considered for this work. This paper generalizes SVPWM that could be applied for CSI, CSR and CSMLI. The intense computation involved in framing a generalized space vector control are discussed in detail. The algorithm includes determination of band, region, subregions and vectors. The algorithm is validated by simulation using MATLAB /SIMULINK for CSR 5, 7, 13 level CSMLI and for CSR fed CSI.
Selection of References in Wind Turbine Model Predictive Control Design
DEFF Research Database (Denmark)
Odgaard, Peter Fogh; Hovgaard, Tobias
2015-01-01
a model predictive controller for a wind turbine. One of the important aspects for a tracking control problem is how to setup the optimal reference tracking problem, as it might be relevant to track, e.g., the three concurrent references: optimal pitch angle, optimal rotational speed, and optimal power......Lowering the cost of energy is one of the major focus areas in the wind turbine industry. Recent research has indicated that wind turbine controllers based on model predictive control methods can be useful in obtaining this objective. A number of design considerations have to be made when designing....... The importance if the individual references differ depending in particular on the wind speed. In this paper we investigate the performance of a reference tracking model predictive controller with two different setups of the used optimal reference signals. The controllers are evaluated using an industrial high...
Moment based model predictive control for systems with additive uncertainty
Saltik, M.B.; Ozkan, L.; Weiland, S.; Ludlage, J.H.A.
2017-01-01
In this paper, we present a model predictive control (MPC) strategy based on the moments of the state variables and the cost functional. The statistical properties of the state predictions are calculated through the open loop iteration of dynamics and used in the formulation of MPC cost function. We
Rate-Based Model Predictive Control of Turbofan Engine Clearance
DeCastro, Jonathan A.
2006-01-01
An innovative model predictive control strategy is developed for control of nonlinear aircraft propulsion systems and sub-systems. At the heart of the controller is a rate-based linear parameter-varying model that propagates the state derivatives across the prediction horizon, extending prediction fidelity to transient regimes where conventional models begin to lose validity. The new control law is applied to a demanding active clearance control application, where the objectives are to tightly regulate blade tip clearances and also anticipate and avoid detrimental blade-shroud rub occurrences by optimally maintaining a predefined minimum clearance. Simulation results verify that the rate-based controller is capable of satisfying the objectives during realistic flight scenarios where both a conventional Jacobian-based model predictive control law and an unconstrained linear-quadratic optimal controller are incapable of doing so. The controller is evaluated using a variety of different actuators, illustrating the efficacy and versatility of the control approach. It is concluded that the new strategy has promise for this and other nonlinear aerospace applications that place high importance on the attainment of control objectives during transient regimes.
2013-01-01
Background The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population. Methods We analyzed a previous dataset based on a population sample consisted of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN analysis. Performances of these prediction models were evaluated in the validation set. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with CA dysfunction (P < 0.05). The mean area under the receiver-operating curve was 0.762 (95% CI 0.732–0.793) for prediction model developed using ANN analysis. The mean sensitivity, specificity, positive and negative predictive values were similar in the prediction models was 0.751, 0.665, 0.330 and 0.924, respectively. All HL statistics were less than 15.0. Conclusion ANN is an effective tool for developing prediction models with high value for predicting CA dysfunction among the general population. PMID:23902963
Model Predictive Control for Integrating Traffic Control Measures
Hegyi, A.
2004-01-01
Dynamic traffic control measures, such as ramp metering and dynamic speed limits, can be used to better utilize the available road capacity. Due to the increasing traffic volumes and the increasing number of traffic jams the interaction between the control measures has increased such that local
Model Predictive Control of a Wave Energy Converter
DEFF Research Database (Denmark)
Andersen, Palle; Pedersen, Tom Søndergård; Nielsen, Kirsten Mølgaard
2015-01-01
In this paper reactive control and Model Predictive Control (MPC) for a Wave Energy Converter (WEC) are compared. The analysis is based on a WEC from Wave Star A/S designed as a point absorber. The model predictive controller uses wave models based on the dominating sea states combined with a model...... connecting undisturbed wave sequences to sequences of torque. Losses in the conversion from mechanical to electrical power are taken into account in two ways. Conventional reactive controllers are tuned for each sea state with the assumption that the converter has the same efficiency back and forth. MPC...
A multicontroller structure for teaching and designing predictive control strategies
International Nuclear Information System (INIS)
Hodouin, D.; Desbiens, A.
1999-01-01
The paper deals with the unification of the existing linear control algorithms in order to facilitate their transfer to the engineering students and to industry's engineers. The resulting control algorithm is the Global Predictive Control (GlobPC), which is now taught at the graduate and continuing education levels. GlobPC is based on an internal model framework where three independent control criteria are minimized: one for tracking, one for regulation and one for feedforward. This structure allows to obtain desired tracking, regulation and feedforward behaviors in an optimal way while keeping them perfectly separated. It also cleanly separates the deterministic and stochastic predictions of the process model output. (author)
Robust Output Model Predictive Control of an Unstable Rijke Tube
Directory of Open Access Journals (Sweden)
Fabian Jarmolowitz
2012-01-01
Full Text Available This work investigates the active control of an unstable Rijke tube using robust output model predictive control (RMPC. As internal model a polytopic linear system with constraints is assumed to account for uncertainties. For guaranteed stability, a linear state feedback controller is designed using linear matrix inequalities and used within a feedback formulation of the model predictive controller. For state estimation a robust gain-scheduled observer is developed. It is shown that the proposed RMPC ensures robust stability under constraints over the considered operating range.
Synchronization of generalized Henon map using polynomial controller
International Nuclear Information System (INIS)
Lam, H.K.
2010-01-01
This Letter presents the chaos synchronization of two discrete-time generalized Henon map, namely the drive and response systems. A polynomial controller is proposed to drive the system states of the response system to follow those of the drive system. The system stability of the error system formed by the drive and response systems and the synthesis of the polynomial controller are investigated using the sum-of-squares (SOS) technique. Based on the Lyapunov stability theory, stability conditions in terms of SOS are derived to guarantee the system stability and facilitate the controller synthesis. By satisfying the SOS-based stability conditions, chaotic synchronization is achieved. The solution of the SOS-based stability conditions can be found numerically using the third-party Matlab toolbox SOSTOOLS. A simulation example is given to illustrate the merits of the proposed polynomial control approach.
Generalized approach to bilateral control for EMG driven exoskeleton
Directory of Open Access Journals (Sweden)
Gradetsky Valery
2017-01-01
Full Text Available The paper discusses a generalized approach to bilateral control for EMG driven exoskeleton systems. In this paper we consider a semi-automatic mechatronic system that is controlled via human muscle activity (EMG level. The problem is to understand how the movement of the exoskeleton effects on the control. The considered system can be described in terms of bilateral control. This means the existence of force feedback from the object via the exoskeleton links and drives to operator. The simulation of the considered model was held on the MATLAB Simulink. The mathematical model of the bilateral system with exoskeleton and operator was developed. Transient functions for different dynamic parameters were obtained. It was shown that force feedback is essential for the R&D of such systems.
Asthma control in general practice -- GP and patient perspectives compared.
Henderson, Joan; Hancock, Kerry L; Armour, Carol; Harrison, Christopher; Miller, Graeme
2013-10-01
How general practitioners (GPs) and patients perceive asthma control, and concordance between these perceptions, may influence asthma management and medication adherence. The aims of this study were to determine asthma prevalence in adult patients, measure patient asthma control and the correlation between GP and patient perceptions of asthma control or impact. A Supplementary Analysis of Nominated Data (SAND) sub-study of the Bettering the Evaluation and Care of Health (BEACH) program surveyed 2563 patients from 103 GPs. Asthma control was measured using the Asthma Control Questionnaire 5-item version (ACQ-5), and medication adherence by patient self-report. Survey procedures in SAS software and Pearson's correlation statistics were used. Asthma prevalence was 12.7% (95% confidence interval: 10.9-14.5), with good correlation between GP and patient perceptions of asthma control/impact, and with raw ACQ-5 scores. Grouped ACQ-5 scores showed higher levels of uncontrolled asthma. Medication adherence was sub-optimal. The ACQ-5 questions are useful for assessing asthma control, for prompting medication reviews, and for reinforcing benefits of medication compliance to improve long-term asthma control.
Higher-Order Generalized Invexity in Control Problems
Directory of Open Access Journals (Sweden)
S. K. Padhan
2011-01-01
Full Text Available We introduce a higher-order duality (Mangasarian type and Mond-Weir type for the control problem. Under the higher-order generalized invexity assumptions on the functions that compose the primal problems, higher-order duality results (weak duality, strong duality, and converse duality are derived for these pair of problems. Also, we establish few examples in support of our investigation.
DEFF Research Database (Denmark)
Rosthøj, Susanne; Keiding, Niels
2004-01-01
When studying a regression model measures of explained variation are used to assess the degree to which the covariates determine the outcome of interest. Measures of predictive accuracy are used to assess the accuracy of the predictions based on the covariates and the regression model. We give a ...... a detailed and general introduction to the two measures and the estimation procedures. The framework we set up allows for a study of the effect of misspecification on the quantities estimated. We also introduce a generalization to survival analysis....
PLMPC - supervisor predictive control; PLMPC - controle supervisorio preditivo
Energy Technology Data Exchange (ETDEWEB)
Ferreira, Amalia Burger Santa Brigida; Matuck, Fuad Jorge [White Martins S.A., Rio de Janeiro, RJ (Brazil)
2010-07-01
MPC is the latest and most sophisticated technology for controlling chemical plants with several interactive variables. Since 1984, over 2000 MPC systems have been installed worldwide, mostly at oil refineries and large petrochemical facilities. Praxair was the first company to apply MPC technology to the air separation industry. MPC technology is now Praxair's standard platform for supervisory control of cryogenic air separation plants. Most new Praxair plants are controlled by MPC systems. The Pipeline MPC (PLMPC) drives at least 2 plants, A and B, GO2 production towards optimum targets during the pipeline variations. The purpose of the PLMPC is to optimize gas oxygen (GO2) production according to demand, while ensuring a quickly pipeline response. It is implemented using AspenTech DMCPlus software, which is configured with a model file and a controller configuration file, that executes periodically. (author)
Generalized perturbation theory error control within PWR core-loading pattern optimization
International Nuclear Information System (INIS)
Imbriani, J.S.; Turinsky, P.J.; Kropaczek, D.J.
1995-01-01
The fuel management optimization code FORMOSA-P has been developed to determine the family of near-optimum loading patterns for PWR reactors. The code couples the optimization technique of simulated annealing (SA) with a generalized perturbation theory (GPT) model for evaluating core physics characteristics. To ensure the accuracy of the GPT predictions, as well as to maximize the efficient of the SA search, a GPT error control method has been developed
Gravitational redshift of galaxies in clusters as predicted by general relativity.
Wojtak, Radosław; Hansen, Steen H; Hjorth, Jens
2011-09-28
The theoretical framework of cosmology is mainly defined by gravity, of which general relativity is the current model. Recent tests of general relativity within the Lambda Cold Dark Matter (ΛCDM) model have found a concordance between predictions and the observations of the growth rate and clustering of the cosmic web. General relativity has not hitherto been tested on cosmological scales independently of the assumptions of the ΛCDM model. Here we report an observation of the gravitational redshift of light coming from galaxies in clusters at the 99 per cent confidence level, based on archival data. Our measurement agrees with the predictions of general relativity and its modification created to explain cosmic acceleration without the need for dark energy (the f(R) theory), but is inconsistent with alternative models designed to avoid the presence of dark matter. © 2011 Macmillan Publishers Limited. All rights reserved
Wind turbine control with constraint handling: a model predictive control approach
DEFF Research Database (Denmark)
Henriksen, Lars Christian; Hansen, Morten Hartvig; Poulsen, Niels Kjølstad
2012-01-01
on model predictive control, a control method well suited for constraint handling. The performance of the presented controller during an extreme operating gust is compared to that of a proportional-integral controller with integrator anti-windup. Furthermore, the presented controller-s capability...
Relativistic theory of gravitation and nonuniqueness of the predictions of general relativity theory
International Nuclear Information System (INIS)
Logunov, A.A.; Loskutov, Yu.M.
1986-01-01
It is shown that while the predictions of relativistic theory of gravitation (RTG) for the gravitational effects are unique and consistent with the experimental data available, the relevant predictions of general relativity theory are not unique. Therewith the above nonuniqueness manifests itself in some effects in the first order in the gravitational interaction constant in others in the second one. The absence in GRT of the energy-momentum and angular momentum conservation laws for the matter and gravitational field taken together and its inapplicability to give uniquely determined predictions for the gravitational phenomena compel to reject GRT as a physical theory
Application of General Regression Neural Network to the Prediction of LOD Change
Zhang, Xiao-Hong; Wang, Qi-Jie; Zhu, Jian-Jun; Zhang, Hao
2012-01-01
Traditional methods for predicting the change in length of day (LOD change) are mainly based on some linear models, such as the least square model and autoregression model, etc. However, the LOD change comprises complicated non-linear factors and the prediction effect of the linear models is always not so ideal. Thus, a kind of non-linear neural network — general regression neural network (GRNN) model is tried to make the prediction of the LOD change and the result is compared with the predicted results obtained by taking advantage of the BP (back propagation) neural network model and other models. The comparison result shows that the application of the GRNN to the prediction of the LOD change is highly effective and feasible.
Rinaldo, A.; Gatto, M.; Mari, L.; Casagrandi, R.; Righetto, L.; Bertuzzo, E.; Rodriguez-Iturbe, I.
2012-12-01
Metacommunity and individual-based theoretical models are studied in the context of the spreading of infections of water-borne diseases along the ecological corridors defined by river basins and networks of human mobility. The overarching claim is that mathematical models can indeed provide predictive insight into the course of an ongoing epidemic, potentially aiding real-time emergency management in allocating health care resources and by anticipating the impact of alternative interventions. To support the claim, we examine the ex-post reliability of published predictions of the 2010-2011 Haiti cholera outbreak from four independent modeling studies that appeared almost simultaneously during the unfolding epidemic. For each modeled epidemic trajectory, it is assessed how well predictions reproduced the observed spatial and temporal features of the outbreak to date. The impact of different approaches is considered to the modeling of the spatial spread of V. cholera, the mechanics of cholera transmission and in accounting for the dynamics of susceptible and infected individuals within different local human communities. A generalized model for Haitian epidemic cholera and the related uncertainty is thus constructed and applied to the year-long dataset of reported cases now available. Specific emphasis will be dedicated to models of human mobility, a fundamental infection mechanism. Lessons learned and open issues are discussed and placed in perspective, supporting the conclusion that, despite differences in methods that can be tested through model-guided field validation, mathematical modeling of large-scale outbreaks emerges as an essential component of future cholera epidemic control. Although explicit spatial modeling is made routinely possible by widespread data mapping of hydrology, transportation infrastructure, population distribution, and sanitation, the precise condition under which a waterborne disease epidemic can start in a spatially explicit setting is
Walach, H
2003-08-01
Homeopathy is scientifically banned, both for lack of consistent empirical findings, but more so for lack of a sound theoretical model to explain its purported effects. This paper makes an attempt to introduce an explanatory idea based on a generalized version of quantum mechanics (QM), the weak quantum theory (WQT). WQT uses the algebraic formalism of QM proper, but drops some restrictions and definitions typical for QM. This results in a general axiomatic framework similar to QM, but more generalized and applicable to all possible systems. Most notably, WQT predicts entanglement, which in QM is known as Einstein-Podolsky-Rosen (EPR) correlatedness within quantum systems. According to WQT, this entanglement is not only tied to quantum systems, but is to be expected whenever a global and a local variable describing a system are complementary. This idea is used here to reconstruct homeopathy as an exemplification of generalized entanglement as predicted by WQT. It transpires that homeopathy uses two instances of generalized entanglement: one between the remedy and the original substance (potentiation principle) and one between the individual symptoms of a patient and the general symptoms of a remedy picture (similarity principle). By bringing these two elements together, double entanglement ensues, which is reminiscent of cryptographic and teleportation applications of entanglement in QM proper. Homeopathy could be a macroscopic analogue to quantum teleportation. This model is exemplified and some predictions are derived, which make it possible to test the model. Copyright 2003 S. Karger GmbH, Freiburg
Model Predictive Control of Buoy Type Wave Energy Converter
DEFF Research Database (Denmark)
Soltani, Mohsen; Sichani, Mahdi Teimouri; Mirzaei, Mahmood
2014-01-01
by forcing this condition. In the paper the theoretical framework for this principal is shown. The optimal controller requires information of the sea state for infinite horizon which is not applicable. Model Predictive Controllers (MPC) can have finite horizon which crosses out this requirement....... This approach is then taken into account and an MPC controller is designed for a model WEC and implemented on a numerical example. Further, the power outtake of this controller is compared to the optimal controller as an indicator of the performance of the designed controller....
Model Predictive Control of Buoy Type Wave Energy Converter
DEFF Research Database (Denmark)
Soltani, Mohsen N.; Sichani, Mahdi T.; Mirzaei, Mahmood
2014-01-01
by forcing this condition. In the paper the theoretical framework for this principal is shown. The optimal controller requires information of the sea state for infinite horizon which is not applicable. Model Predictive Controllers (MPC) can have finite horizon which crosses out this requirement....... This approach is then taken into account and an MPC controller is designed for a model wave energy converter and implemented on a numerical example. Further, the power outtake of this controller is compared to the optimal controller as an indicator of the performance of the designed controller....
Predictive control, with restrictions for the climate of a greenhouse
International Nuclear Information System (INIS)
Pinon, Sandra; Pena, Miguel; Kuchen, Benjamin
2002-01-01
A proposal for controlling nonlinear systems under constraints is presented. a combination of model predictive control and feedback linearization is used. An alternative that uses extended kalman filter as non-measured variable estimator is applied for performing the constrained optimization. Finally, an observability analysis is done in closed loop in order to demonstrate observer convergence
Neural networks for predictive control of the mechanism of ...
African Journals Online (AJOL)
In this paper, we are interested in the study of the control of orientation of a wind turbine like means of optimization of his output/input ratio (efficiency). The approach suggested is based on the neural predictive control which is justified by the randomness of the wind on the one hand, and on the other hand by the capacity of ...
Sparsely-Packetized Predictive Control by Orthogonal Matching Pursuit
DEFF Research Database (Denmark)
Nagahara, Masaaki; Quevedo, Daniel; Østergaard, Jan
2012-01-01
We study packetized predictive control, known to be robust against packet dropouts in networked systems. To obtain sparse packets for rate-limited networks, we design control packets via an ℓ0 optimization, which can be eectively solved by orthogonal matching pursuit. Our formulation ensures...
Predicting worsening asthma control following the common cold
Walter, M. J.; Castro, M.; Kunselman, S. J.; Chinchilli, V. M.; Reno, M.; Ramkumar, T. P.; Avila, P. C.; Boushey, H. A.; Ameredes, B. T.; Bleecker, E. R.; Calhoun, W. J.; Cherniack, R. M.; Craig, T. J.; Denlinger, L. C.; Israel, E.; Fahy, J. V.; Jarjour, N. N.; Kraft, M.; Lazarus, S. C.; Lemanske, R. F.; Martin, R. J.; Peters, S. P.; Ramsdell, J. W.; Sorkness, C. A.; Sutherland, E. R.; Szefler, S. J.; Wasserman, S. I.; Wechsler, M. E.
2008-01-01
The asthmatic response to the common cold is highly variable, and early characteristics that predict worsening of asthma control following a cold have not been identified. In this prospective multicentric cohort study of 413 adult subjects with asthma, the mini-Asthma Control Questionnaire
Model Predictive Control for Offset-Free Reference Tracking
Czech Academy of Sciences Publication Activity Database
Belda, Květoslav
2016-01-01
Roč. 5, č. 1 (2016), s. 8-13 ISSN 1805-3386 Institutional support: RVO:67985556 Keywords : offset-free reference tracking * predictive control * ARX model * state-space model * multi-input multi-output system * robotic system * mechatronic system Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2016/AS/belda-0458355.pdf
Neural Network Predictive Control for Vanadium Redox Flow Battery
Directory of Open Access Journals (Sweden)
Hai-Feng Shen
2013-01-01
Full Text Available The vanadium redox flow battery (VRB is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.
Modeling, Prediction, and Control of Heating Temperature for Tube Billet
Directory of Open Access Journals (Sweden)
Yachun Mao
2015-01-01
Full Text Available Annular furnaces have multivariate, nonlinear, large time lag, and cross coupling characteristics. The prediction and control of the exit temperature of a tube billet are important but difficult. We establish a prediction model for the final temperature of a tube billet through OS-ELM-DRPLS method. We address the complex production characteristics, integrate the advantages of PLS and ELM algorithms in establishing linear and nonlinear models, and consider model update and data lag. Based on the proposed model, we design a prediction control algorithm for tube billet temperature. The algorithm is validated using the practical production data of Baosteel Co., Ltd. Results show that the model achieves the precision required in industrial applications. The temperature of the tube billet can be controlled within the required temperature range through compensation control method.
Two stage neural network modelling for robust model predictive control.
Patan, Krzysztof
2018-01-01
The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Differing Air Traffic Controller Responses to Similar Trajectory Prediction Errors
Mercer, Joey; Hunt-Espinosa, Sarah; Bienert, Nancy; Laraway, Sean
2016-01-01
A Human-In-The-Loop simulation was conducted in January of 2013 in the Airspace Operations Laboratory at NASA's Ames Research Center. The simulation airspace included two en route sectors feeding the northwest corner of Atlanta's Terminal Radar Approach Control. The focus of this paper is on how uncertainties in the study's trajectory predictions impacted the controllers ability to perform their duties. Of particular interest is how the controllers interacted with the delay information displayed in the meter list and data block while managing the arrival flows. Due to wind forecasts with 30-knot over-predictions and 30-knot under-predictions, delay value computations included errors of similar magnitude, albeit in opposite directions. However, when performing their duties in the presence of these errors, did the controllers issue clearances of similar magnitude, albeit in opposite directions?
Shi, Shengling; Lazar, Mircea
2017-01-01
This paper proposes a distributed model predictive control algorithm for vehicle platooning and more generally networked systems in a chain structure. The distributed models of the vehicle platoon are coupled through the input of the preceding vehicles. Using the principles of robust model
Predictive value of the official cancer alarm symptoms in general practice
DEFF Research Database (Denmark)
Krasnik Huggenberger, Ivan; Andersen, John Sahl
2015-01-01
Introduction: The objective of this study was to investigate the evidence for positive predictive value (PPV) of alarm symptoms and combinations of symptoms for colorectal cancer, breast cancer, prostate cancer and lung cancer in general practice. Methods: This study is based on a literature search...
Bayesian prediction of spatial count data using generalized linear mixed models
DEFF Research Database (Denmark)
Christensen, Ole Fredslund; Waagepetersen, Rasmus Plenge
2002-01-01
Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, ...
Development of predictive control strategies for building climate control
NAGPAL, HIMANSHU
2018-01-01
APPROVED The rapid growth in energy usage and CO2 emissions has become a critical issue for the whole world. It is noteworthy that buildings are a major contributor to global primary energy consumption. Among building services, use of energy in heating-ventilation-air-conditioning (HVAC) system is particularly significant (about 50\\% of the total building energy consumption). Therefore, the development and implementation of effective control strategies to optimize the operation of HVAC sys...
Predictive control and identification: Applications to steering dynamics
DEFF Research Database (Denmark)
Hansen, Anca Daniela
1996-01-01
and of the loss function, which defines the optimality of the control. Some guidelines on how to choose the design parameters, depending on the type of process to be controlled and on the required control performance, are presented. A predictive track keeping system for a Mariner Class Vessel is formulated based...... the under- standing of the connection between identification and control, analysed in Chapter 7. Chapter 7 focuses on how to make the on-line identification for predictive control more robust towards unmodelled dynamics. The theory is verified via simulation studies on a Mariner Class Vessel. The effects...... and the need of a prefilter in the estimation are analysed and illustrated. Based on the idea that the control criterion must be dual to the estimation criterion, an iterative optimal prefilter is designed. This seems to be an appealing way to tune the model towards the objective for which the model...
Offset Free Tracking Predictive Control Based on Dynamic PLS Framework
Directory of Open Access Journals (Sweden)
Jin Xin
2017-10-01
Full Text Available This paper develops an offset free tracking model predictive control based on a dynamic partial least square (PLS framework. First, state space model is used as the inner model of PLS to describe the dynamic system, where subspace identification method is used to identify the inner model. Based on the obtained model, multiple independent model predictive control (MPC controllers are designed. Due to the decoupling character of PLS, these controllers are running separately, which is suitable for distributed control framework. In addition, the increment of inner model output is considered in the cost function of MPC, which involves integral action in the controller. Hence, the offset free tracking performance is guaranteed. The results of an industry background simulation demonstrate the effectiveness of proposed method.
Catalytic cracking models developed for predictive control purposes
Directory of Open Access Journals (Sweden)
Dag Ljungqvist
1993-04-01
Full Text Available The paper deals with state-space modeling issues in the context of model-predictive control, with application to catalytic cracking. Emphasis is placed on model establishment, verification and online adjustment. Both the Fluid Catalytic Cracking (FCC and the Residual Catalytic Cracking (RCC units are discussed. Catalytic cracking units involve complex interactive processes which are difficult to operate and control in an economically optimal way. The strong nonlinearities of the FCC process mean that the control calculation should be based on a nonlinear model with the relevant constraints included. However, the model can be simple compared to the complexity of the catalytic cracking plant. Model validity is ensured by a robust online model adjustment strategy. Model-predictive control schemes based on linear convolution models have been successfully applied to the supervisory dynamic control of catalytic cracking units, and the control can be further improved by the SSPC scheme.
Nonlinear Predictive Sliding Mode Control for Active Suspension System
Directory of Open Access Journals (Sweden)
Dazhuang Wang
2018-01-01
Full Text Available An active suspension system is important in meeting the requirements of the ride comfort and handling stability for vehicles. In this work, a nonlinear model of active suspension system and a corresponding nonlinear robust predictive sliding mode control are established for the control problem of active suspension. Firstly, a seven-degree-of-freedom active suspension model is established considering the nonlinear effects of springs and dampers; and secondly, the dynamic model is expanded in the time domain, and the corresponding predictive sliding mode control is established. The uncertainties in the controller are approximated by the fuzzy logic system, and the adaptive controller reduces the approximation error to increase the robustness of the control system. Finally, the simulation results show that the ride comfort and handling stability performance of the active suspension system is better than that of the passive suspension system and the Skyhook active suspension. Thus, the system can obviously improve the shock absorption performance of vehicles.
Fuzzy predictive filtering in nonlinear economic model predictive control for demand response
DEFF Research Database (Denmark)
Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.
2016-01-01
problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...
Rana, Narender; Zhang, Yunlin; Wall, Donald; Dirahoui, Bachir; Bailey, Todd C.
2015-03-01
Integrate circuit (IC) technology is going through multiple changes in terms of patterning techniques (multiple patterning, EUV and DSA), device architectures (FinFET, nanowire, graphene) and patterning scale (few nanometers). These changes require tight controls on processes and measurements to achieve the required device performance, and challenge the metrology and process control in terms of capability and quality. Multivariate data with complex nonlinear trends and correlations generally cannot be described well by mathematical or parametric models but can be relatively easily learned by computing machines and used to predict or extrapolate. This paper introduces the predictive metrology approach which has been applied to three different applications. Machine learning and predictive analytics have been leveraged to accurately predict dimensions of EUV resist patterns down to 18 nm half pitch leveraging resist shrinkage patterns. These patterns could not be directly and accurately measured due to metrology tool limitations. Machine learning has also been applied to predict the electrical performance early in the process pipeline for deep trench capacitance and metal line resistance. As the wafer goes through various processes its associated cost multiplies. It may take days to weeks to get the electrical performance readout. Predicting the electrical performance early on can be very valuable in enabling timely actionable decision such as rework, scrap, feedforward, feedback predicted information or information derived from prediction to improve or monitor processes. This paper provides a general overview of machine learning and advanced analytics application in the advanced semiconductor development and manufacturing.
Range-Space Predictive Control for Optimal Robot Motion
Czech Academy of Sciences Publication Activity Database
Belda, Květoslav; Böhm, Josef
2008-01-01
Roč. 1, č. 1 (2008), s. 1-7 ISSN 1998-0140 R&D Projects: GA ČR GP102/06/P275 Institutional research plan: CEZ:AV0Z10750506 Keywords : Accurate manipulation * Industrial robotics * Predictive control * Range-space control Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/historie/belda-0305644.pdf
Recent Advances in Explicit Multiparametric Nonlinear Model Predictive Control
Domínguez, Luis F.
2011-01-19
In this paper we present recent advances in multiparametric nonlinear programming (mp-NLP) algorithms for explicit nonlinear model predictive control (mp-NMPC). Three mp-NLP algorithms for NMPC are discussed, based on which novel mp-NMPC controllers are derived. The performance of the explicit controllers are then tested and compared in a simulation example involving the operation of a continuous stirred-tank reactor (CSTR). © 2010 American Chemical Society.
A weighted generalized score statistic for comparison of predictive values of diagnostic tests.
Kosinski, Andrzej S
2013-03-15
Positive and negative predictive values are important measures of a medical diagnostic test performance. We consider testing equality of two positive or two negative predictive values within a paired design in which all patients receive two diagnostic tests. The existing statistical tests for testing equality of predictive values are either Wald tests based on the multinomial distribution or the empirical Wald and generalized score tests within the generalized estimating equations (GEE) framework. As presented in the literature, these test statistics have considerably complex formulas without clear intuitive insight. We propose their re-formulations that are mathematically equivalent but algebraically simple and intuitive. As is clearly seen with a new re-formulation we presented, the generalized score statistic does not always reduce to the commonly used score statistic in the independent samples case. To alleviate this, we introduce a weighted generalized score (WGS) test statistic that incorporates empirical covariance matrix with newly proposed weights. This statistic is simple to compute, always reduces to the score statistic in the independent samples situation, and preserves type I error better than the other statistics as demonstrated by simulations. Thus, we believe that the proposed WGS statistic is the preferred statistic for testing equality of two predictive values and for corresponding sample size computations. The new formulas of the Wald statistics may be useful for easy computation of confidence intervals for difference of predictive values. The introduced concepts have potential to lead to development of the WGS test statistic in a general GEE setting. Copyright © 2012 John Wiley & Sons, Ltd.
Wang, JianLi; Sareen, Jitender; Patten, Scott; Bolton, James; Schmitz, Norbert; Birney, Arden
2014-05-01
Prediction algorithms are useful for making clinical decisions and for population health planning. However, such prediction algorithms for first onset of major depression do not exist. The objective of this study was to develop and validate a prediction algorithm for first onset of major depression in the general population. Longitudinal study design with approximate 3-year follow-up. The study was based on data from a nationally representative sample of the US general population. A total of 28 059 individuals who participated in Waves 1 and 2 of the US National Epidemiologic Survey on Alcohol and Related Conditions and who had not had major depression at Wave 1 were included. The prediction algorithm was developed using logistic regression modelling in 21 813 participants from three census regions. The algorithm was validated in participants from the 4th census region (n=6246). Major depression occurred since Wave 1 of the National Epidemiologic Survey on Alcohol and Related Conditions, assessed by the Alcohol Use Disorder and Associated Disabilities Interview Schedule-diagnostic and statistical manual for mental disorders IV. A prediction algorithm containing 17 unique risk factors was developed. The algorithm had good discriminative power (C statistics=0.7538, 95% CI 0.7378 to 0.7699) and excellent calibration (F-adjusted test=1.00, p=0.448) with the weighted data. In the validation sample, the algorithm had a C statistic of 0.7259 and excellent calibration (Hosmer-Lemeshow χ(2)=3.41, p=0.906). The developed prediction algorithm has good discrimination and calibration capacity. It can be used by clinicians, mental health policy-makers and service planners and the general public to predict future risk of having major depression. The application of the algorithm may lead to increased personalisation of treatment, better clinical decisions and more optimal mental health service planning.
Model Predictive Control for a Small Scale Unmanned Helicopter
Directory of Open Access Journals (Sweden)
Jianfu Du
2008-11-01
Full Text Available Kinematical and dynamical equations of a small scale unmanned helicoper are presented in the paper. Based on these equations a model predictive control (MPC method is proposed for controlling the helicopter. This novel method allows the direct accounting for the existing time delays which are used to model the dynamics of actuators and aerodynamics of the main rotor. Also the limits of the actuators are taken into the considerations during the controller design. The proposed control algorithm was verified in real flight experiments where good perfomance was shown in postion control mode.
Supervisory Model Predictive Control of the Heat Integrated Distillation Column
DEFF Research Database (Denmark)
Meyer, Kristian; Bisgaard, Thomas; Huusom, Jakob Kjøbsted
2017-01-01
This paper benchmarks a centralized control system based on model predictive control for the operation of the heat integrated distillation column (HIDiC) against a fully decentralized control system using the most complete column model currently available in the literature. The centralized control...... system outperforms the decentralized system, because it handles the interactions in the HIDiC process better. The integral absolute error (IAE) is reduced by a factor of 2 and a factor of 4 for control of the top and bottoms compositions, respectively....
Advanced control room evaluation: General approach and rationale
International Nuclear Information System (INIS)
O'Hara, J.M.; Wachtel, J.
1991-01-01
Advanced control rooms (ACRs) for future nuclear power plants (NPPs) are being designed utilizing computer-based technologies. The US Nuclear Regulatory Commission reviews the human engineering aspects of such control rooms to ensure that they are designed to good human factors engineering principles and that operator performance and reliability are appropriately supported in order to protect public health and safety. This paper describes the rationale and general approach to the development of a human factors review guideline for ACRs. The factors influencing the guideline development are discussed, including the review environment, the types of advanced technologies being addressed, the human factors issues associated with advanced technology, and the current state-of-the-art of human factors guidelines for advanced human-system interfaces (HSIs). The proposed approach to ACR review would track the design and implementation process through the application of review guidelines reflecting four review modules: planning, design process analysis, human factors engineering review, and dynamic performance evaluation. 21 refs
Nonlinear Economic Model Predictive Control Strategy for Active Smart Buildings
DEFF Research Database (Denmark)
Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.
2016-01-01
Nowadays, the development of advanced and innovative intelligent control techniques for energy management in buildings is a key issue within the smart grid topic. A nonlinear economic model predictive control (EMPC) scheme, based on the branch-and-bound tree search used as optimization algorithm ...... controller is shown very reliable keeping the comfort levels in the two considered seasons and shifting the load away from peak hours in order to achieve the desired flexible electricity consumption.......Nowadays, the development of advanced and innovative intelligent control techniques for energy management in buildings is a key issue within the smart grid topic. A nonlinear economic model predictive control (EMPC) scheme, based on the branch-and-bound tree search used as optimization algorithm...
Enhanced pid vs model predictive control applied to bldc motor
Gaya, M. S.; Muhammad, Auwal; Aliyu Abdulkadir, Rabiu; Salim, S. N. S.; Madugu, I. S.; Tijjani, Aminu; Aminu Yusuf, Lukman; Dauda Umar, Ibrahim; Khairi, M. T. M.
2018-01-01
BrushLess Direct Current (BLDC) motor is a multivariable and highly complex nonlinear system. Variation of internal parameter values with environment or reference signal increases the difficulty in controlling the BLDC effectively. Advanced control strategies (like model predictive control) often have to be integrated to satisfy the control desires. Enhancing or proper tuning of a conventional algorithm results in achieving the desired performance. This paper presents a performance comparison of Enhanced PID and Model Predictive Control (MPC) applied to brushless direct current motor. The simulation results demonstrated that the PSO-PID is slightly better than the PID and MPC in tracking the trajectory of the reference signal. The proposed scheme could be useful algorithms for the system.
Modeling and Control of CSTR using Model based Neural Network Predictive Control
Shrivastava, Piyush
2012-01-01
This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some commen...
Finite-Control-Set Model Predictive Control (FCS-MPC) for Islanded Hybrid Microgrids
Yi, Zhehan; Babqi, Abdulrahman J.; Wang, Yishen; Shi, Di; Etemadi, Amir H.; Wang, Zhiwei; Huang, Bibin
2018-01-01
Microgrids consisting of multiple distributed energy resources (DERs) provide a promising solution to integrate renewable energies, e.g., solar photovoltaic (PV) systems. Hybrid AC/DC microgrids leverage the merits of both AC and DC power systems. In this paper, a control strategy for islanded multi-bus hybrid microgrids is proposed based on the Finite-Control-Set Model Predictive Control (FCS-MPC) technologies. The control loops are expedited by predicting the future states and determining t...
Proposal of computation chart for general use for diffusion prediction of discharged warm water
International Nuclear Information System (INIS)
Wada, Akira; Kadoyu, Masatake
1976-01-01
The authors have developed the unique simulation analysis method using the numerical models for the prediction of discharged warm water diffusion. At the present stage, the method is adopted for the precise analysis computation in order to make the prediction of the diffusion of discharged warm water at each survey point, but instead of this method, it is strongly requested that some simple and easy prediction methods should be established. For the purpose of meeting this demand, in this report, the computation chart for general use is given to predict simply the diffusion range of discharged warm water, after classifying the semi-infinite sea region into several flow patterns according to the sea conditions and conducting the systematic simulation analysis with the numerical model of each pattern, respectively. (1) Establishment of the computation conditions: The special sea region was picked up as the area to be investigated, which is semi-infinite facing the outer sea and along the rectilineal coast line from many sea regions surrounding Japan, and from the viewpoint of the flow and the diffusion characteristics, the sea region was classified into three patterns. 51 cases in total various parameters were obtained, and finally the simulation analysis was performed. (2) Drawing up the general use chart: 28 sheets of the computation chart for general use were drawn, which are available for computing the approximate temperature rise caused by the discharged warm water diffusion. The example of Anegasaki Thermal Power Station is given. (Kako, I.)
Domestic appliances energy optimization with model predictive control
International Nuclear Information System (INIS)
Rodrigues, E.M.G.; Godina, R.; Pouresmaeil, E.; Ferreira, J.R.; Catalão, J.P.S.
2017-01-01
Highlights: • An alternative power management control for home appliances that require thermal regulation is presented. • A Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat. • Problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. • A modulation scheme of a two-level Model Predictive Control signal as an interface block is presented. • The implementation costs in home appliances with thermal regulation requirements are reduced. - Abstract: A vital element in making a sustainable world is correctly managing the energy in the domestic sector. Thus, this sector evidently stands as a key one for to be addressed in terms of climate change goals. Increasingly, people are aware of electricity savings by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Much of the reduction was due to technological improvements, however with the advancing of the years new types of control arise. Domestic appliances with the purpose of heating and cooling rely on thermostatic regulation technique. The study in this paper is focused on the subject of an alternative power management control for home appliances that require thermal regulation. In this paper a Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat with the aim of minimizing the cooling energy consumption through the minimization of the energy cost while satisfying the adequate temperature range for the human comfort. In addition, the Model Predictive Control problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. For this purpose, the typical consumption of a 24 h period of a summer day was simulated a three-level tariff scheme was used. The new
Networked Predictive Control for Nonlinear Systems With Arbitrary Region Quantizers.
Yang, Hongjiu; Xu, Yang; Xia, Yuanqing; Zhang, Jinhui
2017-04-06
In this paper, networked predictive control is investigated for planar nonlinear systems with quantization by an extended state observer (ESO). The ESO is used not only to deal with nonlinear terms but also to generate predictive states for dealing with network-induced delays. Two arbitrary region quantizers are applied to take effective values of signals in forward channel and feedback channel, respectively. Based on a "zoom" strategy, sufficient conditions are given to guarantee stabilization of the closed-loop networked control system with quantization. A simulation example is proposed to exhibit advantages and availability of the results.
Prediction-Based Control for Nonlinear Systems with Input Delay
Directory of Open Access Journals (Sweden)
I. Estrada-Sánchez
2017-01-01
Full Text Available This work has two primary objectives. First, it presents a state prediction strategy for a class of nonlinear Lipschitz systems subject to constant time delay in the input signal. As a result of a suitable change of variable, the state predictor asymptotically provides the value of the state τ units of time ahead. Second, it proposes a solution to the stabilization and trajectory tracking problems for the considered class of systems using predicted states. The predictor-controller convergence is proved by considering a complete Lyapunov functional. The proposed predictor-based controller strategy is evaluated using numerical simulations.
Model Predictive Control based on Finite Impulse Response Models
DEFF Research Database (Denmark)
Prasath, Guru; Jørgensen, John Bagterp
2008-01-01
We develop a regularized l2 finite impulse response (FIR) predictive controller with input and input-rate constraints. Feedback is based on a simple constant output disturbance filter. The performance of the predictive controller in the face of plant-model mismatch is investigated by simulations...... and related to the uncertainty of the impulse response coefficients. The simulations can be used to benchmark l2 MPC against FIR based robust MPC as well as to estimate the maximum performance improvements by robust MPC....
An Application to the Prediction of LOD Change Based on General Regression Neural Network
Zhang, X. H.; Wang, Q. J.; Zhu, J. J.; Zhang, H.
2011-07-01
Traditional prediction of the LOD (length of day) change was based on linear models, such as the least square model and the autoregressive technique, etc. Due to the complex non-linear features of the LOD variation, the performances of the linear model predictors are not fully satisfactory. This paper applies a non-linear neural network - general regression neural network (GRNN) model to forecast the LOD change, and the results are analyzed and compared with those obtained with the back propagation neural network and other models. The comparison shows that the performance of the GRNN model in the prediction of the LOD change is efficient and feasible.
REALIGNED MODEL PREDICTIVE CONTROL OF A PROPYLENE DISTILLATION COLUMN
Directory of Open Access Journals (Sweden)
A. I. Hinojosa
Full Text Available Abstract In the process industry, advanced controllers usually aim at an economic objective, which usually requires closed-loop stability and constraints satisfaction. In this paper, the application of a MPC in the optimization structure of an industrial Propylene/Propane (PP splitter is tested with a controller based on a state space model, which is suitable for heavily disturbed environments. The simulation platform is based on the integration of the commercial dynamic simulator Dynsim® and the rigorous steady-state optimizer ROMeo® with the real-time facilities of Matlab. The predictive controller is the Infinite Horizon Model Predictive Control (IHMPC, based on a state-space model that that does not require the use of a state observer because the non-minimum state is built with the past inputs and outputs. The controller considers the existence of zone control of the outputs and optimizing targets for the inputs. We verify that the controller is efficient to control the propylene distillation system in a disturbed scenario when compared with a conventional controller based on a state observer. The simulation results show a good performance in terms of stability of the controller and rejection of large disturbances in the composition of the feed of the propylene distillation column.
Exponential stability for formation control systems with generalized controllers: A unified approach
Sun, Zhiyong; Mou, Shaoshuai; Anderson, Brian D.O.; Cao, Ming
2016-01-01
This paper discusses generalized controllers for distance-based rigid formation shape stabilization and aims to provide a unified approach for the convergence analysis. We consider two types of formation control systems according to different characterizations of target formations: minimally rigid
Rosenkrantz, Andrew B; Doshi, Ankur M; Ginocchio, Luke A; Aphinyanaphongs, Yindalon
2016-12-01
This study aimed to assess the performance of a text classification machine-learning model in predicting highly cited articles within the recent radiological literature and to identify the model's most influential article features. We downloaded from PubMed the title, abstract, and medical subject heading terms for 10,065 articles published in 25 general radiology journals in 2012 and 2013. Three machine-learning models were applied to predict the top 10% of included articles in terms of the number of citations to the article in 2014 (reflecting the 2-year time window in conventional impact factor calculations). The model having the highest area under the curve was selected to derive a list of article features (words) predicting high citation volume, which was iteratively reduced to identify the smallest possible core feature list maintaining predictive power. Overall themes were qualitatively assigned to the core features. The regularized logistic regression (Bayesian binary regression) model had highest performance, achieving an area under the curve of 0.814 in predicting articles in the top 10% of citation volume. We reduced the initial 14,083 features to 210 features that maintain predictivity. These features corresponded with topics relating to various imaging techniques (eg, diffusion-weighted magnetic resonance imaging, hyperpolarized magnetic resonance imaging, dual-energy computed tomography, computed tomography reconstruction algorithms, tomosynthesis, elastography, and computer-aided diagnosis), particular pathologies (prostate cancer; thyroid nodules; hepatic adenoma, hepatocellular carcinoma, non-alcoholic fatty liver disease), and other topics (radiation dose, electroporation, education, general oncology, gadolinium, statistics). Machine learning can be successfully applied to create specific feature-based models for predicting articles likely to achieve high influence within the radiological literature. Copyright © 2016 The Association of University
Nonlinear Model Predictive Control with Constraint Satisfactions for a Quadcopter
Wang, Ye; Ramirez-Jaime, Andres; Xu, Feng; Puig, Vicenç
2017-01-01
This paper presents a nonlinear model predictive control (NMPC) strategy combined with constraint satisfactions for a quadcopter. The full dynamics of the quadcopter describing the attitude and position are nonlinear, which are quite sensitive to changes of inputs and disturbances. By means of constraint satisfactions, partial nonlinearities and modeling errors of the control-oriented model of full dynamics can be transformed into the inequality constraints. Subsequently, the quadcopter can be controlled by an NMPC controller with the updated constraints generated by constraint satisfactions. Finally, the simulation results applied to a quadcopter simulator are provided to show the effectiveness of the proposed strategy.
Economic Model Predictive Control for Smart Energy Systems
DEFF Research Database (Denmark)
Halvgaard, Rasmus
Model Predictive Control (MPC) can be used to control the energy distribution in a Smart Grid with a high share of stochastic energy production from renewable energy sources like wind. Heat pumps for heating residential buildings can exploit the slow heat dynamics of a building to store heat and ...... and hereby shift the heat pump power consumption to periods with both low electricity prices and a high fraction of green energy in the grid.......Model Predictive Control (MPC) can be used to control the energy distribution in a Smart Grid with a high share of stochastic energy production from renewable energy sources like wind. Heat pumps for heating residential buildings can exploit the slow heat dynamics of a building to store heat...
Multimodel Predictive Control Approach for UAV Formation Flight
Directory of Open Access Journals (Sweden)
Chang-jian Ru
2014-01-01
Full Text Available Formation flight problem is the most important and interesting problem of multiple UAVs (unmanned aerial vehicles cooperative control. In this paper, a novel approach for UAV formation flight based on multimodel predictive control is designed. Firstly, the state equation of relative motion is obtained and then discretized. By the geometrical method, the characteristic points of state are determined. Afterwards, based on the linearization technique, the standard linear discrete model is obtained at each characteristic state point. Then, weighted model set is proposed using the idea of T-S (Takagi-Sugeno fuzzy control and the predictive control is carried out based on the multimodel method. Finally, to verify the performance of the proposed method, two different simulation scenarios are performed.
The predictive value of general movement tasks in assessing occupational task performance.
Frost, David M; Beach, Tyson A C; McGill, Stuart M; Callaghan, Jack P
2015-01-01
Within the context of evaluating individuals' movement behavior it is generally assumed that the tasks chosen will predict their competency to perform activities relevant to their occupation. This study sought to examine whether a battery of general tasks could be used to predict the movement patterns employed by firefighters to perform select job-specific skills. Fifty-two firefighters performed a battery of general and occupation-specific tasks that simulated the demands of firefighting. Participants' peak lumbar spine and frontal plane knee motion were compared across tasks. During 85% of all comparisons, the magnitude of spine and knee motion was greater during the general movement tasks than observed during the firefighting skills. Certain features of a worker's movement behavior may be exhibited across a range of tasks. Therefore, provided that a movement screen's tasks expose the motions of relevance for the population being tested, general evaluations could offer valuable insight into workers' movement competency or facilitate an opportunity to establish an evidence-informed intervention.
Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current
Directory of Open Access Journals (Sweden)
Wenbiao Li
2016-04-01
Full Text Available Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC balancing. To solve these problems, multi-objective predictive balancing control (MOPBC based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process.
Integration of Predictive Display and Aircraft Flight Control System
Directory of Open Access Journals (Sweden)
Efremov A.V.
2017-01-01
Full Text Available The synthesis of predictive display information and direct lift control system are considered for the path control tracking tasks (in particular landing task. The both solutions are based on pilot-vehicle system analysis and requirements to provide the highest accuracy and lowest pilot workload. The investigation was carried out for cases with and without time delay in aircraft dynamics. The efficiency of the both ways for the flying qualities improvement and their integration is tested by ground based simulation.
DEFF Research Database (Denmark)
Manzolli Leite, Fabio Renato; Peres, Karen Glazer; Do, Loc Giang
2017-01-01
BACKGROUND: Prediction of periodontitis development is challenging. Use of oral health-related data alone, especially in a young population, might underestimate disease risk. This study investigates accuracy of oral, systemic, and socioeconomic data on estimating periodontitis development...... in a population-based prospective cohort. METHODS: General health history and sociodemographic information were collected throughout the life-course of individuals. Oral examinations were performed at ages 24 and 31 years in the Pelotas 1982 birth cohort. Periodontitis at age 31 years according to six...... classifications was used as the gold standard to compute area under the receiver operating characteristic curve (AUC). Multivariable binomial regression models were used to evaluate the effects of oral health, general health, and socioeconomic characteristics on accuracy of periodontitis development prediction...
Model Predictive Control of Mineral Column Flotation Process
Directory of Open Access Journals (Sweden)
Yahui Tian
2018-06-01
Full Text Available Column flotation is an efficient method commonly used in the mineral industry to separate useful minerals from ores of low grade and complex mineral composition. Its main purpose is to achieve maximum recovery while ensuring desired product grade. This work addresses a model predictive control design for a mineral column flotation process modeled by a set of nonlinear coupled heterodirectional hyperbolic partial differential equations (PDEs and ordinary differential equations (ODEs, which accounts for the interconnection of well-stirred regions represented by continuous stirred tank reactors (CSTRs and transport systems given by heterodirectional hyperbolic PDEs, with these two regions combined through the PDEs’ boundaries. The model predictive control considers both optimality of the process operations and naturally present input and state/output constraints. For the discrete controller design, spatially varying steady-state profiles are obtained by linearizing the coupled ODE–PDE model, and then the discrete system is obtained by using the Cayley–Tustin time discretization transformation without any spatial discretization and/or without model reduction. The model predictive controller is designed by solving an optimization problem with input and state/output constraints as well as input disturbance to minimize the objective function, which leads to an online-solvable finite constrained quadratic regulator problem. Finally, the controller performance to keep the output at the steady state within the constraint range is demonstrated by simulation studies, and it is concluded that the optimal control scheme presented in this work makes this flotation process more efficient.
Prediction of active control of subsonic centrifugal compressor rotating stall
Lawless, Patrick B.; Fleeter, Sanford
1993-01-01
A mathematical model is developed to predict the suppression of rotating stall in a centrifugal compressor with a vaned diffuser. This model is based on the employment of a control vortical waveform generated upstream of the impeller inlet to damp weak potential disturbances that are the early stages of rotating stall. The control system is analyzed by matching the perturbation pressure in the compressor inlet and exit flow fields with a model for the unsteady behavior of the compressor. The model was effective at predicting the stalling behavior of the Purdue Low Speed Centrifugal Compressor for two distinctly different stall patterns. Predictions made for the effect of a controlled inlet vorticity wave on the stability of the compressor show that for minimum control wave magnitudes, on the order of the total inlet disturbance magnitude, significant damping of the instability can be achieved. For control waves of sufficient amplitude, the control phase angle appears to be the most important factor in maintaining a stable condition in the compressor.
Model Predictive Control of the Hybrid Ventilation for Livestock
DEFF Research Database (Denmark)
Wu, Zhuang; Stoustrup, Jakob; Trangbæk, Klaus
2006-01-01
In this paper, design and simulation results of Model Predictive Control (MPC) strategy for livestock hybrid ventilation systems and associated indoor climate through variable valve openings and exhaust fans are presented. The design is based on thermal comfort parameters for poultry in barns...
Predictive Models of Procedural Human Supervisory Control Behavior
2011-01-01
821708, Brest , France. Page 139 of 150 Boussemart, Y. and M. L. Cummings (2010). "Predicting Supervisory Control Behavior with Hidden Markov Models...Strategies for Strike Planning. COGIS 2006 - Cognitive Systems with Interactive Sensors, Paris . Burges, C. (1998). "A Tutorial on Support Vector Machines
Real-Time Optimization for Economic Model Predictive Control
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Edlund, Kristian; Frison, Gianluca
2012-01-01
In this paper, we develop an efficient homogeneous and self-dual interior-point method for the linear programs arising in economic model predictive control. To exploit structure in the optimization problems, the algorithm employs a highly specialized Riccati iteration procedure. Simulations show...
Model predictive control of hybrid systems : stability and robustness
Lazar, M.
2006-01-01
This thesis considers the stabilization and the robust stabilization of certain classes of hybrid systems using model predictive control. Hybrid systems represent a broad class of dynamical systems in which discrete behavior (usually described by a finite state machine) and continuous behavior
Stochastic Predictive Control of Multi-Microgrid Systems
DEFF Research Database (Denmark)
Bazmohammadi, Najmeh; Tahsiri, Ahmadreza; Anvari-Moghaddam, Amjad
2018-01-01
This paper presents a stochastic predictive control algorithm for a number of microgrids connected to the same distribution system. Each microgrid includes a variety of distributed resources such as wind turbine, photo voltaic units, energy storage devices and loads. Considering the uncertainty...
Use of Ocean Remote Sensing Data to Enhance Predictions with a Coupled General Circulation Model
Rienecker, Michele M.
1999-01-01
Surface height, sea surface temperature and surface wind observations from satellites have given a detailed time sequence of the initiation and evolution of the 1997/98 El Nino. The data have beet complementary to the subsurface TAO moored data in their spatial resolution and extent. The impact of satellite observations on seasonal prediction in the tropical Pacific using a coupled ocean-atmosphere general circulation model will be presented.
When predictions take control: The effect of task predictions on task switching performance
Directory of Open Access Journals (Sweden)
Wout eDuthoo
2012-08-01
Full Text Available In this paper, we aimed to investigate the role of self-generated predictions in the flexible control of behaviour. Therefore, we ran a task switching experiment in which participants were asked to try to predict the upcoming task in three conditions varying in switch rate (30%, 50% and 70%. Irrespective of their predictions, the colour of the target indicated which task participants had to perform. In line with previous studies (Mayr, 2006; Monsell & Mizon, 2006, the switch cost was attenuated as the switch rate increased. Importantly, a clear task repetition bias was found in all conditions, yet the task repetition prediction rate dropped from 78% over 66% to 49% with increasing switch probability in the three conditions. Irrespective of condition, the switch cost was strongly reduced in expectation of a task alternation compared to the cost of an unexpected task alternation following repetition predictions. Hence, our data suggest that the reduction in the switch cost with increasing switch probability is caused by a diminished expectancy for the task to repeat. Taken together, this paper highlights the importance of predictions in the flexible control of behaviour, and suggests a crucial role for task repetition expectancy in the context-sensitive adjusting of task switching performance.
Control and prediction components of movement planning in stuttering vs. nonstuttering adults
Daliri, Ayoub; Prokopenko, Roman A.; Flanagan, J. Randall; Max, Ludo
2014-01-01
Purpose Stuttering individuals show speech and nonspeech sensorimotor deficiencies. To perform accurate movements, the sensorimotor system needs to generate appropriate control signals and correctly predict their sensory consequences. Using a reaching task, we examined the integrity of these control and prediction components, separately, for movements unrelated to the speech motor system. Method Nine stuttering and nine nonstuttering adults made fast reaching movements to visual targets while sliding an object under the index finger. To quantify control, we determined initial direction error and end-point error. To quantify prediction, we calculated the correlation between vertical and horizontal forces applied to the object—an index of how well vertical force (preventing slip) anticipated direction-dependent variations in horizontal force (moving the object). Results Directional and end-point error were significantly larger for the stuttering group. Both groups performed similarly in scaling vertical force with horizontal force. Conclusions The stuttering group's reduced reaching accuracy suggests limitations in generating control signals for voluntary movements, even for non-orofacial effectors. Typical scaling of vertical force with horizontal force suggests an intact ability to predict the consequences of planned control signals. Stuttering may be associated with generalized deficiencies in planning control signals rather than predicting the consequences of those signals. PMID:25203459
A Generalized Correlation Plot Package for the CEBAF Control System
International Nuclear Information System (INIS)
D. Wu; W. Akers; S. Schaffner; H. Shoaee; W. A. Watson; D. Wetherholt
1996-01-01
The Correlation Package is a general facility for data acquisition and analysis serving as an online environment for performing a wide variety of machine physics experiments and engineering diagnostics. Typical correlation experiments consist of an initial set of actions followed by stepping one or two accelerator parameters while measuring up to several hundred control system parameters. The package utilizes the CDEV [1] device API to access accelerator systems. A variety of analysis and graphics tools are included through integration with the Matlab math modeling package. A post- acquisition script capability is available to automate the data reduction process. A callable interface allows this facility to serve as the data acquisition and analysis engine for high level applications. A planned interface to archived accelerator data will allow the same analysis and graphics tools to be used for viewing and correlating history data. The object oriented design and C++ implementation details as well as the current status of the Correlation Package will be presented
A confirmation of the general relativistic prediction of the Lense-Thirring effect.
Ciufolini, I; Pavlis, E C
2004-10-21
An important early prediction of Einstein's general relativity was the advance of the perihelion of Mercury's orbit, whose measurement provided one of the classical tests of Einstein's theory. The advance of the orbital point-of-closest-approach also applies to a binary pulsar system and to an Earth-orbiting satellite. General relativity also predicts that the rotation of a body like Earth will drag the local inertial frames of reference around it, which will affect the orbit of a satellite. This Lense-Thirring effect has hitherto not been detected with high accuracy, but its detection with an error of about 1 per cent is the main goal of Gravity Probe B--an ongoing space mission using orbiting gyroscopes. Here we report a measurement of the Lense-Thirring effect on two Earth satellites: it is 99 +/- 5 per cent of the value predicted by general relativity; the uncertainty of this measurement includes all known random and systematic errors, but we allow for a total +/- 10 per cent uncertainty to include underestimated and unknown sources of error.
King, Michael; Marston, Louise; Švab, Igor; Maaroos, Heidi-Ingrid; Geerlings, Mirjam I; Xavier, Miguel; Benjamin, Vicente; Torres-Gonzalez, Francisco; Bellon-Saameno, Juan Angel; Rotar, Danica; Aluoja, Anu; Saldivia, Sandra; Correa, Bernardo; Nazareth, Irwin
2011-01-01
Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women. 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.
Directory of Open Access Journals (Sweden)
Michael King
Full Text Available Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL for the development of hazardous drinking in safe drinkers.A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women.69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873. The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51. External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846 and Hedge's g of 0.68 (95% CI 0.57, 0.78.The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.
Economic Model Predictive Control for Spray Drying Plants
DEFF Research Database (Denmark)
Petersen, Lars Norbert
and a complexity reduced control model is used for state estimation and prediction in the controllers. These models facilitate development and comparison of control strategies. We develop two MPC strategies; a linear tracking MPC with a Real-Time Optimization layer (MPC with RTO) and an Economic Nonlinear MPC (E...... horizon, out of which only the first input is applied to the dryer. This procedure is repeated at each sample instant and is solved numerically in real-time. The MPC with RTO tracks a target that optimizes the cost of operation at steady-state. The E-MPC optimizes the cost of operation directly by having...... this objective directly in the controller. The need for the RTO layer is then eliminated. We demonstrate the application of the proposed MPC with RTO to control an industrial GEA MSDTM-1250 spray dryer, which produces approximately 7500 kg/hr of enriched milk powder. Compared to the conventional PI controller...
Adaptive model predictive process control using neural networks
Buescher, K.L.; Baum, C.C.; Jones, R.D.
1997-08-19
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.
Composite control for raymond mill based on model predictive control and disturbance observer
Directory of Open Access Journals (Sweden)
Dan Niu
2016-03-01
Full Text Available In the raymond mill grinding process, precise control of operating load is vital for the high product quality. However, strong external disturbances, such as variations of ore size and ore hardness, usually cause great performance degradation. It is not easy to control the current of raymond mill constant. Several control strategies have been proposed. However, most of them (such as proportional–integral–derivative and model predictive control reject disturbances just through feedback regulation, which may lead to poor control performance in the presence of strong disturbances. For improving disturbance rejection, a control method based on model predictive control and disturbance observer is put forward in this article. The scheme employs disturbance observer as feedforward compensation and model predictive control controller as feedback regulation. The test results illustrate that compared with model predictive control method, the proposed disturbance observer–model predictive control method can obtain significant superiority in disturbance rejection, such as shorter settling time and smaller peak overshoot under strong disturbances.
Frequency weighted model predictive control of wind turbine
DEFF Research Database (Denmark)
Klauco, Martin; Poulsen, Niels Kjølstad; Mirzaei, Mahmood
2013-01-01
This work is focused on applying frequency weighted model predictive control (FMPC) on three blade horizontal axis wind turbine (HAWT). A wind turbine is a very complex, non-linear system influenced by a stochastic wind speed variation. The reduced dynamics considered in this work are the rotatio...... predictive controller are presented. Statistical comparison between frequency weighted MPC, standard MPC and baseline PI controller is shown as well.......This work is focused on applying frequency weighted model predictive control (FMPC) on three blade horizontal axis wind turbine (HAWT). A wind turbine is a very complex, non-linear system influenced by a stochastic wind speed variation. The reduced dynamics considered in this work...... are the rotational degree of freedom of the rotor and the tower for-aft movement. The MPC design is based on a receding horizon policy and a linearised model of the wind turbine. Due to the change of dynamics according to wind speed, several linearisation points must be considered and the control design adjusted...
Introducing Model Predictive Control for Improving Power Plant Portfolio Performance
DEFF Research Database (Denmark)
Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon
2008-01-01
This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...... reference tracking and disturbance rejection in an economically optimal way. The performance function is chosen as a mixture of the `1-norm and a linear weighting to model the economics of the system. Simulations show a significant improvement of the performance of the MPC compared to the current...
MODELLING OF DYNAMIC SPEED LIMITS USING THE MODEL PREDICTIVE CONTROL
Directory of Open Access Journals (Sweden)
Andrey Borisovich Nikolaev
2017-09-01
Full Text Available The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS, which consist of interacting intelligent vehicles (IV and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering. Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC. The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic.
Thermal Storage Power Balancing with Model Predictive Control
DEFF Research Database (Denmark)
Halvgaard, Rasmus; Poulsen, Niels Kjølstad; Madsen, Henrik
2013-01-01
The method described in this paper balances power production and consumption with a large number of thermal loads. Linear controllers are used for the loads to track a temperature set point, while Model Predictive Control (MPC) and model estimation of the load behavior are used for coordination....... The total power consumption of all loads is controlled indirectly through a real-time price. The MPC incorporates forecasts of the power production and disturbances that influence the loads, e.g. time-varying weather forecasts, in order to react ahead of time. A simulation scenario demonstrates...
Model Predictive Control of Wind Turbines using Uncertain LIDAR Measurements
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Soltani, Mohsen; Poulsen, Niels Kjølstad
2013-01-01
, we simplify state prediction for the MPC. Consequently, the control problem of the nonlinear system is simplified into a quadratic programming. We consider uncertainty in the wind propagation time, which is the traveling time of wind from the LIDAR measurement point to the rotor. An algorithm based......The problem of Model predictive control (MPC) of wind turbines using uncertain LIDAR (LIght Detection And Ranging) measurements is considered. A nonlinear dynamical model of the wind turbine is obtained. We linearize the obtained nonlinear model for different operating points, which are determined...... on wind speed estimation and measurements from the LIDAR is devised to find an estimate of the delay and compensate for it before it is used in the controller. Comparisons between the MPC with error compensation, the MPC without error compensation and an MPC with re-linearization at each sample point...
Electric vehicle charge planning using Economic Model Predictive Control
DEFF Research Database (Denmark)
Halvgaard, Rasmus; Poulsen, Niels K.; Madsen, Henrik
2012-01-01
Economic Model Predictive Control (MPC) is very well suited for controlling smart energy systems since electricity price and demand forecasts are easily integrated in the controller. Electric vehicles (EVs) are expected to play a large role in the future Smart Grid. They are expected to provide...... grid services, both for peak reduction and for ancillary services, by absorbing short term variations in the electricity production. In this paper the Economic MPC minimizes the cost of electricity consumption for a single EV. Simulations show savings of 50–60% of the electricity costs compared...... to uncontrolled charging from load shifting based on driving pattern predictions. The future energy system in Denmark will most likely be based on renewable energy sources e.g. wind and solar power. These green energy sources introduce stochastic fluctuations in the electricity production. Therefore, energy...
Robust Model Predictive Control of a Wind Turbine
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2012-01-01
In this work the problem of robust model predictive control (robust MPC) of a wind turbine in the full load region is considered. A minimax robust MPC approach is used to tackle the problem. Nonlinear dynamics of the wind turbine are derived by combining blade element momentum (BEM) theory...... of the uncertain system is employed and a norm-bounded uncertainty model is used to formulate a minimax model predictive control. The resulting optimization problem is simplified by semidefinite relaxation and the controller obtained is applied on a full complexity, high fidelity wind turbine model. Finally...... and first principle modeling of the turbine flexible structure. Thereafter the nonlinear model is linearized using Taylor series expansion around system operating points. Operating points are determined by effective wind speed and an extended Kalman filter (EKF) is employed to estimate this. In addition...
One-Step-Ahead Predictive Control for Hydroturbine Governor
Directory of Open Access Journals (Sweden)
Zhihuai Xiao
2015-01-01
Full Text Available The hydroturbine generator regulating system can be considered as one system synthetically integrating water, machine, and electricity. It is a complex and nonlinear system, and its configuration and parameters are time-dependent. A one-step-ahead predictive control based on on-line trained neural networks (NNs for hydroturbine governor with variation in gate position is described in this paper. The proposed control algorithm consists of a one-step-ahead neuropredictor that tracks the dynamic characteristics of the plant and predicts its output and a neurocontroller to generate the optimal control signal. The weights of two NNs, initially trained off-line, are updated on-line according to the scalar error. The proposed controller can thus track operating conditions in real-time and produce the optimal control signal over the wide operating range. Only the inputs and outputs of the generator are measured and there is no need to determine the other states of the generator. Simulations have been performed with varying operating conditions and different disturbances to compare the performance of the proposed controller with that of a conventional PID controller and validate the feasibility of the proposed approach.
Robust predictive control of a gasoline debutanizer column
Directory of Open Access Journals (Sweden)
E. Almeida Neto
2000-12-01
Full Text Available This paper studies the application of Model Predictive Control to moderately nonlinear processes. The system used in this work is an industrial gasoline debutanizer column. The paper presents two new formulations of MPC: MMPC (Multi-Model Predictive Controller and RSMPC (Robust Stable MPC. The approach is based on the concepts of Linear Matrix Inequalities (LMI, which have been recently introduced in the MPC field. Model uncertainty is considered by assuming that the true process model belongs to a convex set (polytope of possible plants. The controller has guaranteed stability when a Lyapunov type inequality constraint is included in the MPC problem. In the debutanizer column, several nonlinearities are present in the advanced control level when the manipulated inputs are the reflux flow and the reboiler heat duty. In most cases the controlled outputs are the contents of C5+ (pentane and heavier hydrocarbons in the LPG (Liquefied Petroleum Gas and the gasoline vapor pressure (P VR. In this case the QDMC algorithm which is usually applied to the debutanizer column has a poor performance and stability problems reflected in an oscillatory behavior of the process. The new approach considers several process models representing different operating conditions where linear models are identified. The results presented here show that the multimodel controller is capable of controlling the process in the entire operating window while the conventional MPC has a limited operating range.
Robust stability in predictive control with soft constraints
DEFF Research Database (Denmark)
Thomsen, Sven Creutz; Niemann, Hans Henrik; Poulsen, Niels Kjølstad
2010-01-01
In this paper we take advantage of the primary and dual Youla parameterizations for setting up a soft constrained model predictive control (MPC) scheme for which stability is guaranteed in face of norm-bounded uncertainties. Under special conditions guarantees are also given for hard input...... constraints. In more detail, we parameterize the MPC predictions in terms of the primary Youla parameter and use this parameter as the online optimization variable. The uncertainty is parameterized in terms of the dual Youla parameter. Stability can then be guaranteed through small gain arguments on the loop...
Robust stability in constrained predictive control through the Youla parameterisations
DEFF Research Database (Denmark)
Thomsen, Sven Creutz; Niemann, Hans Henrik; Poulsen, Niels Kjølstad
2011-01-01
In this article we take advantage of the primary and dual Youla parameterisations to set up a soft constrained model predictive control (MPC) scheme. In this framework it is possible to guarantee stability in face of norm-bounded uncertainties. Under special conditions guarantees are also given...... for hard input constraints. In more detail, we parameterise the MPC predictions in terms of the primary Youla parameter and use this parameter as the on-line optimisation variable. The uncertainty is parameterised in terms of the dual Youla parameter. Stability can then be guaranteed through small gain...
High level model predictive control for plug-and-play process control with stability guaranty
DEFF Research Database (Denmark)
Michelsen, Axel Gottlieb; Stoustrup, Jakob
2010-01-01
In this paper a method for designing a stabilizing high level model predictive controller for a hierarchical plug- and-play process is presented. This is achieved by abstracting the lower layers of the controller structure as low order models with uncertainty and by using a robust model predictive...... controller for generating the references for these. A simulation example, in which the actuators in a process control system are changed, is reported to show the potential of this approach for plug and play process control....
Nonlinear Dynamic Inversion Baseline Control Law: Architecture and Performance Predictions
Miller, Christopher J.
2011-01-01
A model reference dynamic inversion control law has been developed to provide a baseline control law for research into adaptive elements and other advanced flight control law components. This controller has been implemented and tested in a hardware-in-the-loop simulation; the simulation results show excellent handling qualities throughout the limited flight envelope. A simple angular momentum formulation was chosen because it can be included in the stability proofs for many basic adaptive theories, such as model reference adaptive control. Many design choices and implementation details reflect the requirements placed on the system by the nonlinear flight environment and the desire to keep the system as basic as possible to simplify the addition of the adaptive elements. Those design choices are explained, along with their predicted impact on the handling qualities.
Low trait self-control predicts self-handicapping.
Uysal, Ahmet; Knee, C Raymond
2012-02-01
Past research has shown that self-handicapping stems from uncertainty about one's ability and self-presentational concerns. The present studies suggest that low dispositional self-control is also associated with self-handicapping. In 3 studies (N = 289), the association between self-control and self-handicapping was tested. Self-control was operationalized as trait self-control, whereas self-handicapping was operationalized as trait self-handicapping in Study 1 (N = 160), self-reported self-handicapping in Study 2 (N = 74), and behavioral self-handicapping in Study 3 (N = 55). In all 3 studies, hierarchical regression analyses revealed that low self-control predicts self-handicapping, independent of self-esteem, self-doubt, social desirability, and gender. © 2012 The Authors. Journal of Personality © 2012, Wiley Periodicals, Inc.
Health-aware Model Predictive Control of Pasteurization Plant
Karimi Pour, Fatemeh; Puig, Vicenç; Ocampo-Martinez, Carlos
2017-01-01
In order to optimize the trade-off between components life and energy consumption, the integration of a system health management and control modules is required. This paper proposes the integration of model predictive control (MPC) with a fatigue estimation approach that minimizes the damage of the components of a pasteurization plant. The fatigue estimation is assessed with the rainflow counting algorithm. Using data from this algorithm, a simplified model that characterizes the health of the system is developed and integrated with MPC. The MPC controller objective is modified by adding an extra criterion that takes into account the accumulated damage. But, a steady-state offset is created by adding this extra criterion. Finally, by including an integral action in the MPC controller, the steady-state error for regulation purpose is eliminated. The proposed control scheme is validated in simulation using a simulator of a utility-scale pasteurization plant.
A LIDAR-assisted model predictive controller added on a traditional wind turbine controller
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Hansen, Morten Hartvig
2016-01-01
control and opens the market of retrofitting existing wind turbines with the new technology. In this paper, we suggest a model predictive controller (MPC) that is added to the basic gain scheduled PI controller of a WT to enhance the performance of the closed loop system using LIDAR measurements...
Model Predictive Control of Integrated Gasification Combined Cycle Power Plants
Energy Technology Data Exchange (ETDEWEB)
B. Wayne Bequette; Priyadarshi Mahapatra
2010-08-31
The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.
Siddiqui, Bilal A.; El-Ferik, Sami; Abdelkader, Mohamed
2016-01-01
In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.
Siddiqui, Bilal A.
2016-07-26
In this work, a cascade structure of a time-scale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.
Importance of Perioperative Glycemic Control in General Surgery
Kwon, Steve; Thompson, Rachel; Dellinger, Patchen; Yanez, David; Farrohki, Ellen; Flum, David
2014-01-01
Objective To determine the relationship of perioperative hyperglycemia and insulin administration on outcomes in elective colon/rectal and bariatric operations. Background There is limited evidence to characterize the impact of perioperative hyperglycemia and insulin on adverse outcomes in patients, with and without diabetes, undergoing general surgical procedures. Methods The Surgical Care and Outcomes Assessment Program is a Washington State quality improvement benchmarking-based initiative. We evaluated the relationship of perioperative hyperglycemia (>180 mg/dL) and insulin administration on mortality, reoperative interventions, and infections for patients undergoing elective colorectal and bariatric surgery at 47 participating hospitals between fourth quarter of 2005 and fourth quarter of 2010. Results Of the 11,633 patients (55.4 ± 15.3 years; 65.7% women) with a serum glucose determination on the day of surgery, postoperative day 1, or postoperative day 2, 29.1% of patients were hyperglycemic. After controlling for clinical factors, those with hyperglycemia had a significantly increased risk of infection [odds ratio (OR) 2.0; 95% confidence interval (CI), 1.63–2.44], reoperative interventions (OR, 1.8; 95% CI, 1.41–2.3), and death (OR, 2.71; 95% CI, 1.72–4.28). Increased risk of poor outcomes was observed both for patients with and without diabetes. Those with hyperglycemia on the day of surgery who received insulin had no significant increase in infections (OR, 1.01; 95% CI, 0.72–1.42), reoperative interventions (OR, 1.29; 95% CI, 0.89–1.89), or deaths (OR, 1.21; 95% CI, 0.61–2.42). A dose-effect relationship was found between the effectiveness of insulin-related glucose control (worst 180–250 mg/dL, best adverse outcomes. Conclusions Perioperative hyperglycemia was associated with adverse outcomes in general surgery patients with and without diabetes. However, patients with hyperglycemia who received insulin were at no greater risk than
Model predictive control of a wind turbine modelled in Simpack
International Nuclear Information System (INIS)
Jassmann, U; Matzke, D; Reiter, M; Abel, D; Berroth, J; Schelenz, R; Jacobs, G
2014-01-01
Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine
Model predictive control of a wind turbine modelled in Simpack
Jassmann, U.; Berroth, J.; Matzke, D.; Schelenz, R.; Reiter, M.; Jacobs, G.; Abel, D.
2014-06-01
Wind turbines (WT) are steadily growing in size to increase their power production, which also causes increasing loads acting on the turbine's components. At the same time large structures, such as the blades and the tower get more flexible. To minimize this impact, the classical control loops for keeping the power production in an optimum state are more and more extended by load alleviation strategies. These additional control loops can be unified by a multiple-input multiple-output (MIMO) controller to achieve better balancing of tuning parameters. An example for MIMO control, which has been paid more attention to recently by wind industry, is Model Predictive Control (MPC). In a MPC framework a simplified model of the WT is used to predict its controlled outputs. Based on a user-defined cost function an online optimization calculates the optimal control sequence. Thereby MPC can intrinsically incorporate constraints e.g. of actuators. Turbine models used for calculation within the MPC are typically simplified. For testing and verification usually multi body simulations, such as FAST, BLADED or FLEX5 are used to model system dynamics, but they are still limited in the number of degrees of freedom (DOF). Detailed information about load distribution (e.g. inside the gearbox) cannot be provided by such models. In this paper a Model Predictive Controller is presented and tested in a co-simulation with SlMPACK, a multi body system (MBS) simulation framework used for detailed load analysis. The analysis are performed on the basis of the IME6.0 MBS WT model, described in this paper. It is based on the rotor of the NREL 5MW WT and consists of a detailed representation of the drive train. This takes into account a flexible main shaft and its main bearings with a planetary gearbox, where all components are modelled flexible, as well as a supporting flexible main frame. The wind loads are simulated using the NREL AERODYN v13 code which has been implemented as a routine to
Predictive validity of the Work Ability Index and its individual items in the general population.
Lundin, Andreas; Leijon, Ola; Vaez, Marjan; Hallgren, Mats; Torgén, Margareta
2017-06-01
This study assesses the predictive ability of the full Work Ability Index (WAI) as well as its individual items in the general population. The Work, Health and Retirement Study (WHRS) is a stratified random national sample of 25-75-year-olds living in Sweden in 2000 that received a postal questionnaire ( n = 6637, response rate = 53%). Current and subsequent sickness absence was obtained from registers. The ability of the WAI to predict long-term sickness absence (LTSA; ⩾ 90 consecutive days) during a period of four years was analysed by logistic regression, from which the Area Under the Receiver Operating Characteristic curve (AUC) was computed. There were 313 incident LTSA cases among 1786 employed individuals. The full WAI had acceptable ability to predict LTSA during the 4-year follow-up (AUC = 0.79; 95% CI 0.76 to 0.82). Individual items were less stable in their predictive ability. However, three of the individual items: current work ability compared with lifetime best, estimated work impairment due to diseases, and number of diagnosed current diseases, exceeded AUC > 0.70. Excluding the WAI item on number of days on sickness absence did not result in an inferior predictive ability of the WAI. The full WAI has acceptable predictive validity, and is superior to its individual items. For public health surveys, three items may be suitable proxies of the full WAI; current work ability compared with lifetime best, estimated work impairment due to diseases, and number of current diseases diagnosed by a physician.
Computationally efficient model predictive control algorithms a neural network approach
Ławryńczuk, Maciej
2014-01-01
This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: · A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. · Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. · The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). · The MPC algorithms with neural approximation with no on-line linearization. · The MPC algorithms with guaranteed stability and robustness. · Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require d...
Finite element predictions of active buckling control of stiffened panels
Thompson, Danniella M.; Griffin, O. H., Jr.
1993-04-01
Materials systems and structures that can respond 'intelligently' to their environment are currently being proposed and investigated. A series of finite element analyses was performed to investigate the potential for active buckling control of two different stiffened panels by embedded shape memory alloy (SMA) rods. Changes in the predicted buckling load increased with the magnitude of the actuation level for a given structural concept. Increasing the number of actuators for a given concept yielded greater predicted increases in buckling load. Considerable control authority was generated with a small number of actuators, with greater authority demonstrated for those structural concepts where the activated SMA rods could develop greater forces and moments on the structure. Relatively simple and inexpensive analyses were performed with standard finite elements to determine such information, indicating the viability of these types of models for design purposes.
Interpreting Disruption Prediction Models to Improve Plasma Control
Parsons, Matthew
2017-10-01
In order for the tokamak to be a feasible design for a fusion reactor, it is necessary to minimize damage to the machine caused by plasma disruptions. Accurately predicting disruptions is a critical capability for triggering any mitigative actions, and a modest amount of attention has been given to efforts that employ machine learning techniques to make these predictions. By monitoring diagnostic signals during a discharge, such predictive models look for signs that the plasma is about to disrupt. Typically these predictive models are interpreted simply to give a `yes' or `no' response as to whether a disruption is approaching. However, it is possible to extract further information from these models to indicate which input signals are more strongly correlated with the plasma approaching a disruption. If highly accurate predictive models can be developed, this information could be used in plasma control schemes to make better decisions about disruption avoidance. This work was supported by a Grant from the 2016-2017 Fulbright U.S. Student Program, administered by the Franco-American Fulbright Commission in France.
Artificial neural network implementation of a near-ideal error prediction controller
Mcvey, Eugene S.; Taylor, Lynore Denise
1992-01-01
responses be known for a particular input and modeled plant. These responses are used in the error prediction controller. An analysis was done on the general dynamic behavior that results from including a digital error predictor in a control loop and these were compared to those including the near-ideal Neural Network error predictor. This analysis was done for a second and third order system.
Stochastic Model Predictive Control with Applications in Smart Energy Systems
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Edlund, Kristian; Mølbak, Tommy
2012-01-01
to cover more than 50% of the total consumption by 2050. Energy systems based on significant amounts of renewable energy sources are subject to uncertainties. To accommodate the need for model predictive control (MPC) of such systems, the effect of the stochastic effects on the constraints must...... study, we consider a system consisting of fuel-fired thermal power plants, wind farms and electric vehicles....
Predictability of solute transport in diffusion-controlled hydrogeologic regimes
International Nuclear Information System (INIS)
Gillham, R.W.; Cherry, J.A.
1983-01-01
Hydrogeologic regimes that are favourable for the subsurface management of low-level radioactive wastes must have transport properties that will limit the migration velocity of contaminants to some acceptably low value. Of equal importance, for the purpose of impact assessment and licensing, is the need to be able to predict, with a reasonable degree of certainty and over long time periods, what the migration velocity of the various contaminants of interest will be. This paper presents arguments to show that in addition to having favourable velocity characteristics, transport in saturated, diffusion-controlled hydrogeologic regimes is considerably more predictable than in the most common alternatives. The classical transport models for unsaturated, saturated-advection-controlled and saturated-diffusion-controlled environments are compared, with particular consideration being given to the difficulties associated with the characterization of the respective transport parameters. Results are presented which show that the diffusion of non-reactive solutes and solutes that react according to a constant partitioning ratio (K/sub d/) are highly predictable under laboratory conditions and that the diffusion coefficients for the reactive solutes can be determined with a reasonable degree of accuracy from independent measurements of bulk density, porosity, distribution coefficient and tortuosity. Field evidence is presented which shows that the distribution of environmental isotopes and chloride in thick clayey deposits is consistent with a diffusion-type transport process in these media. These results are particularly important in that they not only demonstrate the occurrence of diffusion-controlled hydrogeologic regimes, but they also demonstrate the predictability of the migration characteristics over very long time periods
New predictions for generalized spin polarizabilities from heavy baryon chiral perturbation theory
International Nuclear Information System (INIS)
Chung-Wen Kao; Barbara Pasquini; Marc Vanderhaeghen
2004-01-01
We extract the next-to-next-to-leading order results for spin-flip generalized polarizabilities (GPs) of the nucleon from the spin-dependent amplitudes for virtual Compton scattering (VCS) at Ο(p 4 ) in heavy baryon chiral perturbation theory. At this order, no unknown low energy constants enter the theory, allowing us to make absolute predictions for all spin-flip GPs. Furthermore, by using constraint equations between the GPs due to nucleon crossing combined with charge conjugation symmetry of the VCS amplitudes, we get a next-to-next-to-next-to-leading order prediction for one of the GPs. We provide estimates for forthcoming double polarization experiments which allow to access these spin-flip GPs of the nucleon
Directory of Open Access Journals (Sweden)
Amany E. Aly
2016-04-01
Full Text Available When a system consisting of independent components of the same type, some appropriate actions may be done as soon as a portion of them have failed. It is, therefore, important to be able to predict later failure times from earlier ones. One of the well-known failure distributions commonly used to model component life, is the modified Weibull distribution (MWD. In this paper, two pivotal quantities are proposed to construct prediction intervals for future unobservable lifetimes based on generalized order statistics (gos from MWD. Moreover, a pivotal quantity is developed to reconstruct missing observations at the beginning of experiment. Furthermore, Monte Carlo simulation studies are conducted and numerical computations are carried out to investigate the efficiency of presented results. Finally, two illustrative examples for real data sets are analyzed.
Time scaling internal state predictive control of a solar plant
Energy Technology Data Exchange (ETDEWEB)
Silva, R.N. [DEE-FCT/UNL, Caparica (Portugal); Rato, L.M. [INESC-ID/University, Evora (Portugal); Lemos, J.M. [INESC-ID/IST, Lisboa (Portugal)
2003-12-01
The control of a distributed collector solar field is addressed in this work, exploiting the plant's transport characteristic. The plant is modeled by a hyperbolic type partial differential equation (PDE) where the transport speed is the manipulated flow, i.e. the controller output. The model has an external distributed source, which is the solar radiation captured along the collector, approximated to depend only of time. From the solution of the PDE, a linear discrete state space model is obtained by using time-scaling and the redefinition of the control input. This method allows overcoming the dependency of the time constants with the operating point. A model-based predictive adaptive controller is derived with the internal temperature distribution estimated with a state observer. Experimental results at the solar power plant are presented, illustrating the advantages of the approach under consideration. (author)
Data-Driven Predictive Direct Load Control of Refrigeration Systems
DEFF Research Database (Denmark)
Shafiei, Seyed Ehsan; Knudsen, Torben; Wisniewski, Rafal
2015-01-01
A predictive control using subspace identification is applied for the smart grid integration of refrigeration systems under a direct load control scheme. A realistic demand response scenario based on regulation of the electrical power consumption is considered. A receding horizon optimal control...... is proposed to fulfil two important objectives: to secure high coefficient of performance and to participate in power consumption management. Moreover, a new method for design of input signals for system identification is put forward. The control method is fully data driven without an explicit use of model...... against real data. The performance improvement results in a 22% reduction in the energy consumption. A comparative simulation is accomplished showing the superiority of the method over the existing approaches in terms of the load following performance....
Predictive Smart Grid Control with Exact Aggregated Power Constraints
DEFF Research Database (Denmark)
Trangbæk, K; Petersen, Mette Højgaard; Bendtsen, Jan Dimon
2012-01-01
of autonomous consumers. The control system is tasked with balancing electric power production and consumption within the smart grid, and makes active use of the flexibility of a large number of power producing and/or power consuming units. The load variations on the grid arise on one hand from varying......This chapter deals with hierarchical model predictive control (MPC) of smart grid systems. The design consists of a high-level MPC controller, a second level of so-called aggregators,which reduces the computational and communication related load on the high-level control, and a lower level...... consumption, and on the other hand from natural variations in power production from e.g. wind turbines. The consumers represent energy-consuming units such as heat pumps, car batteries etc. These units obviously have limits on how much power and energy they can consume at any given time, which impose...
Model predictive control approach for a CPAP-device
Directory of Open Access Journals (Sweden)
Scheel Mathias
2017-09-01
Full Text Available The obstructive sleep apnoea syndrome (OSAS is characterized by a collapse of the upper respiratory tract, resulting in a reduction of the blood oxygen- and an increase of the carbon dioxide (CO2 - concentration, which causes repeated sleep disruptions. The gold standard to treat the OSAS is the continuous positive airway pressure (CPAP therapy. The continuous pressure keeps the upper airway open and prevents the collapse of the upper respiratory tract and the pharynx. Most of the available CPAP-devices cannot maintain the pressure reference [1]. In this work a model predictive control approach is provided. This control approach has the possibility to include the patient’s breathing effort into the calculation of the control variable. Therefore a patient-individualized control strategy can be developed.
Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems
DEFF Research Database (Denmark)
Morstyn, Thomas; Hredzak, Branislav; Aguilera, Ricardo P.
2018-01-01
, and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical......This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear $d$ – $q$ reference frame voltage...... feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real...
Towards a general theory of neural computation based on prediction by single neurons.
Directory of Open Access Journals (Sweden)
Christopher D Fiorillo
Full Text Available Although there has been tremendous progress in understanding the mechanics of the nervous system, there has not been a general theory of its computational function. Here I present a theory that relates the established biophysical properties of single generic neurons to principles of Bayesian probability theory, reinforcement learning and efficient coding. I suggest that this theory addresses the general computational problem facing the nervous system. Each neuron is proposed to mirror the function of the whole system in learning to predict aspects of the world related to future reward. According to the model, a typical neuron receives current information about the state of the world from a subset of its excitatory synaptic inputs, and prior information from its other inputs. Prior information would be contributed by synaptic inputs representing distinct regions of space, and by different types of non-synaptic, voltage-regulated channels representing distinct periods of the past. The neuron's membrane voltage is proposed to signal the difference between current and prior information ("prediction error" or "surprise". A neuron would apply a Hebbian plasticity rule to select those excitatory inputs that are the most closely correlated with reward but are the least predictable, since unpredictable inputs provide the neuron with the most "new" information about future reward. To minimize the error in its predictions and to respond only when excitation is "new and surprising," the neuron selects amongst its prior information sources through an anti-Hebbian rule. The unique inputs of a mature neuron would therefore result from learning about spatial and temporal patterns in its local environment, and by extension, the external world. Thus the theory describes how the structure of the mature nervous system could reflect the structure of the external world, and how the complexity and intelligence of the system might develop from a population of
Control System Design for Cylindrical Tank Process Using Neural Model Predictive Control Technique
Directory of Open Access Journals (Sweden)
M. Sridevi
2010-10-01
Full Text Available Chemical manufacturing and process industry requires innovative technologies for process identification. This paper deals with model identification and control of cylindrical process. Model identification of the process was done using ARMAX technique. A neural model predictive controller was designed for the identified model. The performance of the controllers was evaluated using MATLAB software. The performance of NMPC controller was compared with Smith Predictor controller and IMC controller based on rise time, settling time, overshoot and ISE and it was found that the NMPC controller is better suited for this process.
Dynamics and control of quadcopter using linear model predictive control approach
Islam, M.; Okasha, M.; Idres, M. M.
2017-12-01
This paper investigates the dynamics and control of a quadcopter using the Model Predictive Control (MPC) approach. The dynamic model is of high fidelity and nonlinear, with six degrees of freedom that include disturbances and model uncertainties. The control approach is developed based on MPC to track different reference trajectories ranging from simple ones such as circular to complex helical trajectories. In this control technique, a linearized model is derived and the receding horizon method is applied to generate the optimal control sequence. Although MPC is computer expensive, it is highly effective to deal with the different types of nonlinearities and constraints such as actuators’ saturation and model uncertainties. The MPC parameters (control and prediction horizons) are selected by trial-and-error approach. Several simulation scenarios are performed to examine and evaluate the performance of the proposed control approach using MATLAB and Simulink environment. Simulation results show that this control approach is highly effective to track a given reference trajectory.
Thermodynamic scaling of dynamics in polymer melts: predictions from the generalized entropy theory.
Xu, Wen-Sheng; Freed, Karl F
2013-06-21
Many glass-forming fluids exhibit a remarkable thermodynamic scaling in which dynamic properties, such as the viscosity, the relaxation time, and the diffusion constant, can be described under different thermodynamic conditions in terms of a unique scaling function of the ratio ρ(γ)∕T, where ρ is the density, T is the temperature, and γ is a material dependent constant. Interest in the scaling is also heightened because the exponent γ enters prominently into considerations of the relative contributions to the dynamics from pressure effects (e.g., activation barriers) vs. volume effects (e.g., free volume). Although this scaling is clearly of great practical use, a molecular understanding of the scaling remains elusive. Providing this molecular understanding would greatly enhance the utility of the empirically observed scaling in assisting the rational design of materials by describing how controllable molecular factors, such as monomer structures, interactions, flexibility, etc., influence the scaling exponent γ and, hence, the dynamics. Given the successes of the generalized entropy theory in elucidating the influence of molecular details on the universal properties of glass-forming polymers, this theory is extended here to investigate the thermodynamic scaling in polymer melts. The predictions of theory are in accord with the appearance of thermodynamic scaling for pressures not in excess of ~50 MPa. (The failure at higher pressures arises due to inherent limitations of a lattice model.) In line with arguments relating the magnitude of γ to the steepness of the repulsive part of the intermolecular potential, the abrupt, square-well nature of the lattice model interactions lead, as expected, to much larger values of the scaling exponent. Nevertheless, the theory is employed to study how individual molecular parameters affect the scaling exponent in order to extract a molecular understanding of the information content contained in the exponent. The chain
Stability theory and transition prediction applied to a general aviation fuselage
Spall, R. E.; Wie, Y.-S.
1993-01-01
The linear stability of a fully three-dimensional boundary layer formed over a general aviation fuselage was investigated. The location of the onset of transition was estimated using the N-factor method. The results were compared with existing experimental data and indicate N-factors of approximately 8.5 on the side of the fuselage and 3.0 near the top. Considerable crossflow existed along the side of the body, which significantly affected the unstable modes present in the boundary layer. Fair agreement was found between the predicted frequency range of linear instability modes and available experimental data concerning the spectral content of the boundary layer.
DEFF Research Database (Denmark)
Calle-Vallejo, Federico; Martinez, Jose I.; García Lastra, Juan Maria
2014-01-01
of the generalized coordination numbers of the surface sites. This simple and predictive descriptor links the geometric arrangement of a surface to its adsorption properties. It generates linear adsorption-energy trends, captures finite-size effects, and provides more accurate descriptions than d-band centers...... in simple terms, while being able to compare these trends with those of extended surfaces. The trends in the adsorption energies of small oxygen- and hydrogen-containing adsorbates on Pt nanoparticles of various sizes and on extended surfaces were analyzed through DFT calculations by making use...... and usual coordination numbers. Unlike electronic-structure descriptors, which require knowledge of the densities of states, it is calculated manually. Finally, it was shown that an approximate equivalence exists between generalized coordination numbers and d-band centers....
Wendy Johnson
2018-01-01
The debate about the roles of general and specific abilities in predicting important outcomes is a tempest in a ladle because we cannot measure abilities without also measuring skills. Skills always develop through exposure, are specific rather than general, and are executed using different strategies by different people, thus tapping into varied specific abilities. Relative predictive validities of measurement formats depend on the purpose: the more general and long-term the purpose, the bet...
Lin, Yai-Tin; Kalhan, Ashish Chetan; Lin, Yng-Tzer Joseph; Kalhan, Tosha Ashish; Chou, Chein-Chin; Gao, Xiao Li; Hsu, Chin-Ying Stephen
2018-05-08
Oral rehabilitation under general anaesthesia (GA), commonly employed to treat high caries-risk children, has been associated with high economic and individual/family burden, besides high post-GA caries recurrence rates. As there is no caries prediction model available for paediatric GA patients, this study was performed to build caries risk assessment/prediction models using pre-GA data and to explore mid-term prognostic factors for early identification of high-risk children prone to caries relapse post-GA oral rehabilitation. Ninety-two children were identified and recruited with parental consent before oral rehabilitation under GA. Biopsychosocial data collection at baseline and the 6-month follow-up were conducted using questionnaire (Q), microbiological assessment (M) and clinical examination (C). The prediction models constructed using data collected from Q, Q + M and Q + M + C demonstrated an accuracy of 72%, 78% and 82%, respectively. Furthermore, of the 83 (90.2%) patients recalled 6 months after GA intervention, recurrent caries was identified in 54.2%, together with reduced bacterial counts, lower plaque index and increased percentage of children toothbrushing for themselves (all P < 0.05). Additionally, meal-time and toothbrushing duration were shown, through bivariate analyses, to be significant prognostic determinants for caries recurrence (both P < 0.05). Risk assessment/prediction models built using pre-GA data may be promising in identifying high-risk children prone to post-GA caries recurrence, although future internal and external validation of predictive models is warranted. © 2018 FDI World Dental Federation.
Generalized method for calculation and prediction of vapour-liquid equilibria at high pressures
Energy Technology Data Exchange (ETDEWEB)
Drahos, J; Wichterle, I; Hala, E
1978-02-01
Following the approaches of K.C. Chao and J.D. Seader (see Gas Abstr. 18,24 (1962) Jan.) and B.I. Lee, J.H. Erbar, and W.C. Edmister (see Gas Abst. 29, 73-0331), the Czechoslovak Academy of Sciences developed a generalized method for prediction of vapor-liquid equilibria in hydrocarbon mixtures containing some nonhydrocarbon gases at high pressures. The method proposed is based on three equations: (1) a generalized equation of state for vapor-phase calculations; (2) a generalized expression for the pure-liquid fugacity coefficient; and (3) an activity coefficient expression based on a surface modification of the regular solution model. The equations used contain only one partially generalized binary parameter, which was evaluated from experimental K-value data. Researchers tested the proposed method by computing K-values and pressures in binary and multicomponent systems consisting of 13 hydrocarbons and 3 nonhydrocarbon gases. The results show that the method is applicable over a wide range of conditions with a degree of accuracy comparable with that of more complicated methods.
Goudswaard, AN; Stolk, RP; Zuithoff, P; Rutten, GEHM
Many diabetic patients in general practice do not achieve good glycaemic control. The aim of this study was to assess which characteristics of type 2 diabetes patients treated in primary care predict poor glycaemic control (HbA(1c) greater than or equal to7%). Data were collected from the medical
Petersen, Bjørn Molt; Boel, Mikkel; Montag, Markus; Gardner, David K.
2016-01-01
STUDY QUESTION Can a generally applicable morphokinetic algorithm suitable for Day 3 transfers of time-lapse monitored embryos originating from different culture conditions and fertilization methods be developed for the purpose of supporting the embryologist's decision on which embryo to transfer back to the patient in assisted reproduction? SUMMARY ANSWER The algorithm presented here can be used independently of culture conditions and fertilization method and provides predictive power not surpassed by other published algorithms for ranking embryos according to their blastocyst formation potential. WHAT IS KNOWN ALREADY Generally applicable algorithms have so far been developed only for predicting blastocyst formation. A number of clinics have reported validated implantation prediction algorithms, which have been developed based on clinic-specific culture conditions and clinical environment. However, a generally applicable embryo evaluation algorithm based on actual implantation outcome has not yet been reported. STUDY DESIGN, SIZE, DURATION Retrospective evaluation of data extracted from a database of known implantation data (KID) originating from 3275 embryos transferred on Day 3 conducted in 24 clinics between 2009 and 2014. The data represented different culture conditions (reduced and ambient oxygen with various culture medium strategies) and fertilization methods (IVF, ICSI). The capability to predict blastocyst formation was evaluated on an independent set of morphokinetic data from 11 218 embryos which had been cultured to Day 5. PARTICIPANTS/MATERIALS, SETTING, METHODS The algorithm was developed by applying automated recursive partitioning to a large number of annotation types and derived equations, progressing to a five-fold cross-validation test of the complete data set and a validation test of different incubation conditions and fertilization methods. The results were expressed as receiver operating characteristics curves using the area under the
General correlation for prediction of critical heat flux ratio in water cooled channels
Energy Technology Data Exchange (ETDEWEB)
Pernica, R.; Cizek, J.
1995-09-01
The paper present the general empirical Critical Heat Flux Ration (CHFR) correlation which is valid for vertical water upflow through tubes, internally heated concentric annuli and rod bundles geometries with both wide and very tight square and triangular rods lattices. The proposed general PG correlation directly predicts the CHFR, it comprises axial and radial non-uniform heating, and is valid in a wider range of thermal hydraulic conditions than previously published critical heat flux correlations. The PG correlation has been developed using the critical heat flux Czech data bank which includes more than 9500 experimental data on tubes, 7600 data on rod bundles and 713 data on internally heated concentric annuli. Accuracy of the CHFR prediction, statistically assessed by the constant dryout conditions approach, is characterized by the mean value nearing 1.00 and the standard deviation less than 0.06. Moverover, a subchannel form of the PG correlations is statistically verified on Westinghouse and Combustion Engineering rod bundle data bases, i.e. more than 7000 experimental CHF points of Columbia University data bank were used.
A Traffic Prediction Algorithm for Street Lighting Control Efficiency
Directory of Open Access Journals (Sweden)
POPA Valentin
2013-01-01
Full Text Available This paper presents the development of a traffic prediction algorithm that can be integrated in a street lighting monitoring and control system. The prediction algorithm must enable the reduction of energy costs and improve energy efficiency by decreasing the light intensity depending on the traffic level. The algorithm analyses and processes the information received at the command center based on the traffic level at different moments. The data is collected by means of the Doppler vehicle detection sensors integrated within the system. Thus, two methods are used for the implementation of the algorithm: a neural network and a k-NN (k-Nearest Neighbor prediction algorithm. For 500 training cycles, the mean square error of the neural network is 9.766 and for 500.000 training cycles the error amounts to 0.877. In case of the k-NN algorithm the error increases from 8.24 for k=5 to 12.27 for a number of 50 neighbors. In terms of a root means square error parameter, the use of a neural network ensures the highest performance level and can be integrated in a street lighting control system.
Model predictive control of wind energy conversion systems
Yaramasu, Venkata Narasimha R
2017-01-01
The authors provide a comprehensive analysis on the model predictive control of power converters employed in a wide variety of variable-speed wind energy conversion systems (WECS). The contents of this book includes an overview of wind energy system configurations, power converters for variable-speed WECS, digital control techniques, MPC, modeling of power converters and wind generators for MPC design. Other topics include the mapping of continuous-time models to discrete-time models by various exact, approximate, and quasi-exact discretization methods, modeling and control of wind turbine grid-side two-level and multilevel voltage source converters. The authors also focus on the MPC of several power converter configurations for full variable-speed permanent magnet synchronous generator based WECS, squirrel-cage induction generator based WECS, and semi-variable-speed doubly fed induction generator based WECS.
Modeling Smart Energy Systems for Model Predictive Control
DEFF Research Database (Denmark)
Halvgaard, Rasmus; Poulsen, Niels Kjølstad; Madsen, Henrik
2012-01-01
as it is produced requires a very exible and controllable power consumption. Examples of controllable electric loads are heat pumps in buildings and Electric Vehicles (EVs) that are expected to play a large role in the future danish energy system. These units in a smart energy system can potentially oer exibility...... on a time scale ranging from seconds to several days by moving power consumption, exploiting thermal inertia or battery storage capacity, respectively. Using advanced control algorithms these systems are able to reduce their own electricity costs by planning ahead and moving consumption to periods...... future price should also be available in order for the individual units to plan ahead in the most feasible way. This is necessary since Economic MPCs do not respond to the absolute cost of electricity, but to variations of the price over the prediction horizon. Economic MPC is ideal for price responsive...
Structural Acoustic Prediction and Interior Noise Control Technology
Mathur, G. P.; Chin, C. L.; Simpson, M. A.; Lee, J. T.; Palumbo, Daniel L. (Technical Monitor)
2001-01-01
This report documents the results of Task 14, "Structural Acoustic Prediction and Interior Noise Control Technology". The task was to evaluate the performance of tuned foam elements (termed Smart Foam) both analytically and experimentally. Results taken from a three-dimensional finite element model of an active, tuned foam element are presented. Measurements of sound absorption and sound transmission loss were taken using the model. These results agree well with published data. Experimental performance data were taken in Boeing's Interior Noise Test Facility where 12 smart foam elements were applied to a 757 sidewall. Several configurations were tested. Noise reductions of 5-10 dB were achieved over the 200-800 Hz bandwidth of the controller. Accelerometers mounted on the panel provided a good reference for the controller. Configurations with far-field error microphones outperformed near-field cases.
System Identification, Prediction, Simulation and Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1997-01-01
a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...
Model Predictive Voltage Control of Wind Power Plants
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei
2018-01-01
the efficacy of the proposed WFVC, two case scenarios were designed: the wind farm is under normal operating conditions and the internal wind power fluctuation is considered; and besides internal power fluctuation, the impact of the external grid on the wind farm is considered.......This chapter proposes an autonomous wind farm voltage controller (WFVC) based on model predictive control (MPC). It also introduces the analytical expressions for the voltage sensitivity to tap positions of a transformer. The chapter then describes the discrete models for the wind turbine...... generators (WTGs) and static var compensators (SVCs)/static var generators (SVGs). Next, it describes the implementation of the on‐load tap changing (OLTC) in the MPC. Furthermore, the chapter examines the cost function as well as the constraints of the MPC‐based WFVC for both control modes. In order to test...
Explicit Nonlinear Model Predictive Control Theory and Applications
Grancharova, Alexandra
2012-01-01
Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behind explicit NMPC is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real time. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation and the possibility to design embedded control systems with low software and hardware complexity. This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations: Ø Nonlinear systems described by first-principles models and nonlinear systems described by black-box models; �...
General model and control of an n rotor helicopter
International Nuclear Information System (INIS)
Sidea, A G; Brogaard, R Yding; Andersen, N A; Ravn, O
2014-01-01
The purpose of this study was to create a dynamic, nonlinear mathematical model of a multirotor that would be valid for different numbers of rotors. Furthermore, a set of Single Input Single Output (SISO) controllers were implemented for attitude control. Both model and controllers were tested experimentally on a quadcopter. Using the combined model and controllers, simple system simulation and control is possible, by replacing the physical values for the individual systems
General model and control of an n rotor helicopter
Sidea, A. G.; Yding Brogaard, R.; Andersen, N. A.; Ravn, O.
2014-12-01
The purpose of this study was to create a dynamic, nonlinear mathematical model of a multirotor that would be valid for different numbers of rotors. Furthermore, a set of Single Input Single Output (SISO) controllers were implemented for attitude control. Both model and controllers were tested experimentally on a quadcopter. Using the combined model and controllers, simple system simulation and control is possible, by replacing the physical values for the individual systems.
Directory of Open Access Journals (Sweden)
Fitri Yakub
2016-01-01
Full Text Available We present a comparative study of model predictive control approaches of two-wheel steering, four-wheel steering, and a combination of two-wheel steering with direct yaw moment control manoeuvres for path-following control in autonomous car vehicle dynamics systems. Single-track mode, based on a linearized vehicle and tire model, is used. Based on a given trajectory, we drove the vehicle at low and high forward speeds and on low and high road friction surfaces for a double-lane change scenario in order to follow the desired trajectory as close as possible while rejecting the effects of wind gusts. We compared the controller based on both simple and complex bicycle models without and with the roll vehicle dynamics for different types of model predictive control manoeuvres. The simulation result showed that the model predictive control gave a better performance in terms of robustness for both forward speeds and road surface variation in autonomous path-following control. It also demonstrated that model predictive control is useful to maintain vehicle stability along the desired path and has an ability to eliminate the crosswind effect.
A general framework for predicting delayed responses of ecological communities to habitat loss.
Chen, Youhua; Shen, Tsung-Jen
2017-04-20
Although biodiversity crisis at different spatial scales has been well recognised, the phenomena of extinction debt and immigration credit at a crossing-scale context are, at best, unclear. Based on two community patterns, regional species abundance distribution (SAD) and spatial abundance distribution (SAAD), Kitzes and Harte (2015) presented a macroecological framework for predicting post-disturbance delayed extinction patterns in the entire ecological community. In this study, we further expand this basic framework to predict diverse time-lagged effects of habitat destruction on local communities. Specifically, our generalisation of KH's model could address the questions that could not be answered previously: (1) How many species are subjected to delayed extinction in a local community when habitat is destructed in other areas? (2) How do rare or endemic species contribute to extinction debt or immigration credit of the local community? (3) How will species differ between two local areas? From the demonstrations using two SAD models (single-parameter lognormal and logseries), the predicted patterns of the debt, credit, and change in the fraction of unique species can vary, but with consistencies and depending on several factors. The general framework deepens the understanding of the theoretical effects of habitat loss on community dynamic patterns in local samples.
International Nuclear Information System (INIS)
Grotch, S.L.
1991-01-01
This study is a detailed intercomparison of the results produced by four general circulation models (GCMs) that have been used to estimate the climatic consequences of a doubling of the CO 2 concentration. Two variables, surface air temperature and precipitation, annually and seasonally averaged, are compared for both the current climate and for the predicted equilibrium changes after a doubling of the atmospheric CO 2 concentration. The major question considered here is: how well do the predictions from different GCMs agree with each other and with historical climatology over different areal extents, from the global scale down to the range of only several gridpoints? Although the models often agree well when estimating averages over large areas, substantial disagreements become apparent as the spatial scale is reduced. At scales below continental, the correlations observed between different model predictions are often very poor. The implications of this work for investigation of climatic impacts on a regional scale are profound. For these two important variables, at least, the poor agreement between model simulations of the current climate on the regional scale calls into question the ability of these models to quantitatively estimate future climatic change on anything approaching the scale of a few (< 10) gridpoints, which is essential if these results are to be used in meaningful resource-assessment studies. A stronger cooperative effort among the different modeling groups will be necessary to assure that we are getting better agreement for the right reasons, a prerequisite for improving confidence in model projections. 11 refs.; 10 figs
International Nuclear Information System (INIS)
Grotch, S.L.
1990-01-01
This study is a detailed intercomparison of the results produced by four general circulation models (GCMs) that have been used to estimate the climatic consequences of a doubling of the CO 2 concentration. Two variables, surface air temperature and precipitation, annually and seasonally averaged, are compared for both the current climate and for the predicted equilibrium changes after a doubling of the atmospheric CO 2 concentration. The major question considered here is: how well do the predictions from different GCMs agree with each other and with historical climatology over different areal extents, from the global scale down to the range of only several gridpoints? Although the models often agree well when estimating averages over large areas, substantial disagreements become apparent as the spatial scale is reduced. At scales below continental, the correlations observed between different model predictions are often very poor. The implications of this work for investigation of climatic impacts on a regional scale are profound. For these two important variables, at least, the poor agreement between model simulations of the current climate on the regional scale calls into question the ability of these models to quantitatively estimate future climatic change on anything approaching the scale of a few (< 10) gridpoints, which is essential if these results are to be used in meaningful resource-assessment studies. A stronger cooperative effort among the different modeling groups will be necessary to assure that we are getting better agreement for the right reasons, a prerequisite for improving confidence in model projections
Enhanced Voltage Control of VSC-HVDC Connected Offshore Wind Farms Based on Model Predictive Control
DEFF Research Database (Denmark)
Guo, Yifei; Gao, Houlei; Wu, Qiuwei
2018-01-01
This paper proposes an enhanced voltage control strategy (EVCS) based on model predictive control (MPC) for voltage source converter based high voltage direct current (VSCHVDC) connected offshore wind farms (OWFs). In the proposed MPC based EVCS, all wind turbine generators (WTGs) as well...... as the wind farm side VSC are optimally coordinated to keep voltages within the feasible range and reduce system power losses. Considering the high ratio of the OWF collector system, the effects of active power outputs of WTGs on voltage control are also taken into consideration. The predictive model of VSC...
DEFF Research Database (Denmark)
Jensen, Christian Gaden; Niclasen, Janni; Vangkilde, Signe
2016-01-01
The Mindful Attention Awareness Scale (MAAS) measures perceived degree of inattentiveness in different contexts and is often used as a reversed indicator of mindfulness. MAAS is hypothesized to reflect a psychological trait or disposition when used outside attentional training contexts, but the l......The Mindful Attention Awareness Scale (MAAS) measures perceived degree of inattentiveness in different contexts and is often used as a reversed indicator of mindfulness. MAAS is hypothesized to reflect a psychological trait or disposition when used outside attentional training contexts......, but the long-term test-retest reliability of MAAS scores is virtually untested. It is unknown whether MAAS predicts psychological health after controlling for standardized socioeconomic status classifications. First, MAAS translated to Danish was validated psychometrically within a randomly invited healthy...... adult community sample (N = 490). Factor analysis confirmed that MAAS scores quantified a unifactorial construct of excellent composite reliability and consistent convergent validity. Structural equation modeling revealed that MAAS scores contributed independently to predicting psychological distress...
Online prediction and control in nonlinear stochastic systems
DEFF Research Database (Denmark)
Nielsen, Torben Skov
2002-01-01
speed and the relationship between (primarily) wind speed and wind power (the power curve). In paper G the model parameters are estimated using a RLS algorithm and any systematic time-variation of the model parameters is disregarded. Two di erent parameterizations of the power curve is considered...... are estimated using the algorithm proposed in paper C. The power curve and the diurnal variation of wind speed is estimated separately using the local polynomial regression procedure described in paper A . In paper J the parameters of the prediction model is assumed to be smooth functions of wind direction (and......The present thesis consists of a summary report and ten research papers. The subject of the thesis is on-line prediction and control of non-linear and non-stationary systems based on stochastic modelling. The thesis consists of three parts where the rst part deals with on-line estimation in linear...
Maljaars, E.; Felici, F.; Blanken, T.C.; Galperti, C.; Sauter, O.; de Baar, M.R.; Carpanese, F.; Goodman, T.P.; Kim, D.; Kim, S.H.; Kong, M.G.; Mavkov, B.; Merle, A.; Moret, J.M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A.A.; Vu, N.M.T.
2017-01-01
The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety
Maljaars, B.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J.; Nouailletas, R.; Scheffer, M.; Teplukhina, A.; Vu, T.
2017-01-01
The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety
Dinucleotide controlled null models for comparative RNA gene prediction
Directory of Open Access Journals (Sweden)
Gesell Tanja
2008-05-01
Full Text Available Abstract Background Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. Results We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. Conclusion SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require
Dinucleotide controlled null models for comparative RNA gene prediction.
Gesell, Tanja; Washietl, Stefan
2008-05-27
Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. SISSIz
Edelman, N L; Cassell, J A; Mercer, C H; Bremner, S A; Jones, C I; Gersten, A; deVisser, R O
2018-07-01
Some women attending General Practices (GPs) are at higher risk of unintended pregnancy (RUIP) and sexually transmitted infections (STI) than others. A clinical prediction rule (CPR) may help target resources using psychosocial questions as an acceptable, effective means of assessment. The aim was to derive a CPR that discriminates women who would benefit from sexual health discussion and intervention. Participants were recruited to a cross-sectional survey from six GPs in a city in South-East England in 2016. On arrival, female patients aged 16-44 years were invited to complete a questionnaire that addressed psychosocial factors, and the following self-reported outcomes: 2+ sexual partners in the last year (2PP) and RUIP. For each sexual risk, psychosocial questions were retained from logistic regression modelling which best discriminated women at risk using the C-statistic. Sensitivity and specificity were established in consultation with GP staff. The final sample comprised N = 1238 women. 2PP was predicted by 11 questions including age, binge-drinking weekly, ever having a partner who insulted you often, current smoking, and not cohabiting (C-statistic = 0.83, sensitivity = 73% and specificity = 77%). RUIP was predicted by 5 questions including sexual debut years, and emergency contraception use in the last 6 months (C-statistic = 0.70, sensitivity = 69% and specificity = 57%). 2PP was better discriminated than RUIP but neither to a clinically-useful degree. The finding that different psychosocial factors predicted each outcome has implications for prevention strategies. Further research should investigate causal links between psychosocial factors and sexual risk. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.
Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun
2017-01-01
Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data. The proposed model employs a spike-and-slab mixture double-exponential prior for coefficients that can induce weak shrinkage on large coefficients, and strong shrinkage on irrelevant coefficients. We have developed a fast and stable algorithm to fit large-scale hierarchal GLMs by incorporating expectation-maximization (EM) steps into the fast cyclic coordinate descent algorithm. The proposed approach integrates nice features of two popular methods, i.e., penalized lasso and Bayesian spike-and-slab variable selection. The performance of the proposed method is assessed via extensive simulation studies. The results show that the proposed approach can provide not only more accurate estimates of the parameters, but also better prediction. We demonstrate the proposed procedure on two cancer data sets: a well-known breast cancer data set consisting of 295 tumors, and expression data of 4919 genes; and the ovarian cancer data set from TCGA with 362 tumors, and expression data of 5336 genes. Our analyses show that the proposed procedure can generate powerful models for predicting outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Copyright © 2017 by the Genetics Society of America.
Prediction of Monte Carlo errors by a theory generalized to treat track-length estimators
International Nuclear Information System (INIS)
Booth, T.E.; Amster, H.J.
1978-01-01
Present theories for predicting expected Monte Carlo errors in neutron transport calculations apply to estimates of flux-weighted integrals sampled directly by scoring individual collisions. To treat track-length estimators, the recent theory of Amster and Djomehri is generalized to allow the score distribution functions to depend on the coordinates of two successive collisions. It has long been known that the expected track length in a region of phase space equals the expected flux integrated over that region, but that the expected statistical error of the Monte Carlo estimate of the track length is different from that of the flux integral obtained by sampling the sum of the reciprocals of the cross sections for all collisions in the region. These conclusions are shown to be implied by the generalized theory, which provides explicit equations for the expected values and errors of both types of estimators. Sampling expected contributions to the track-length estimator is also treated. Other general properties of the errors for both estimators are derived from the equations and physically interpreted. The actual values of these errors are then obtained and interpreted for a simple specific example
Czech Academy of Sciences Publication Activity Database
Pčolka, M.; Žáčeková, E.; Robinett, R.; Čelikovský, Sergej; Šebek, M.
2016-01-01
Roč. 53, č. 1 (2016), s. 124-138 ISSN 0967-0661 R&D Projects: GA ČR GA13-20433S Institutional support: RVO:67985556 Keywords : Model predictive control * Identification for control * Building climatecontrol Subject RIV: BC - Control Systems Theory Impact factor: 2.602, year: 2016 http://library.utia.cas.cz/separaty/2016/TR/celikovsky-0460306.pdf
Local Model Predictive Control for T-S Fuzzy Systems.
Lee, Donghwan; Hu, Jianghai
2017-09-01
In this paper, a new linear matrix inequality-based model predictive control (MPC) problem is studied for discrete-time nonlinear systems described as Takagi-Sugeno fuzzy systems. A recent local stability approach is applied to improve the performance of the proposed MPC scheme. At each time k , an optimal state-feedback gain that minimizes an objective function is obtained by solving a semidefinite programming problem. The local stability analysis, the estimation of the domain of attraction, and feasibility of the proposed MPC are proved. Examples are given to demonstrate the advantages of the suggested MPC over existing approaches.
Predictive control of thermal state of blast furnace
Barbasova, T. A.; Filimonova, A. A.
2018-05-01
The work describes the structure of the model for predictive control of the thermal state of a blast furnace. The proposed model contains the following input parameters: coke rate; theoretical combustion temperature, comprising: natural gas consumption, blasting temperature, humidity, oxygen, blast furnace cooling water; blast furnace gas utilization rate. The output parameter is the cast iron temperature. The results for determining the cast iron temperature were obtained following the identification using the Hammerstein-Wiener model. The result of solving the cast iron temperature stabilization problem was provided for the calculated values of process parameters of the target area of the respective blast furnace operation mode.
Offset-Free Direct Power Control of DFIG Under Continuous-Time Model Predictive Control
DEFF Research Database (Denmark)
Errouissi, Rachid; Al-Durra, Ahmed; Muyeen, S.M.
2017-01-01
This paper presents a robust continuous-time model predictive direct power control for doubly fed induction generator (DFIG). The proposed approach uses Taylor series expansion to predict the stator current in the synchronous reference frame over a finite time horizon. The predicted stator current...... is directly used to compute the required rotor voltage in order to minimize the difference between the actual stator currents and their references over the predictive time. However, as the proposed strategy is sensitive to parameter variations and external disturbances, a disturbance observer is embedded...... into the control loop to remove the steady-state error of the stator current. It turns out that the steady-state and the transient performances can be identified by simple design parameters. In this paper, the reference of the stator current is directly calculated from the desired stator active and reactive powers...
Generalized Anxiety Disorder (GAD): When Worry Gets Out of Control
WHAT IS GAD? Occasional anxiety is a normal part of life. You might worry about things like health, money, or family problems. But people with generalized anxiety disorder (GAD) feel extremely worried or feel nervous ...
Stauch, V. J.; Gwerder, M.; Gyalistras, D.; Oldewurtel, F.; Schubiger, F.; Steiner, P.
2010-09-01
other hand, buildings are affected by particularly local weather conditions at the building site. To overcome this discrepancy, we make use of local measurements to statistically adapt the COSMO-7 model output to the meteorological conditions at the building. For this, we have developed a general correction algorithm that exploits systematic properties of the COSMO-7 prediction error and explicitly estimates the degree of temporal autocorrelation using online recursive estimation. The resulting corrected predictions are improved especially for the first few hours being the most crucial for the predictive controller and, ultimately for the reduction of primary energy consumption using predictive control. The use of numerical weather forecasts in predictive building automation is one example in a wide field of weather dependent advanced energy saving technologies. Our work particularly highlights the need for the development of specifically tailored weather forecast products by (statistical) postprocessing in order to meet the requirements of specific applications.
Predictive Function Control for Communication-Based Train Control (CBTC Systems
Directory of Open Access Journals (Sweden)
Bing Bu
2013-01-01
Full Text Available In Communication-Based Train Control (CBTC systems, random transmission delays and packet drops are inevitable in the wireless networks, which could result in unnecessary traction, brakes or even emergency brakes of trains, losses of line capacity and passenger dissatisfaction. This paper applies predictive function control technology with a mixed H2/∞ control approach to improve the control performances. The controller is in the state feedback form and satisfies the requirement of quadratic input and state constraints. A linear matrix inequality (LMI approach is developed to solve the control problem. The proposed method attenuates disturbances by incorporating H2/∞ into the control scheme. The control command from the automatic train operation (ATO is included in the reward function to optimize the train's running profile. The influence of transmission delays and packet drops is alleviated through improving the performances of the controller. Simulation results show that the method is effective to improve the performances and robustness of CBTC systems.
Are the general equations to predict BMR applicable to patients with anorexia nervosa?
Marra, M; Polito, A; De Filippo, E; Cuzzolaro, M; Ciarapica, D; Contaldo, F; Scalfi, L
2002-03-01
To determine whether the general equations to predict basal metabolic rate (BMR) can be reliably applied to female anorectics. Two hundred and thirty-seven female patients with anorexia nervosa (AN) were divided into an adolescent group [n=43, 13-17 yrs, 39.3+/-5.0 kg, body mass index (BMI) (weight/height) 15.5+/-1.8 kg/m2] and a young-adult group (n=194, 18-40 yrs, 40.5+/-6.1 kg, BMI 15.6+/-1.9 kg/m2). BMR values determined by indirect calorimetry were compared with those predicted according to either the WHO/FAO/UNU or the Harris-Benedict general equations, or using the Schebendach correction formula (proposed for adjusting the Harris-Benedict estimates in anorectics). Measured BMR was 3,658+/-665 kJ/day in the adolescent and 3,907+/-760 kJ/day in the young-adult patients. In the adolescent group, the differences between predicted and measured values were (mean+/-SD) 1,466 529 kJ/day (+44+/-21%) for WHO/FAO/UNU, 1,587+/-552 kJ/day (+47+/-23%) for the Harris-Benedict and -20+/-510 kJ/day for the Schebendach (+1+/-13%), while in the young-adult group the corresponding values were 696+/-570 kJ/day (+24+/-24%), 1,252+/-644 kJ/day (+37+/-27%) and -430+/-640 kJ/day (-9+/-16%). The bias was negatively associated with weight and BMI in both groups when using the WHO/FAO/UNU and Harris-Benedict equations, and with age in the young-adult group for the Harris-Benedict and Schebendach equations. The WHO/FAO/UNU and Harris-Benedict equations greatly overestimate BMR in AN. Accurate estimation is to some extent dependent on individual characteristics such as age, weight or BMI. The Schebendach correction formula accurately predicts BMR in female adolescents, but not in young adult women with AN.
Predicting, monitoring and controlling geomechanical effects of CO2 injection
International Nuclear Information System (INIS)
Streit, J.E.; Siggins, A.F.
2005-01-01
A key objective of geological carbon dioxide (CO 2 ) storage in porous rock is long-term subsurface containment of CO 2 . Fault stability and maximum sustainable pore-fluid pressures should be estimated in geomechanical studies in order to avoid damage to reservoir seals and fault seals of storage sites during CO 2 injection. Such analyses rely on predicting the evolution of effective stresses in rocks and faults during CO 2 injection. However, geomechanical analyses frequently do not incorporate poroelastic behaviour of reservoir rock, as relevant poroelastic properties are rarely known. The knowledge of rock poroelastic properties would allow the use of seismic methods for the accurate measurement of the effective stress evolution during CO 2 injection. This paper discussed key geomechanical effects of CO 2 injection into porous rock, and in particular, focused on the effects that the poroelasticity of reservoir rocks and pore pressure/stress coupling have on effective stresses. Relevant geophysical monitoring techniques were also suggested. The paper also outlined how these techniques could be applied to measure stress changes related to poroelastic rock behaviour during CO 2 injection and to test the predictions of sustainable changes in effective stress in CO 2 storage sites. It was concluded that a combination of predictive geomechanical techniques and application of geophysical monitoring techniques is a valid new concept for controlling and monitoring the geomechanical effects of CO 2 storage. 36 refs., 5 figs
Demand Management Based on Model Predictive Control Techniques
Directory of Open Access Journals (Sweden)
Yasser A. Davizón
2014-01-01
Full Text Available Demand management (DM is the process that helps companies to sell the right product to the right customer, at the right time, and for the right price. Therefore the challenge for any company is to determine how much to sell, at what price, and to which market segment while maximizing its profits. DM also helps managers efficiently allocate undifferentiated units of capacity to the available demand with the goal of maximizing revenue. This paper introduces control system approach to demand management with dynamic pricing (DP using the model predictive control (MPC technique. In addition, we present a proper dynamical system analogy based on active suspension and a stability analysis is provided via the Lyapunov direct method.
Predictive Solar-Integrated Commercial Building Load Control
Energy Technology Data Exchange (ETDEWEB)
Glasgow, Nathan [EdgePower Inc., Aspen, CO (United States)
2017-01-31
This report is the final technical report for the Department of Energy SunShot award number EE0007180 to EdgePower Inc., for the project entitled “Predictive Solar-Integrated Commercial Building Load Control.” The goal of this project was to successfully prove that the integration of solar forecasting and building load control can reduce demand charge costs for commercial building owners with solar PV. This proof of concept Tier 0 project demonstrated its value through a pilot project at a commercial building. This final report contains a summary of the work completed through he duration of the project. Clean Power Research was a sub-recipient on the award.
Fast core prediction simulator for load follow control
International Nuclear Information System (INIS)
Yim, Man Sung; Lee, Sang Hoon; Lee, Un Chul
1990-01-01
An operator-assisting system for the reactor core control under power changing operating condition was developed. The system is consisted of core simulator routine and Xenon and Iodine initial condition generation routine. The initial condition generation routine, without exactly knowing the core status, is capable of providing accurate number densities and axial offset conditions of Xenon and Iodine after several hours of predictor- corrector calculations using the plant instrumentation signals of power level and power axial offset. The core simulator routine, even with the two node core model, gives equivalently accurate results as the one-dimensional model for the core behaviour simulation under power changing condition and can provide proper control strategies for load follow operation. The core simulator can also be used by the operator to develop remedial actions to restore the distorted power distribution by using its prediction capability
Prediction of chronic critical illness in a general intensive care unit
Directory of Open Access Journals (Sweden)
Sérgio H. Loss
2013-06-01
Full Text Available OBJECTIVE: To assess the incidence, costs, and mortality associated with chronic critical illness (CCI, and to identify clinical predictors of CCI in a general intensive care unit. METHODS: This was a prospective observational cohort study. All patients receiving supportive treatment for over 20 days were considered chronically critically ill and eligible for the study. After applying the exclusion criteria, 453 patients were analyzed. RESULTS: There was an 11% incidence of CCI. Total length of hospital stay, costs, and mortality were significantly higher among patients with CCI. Mechanical ventilation, sepsis, Glasgow score < 15, inadequate calorie intake, and higher body mass index were independent predictors for cci in the multivariate logistic regression model. CONCLUSIONS: CCI affects a distinctive population in intensive care units with higher mortality, costs, and prolonged hospitalization. Factors identifiable at the time of admission or during the first week in the intensive care unit can be used to predict CCI.
Predicting mastitis in dairy cows using neural networks and generalized additive models
DEFF Research Database (Denmark)
Anantharama Ankinakatte, Smitha; Norberg, Elise; Løvendahl, Peter
2013-01-01
The aim of this paper is to develop and compare methods for early detection of oncoming mastitis with automated recorded data. The data were collected at the Danish Cattle Research Center (Tjele, Denmark). As indicators of mastitis, electrical conductivity (EC), somatic cell scores (SCS), lactate...... that combines residual components into a score to improve the model. To develop and verify the model, the data are randomly divided into training and validation data sets. To predict the occurrence of mastitis, neural network models (NNs) and generalized additive models (GAMs) are developed using the training...... classification with all indicators, using individual residuals rather than factor scores. When SCS is excluded, GAMs shows better classification result when milk yield is also excluded. In conclusion, the study shows that NNs and GAMs are similar in their ability to detect mastitis, a sensitivity of almost 75...
Self-reported musculoskeletal pain predicts long-term increase in general health care use
DEFF Research Database (Denmark)
Hartvigsen, Jan; Davidsen, Michael; Søgaard, Karen
2014-01-01
reported during the past two weeks from the Danish National Cohort Study were merged with data from the Danish National Health Insurance Registry and the National Patient Registry containing information on consultations in the Danish primary and secondary care sector. Absolute and relative rates for all......Aims: Musculoskeletal pain and disability is a modern epidemic and a major reason for seeking health care. The aim of this study is to determine absolute and relative rates of care seeking over 20 years for adults reporting musculoskeletal complaints. Methods: Interview data on musculoskeletal pain...... to any of the outcomes. CONCLUSIONS SELF-REPORT OF MUSCULOSKELETAL PAIN REPORTED WITHIN THE PAST TWO WEEKS PREDICTS A STATISTICALLY SIGNIFICANT LONG-TERM INCREASE IN GENERAL USE OF HEALTH CARE SERVICES IN BOTH THE PRIMARY AND THE SECONDARY HEALTH CARE SECTOR:...
A residual life prediction model based on the generalized σ -N curved surface
Directory of Open Access Journals (Sweden)
Zongwen AN
2016-06-01
Full Text Available In order to investigate change rule of the residual life of structure under random repeated load, firstly, starting from the statistic meaning of random repeated load, the joint probability density function of maximum stress and minimum stress is derived based on the characteristics of order statistic (maximum order statistic and minimum order statistic; then, based on the equation of generalized σ -N curved surface, considering the influence of load cycles number on fatigue life, a relationship among minimum stress, maximum stress and residual life, that is the σmin(n- σmax(n-Nr(n curved surface model, is established; finally, the validity of the proposed model is demonstrated by a practical case. The result shows that the proposed model can reflect the influence of maximum stress and minimum stress on residual life of structure under random repeated load, which can provide a theoretical basis for life prediction and reliability assessment of structure.
General Quality Control (QC) Guidelines for SAM Methods
Learn more about quality control guidelines and recommendations for the analysis of samples using the methods listed in EPA's Selected Analytical Methods for Environmental Remediation and Recovery (SAM).
International Nuclear Information System (INIS)
Li, Liang; Zhang, Yuanbo; Yang, Chao; Yan, Bingjie; Marina Martinez, C.
2016-01-01
Highlights: • A 7-degree-of-freedom model of hybrid electric vehicle with regenerative braking system is built. • A modified nonlinear model predictive control strategy is developed. • The particle swarm optimization algorithm is employed to solve the optimization problem. • The proposed control strategy is verified by simulation and hardware-in-loop tests. • Test results verify the effectiveness of the proposed control strategy. - Abstract: As one of the main working modes, the energy recovered with regenerative braking system provides an effective approach so as to greatly improve fuel economy of hybrid electric bus. However, it is still a challenging issue to ensure braking stability while maximizing braking energy recovery. To solve this problem, an efficient energy recovery control strategy is proposed based on the modified nonlinear model predictive control method. Firstly, combined with the characteristics of the compound braking process of single-shaft parallel hybrid electric bus, a 7 degrees of freedom model of the vehicle longitudinal dynamics is built. Secondly, considering nonlinear characteristic of the vehicle model and the efficiency of regenerative braking system, the particle swarm optimization algorithm within the modified nonlinear model predictive control is adopted to optimize the torque distribution between regenerative braking system and pneumatic braking system at the wheels. So as to reduce the computational time of modified nonlinear model predictive control, a nearest point method is employed during the braking process. Finally, the simulation and hardware-in-loop test are carried out on road conditions with different tire–road adhesion coefficients, and the proposed control strategy is verified by comparing it with the conventional control method employed in the baseline vehicle controller. The simulation and hardware-in-loop test results show that the proposed strategy can ensure vehicle safety during emergency braking
Predictive Control, Competitive Model Business Planning, and Innovation ERP
DEFF Research Database (Denmark)
Nourani, Cyrus F.; Lauth, Codrina
2015-01-01
is not viewed as the sum of its component elements, but the product of their interactions. The paper starts with introducing a systems approach to business modeling. A competitive business modeling technique, based on the author's planning techniques is applied. Systemic decisions are based on common......New optimality principles are put forth based on competitive model business planning. A Generalized MinMax local optimum dynamic programming algorithm is presented and applied to business model computing where predictive techniques can determine local optima. Based on a systems model an enterprise...... organizational goals, and as such business planning and resource assignments should strive to satisfy higher organizational goals. It is critical to understand how different decisions affect and influence one another. Here, a business planning example is presented where systems thinking technique, using Causal...
Predictability, Force and (Anti-)Resonance in Complex Object Control.
Maurice, Pauline; Hogan, Neville; Sternad, Dagmar
2018-04-18
Manipulation of complex objects as in tool use is ubiquitous and has given humans an evolutionary advantage. This study examined the strategies humans choose when manipulating an object with underactuated internal dynamics, such as a cup of coffee. The object's dynamics renders the temporal evolution complex, possibly even chaotic, and difficult to predict. A cart-and-pendulum model, loosely mimicking coffee sloshing in a cup, was implemented in a virtual environment with a haptic interface. Participants rhythmically manipulated the virtual cup containing a rolling ball; they could choose the oscillation frequency, while the amplitude was prescribed. Three hypotheses were tested: 1) humans decrease interaction forces between hand and object; 2) humans increase the predictability of the object dynamics; 3) humans exploit the resonances of the coupled object-hand system. Analysis revealed that humans chose either a high-frequency strategy with anti-phase cup-and-ball movements or a low-frequency strategy with in-phase cup-and-ball movements. Counter Hypothesis 1, they did not decrease interaction force; instead, they increased the predictability of the interaction dynamics, quantified by mutual information, supporting Hypothesis 2. To address Hypothesis 3, frequency analysis of the coupled hand-object system revealed two resonance frequencies separated by an anti-resonance frequency. The low-frequency strategy exploited one resonance, while the high-frequency strategy afforded more choice, consistent with the frequency response of the coupled system; both strategies avoided the anti-resonance. Hence, humans did not prioritize interaction force, but rather strategies that rendered interactions predictable. These findings highlight that physical interactions with complex objects pose control challenges not present in unconstrained movements.
DEFF Research Database (Denmark)
Ørsted, David Dynnes; Nordestgaard, Børge G; Jensen, Gorm B
2012-01-01
It is largely unknown whether prostate-specific antigen (PSA) level at first date of testing predicts long-term risk of prostate cancer (PCa) incidence and mortality in the general population.......It is largely unknown whether prostate-specific antigen (PSA) level at first date of testing predicts long-term risk of prostate cancer (PCa) incidence and mortality in the general population....
Directory of Open Access Journals (Sweden)
Wu Chi-Yeh
2010-01-01
Full Text Available Abstract Background MicroRNAs (miRNAs are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs over the years. Recently, ab initio approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM are extensively adopted in these ab initio approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention. Results This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G2DE based classifier. The G2DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set. Conclusion Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G2DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G
General model and control of an n rotor helicopter
DEFF Research Database (Denmark)
Sidea, Adriana-Gabriela; Brogaard, Rune Yding; Andersen, Nils Axel
2015-01-01
The purpose of this study was to create a dynamic, nonlinear mathematical model ofa multirotor that would be valid for different numbers of rotors. Furthermore, a set of SingleInput Single Output (SISO) controllers were implemented for attitude control. Both model andcontrollers were tested exper...
Exact Boundary Controllability of Electromagnetic Fields in a General Region
International Nuclear Information System (INIS)
Eller, M. M.; Masters, J. E.
2002-01-01
We prove exact controllability for Maxwell's system with variable coefficients in a bounded domain by a current flux in the boundary. The proof relies on a duality argument which reduces the proof of exact controllability to the proof of continuous observability for the homogeneous adjoint system. There is no geometric restriction imposed on the domain
RoboCon: A general purpose telerobotic control center
International Nuclear Information System (INIS)
Draper, J.V.; Noakes, M.W.; Blair, L.M.
1997-01-01
This report describes human factors issues involved in the design of RoboCon, a multi-purpose control center for use in US Department of Energy remote handling applications. RoboCon is intended to be a flexible, modular control center capable of supporting a wide variety of robotic devices
RoboCon: A general purpose telerobotic control center
Energy Technology Data Exchange (ETDEWEB)
Draper, J.V.; Noakes, M.W. [Oak Ridge National Lab., TN (United States). Robotics and Process Systems Div.; Schempf, H. [Carnegie Mellon Univ., Pittsburgh, PA (United States); Blair, L.M. [Human Machine Interfaces, Inc., Knoxville, TN (United States)
1997-02-01
This report describes human factors issues involved in the design of RoboCon, a multi-purpose control center for use in US Department of Energy remote handling applications. RoboCon is intended to be a flexible, modular control center capable of supporting a wide variety of robotic devices.
Connectivity patterns in cognitive control networks predict naturalistic multitasking ability.
Wen, Tanya; Liu, De-Cyuan; Hsieh, Shulan
2018-06-01
Multitasking is a fundamental aspect of everyday life activities. To achieve a complex, multi-component goal, the tasks must be subdivided into sub-tasks and component steps, a critical function of prefrontal networks. The prefrontal cortex is considered to be organized in a cascade of executive processes from the sensorimotor to anterior prefrontal cortex, which includes execution of specific goal-directed action, to encoding and maintaining task rules, and finally monitoring distal goals. In the current study, we used a virtual multitasking paradigm to tap into real-world performance and relate it to each individual's resting-state functional connectivity in fMRI. While did not find any correlation between global connectivity of any of the major networks with multitasking ability, global connectivity of the lateral prefrontal cortex (LPFC) was predictive of multitasking ability. Further analysis showed that multivariate connectivity patterns within the sensorimotor network (SMN), and between-network connectivity of the frontoparietal network (FPN) and dorsal attention network (DAN), predicted individual multitasking ability and could be generalized to novel individuals. Together, these results support previous research that prefrontal networks underlie multitasking abilities and show that connectivity patterns in the cascade of prefrontal networks may explain individual differences in performance. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.
Constrained Fuzzy Predictive Control Using Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Oussama Ait Sahed
2015-01-01
Full Text Available A fuzzy predictive controller using particle swarm optimization (PSO approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.
Study on Noise Prediction Model and Control Schemes for Substation
Gao, Yang; Liu, Songtao
2014-01-01
With the government's emphasis on environmental issues of power transmission and transformation project, noise pollution has become a prominent problem now. The noise from the working transformer, reactor, and other electrical equipment in the substation will bring negative effect to the ambient environment. This paper focuses on using acoustic software for the simulation and calculation method to control substation noise. According to the characteristics of the substation noise and the techniques of noise reduction, a substation's acoustic field model was established with the SoundPLAN software to predict the scope of substation noise. On this basis, 4 reasonable noise control schemes were advanced to provide some helpful references for noise control during the new substation's design and construction process. And the feasibility and application effect of these control schemes can be verified by using the method of simulation modeling. The simulation results show that the substation always has the problem of excessive noise at boundary under the conventional measures. The excess noise can be efficiently reduced by taking the corresponding noise reduction methods. PMID:24672356
General-purpose microprocessor-based control chassis
International Nuclear Information System (INIS)
Halbig, J.K.; Klosterbuer, S.F.; Swenson, D.A.
1979-12-01
The objective of the Pion Generation for Medical Irradiations (PIGMI) program at the Los Alamos Scientific Laboratory is to develop the technology to build smaller, less expensive, and more reliable proton linear accelerators for medical applications. For this program, a powerful, simple, inexpensive, and reliable control and data acquisition system was developed. The system has a NOVA 3D computer with a real time disk-operating system (RDOS) that communicates with distributed microprocessor-based controllers which directly control data input/output chassis. At the heart of the controller is a microprocessor crate which was conceived at the Fermi National Accelerator Laboratory. This idea was applied to the design of the hardware and software of the controller
International Nuclear Information System (INIS)
Moon, Jin Woo; Yoon, Younju; Jeon, Young-Hoon; Kim, Sooyoung
2017-01-01
Highlights: • Initial ANN model was developed for predicting the time to the setback temperature. • Initial model was optimized for producing accurate output. • Optimized model proved its prediction accuracy. • ANN-based algorithms were developed and tested their performance. • ANN-based algorithms presented superior thermal comfort or energy efficiency. - Abstract: In this study, a temperature control algorithm was developed to apply a setback temperature predictively for the cooling system of a residential building during occupied periods by residents. An artificial neural network (ANN) model was developed to determine the required time for increasing the current indoor temperature to the setback temperature. This study involved three phases: development of the initial ANN-based prediction model, optimization and testing of the initial model, and development and testing of three control algorithms. The development and performance testing of the model and algorithm were conducted using TRNSYS and MATLAB. Through the development and optimization process, the final ANN model employed indoor temperature and the temperature difference between the current and target setback temperature as two input neurons. The optimal number of hidden layers, number of neurons, learning rate, and moment were determined to be 4, 9, 0.6, and 0.9, respectively. The tangent–sigmoid and pure-linear transfer function was used in the hidden and output neurons, respectively. The ANN model used 100 training data sets with sliding-window method for data management. Levenberg-Marquart training method was employed for model training. The optimized model had a prediction accuracy of 0.9097 root mean square errors when compared with the simulated results. Employing the ANN model, ANN-based algorithms maintained indoor temperatures better within target ranges. Compared to the conventional algorithm, the ANN-based algorithms reduced the duration of time, in which the indoor temperature
TACO: a general-purpose tool for predicting cell-type-specific transcription factor dimers.
Jankowski, Aleksander; Prabhakar, Shyam; Tiuryn, Jerzy
2014-03-19
Cooperative binding of transcription factor (TF) dimers to DNA is increasingly recognized as a major contributor to binding specificity. However, it is likely that the set of known TF dimers is highly incomplete, given that they were discovered using ad hoc approaches, or through computational analyses of limited datasets. Here, we present TACO (Transcription factor Association from Complex Overrepresentation), a general-purpose standalone software tool that takes as input any genome-wide set of regulatory elements and predicts cell-type-specific TF dimers based on enrichment of motif complexes. TACO is the first tool that can accommodate motif complexes composed of overlapping motifs, a characteristic feature of many known TF dimers. Our method comprehensively outperforms existing tools when benchmarked on a reference set of 29 known dimers. We demonstrate the utility and consistency of TACO by applying it to 152 DNase-seq datasets and 94 ChIP-seq datasets. Based on these results, we uncover a general principle governing the structure of TF-TF-DNA ternary complexes, namely that the flexibility of the complex is correlated with, and most likely a consequence of, inter-motif spacing.
Non linear predictive control of a LEGO mobile robot
Merabti, H.; Bouchemal, B.; Belarbi, K.; Boucherma, D.; Amouri, A.
2014-10-01
Metaheuristics are general purpose heuristics which have shown a great potential for the solution of difficult optimization problems. In this work, we apply the meta heuristic, namely particle swarm optimization, PSO, for the solution of the optimization problem arising in NLMPC. This algorithm is easy to code and may be considered as alternatives for the more classical solution procedures. The PSO- NLMPC is applied to control a mobile robot for the tracking trajectory and obstacles avoidance. Experimental results show the strength of this approach.
Distributed Model Predictive Control for Active Power Control of Wind Farm
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei; Rasmussen, Claus Nygaard
2014-01-01
This paper presents the active power control of a wind farm using the Distributed Model Predictive Controller (D- MPC) via dual decomposition. Different from the conventional centralized wind farm control, multiple objectives such as power reference tracking performance and wind turbine load can...... be considered to achieve a trade-off between them. Additionally, D- MPC is based on communication among the subsystems. Through the interaction among the neighboring subsystems, the global optimization could be achieved, which significantly reduces the computation burden. It is suitable for the modern large......-scale wind farm control....
General man-machine interface used in accelerators controls
International Nuclear Information System (INIS)
Boutheon, M.; Di Maio, F.; Pace, A.
1992-01-01
A large community is now using Workstations as Accelerators Computer Controls Interface, through the concepts of windows - menus - synoptics - icons. Some standards were established for the CERN-PS control systems rejuvenation. The Booster-to-PS transfer and injection process is now entirely operated with these tools. This application constitutes a global environment providing the users with the controls, analysis, visualization of a part of an accelerator. Individual commands, measurements, and specialized programs including complex treatments are available in a homogeneous frame. Some months of experience in current operation have shown that this model can be extended to the whole project. (author)
Design and Application of Offset-Free Model Predictive Control Disturbance Observation Method
Directory of Open Access Journals (Sweden)
Xue Wang
2016-01-01
Full Text Available Model predictive control (MPC with its lower request to the mathematical model, excellent control performance, and convenience online calculation has developed into a very important subdiscipline with rich theory foundation and practical application. However, unmeasurable disturbance is widespread in industrial processes, which is difficult to deal with directly at present. In most of the implemented MPC strategies, the method of incorporating a constant output disturbance into the process model is introduced to solve this problem, but it fails to achieve offset-free control once the unmeasured disturbances access the process. Based on the Kalman filter theory, the problem is solved by using a more general disturbance model which is superior to the constant output disturbance model. This paper presents the necessary conditions for offset-free model predictive control based on the model. By applying disturbance model, the unmeasurable disturbance vectors are augmented as the states of control system, and the Kalman filer is used to estimate unmeasurable disturbance and its effect on the output. Then, the dynamic matrix control (DMC algorithm is improved by utilizing the feed-forward compensation control strategy with the disturbance estimated.
CONTROL PREDICTIVO DE UN ROBOT TIPO SCARA PREDICTIVE CONTROL OF A SCARA ROBOT
Directory of Open Access Journals (Sweden)
Oscar Andrés Vivas Albán
2006-08-01
Full Text Available Este artículo presenta una aplicación eficiente de un control por modelo de referencia sobre un robot de tipo SCARA. El control estudiado es un control predictivo funcional, el que hace uso de un modelo dinámico simplificado del robot. Los ensayos simulados se realizan sobre un robot de cuatro grados de libertad, tipo SCARA. Con el fin de comparar diferentes estrategias de control, se diseña un controlador clásico tipo PID y dos controladores basados en el modelo de referencia. En este último caso el sistema se linealiza y se desacoplada por realimentación, lo que transforma el sistema a controlar en un simple par de integradores. Al sistema lineal y desacoplado resultante se le aplica el control por par calculado y el control predictivo funcional. Los tres controladores estudiados se simulan sobre el robot SCARA con valores numéricos reales. Las pruebas permiten valorar las respuestas de estos controladores en seguimiento de trayectoria, rechazo de perturbaciones y presencia de errores en el modelado con consignas complejas similares a las utilizadas en procesos de fabricación.This paper describes an efficient approach for model based control, applied on a SCARA robot. The studied control is the predictive functional control which uses a simplified dynamical model of the robot. The simulated tests are made on a SCARA type robot, with four DOF. To compare several control strategies, a classical PID control and two model based controllers are designed. In the last case, the model is first linearized and decoupled by feedback, transforming the system into a double set of integrators. Computed torque control and predictive functional control are applied to the linear and decoupled system. The three studied controllers are simulated on the SCARA robot with real numerical values. Tracking performance, disturbance rejection and model robot mismatch are enlightened, using complex machining tasks trajectories and error presence in the modelling
Combined Active and Reactive Power Control of Wind Farms based on Model Predictive Control
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei; Wang, Jianhui
2017-01-01
This paper proposes a combined wind farm controller based on Model Predictive Control (MPC). Compared with the conventional decoupled active and reactive power control, the proposed control scheme considers the significant impact of active power on voltage variations due to the low X=R ratio...... of wind farm collector systems. The voltage control is improved. Besides, by coordination of active and reactive power, the Var capacity is optimized to prevent potential failures due to Var shortage, especially when the wind farm operates close to its full load. An analytical method is used to calculate...... the sensitivity coefficients to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both normal and emergency conditions. A wind farm with 20 wind turbines was used to verify the proposed combined control scheme....
Inlet Flow Control and Prediction Technologies for Embedded Propulsion Systems
McMillan, Michelle L.; Mackie, Scott A.; Gissen, Abe; Vukasinovic, Bojan; Lakebrink, Matthew T.; Glezer, Ari; Mani, Mori; Mace, James L.
2011-01-01
Fail-safe, hybrid, flow control (HFC) is a promising technology for meeting high-speed cruise efficiency, low-noise signature, and reduced fuel-burn goals for future, Hybrid-Wing-Body (HWB) aircraft with embedded engines. This report details the development of HFC technology that enables improved inlet performance in HWB vehicles with highly integrated inlets and embedded engines without adversely affecting vehicle performance. In addition, new test techniques for evaluating Boundary-Layer-Ingesting (BLI)-inlet flow-control technologies developed and demonstrated through this program are documented, including the ability to generate a BLI-like inlet-entrance flow in a direct-connect, wind-tunnel facility, as well as, the use of D-optimal, statistically designed experiments to optimize test efficiency and enable interpretation of results. Validated improvements in numerical analysis tools and methods accomplished through this program are also documented, including Reynolds-Averaged Navier-Stokes CFD simulations of steady-state flow physics for baseline, BLI-inlet diffuser flow, as well as, that created by flow-control devices. Finally, numerical methods were employed in a ground-breaking attempt to directly simulate dynamic distortion. The advances in inlet technologies and prediction tools will help to meet and exceed "N+2" project goals for future HWB aircraft.
Outdoor flocking of quadcopter drones with decentralized model predictive control.
Yuan, Quan; Zhan, Jingyuan; Li, Xiang
2017-11-01
In this paper, we present a multi-drone system featured with a decentralized model predictive control (DMPC) flocking algorithm. The drones gather localized information from neighbors and update their velocities using the DMPC flocking algorithm. In the multi-drone system, data packages are transmitted through XBee ® wireless modules in broadcast mode, yielding such an anonymous and decentralized system where all the calculations and controls are completed on an onboard minicomputer of each drone. Each drone is a double-layered agent system with the coordination layer running multi-drone flocking algorithms and the flight control layer navigating the drone, and the final formation of the flock relies on both the communication range and the desired inter-drone distance. We give both numerical simulations and field tests with a flock of five drones, showing that the DMPC flocking algorithm performs well on the presented multi-drone system in both the convergence rate and the ability of tracking a desired path. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Maljaars, E.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J. M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A. A.; Vu, N. M. T.; The EUROfusion MST1-team; The TCV-team
2017-12-01
The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety factor profile (q-profile) and kinetic plasma parameters such as the plasma beta. This demands to establish reliable profile control routines in presently operational tokamaks. We present a model predictive profile controller that controls the q-profile and plasma beta using power requests to two clusters of gyrotrons and the plasma current request. The performance of the controller is analyzed in both simulation and TCV L-mode discharges where successful tracking of the estimated inverse q-profile as well as plasma beta is demonstrated under uncertain plasma conditions and the presence of disturbances. The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.
A General Theory of Markovian Time Inconsistent Stochastic Control Problems
DEFF Research Database (Denmark)
Björk, Tomas; Murgochi, Agatha
We develop a theory for stochastic control problems which, in various ways, are time inconsistent in the sense that they do not admit a Bellman optimality principle. We attach these problems by viewing them within a game theoretic framework, and we look for Nash subgame perfect equilibrium points...... examples of time inconsistency in the literature are easily seen to be special cases of the present theory. We also prove that for every time inconsistent problem, there exists an associated time consistent problem such that the optimal control and the optimal value function for the consistent problem...
Lu, Chao; Li, Xubin; Wu, Dongsheng; Zheng, Lianqing; Yang, Wei
2016-01-12
In aqueous solution, solute conformational transitions are governed by intimate interplays of the fluctuations of solute-solute, solute-water, and water-water interactions. To promote molecular fluctuations to enhance sampling of essential conformational changes, a common strategy is to construct an expanded Hamiltonian through a series of Hamiltonian perturbations and thereby broaden the distribution of certain interactions of focus. Due to a lack of active sampling of configuration response to Hamiltonian transitions, it is challenging for common expanded Hamiltonian methods to robustly explore solvent mediated rare conformational events. The orthogonal space sampling (OSS) scheme, as exemplified by the orthogonal space random walk and orthogonal space tempering methods, provides a general framework for synchronous acceleration of slow configuration responses. To more effectively sample conformational transitions in aqueous solution, in this work, we devised a generalized orthogonal space tempering (gOST) algorithm. Specifically, in the Hamiltonian perturbation part, a solvent-accessible-surface-area-dependent term is introduced to implicitly perturb near-solute water-water fluctuations; more importantly in the orthogonal space response part, the generalized force order parameter is generalized as a two-dimension order parameter set, in which essential solute-solvent and solute-solute components are separately treated. The gOST algorithm is evaluated through a molecular dynamics simulation study on the explicitly solvated deca-alanine (Ala10) peptide. On the basis of a fully automated sampling protocol, the gOST simulation enabled repetitive folding and unfolding of the solvated peptide within a single continuous trajectory and allowed for detailed constructions of Ala10 folding/unfolding free energy surfaces. The gOST result reveals that solvent cooperative fluctuations play a pivotal role in Ala10 folding/unfolding transitions. In addition, our assessment
Robust Model Predictive Control Schemes for Tracking Setpoints
Directory of Open Access Journals (Sweden)
Vu Trieu Minh
2010-01-01
Full Text Available This paper briefly reviews the development of nontracking robust model predictive control (RMPC schemes for uncertain systems using linear matrix inequalities (LMIs subject to input saturated and softened state constraints. Then we develop two new tracking setpoint RMPC schemes with common Lyapunov function and with zero terminal equality subject to input saturated and softened state constraints. The novel tracking setpoint RMPC schemes are able to stabilize uncertain systems once the output setpoints lead to the violation of the state constraints. The state violation can be regulated by changing the value of the weighting factor. A brief comparative simulation study of the two tracking setpoint RMPC schemes is done via simple examples to demonstrate the ability of the softened state constraint schemes. Finally, some features of future research from this study are discussed.
Numerical Prediction of Hydromechanical Behaviour of Controllable Pitch Propeller
Directory of Open Access Journals (Sweden)
Saman Tarbiat
2014-01-01
Full Text Available The research described in this paper was carried out to predict hydrodynamic and frictional forces of controllable pitch propeller (CPP that bring about fretting problems in a blade bearing. The governing equations are Reynolds-averaged Navier-Stokes (RANS and are solved by OpenFOAM solver for hydrodynamic forces behind the ship’s wake. Frictional forces are calculated by practical mechanical formulae. Different advance velocities with constant rotational speed for blades are used to achieve hydrodynamic coefficients in open water and the wake behind the propeller. Results are compared at four different pitches. Detailed numerical results of 3D modelling of the propeller, hydrodynamic characteristics, and probability of the fretting motion in the propeller are presented. Results show that the probability of the fretting movement is related to the pitch.
Prediction-based association control scheme in dense femtocell networks
Pham, Ngoc-Thai; Huynh, Thong; Hwang, Won-Joo; You, Ilsun; Choo, Kim-Kwang Raymond
2017-01-01
The deployment of large number of femtocell base stations allows us to extend the coverage and efficiently utilize resources in a low cost manner. However, the small cell size of femtocell networks can result in frequent handovers to the mobile user, and consequently throughput degradation. Thus, in this paper, we propose predictive association control schemes to improve the system’s effective throughput. Our design focuses on reducing handover frequency without impacting on throughput. The proposed schemes determine handover decisions that contribute most to the network throughput and are proper for distributed implementations. The simulation results show significant gains compared with existing methods in terms of handover frequency and network throughput perspective. PMID:28328992
Directory of Open Access Journals (Sweden)
César Hernández-Hernández
2017-06-01
Full Text Available Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.
An FPGA- Based General-Purpose Data Acquisition Controller
Robson, C. C. W.; Bousselham, A.; Bohm
2006-08-01
System development in advanced FPGAs allows considerable flexibility, both during development and in production use. A mixed firmware/software solution allows the developer to choose what shall be done in firmware or software, and to make that decision late in the process. However, this flexibility comes at the cost of increased complexity. We have designed a modular development framework to help to overcome these issues of increased complexity. This framework comprises a generic controller that can be adapted for different systems by simply changing the software or firmware parts. The controller can use both soft and hard processors, with or without an RTOS, based on the demands of the system to be developed. The resulting system uses the Internet for both control and data acquisition. In our studies we developed the embedded system in a Xilinx Virtex-II Pro FPGA, where we used both PowerPC and MicroBlaze cores, http, Java, and LabView for control and communication, together with the MicroC/OS-II and OSE operating systems
Control rod computer code IAMCOS: general theory and numerical methods
International Nuclear Information System (INIS)
West, G.
1982-11-01
IAMCOS is a computer code for the description of mechanical and thermal behavior of cylindrical control rods for fast breeders. This code version was applied, tested and modified from 1979 to 1981. In this report are described the basic model (02 version), theoretical definitions and computation methods [fr
A General Model for Repeated Audit Controls Using Monotone Subsampling
Raats, V.M.; van der Genugten, B.B.; Moors, J.J.A.
2002-01-01
In categorical repeated audit controls, fallible auditors classify sample elements in order to estimate the population fraction of elements in certain categories.To take possible misclassifications into account, subsequent checks are performed with a decreasing number of observations.In this paper a
IFLA General Conference, 1986. Bibliographic Control Division. Section: Bibliography. Papers.
International Federation of Library Associations and Institutions, The Hague (Netherlands).
Papers on bibliographic control presented at the 1986 International Federation of Library Associations (IFLA) conference include: (1) "Bibliographic Interchange/Coordination in Southeast Asia (Huck Tee Lim, Malaysia); (2) "Project for 'Chinese National Bibliography' and Its Progress" (Huang Jungui, China); (3) "Subject…
Systems control with generalized probabilistic fuzzy-reinforcement learning
Hinojosa, J.; Nefti, S.; Kaymak, U.
2011-01-01
Reinforcement learning (RL) is a valuable learning method when the systems require a selection of control actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems and RL, the environment is considered to be
Model-free adaptive sliding mode controller design for generalized ...
Indian Academy of Sciences (India)
L M WANG
2017-08-16
Aug 16, 2017 ... The main contribution of this paper is that a control strategy is provided for ... differential equation provides a new mathematical tool, ... methods of synchronization and GPS, which only use ... the effectiveness of the proposed method by numerical ...... [24] M Anthony and P L Bartlett, Neural network learning.
Steering Angle Control of Car for Dubins Path-tracking Using Model Predictive Control
Kusuma Rahma Putri, Dian; Subchan; Asfihani, Tahiyatul
2018-03-01
Car as one of transportation is inseparable from technological developments. About ten years, there are a lot of research and development on lane keeping system(LKS) which is a system that automaticaly controls the steering to keep the vehicle especially car always on track. This system can be developed for unmanned cars. Unmanned system car requires navigation, guidance and control which is able to direct the vehicle to move toward the desired path. The guidance system is represented by using Dubins-Path that will be controlled by using Model Predictive Control. The control objective is to keep the car’s movement that represented by dinamic lateral motion model so car can move according to the path appropriately. The simulation control on the four types of trajectories that generate the value for steering angle and steering angle changes are at the specified interval.
Gorlin, Eugenia I; Teachman, Bethany A
2015-07-01
The current study brings together two typically distinct lines of research. First, social anxiety is inconsistently associated with behavioral deficits in social performance, and the factors accounting for these deficits remain poorly understood. Second, research on selective processing of threat cues, termed cognitive biases, suggests these biases typically predict negative outcomes, but may sometimes be adaptive, depending on the context. Integrating these research areas, the current study examined whether conscious and/or unconscious threat interference biases (indexed by the unmasked and masked emotional Stroop) can explain unique variance, beyond self-reported anxiety measures, in behavioral avoidance and observer-rated anxious behavior during a public speaking task. Minute of speech and general inhibitory control (indexed by the color-word Stroop) were examined as within-subject and between-subject moderators, respectively. Highly socially anxious participants (N=135) completed the emotional and color-word Stroop blocks prior to completing a 4-minute videotaped speech task, which was later coded for anxious behaviors (e.g., speech dysfluency). Mixed-effects regression analyses revealed that general inhibitory control moderated the relationship between both conscious and unconscious threat interference bias and anxious behavior (though not avoidance), such that lower threat interference predicted higher levels of anxious behavior, but only among those with relatively weaker (versus stronger) inhibitory control. Minute of speech further moderated this relationship for unconscious (but not conscious) social-threat interference, such that lower social-threat interference predicted a steeper increase in anxious behaviors over the course of the speech (but only among those with weaker inhibitory control). Thus, both trait and state differences in inhibitory control resources may influence the behavioral impact of threat biases in social anxiety. Copyright © 2015
Pohjoranta, Antti; Halinen, Matias; Pennanen, Jari; Kiviaho, Jari
2015-03-01
Generalized predictive control (GPC) is applied to control the maximum temperature in a solid oxide fuel cell (SOFC) stack and the temperature difference over the stack. GPC is a model predictive control method and the models utilized in this work are ARX-type (autoregressive with extra input), multiple input-multiple output, polynomial models that were identified from experimental data obtained from experiments with a complete SOFC system. The proposed control is evaluated by simulation with various input-output combinations, with and without constraints. A comparison with conventional proportional-integral-derivative (PID) control is also made. It is shown that if only the stack maximum temperature is controlled, a standard PID controller can be used to obtain output performance comparable to that obtained with the significantly more complex model predictive controller. However, in order to control the temperature difference over the stack, both the stack minimum and the maximum temperature need to be controlled and this cannot be done with a single PID controller. In such a case the model predictive controller provides a feasible and effective solution.
Downey, Brandon; Schmitt, John; Beller, Justin; Russell, Brian; Quach, Anthony; Hermann, Elizabeth; Lyon, David; Breit, Jeffrey
2017-11-01
As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers.
Estimation of CANDU reactor zone controller level by generalized perturbation method
International Nuclear Information System (INIS)
Kim, Do Heon; Kim, Jong Kyung; Choi, Hang Bok; Roh, Gyu Hong; Yang, Won Sik
1998-01-01
The zone controller level change due to refueling operation has been studied using a generalized perturbation method. The generalized perturbation method provides sensitivity of zone power to individual refueling operation and incremental change of zone controller level. By constructing a system equation for each zone power, the zone controller level change was obtained. The details and a proposed model for future work are described
Coordinated Voltage Control of a Wind Farm based on Model Predictive Control
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai
2016-01-01
This paper presents an autonomous wind farm voltage controller based on Model Predictive Control (MPC). The reactive power compensation and voltage regulation devices of the wind farm include Static Var Compensators (SVCs), Static Var Generators (SVGs), Wind Turbine Generators (WTGs) and On...... are calculated based on an analytical method to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both voltage violated and normal operation conditions. A wind farm with 20 wind turbines was used to conduct case studies to verify the proposed coordinated...
Takase, Hiroyuki; Sugiura, Tomonori; Kimura, Genjiro; Ohte, Nobuyuki; Dohi, Yasuaki
2015-07-29
Although there is a close relationship between dietary sodium and hypertension, the concept that persons with relatively high dietary sodium are at increased risk of developing hypertension compared with those with relatively low dietary sodium has not been studied intensively in a cohort. We conducted an observational study to investigate whether dietary sodium intake predicts future blood pressure and the onset of hypertension in the general population. Individual sodium intake was estimated by calculating 24-hour urinary sodium excretion from spot urine in 4523 normotensive participants who visited our hospital for a health checkup. After a baseline examination, they were followed for a median of 1143 days, with the end point being development of hypertension. During the follow-up period, hypertension developed in 1027 participants (22.7%). The risk of developing hypertension was higher in those with higher rather than lower sodium intake (hazard ratio 1.25, 95% CI 1.04 to 1.50). In multivariate Cox proportional hazards regression analysis, baseline sodium intake and the yearly change in sodium intake during the follow-up period (as continuous variables) correlated with the incidence of hypertension. Furthermore, both the yearly increase in sodium intake and baseline sodium intake showed significant correlations with the yearly increase in systolic blood pressure in multivariate regression analysis after adjustment for possible risk factors. Both relatively high levels of dietary sodium intake and gradual increases in dietary sodium are associated with future increases in blood pressure and the incidence of hypertension in the Japanese general population. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.
Memory controllers for real-time embedded systems predictable and composable real-time systems
Akesson, Benny
2012-01-01
Verification of real-time requirements in systems-on-chip becomes more complex as more applications are integrated. Predictable and composable systems can manage the increasing complexity using formal verification and simulation. This book explains the concepts of predictability and composability and shows how to apply them to the design and analysis of a memory controller, which is a key component in any real-time system. This book is generally intended for readers interested in Systems-on-Chips with real-time applications. It is especially well-suited for readers looking to use SDRAM memories in systems with hard or firm real-time requirements. There is a strong focus on real-time concepts, such as predictability and composability, as well as a brief discussion about memory controller architectures for high-performance computing. Readers will learn step-by-step how to go from an unpredictable SDRAM memory, offering highly variable bandwidth and latency, to a predictable and composable shared memory...
International Nuclear Information System (INIS)
Westervelt, Robert; Klein, William; Kroupa, Michael; Olsson, Eric; Rothrock, Rick
1999-01-01
Vista Control Systems, Inc. has developed a portable system for intelligent accelerator control. The design is general in scope and is thus configurable to a wide range of accelerator facilities and control problems. The control system employs a multi-layer organization in which knowledge-based decision making is used to dynamically configure lower level optimization and control algorithms
Using a cognitive architecture for general purpose service robot control
Puigbo, Jordi-Ysard; Pumarola, Albert; Angulo, Cecilio; Tellez, Ricardo
2015-04-01
A humanoid service robot equipped with a set of simple action skills including navigating, grasping, recognising objects or people, among others, is considered in this paper. By using those skills the robot should complete a voice command expressed in natural language encoding a complex task (defined as the concatenation of a number of those basic skills). As a main feature, no traditional planner has been used to decide skills to be activated, as well as in which sequence. Instead, the SOAR cognitive architecture acts as the reasoner by selecting which action the robot should complete, addressing it towards the goal. Our proposal allows to include new goals for the robot just by adding new skills (without the need to encode new plans). The proposed architecture has been tested on a human-sized humanoid robot, REEM, acting as a general purpose service robot.
Directory of Open Access Journals (Sweden)
Ernest Novak
2001-12-01
Full Text Available Background. Malnutrition has serious implications for recovery after surgery. Early detection of malnutrition with nutritional support minimizes postoperative complications. Nutritional assessment tools need to be simple and suitable for use in everyday practice. In our study we wanted to determine, how many patients might benefit from nutritional support.Methods. From April to August 1999 fifty consecutively admitted patients predicted to major abdominal surgery have been examined. We used Mini nutritional assessment (MNA, Buzby’s nutrition risk index (NRI, blood albumin level and weight loss in the last 3 months period prior to the examination, to assess nutritional status.Results. We examined 50 patients (27 males and 23 females, age 76.5 ± 16.5 and confirmed malnutrition in 40% of patients with MNA and serum albumin level. The increased risk for nutrition-associated complications was confirmed by NRI and weight loss in 44%.Conclusions. A confident diagnosis of malnutrition and increased risk for nutrition-associated complications can be established by using a combination of simple methods like MNA, NRI, weight loss and serum albumin level. Almost half of the patients admitted for major abdominal surgery in General hospital Celje suffer from malnutrition and they may benefit with early nutritional intervention.
Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D
2017-09-11
Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.
Transforming the ASDEX Upgrade discharge control system to a general-purpose plasma control platform
International Nuclear Information System (INIS)
Treutterer, Wolfgang; Cole, Richard; Gräter, Alexander; Lüddecke, Klaus; Neu, Gregor; Rapson, Christopher; Raupp, Gerhard; Zasche, Dieter; Zehetbauer, Thomas
2015-01-01
Highlights: • Control framework split in core and custom part. • Core framework deployable in other fusion device environments. • Adaptible through customizable modules, plug-in support and generic interfaces. - Abstract: The ASDEX Upgrade Discharge Control System DCS is a modern and mature product, originally designed to regulate and supervise ASDEX Upgrade Tokamak plasma operation. In its core DCS is based on a generic, versatile real-time software framework with a plugin architecture that allows to easily combine, modify and extend control function modules in order to tailor the system to required features and let it continuously evolve with the progress of an experimental fusion device. Due to these properties other fusion experiments like the WEST project have expressed interest in adopting DCS. For this purpose, essential parts of DCS must be unpinned from the ASDEX Upgrade environment by exposure or introduction of generalised interfaces. Re-organisation of DCS modules allows distinguishing between intrinsic framework core functions and device-specific applications. In particular, DCS must be prepared for deployment in different system environments with their own realisations for user interface, pulse schedule preparation, parameter server, time and event distribution, diagnostic and actuator systems, network communication and data archiving. The article explains the principles of the revised DCS structure, derives the necessary interface definitions and describes major steps to achieve the separation between general-purpose framework and fusion device specific components.
Transforming the ASDEX Upgrade discharge control system to a general-purpose plasma control platform
Energy Technology Data Exchange (ETDEWEB)
Treutterer, Wolfgang, E-mail: Wolfgang.Treutterer@ipp.mpg.de [Max-Planck-Institut für Plasmaphysik, Boltzmannstr. 2, 85748 Garching (Germany); Cole, Richard [Unlimited Computer Systems, Seeshaupter Str. 15, 82393 Iffeldorf (Germany); Gräter, Alexander [Max-Planck-Institut für Plasmaphysik, Boltzmannstr. 2, 85748 Garching (Germany); Lüddecke, Klaus [Unlimited Computer Systems, Seeshaupter Str. 15, 82393 Iffeldorf (Germany); Neu, Gregor; Rapson, Christopher; Raupp, Gerhard; Zasche, Dieter; Zehetbauer, Thomas [Max-Planck-Institut für Plasmaphysik, Boltzmannstr. 2, 85748 Garching (Germany)
2015-10-15
Highlights: • Control framework split in core and custom part. • Core framework deployable in other fusion device environments. • Adaptible through customizable modules, plug-in support and generic interfaces. - Abstract: The ASDEX Upgrade Discharge Control System DCS is a modern and mature product, originally designed to regulate and supervise ASDEX Upgrade Tokamak plasma operation. In its core DCS is based on a generic, versatile real-time software framework with a plugin architecture that allows to easily combine, modify and extend control function modules in order to tailor the system to required features and let it continuously evolve with the progress of an experimental fusion device. Due to these properties other fusion experiments like the WEST project have expressed interest in adopting DCS. For this purpose, essential parts of DCS must be unpinned from the ASDEX Upgrade environment by exposure or introduction of generalised interfaces. Re-organisation of DCS modules allows distinguishing between intrinsic framework core functions and device-specific applications. In particular, DCS must be prepared for deployment in different system environments with their own realisations for user interface, pulse schedule preparation, parameter server, time and event distribution, diagnostic and actuator systems, network communication and data archiving. The article explains the principles of the revised DCS structure, derives the necessary interface definitions and describes major steps to achieve the separation between general-purpose framework and fusion device specific components.
Weisburd, Melvin I.
The Field Operations and Enforcement Manual for Air Pollution Control, Volume II, explains in detail the following: technology of source control, modification of operations, particulate control equipment, sulfur dioxide removal systems for power plants, and control equipment for gases and vapors; inspection procedures for general sources, fuel…
Scintillation camera-computer systems: General principles of quality control
International Nuclear Information System (INIS)
Ganatra, R.D.
1992-01-01
Scintillation camera-computer systems are designed to allow the collection, digital analysis and display of the image data from a scintillation camera. The components of the computer in such a system are essentially the same as those of a computer used in any other application, i.e. a central processing unit (CPU), memory and magnetic storage. Additional hardware items necessary for nuclear medicine applications are an analogue-to-digital converter (ADC), which converts the analogue signals from the camera to digital numbers, and an image display. It is possible that the transfer of data from camera to computer degrades the information to some extent. The computer can generate the image for display, but it also provides the capability of manipulating the primary data to improve the display of the image. The first function of conversion from analogue to digital mode is not within the control of the operator, but the second type of manipulation is in the control of the operator. These type of manipulations should be done carefully without sacrificing the integrity of the incoming information
Quality by control: Towards model predictive control of mammalian cell culture bioprocesses.
Sommeregger, Wolfgang; Sissolak, Bernhard; Kandra, Kulwant; von Stosch, Moritz; Mayer, Martin; Striedner, Gerald
2017-07-01
The industrial production of complex biopharmaceuticals using recombinant mammalian cell lines is still mainly built on a quality by testing approach, which is represented by fixed process conditions and extensive testing of the end-product. In 2004 the FDA launched the process analytical technology initiative, aiming to guide the industry towards advanced process monitoring and better understanding of how critical process parameters affect the critical quality attributes. Implementation of process analytical technology into the bio-production process enables moving from the quality by testing to a more flexible quality by design approach. The application of advanced sensor systems in combination with mathematical modelling techniques offers enhanced process understanding, allows on-line prediction of critical quality attributes and subsequently real-time product quality control. In this review opportunities and unsolved issues on the road to a successful quality by design and dynamic control implementation are discussed. A major focus is directed on the preconditions for the application of model predictive control for mammalian cell culture bioprocesses. Design of experiments providing information about the process dynamics upon parameter change, dynamic process models, on-line process state predictions and powerful software environments seem to be a prerequisite for quality by control realization. © 2017 The Authors. Biotechnology Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Directory of Open Access Journals (Sweden)
M’hamed Sekour
2017-01-01
Full Text Available In order to improve the driving performance and the stability of electric vehicles (EVs, a new multimachine robust control, which realizes the acceleration slip regulation (ASR and antilock braking system (ABS functions, based on nonlinear model predictive (NMP direct torque control (DTC, is proposed for four permanent magnet synchronous in-wheel motors. The in-wheel motor provides more possibilities of wheel control. One of its advantages is that it has low response time and almost instantaneous torque generation. Moreover, it can be independently controlled, enhancing the limits of vehicular control. For an EV equipped with four in-wheel electric motors, an advanced control may be envisaged. Taking advantage of the fast and accurate torque of in-wheel electric motors which is directly transmitted to the wheels, a new approach for longitudinal control realized by ASR and ABS is presented in this paper. In order to achieve a high-performance torque control for EVs, the NMP-DTC strategy is proposed. It uses the fuzzy logic control technique that determines online the accurate values of the weighting factors and generates the optimal switching states that optimize the EV drives’ decision. The simulation results built in Matlab/Simulink indicate that the EV can achieve high-performance vehicle longitudinal stability control.
Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control
Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.
1997-01-01
One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.
Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.
Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei
2016-02-01
A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.
Robust entry guidance using linear covariance-based model predictive control
Directory of Open Access Journals (Sweden)
Jianjun Luo
2017-02-01
Full Text Available For atmospheric entry vehicles, guidance design can be accomplished by solving an optimal issue using optimal control theories. However, traditional design methods generally focus on the nominal performance and do not include considerations of the robustness in the design process. This paper proposes a linear covariance-based model predictive control method for robust entry guidance design. Firstly, linear covariance analysis is employed to directly incorporate the robustness into the guidance design. The closed-loop covariance with the feedback updated control command is initially formulated to provide the expected errors of the nominal state variables in the presence of uncertainties. Then, the closed-loop covariance is innovatively used as a component of the cost function to guarantee the robustness to reduce its sensitivity to uncertainties. After that, the models predictive control is used to solve the optimal problem, and the control commands (bank angles are calculated. Finally, a series of simulations for different missions have been completed to demonstrate the high performance in precision and the robustness with respect to initial perturbations as well as uncertainties in the entry process. The 3σ confidence region results in the presence of uncertainties which show that the robustness of the guidance has been improved, and the errors of the state variables are decreased by approximately 35%.
Improvements in cross-infection control in general dental practice.
Bentley, E M; Sarll, D W
1995-07-08
A questionnaire about cross-infection control was sent to all GDPs in five FHSAs in the North Western Region. Replies came from 312 dentists, a response rate of 74%. They worked in 185 practices, a response rate of 85%. Gloves were worn routinely by 86% of dentists and 80% of DSAs. Handpieces were autoclaved between patients in 77% of practices. Much however, remains to be improved. DSAs could be better protected if more ultrasonic cleaners were used, eye protection encouraged and heavy duty gloves were available for cleaning instruments. BDA guidelines were reported as being the most influential factor, though it would appear that the media did persuade many practitioners to use autoclavable handpieces and sterilise them after each use.
Predictive powertrain control using powertrain history and GPS data
Weslati, Feisel; Krupadanam, Ashish A
2015-03-03
A method and powertrain apparatus that predicts a route of travel for a vehicle and uses historical powertrain loads and speeds for the predicted route of travel to optimize at least one powertrain operation for the vehicle.
DEFF Research Database (Denmark)
Benn, Marianne; Nordestgaard, Børge G; Jensen, Gorm Boje
2007-01-01
Apolipoprotein B (apoB) levels predict fatal myocardial infarction. Whether apoB also predicts nonfatal ischemic cardiovascular events is unclear. We tested the following hypotheses: apoB predicts ischemic cardiovascular events, and apoB is a better predictor of ischemic cardiovascular events tha...
Autonomous formation flight of helicopters: Model predictive control approach
Chung, Hoam
Formation flight is the primary movement technique for teams of helicopters. However, the potential for accidents is greatly increased when helicopter teams are required to fly in tight formations and under harsh conditions. This dissertation proposes that the automation of helicopter formations is a realistic solution capable of alleviating risks. Helicopter formation flight operations in battlefield situations are highly dynamic and dangerous, and, therefore, we maintain that both a high-level formation management system and a distributed coordinated control algorithm should be implemented to help ensure safe formations. The starting point for safe autonomous formation flights is to design a distributed control law attenuating external disturbances coming into a formation, so that each vehicle can safely maintain sufficient clearance between it and all other vehicles. While conventional methods are limited to homogeneous formations, our decentralized model predictive control (MPC) approach allows for heterogeneity in a formation. In order to avoid the conservative nature inherent in distributed MPC algorithms, we begin by designing a stable MPC for individual vehicles, and then introducing carefully designed inter-agent coupling terms in a performance index. Thus the proposed algorithm works in a decentralized manner, and can be applied to the problem of helicopter formations comprised of heterogenous vehicles. Individual vehicles in a team may be confronted by various emerging situations that will require the capability for in-flight reconfiguration. We propose the concept of a formation manager to manage separation, join, and synchronization of flight course changes. The formation manager accepts an operator's commands, information from neighboring vehicles, and its own vehicle states. Inside the formation manager, there are multiple modes and complex mode switchings represented as a finite state machine (FSM). Based on the current mode and collected
DEFF Research Database (Denmark)
Solberg, Brian; Andersen, Palle; Maciejowski, Jan
2008-01-01
This paper discusses the application of hybrid model predictive control to control switching between different burner modes in a novel compact marine boiler design. A further purpose of the present work is to point out problems with finite horizon model predictive control applied to systems for w...
An estimator-based distributed voltage-predictive control strategy for ac islanded microgrids
DEFF Research Database (Denmark)
Wang, Yanbo; Chen, Zhe; Wang, Xiongfei
2015-01-01
This paper presents an estimator-based voltage predictive control strategy for AC islanded microgrids, which is able to perform voltage control without any communication facilities. The proposed control strategy is composed of a network voltage estimator and a voltage predictive controller for each...... and has a good capability to reject uncertain perturbations of islanded microgrids....
International Nuclear Information System (INIS)
Huang Gongsheng; Wang Shengwei; Xu Xinhua
2009-01-01
This paper presents a robust model predictive control strategy for improving the supply air temperature control of air-handling units by dealing with the associated uncertainties and constraints directly. This strategy uses a first-order plus time-delay model with uncertain time-delay and system gain to describe air-conditioning process of an air-handling unit usually operating at various weather conditions. The uncertainties of the time-delay and system gain, which imply the nonlinearities and the variable dynamic characteristics, are formulated using an uncertainty polytope. Based on this uncertainty formulation, an offline LMI-based robust model predictive control algorithm is employed to design a robust controller for air-handling units which can guarantee a good robustness subject to uncertainties and constraints. The proposed robust strategy is evaluated in a dynamic simulation environment of a variable air volume air-conditioning system in various operation conditions by comparing with a conventional PI control strategy. The robustness analysis of both strategies under different weather conditions is also presented.
Automatic Power Control for Daily Load-following Operation using Model Predictive Control Method
Energy Technology Data Exchange (ETDEWEB)
Yu, Keuk Jong; Kim, Han Gon [KH, Daejeon (Korea, Republic of)
2009-10-15
Under the circumstances that nuclear power occupies more than 50%, nuclear power plants are required to be operated on load-following operation in order to make the effective management of electric grid system and enhanced responsiveness to rapid changes in power demand. Conventional reactors such as the OPR1000 and APR1400 have a regulating system that controls the average temperature of the reactor core relation to the reference temperature. This conventional method has the advantages of proven technology and ease of implementation. However, this method is unsuitable for controlling the axial power shape, particularly the load following operation. Accordingly, this paper reports on the development of a model predictive control method which is able to control the reactor power and the axial shape index. The purpose of this study is to analyze the behavior of nuclear reactor power and the axial power shape by using a model predictive control method when the power is increased and decreased for a daily load following operation. The study confirms that deviations in the axial shape index (ASI) are within the operating limit.
Potentials of mean force for protein structure prediction vindicated, formalized and generalized.
Directory of Open Access Journals (Sweden)
Thomas Hamelryck
Full Text Available Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge-based potentials based on pairwise distances--so-called "potentials of mean force" (PMFs--have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state--a necessary component of these potentials--is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities "reference ratio distributions" deriving from the application of the "reference ratio method." This new view is not only of theoretical relevance but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures.
Yock, Adam D; Rao, Arvind; Dong, Lei; Beadle, Beth M; Garden, Adam S; Kudchadker, Rajat J; Court, Laurence E
2014-05-01
The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: -11.6%-23.8%) and 14.6% (range: -7.3%-27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: -6.8%-40.3%) and 13.1% (range: -1.5%-52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: -11.1%-20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography images and facilitate improved treatment management.
High performance predictive current control of a three phase VSI: An ...
Indian Academy of Sciences (India)
... current control of a three phase VSI: An experimental assessment ... Voltage source inverter; two level inverter; predictive current control; weighting factor ... Conventionally, for reference current tracking control in a two level VSI, the objective ...
Subjective memory complaints in general practice predicts future dementia: a 4-year follow-up study
DEFF Research Database (Denmark)
Waldorff, Frans Boch; Vogel, Asmus Mejling; Siersma, Volkert Dirk
2012-01-01
Many older patients in general practice have subjective memory complaints (SMC); however, not all share this information with their general practitioner (GP). The association between SMC and future cognitive decline or dementia is not clear, especially in a general practice population. The aim...
Johnson, Erik A.; Elhaddad, Wael M.; Wojtkiewicz, Steven F.
2016-04-01
A variety of strategies have been developed over the past few decades to determine controllable damping device forces to mitigate the response of structures and mechanical systems to natural hazards and other excitations. These "smart" damping devices produce forces through passive means but have properties that can be controlled in real time, based on sensor measurements of response across the structure, to dramatically reduce structural motion by exploiting more than the local "information" that is available to purely passive devices. A common strategy is to design optimal damping forces using active control approaches and then try to reproduce those forces with the smart damper. However, these design forces, for some structures and performance objectives, may achieve high performance by selectively adding energy, which cannot be replicated by a controllable damping device, causing the smart damper performance to fall far short of what an active system would provide. The authors have recently demonstrated that a model predictive control strategy using hybrid system models, which utilize both continuous and binary states (the latter to capture the switching behavior between dissipative and non-dissipative forces), can provide reductions in structural response on the order of 50% relative to the conventional clipped-optimal design strategy. This paper explores the robustness of this newly proposed control strategy through evaluating controllable damper performance when the structure model differs from the nominal one used to design the damping strategy. Results from the application to a two-degree-of-freedom structure model confirms the robustness of the proposed strategy.
Predictive Trailing-Edge Modulation Average Current Control in DC-DC Converters
Directory of Open Access Journals (Sweden)
LASCU, D.
2013-11-01
Full Text Available The paper investigates predictive digital average current control (PDACC in dc/dc converters using trailing-edge modulation (TEM. The study is focused on the recurrence duty cycle equation and then stability analysis is performed. It is demonstrated that average current control using trailing-edge modulation is stable on the whole range of the duty cycle and thus design problems are highly reduced. The analysis is carried out in a general manner, independent of converter topology and therefore the results can then be easily applied for a certain converter (buck, boost, buck-boost, etc.. The theoretical considerations are confirmed for a boost converter first using the MATLAB program based on state-space equations and finally with the CASPOC circuit simulation package.
A General Linear Model (GLM) was used to evaluate the deviation of predicted values from expected values for a complex environmental model. For this demonstration, we used the default level interface of the Regional Mercury Cycling Model (R-MCM) to simulate epilimnetic total mer...
Robinson, Cecil; Rose, Sage
2010-01-01
One leading version of hope theory posits hope to be a general disposition for goal-directed agency and pathways thinking. Domain-specific hope theory suggests that hope operates within context and measures of hope should reflect that context. This study examined three measures of hope to test the predictive, construct, and convergent validity…
Curseu, Petru Lucian; Schruijer, Sandra G. L.
2012-01-01
This study investigates the relationship between the five decision-making styles evaluated by the General Decision-Making Style Inventory, indecisiveness, and rationality in decision making. Using a sample of 102 middle-level managers, the results show that the rational style positively predicts rationality in decision making and negatively…
Wang, Ming; Li, Zheng; Lee, Eun Young; Lewis, Mechelle M; Zhang, Lijun; Sterling, Nicholas W; Wagner, Daymond; Eslinger, Paul; Du, Guangwei; Huang, Xuemei
2017-09-25
It is challenging for current statistical models to predict clinical progression of Parkinson's disease (PD) because of the involvement of multi-domains and longitudinal data. Past univariate longitudinal or multivariate analyses from cross-sectional trials have limited power to predict individual outcomes or a single moment. The multivariate generalized linear mixed-effect model (GLMM) under the Bayesian framework was proposed to study multi-domain longitudinal outcomes obtained at baseline, 18-, and 36-month. The outcomes included motor, non-motor, and postural instability scores from the MDS-UPDRS, and demographic and standardized clinical data were utilized as covariates. The dynamic prediction was performed for both internal and external subjects using the samples from the posterior distributions of the parameter estimates and random effects, and also the predictive accuracy was evaluated based on the root of mean square error (RMSE), absolute bias (AB) and the area under the receiver operating characteristic (ROC) curve. First, our prediction model identified clinical data that were differentially associated with motor, non-motor, and postural stability scores. Second, the predictive accuracy of our model for the training data was assessed, and improved prediction was gained in particularly for non-motor (RMSE and AB: 2.89 and 2.20) compared to univariate analysis (RMSE and AB: 3.04 and 2.35). Third, the individual-level predictions of longitudinal trajectories for the testing data were performed, with ~80% observed values falling within the 95% credible intervals. Multivariate general mixed models hold promise to predict clinical progression of individual outcomes in PD. The data was obtained from Dr. Xuemei Huang's NIH grant R01 NS060722 , part of NINDS PD Biomarker Program (PDBP). All data was entered within 24 h of collection to the Data Management Repository (DMR), which is publically available ( https://pdbp.ninds.nih.gov/data-management ).
Brinck-Claussen, Ursula Ødum; Curth, Nadja Kehler; Davidsen, Annette Sofie; Mikkelsen, John Hagel; Lau, Marianne Engelbrecht; Lundsteen, Merete; Csillag, Claudio; Christensen, Kaj Sparle; Hjorthøj, Carsten; Nordentoft, Merete; Eplov, Lene Falgaard
2017-07-21
Depression is a common illness with great human costs and a significant burden on the public economy. Previous studies have indicated that collaborative care (CC) has a positive effect on symptoms when provided to people with depression, but CC has not yet been applied in a Danish context. We therefore developed a model for CC (the Collabri model) to treat people with depression in general practice in Denmark. Since systematic identification of patients is an "active ingredient" in CC and some literature suggests case finding as the best alternative to standard detection, the two detection methods are examined as part of the study. The aim is to investigate if treatment according to the Collabri model has an effect on depression symptoms when provided to people with depression in general practice in Denmark, and to examine if case finding is a better method to detect depression in general practice than standard detection. The trial is a cluster-randomised, clinical superiority trial investigating the effect of treatment according to the Collabri model for CC, compared to treatment as usual for 480 participants diagnosed with depression in general practice in the Capital Region of Denmark. The primary outcome is depression symptoms (Beck's Depression Inventory (BDI-II)) after 6 months. Secondary outcomes include depression symptoms (BDI-II) after 15 months, anxiety symptoms (Beck's Anxiety Inventory (BAI)), level of functioning (Global Assessment of Function (GAF)) and psychological stress (Symptom Checklist-90-Revised (SCL-90-R)). In addition, case finding (with the recommended screening tool Major Depression Inventory (MDI)) and standard detection of depression is examined in a cluster-randomized controlled design. Here, the primary outcome is the positive predictive value of referral diagnosis. If the Collabri model is shown to be superior to treatment as usual, the study will contribute with important knowledge on how to improve treatment of depression in
Analysis of explicit model predictive control for path-following control
2018-01-01
In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration. PMID:29534080
Analysis of explicit model predictive control for path-following control.
Lee, Junho; Chang, Hyuk-Jun
2018-01-01
In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration.
Althouse, Linda A; McGuinness, Gail A
2008-09-01
This study investigates the predictive validity of the In-Training Examination (ITE). Although studies have confirmed the predictive validity of ITEs in other medical specialties, no study has been done for general pediatrics. Each year, residents in accredited pediatric training programs take the ITE as a self-assessment instrument. The ITE is similar to the American Board of Pediatrics General Pediatrics Certifying Examination. First-time takers of the certifying examination over a 5-year period who took at least 1 ITE examination were included in the sample. Regression models analyzed the predictive value of the ITE. The predictive power of the ITE in the first training year is minimal. However, the predictive power of the ITE increases each year, providing the greatest power in the third year of training. Even though ITE scores provide information regarding the likelihood of passing the certification examination, the data should be used with caution, particularly in the first training year. Other factors also must be considered when predicting performance on the certification examination. This study continues to support the ITE as an assessment tool for program directors, as well as a means of providing residents with feedback regarding their acquisition of pediatric knowledge.
Energy Technology Data Exchange (ETDEWEB)
Teo, Troy; Alayoubi, Nadia; Bruce, Neil; Pistorius, Stephen [University of Manitoba/ CancerCare Manitoba, University of Manitoba, University of Manitoba, University of Manitoba / CancerCare Manitoba (Canada)
2016-08-15
Purpose: In image-guided adaptive radiotherapy systems, prediction of tumor motion is required to compensate for system latencies. However, due to the non-stationary nature of respiration, it is a challenge to predict the associated tumor motions. In this work, a systematic design of the neural network (NN) using a mixture of online data acquired during the initial period of the tumor trajectory, coupled with a generalized model optimized using a group of patient data (obtained offline) is presented. Methods: The average error surface obtained from seven patients was used to determine the input data size and number of hidden neurons for the generalized NN. To reduce training time, instead of using random weights to initialize learning (method 1), weights inherited from previous training batches (method 2) were used to predict tumor position for each sliding window. Results: The generalized network was established with 35 input data (∼4.66s) and 20 hidden nodes. For a prediction horizon of 650 ms, mean absolute errors of 0.73 mm and 0.59 mm were obtained for method 1 and 2 respectively. An average initial learning period of 8.82 s is obtained. Conclusions: A network with a relatively short initial learning time was achieved. Its accuracy is comparable to previous studies. This network could be used as a plug-and play predictor in which (a) tumor positions can be predicted as soon as treatment begins and (b) the need for pretreatment data and optimization for individual patients can be avoided.
International Nuclear Information System (INIS)
Teo, Troy; Alayoubi, Nadia; Bruce, Neil; Pistorius, Stephen
2016-01-01
Purpose: In image-guided adaptive radiotherapy systems, prediction of tumor motion is required to compensate for system latencies. However, due to the non-stationary nature of respiration, it is a challenge to predict the associated tumor motions. In this work, a systematic design of the neural network (NN) using a mixture of online data acquired during the initial period of the tumor trajectory, coupled with a generalized model optimized using a group of patient data (obtained offline) is presented. Methods: The average error surface obtained from seven patients was used to determine the input data size and number of hidden neurons for the generalized NN. To reduce training time, instead of using random weights to initialize learning (method 1), weights inherited from previous training batches (method 2) were used to predict tumor position for each sliding window. Results: The generalized network was established with 35 input data (∼4.66s) and 20 hidden nodes. For a prediction horizon of 650 ms, mean absolute errors of 0.73 mm and 0.59 mm were obtained for method 1 and 2 respectively. An average initial learning period of 8.82 s is obtained. Conclusions: A network with a relatively short initial learning time was achieved. Its accuracy is comparable to previous studies. This network could be used as a plug-and play predictor in which (a) tumor positions can be predicted as soon as treatment begins and (b) the need for pretreatment data and optimization for individual patients can be avoided.
Directory of Open Access Journals (Sweden)
Hiroyuki Goto
2013-07-01
Full Text Available A model predictive control-based scheduler for a class of discrete event systems is designed and developed. We focus on repetitive, multiple-input, multiple-output, and directed acyclic graph structured systems on which capacity constraints can be imposed. The target system’s behaviour is described by linear equations in max-plus algebra, referred to as state-space representation. Assuming that the system’s performance can be improved by paying additional cost, we adjust the system parameters and determine control inputs for which the reference output signals can be observed. The main contribution of this research is twofold, 1: For systems with capacity constraints, we derived an output prediction equation as functions of adjustable variables in a recursive form, 2: Regarding the construct for the system’s representation, we improved the structure to accomplish general operations which are essential for adjusting the system parameters. The result of numerical simulation in a later section demonstrates the effectiveness of the developed controller.
Predicting macropores in space and time by earthworms and abiotic controls
Hohenbrink, Tobias Ludwig; Schneider, Anne-Kathrin; Zangerlé, Anne; Reck, Arne; Schröder, Boris; van Schaik, Loes
2017-04-01
Macropore flow increases infiltration and solute leaching. The macropore density and connectivity, and thereby the hydrological effectiveness, vary in space and time due to earthworms' burrowing activity and their ability to refill their burrows in order to survive drought periods. The aim of our study was to predict the spatiotemporal variability of macropore distributions by a set of potentially controlling abiotic variables and abundances of different earthworm species. We measured earthworm abundances and effective macropore distributions using tracer rainfall infiltration experiments in six measurement campaigns during one year at six field sites in Luxembourg. Hydrologically effective macropores were counted in three soil depths (3, 10, 30 cm) and distinguished into three diameter classes (6 mm). Earthworms were sampled and determined to species-level. In a generalized linear modelling framework, we related macropores to potential spatial and temporal controlling factors. Earthworm species such as Lumbricus terrestris and Aporrectodea longa, local abiotic site conditions (land use, TWI, slope), temporally varying weather conditions (temperature, humidity, precipitation) and soil moisture affected the number of effective macropores. Main controlling factors and explanatory power of the models (uncertainty and model performance) varied depending on the depth and diameter class of macropores. We present spatiotemporal predictions of macropore density by daily-resolved, one year time series of macropore numbers and maps of macropore distributions at specific dates in a small-scale catchment with 5 m resolution.
Predictive control of irrigation canals – robust design and real-time implementation
Aguilar, José V.; Langarita, Pedro; Rodellar, José; Linares, Lorenzo; Horváth, K.
2016-01-01
Predictive control is one of the most commonly used control methods in a variety of application areas, including hydraulic processes such as water distribution canals for irrigation. This article presents the design and application of predictive control for the water discharge entering into an
Efficient predictive model-based and fuzzy control for green urban mobility
Jamshidnejad, A.
2017-01-01
In this thesis, we develop efficient predictive model-based control approaches, including model-predictive control (MPC) andmodel-based fuzzy control, for application in urban traffic networks with the aim of reducing a combination of the total time spent by the vehicles within the network and the
DEFF Research Database (Denmark)
Rivera, M.; Nasir, U.; Tarisciotti, L.
2017-01-01
The classic model predictive control presents a variable switching frequency which could produce high ripple in the controlled waveforms or resonances in the input filter of the matrix converter, affecting the performance of the system. This paper presents two model predictive control strategies...
Davis, Laurie Laughlin
2004-01-01
Choosing a strategy for controlling item exposure has become an integral part of test development for computerized adaptive testing (CAT). This study investigated the performance of six procedures for controlling item exposure in a series of simulated CATs under the generalized partial credit model. In addition to a no-exposure control baseline…
Glaesmer, Heide; Hallensleben, Nina; Forkmann, Thomas; Spangenberg, Lena; Kapusta, Nestor; Teismann, Tobias
2017-03-15
To evaluate the main prediction of the Interpersonal Theory of Suicide (IPTS): 3-way-interaction of perceived burdensomeness (PB), thwarted belongingness (TB), and acquired capability (AC) for the prediction of suicidal behavior in a representative population sample. A total of 2513 participants completed measures of suicidal behavior, TB, PB, acquired capability (AC-FAD), and symptoms of depression and anxiety. The two-way-interaction of TB and PB, and the three-way interaction of TB, PB and AC-FAD predict suicidality. Given the cross-sectional nature of the data, conclusions on causality should be handled carefully. The main prediction of the IPTS has been proven in a general population sample. Copyright © 2017 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Deyin Yao
2014-01-01
Full Text Available This paper deals with the problem of robust model predictive control (RMPC for a class of linear time-varying systems with constraints and data losses. We take the polytopic uncertainties into account to describe the uncertain systems. First, we design a robust state observer by using the linear matrix inequality (LMI constraints so that the original system state can be tracked. Second, the MPC gain is calculated by minimizing the upper bound of infinite horizon robust performance objective in terms of linear matrix inequality conditions. The method of robust MPC and state observer design is illustrated by a numerical example.
Including model uncertainty in the model predictive control with output feedback
Directory of Open Access Journals (Sweden)
Rodrigues M.A.
2002-01-01
Full Text Available This paper addresses the development of an efficient numerical output feedback robust model predictive controller for open-loop stable systems. Stability of the closed loop is guaranteed by using an infinite horizon predictive controller and a stable state observer. The performance and the computational burden of this approach are compared to a robust predictive controller from the literature. The case used for this study is based on an industrial gasoline debutanizer column.
Wheel slip control with torque blending using linear and nonlinear model predictive control
Basrah, M. Sofian; Siampis, Efstathios; Velenis, Efstathios; Cao, Dongpu; Longo, Stefano
2017-11-01
Modern hybrid electric vehicles employ electric braking to recuperate energy during deceleration. However, currently anti-lock braking system (ABS) functionality is delivered solely by friction brakes. Hence regenerative braking is typically deactivated at a low deceleration threshold in case high slip develops at the wheels and ABS activation is required. If blending of friction and electric braking can be achieved during ABS events, there would be no need to impose conservative thresholds for deactivation of regenerative braking and the recuperation capacity of the vehicle would increase significantly. In addition, electric actuators are typically significantly faster responding and would deliver better control of wheel slip than friction brakes. In this work we present a control strategy for ABS on a fully electric vehicle with each wheel independently driven by an electric machine and friction brake independently applied at each wheel. In particular we develop linear and nonlinear model predictive control strategies for optimal performance and enforcement of critical control and state constraints. The capability for real-time implementation of these controllers is assessed and their performance is validated in high fidelity simulation.
Bonne, F.; Alamir, M.; Bonnay, P.
2017-02-01
This paper deals with multivariable constrained model predictive control for Warm Compression Stations (WCS). WCSs are subject to numerous constraints (limits on pressures, actuators) that need to be satisfied using appropriate algorithms. The strategy is to replace all the PID loops controlling the WCS with an optimally designed model-based multivariable loop. This new strategy leads to high stability and fast disturbance rejection such as those induced by a turbine or a compressor stop, a key-aspect in the case of large scale cryogenic refrigeration. The proposed control scheme can be used to achieve precise control of pressures in normal operation or to avoid reaching stopping criteria (such as excessive pressures) under high disturbances (such as a pulsed heat load expected to take place in future fusion reactors, expected in the cryogenic cooling systems of the International Thermonuclear Experimental Reactor ITER or the Japan Torus-60 Super Advanced fusion experiment JT-60SA). The paper details the simulator used to validate this new control scheme and the associated simulation results on the SBTs WCS. This work is partially supported through the French National Research Agency (ANR), task agreement ANR-13-SEED-0005.
Predicting Academic Success: General Intelligence, "Big Five" Personality Traits, and Work Drive
Ridgell, Susan D.; Lounsbury, John W.
2004-01-01
General intelligence, Big Five personality traits, and the construct Work Drive were studied in relation to two measures of collegiate academic performance: a single course grade received by undergraduate students in an introductory psychology course, and self-reported GPA. General intelligence and Work Drive were found to be significantly…
Dynamical predictive power of the generalized Gibbs ensemble revealed in a second quench.
Zhang, J M; Cui, F C; Hu, Jiangping
2012-04-01
We show that a quenched and relaxed completely integrable system is hardly distinguishable from the corresponding generalized Gibbs ensemble in a dynamical sense. To be specific, the response of the quenched and relaxed system to a second quench can be accurately reproduced by using the generalized Gibbs ensemble as a substitute. Remarkably, as demonstrated with the transverse Ising model and the hard-core bosons in one dimension, not only the steady values but even the transient, relaxation dynamics of the physical variables can be accurately reproduced by using the generalized Gibbs ensemble as a pseudoinitial state. This result is an important complement to the previously established result that a quenched and relaxed system is hardly distinguishable from the generalized Gibbs ensemble in a static sense. The relevance of the generalized Gibbs ensemble in the nonequilibrium dynamics of completely integrable systems is then greatly strengthened.
Economic Model Predictive Control for Building Climate Control in a Smart Grid
DEFF Research Database (Denmark)
Halvgaard, Rasmus; Poulsen, Niels Kjølstad; Madsen, Henrik
2012-01-01
Model Predictive Control (MPC) can be used to control a system of energy producers and consumers in a Smart Grid. In this paper, we use heat pumps for heating residential buildings with a floor heating system. We use the thermal capacity of the building to shift the electricity consumptions...... to periods with low energy prices. In this way the heating system of the house becomes a flexible power consumer in the Smart Grid. This scenario is relevant for systems with a significant share of stochastic energy producers, e.g. wind turbines, where the ability to shift power consumption according...... and electricity price. Simulation studies demonstrate the capabilities of the proposed model and algorithm. Compared to traditional operation of heat pumps with constant electricity prices, the optimized operating strategy saves 25-33% of the electricity cost....
DomPep--a general method for predicting modular domain-mediated protein-protein interactions.
Directory of Open Access Journals (Sweden)
Lei Li
Full Text Available Protein-protein interactions (PPIs are frequently mediated by the binding of a modular domain in one protein to a short, linear peptide motif in its partner. The advent of proteomic methods such as peptide and protein arrays has led to the accumulation of a wealth of interaction data for modular interaction domains. Although several computational programs have been developed to predict modular domain-mediated PPI events, they are often restricted to a given domain type. We describe DomPep, a method that can potentially be used to predict PPIs mediated by any modular domains. DomPep combines proteomic data with sequence information to achieve high accuracy and high coverage in PPI prediction. Proteomic binding data were employed to determine a simple yet novel parameter Ligand-Binding Similarity which, in turn, is used to calibrate Domain Sequence Identity and Position-Weighted-Matrix distance, two parameters that are used in constructing prediction models. Moreover, DomPep can be used to predict PPIs for both domains with experimental binding data and those without. Using the PDZ and SH2 domain families as test cases, we show that DomPep can predict PPIs with accuracies superior to existing methods. To evaluate DomPep as a discovery tool, we deployed DomPep to identify interactions mediated by three human PDZ domains. Subsequent in-solution binding assays validated the high accuracy of DomPep in predicting authentic PPIs at the proteome scale. Because DomPep makes use of only interaction data and the primary sequence of a domain, it can be readily expanded to include other types of modular domains.
Peitsch, Lorraine; Tyas, Suzanne L; Menec, Verena H; St John, Philip D
2016-07-01
Low life satisfaction predicts adverse outcomes, and may predict dementia. The objectives were: (1) to determine if life satisfaction predicts dementia over a five year period in those with normal cognition at baseline; and (2) to determine if different aspects of life satisfaction differentially predict dementia. Secondary analysis of an existing population-based cohort study with initial assessment in 1991 and follow-up five years later. Initially, 1,751 adults age 65+ living in the community were sampled from a representative sampling frame. Of these, 1,024 were alive and had complete data at time 2, of whom 96 were diagnosed with dementia. Life satisfaction was measured using the Terrible-Delightful scale, which measures overall life satisfaction on a 7-point scale, as well as various aspects of life satisfaction (e.g. friendships, finances, etc.) Dementia was diagnosed by clinical examination using DSM-IIIR criteria. Logistic regression models were constructed for the outcome of dementia at time 2, and adjusted for age, gender, education, and comorbidities. Overall life satisfaction predicted dementia five years later, at time 2. The unadjusted Odds Ratio (OR; 95% confidence interval) for dementia at time 2 was 0.72 (0.55, 0.95) per point. The adjusted OR for dementia was 0.70 (0.51, 0.96). No individual item on the life satisfaction scale predicted dementia. However, the competing risk of mortality was very high for some items. A global single-item measure of life satisfaction predicts dementia over a five year period in older adults without cognitive impairment.
Image-Based Visual Servoing for Manipulation Via Predictive Control – A Survey of Some Results
Directory of Open Access Journals (Sweden)
Corneliu Lazăr
2016-09-01
Full Text Available In this paper, a review of predictive control algorithms developed by the authors for visual servoing of robots in manipulation applications is presented. Using these algorithms, a control predictive framework was created for image-based visual servoing (IBVS systems. Firstly, considering the point features, in the year 2008 we introduced an internal model predictor based on the interaction matrix. Secondly, distinctly from the set-point trajectory, we introduced in 2011 the reference trajectory using the concept from predictive control. Finally, minimizing a sum of squares of predicted errors, the optimal input trajectory was obtained. The new concept of predictive control for IBVS systems was employed to develop a cascade structure for motion control of robot arms. Simulation results obtained with a simulator for predictive IBVS systems are also presented.
DEFF Research Database (Denmark)
Jiang, Hao; Lin, Jin; Song, Yonghua
2016-01-01
Model predictive control (MPC), that can consider system constraints, is one of the most advanced control technology used nowadays. In power systems, MPC is applied in a way that an optimal control sequence is given every step by an online MPC controller. The main drawback is that the control law...
Maughmer, Mark D.; Ozoroski, L.; Ozoroski, T.; Straussfogel, D.
1990-01-01
Many types of hypersonic aircraft configurations are currently being studied for feasibility of future development. Since the control of the hypersonic configurations throughout the speed range has a major impact on acceptable designs, it must be considered in the conceptual design stage. The ability of the aerodynamic analysis methods contained in an industry standard conceptual design system, APAS II, to estimate the forces and moments generated through control surface deflections from low subsonic to high hypersonic speeds is considered. Predicted control forces and moments generated by various control effectors are compared with previously published wind tunnel and flight test data for three configurations: the North American X-15, the Space Shuttle Orbiter, and a hypersonic research airplane concept. Qualitative summaries of the results are given for each longitudinal force and moment and each control derivative in the various speed ranges. Results show that all predictions of longitudinal stability and control derivatives are acceptable for use at the conceptual design stage. Results for most lateral/directional control derivatives are acceptable for conceptual design purposes; however, predictions at supersonic Mach numbers for the change in yawing moment due to aileron deflection and the change in rolling moment due to rudder deflection are found to be unacceptable. Including shielding effects in the analysis is shown to have little effect on lift and pitching moment predictions while improving drag predictions.
Westenberg, HGM; Stein, DJ; Yang, HC; Li, D; Barbato, LM
This was a randomized double-blind placebo-controlled multicenter study to assess the efficacy, safety, and tolerability of fluvoxamine in a controlled release (CR) formulation for treatment of generalized social anxiety disorder (GSAD). A total of 300 subjects with GSAD were randomly assigned to
A Decoupling Control Method for Shunt Hybrid Active Power Filter Based on Generalized Inverse System
Directory of Open Access Journals (Sweden)
Xin Li
2017-01-01
Full Text Available In this paper, a novel decoupling control method based on generalized inverse system is presented to solve the problem of SHAPF (Shunt Hybrid Active Power Filter possessing the characteristics of 2-input-2-output nonlinearity and strong coupling. Based on the analysis of operation principle, the mathematical model of SHAPF is firstly built, which is verified to be invertible using interactor algorithm; then the generalized inverse system of SHAPF is obtained to connect in series with the original system so that the composite system is decoupled under the generalized inverse system theory. The PI additional controller is finally designed to control the decoupled 1-order pseudolinear system to make it possible to adjust the performance of the subsystem. The simulation results demonstrated by MATLAB show that the presented generalized inverse system strategy can realise the dynamic decoupling of SHAPF. And the control system has fine dynamic and static performance.
Predicting timing performance of advanced mechatronics control systems
Voeten, J.P.M.; Hendriks, T.; Theelen, B.D.; Schuddemat, J.; Tabingh Suermondt, W.; Gemei, J.; Kotterink, C.; Huet, van J.; Eichler, G.; Kuepper, A.; Schau, V.; Fouchal, H.; Unger, H.
2011-01-01
Embedded control is a key product technology differentiator for many high-tech industries, including ASML. The strong increase in complexity of embedded control systems, combined with the occurrence of late changes in control requirements, results in many timing performance problems showing up only
Prediction in Partial Duration Series With Generalized Pareto-Distributed Exceedances
DEFF Research Database (Denmark)
Rosbjerg, Dan; Madsen, Henrik; Rasmussen, Peter Funder
1992-01-01
As a generalization of the common assumption of exponential distribution of the exceedances in Partial duration series the generalized Pareto distribution has been adopted. Estimators for the parameters are presented using estimation by both method of moments and probability-weighted moments......-weighted moments. Maintaining the generalized Pareto distribution as the parent exceedance distribution the T-year event is estimated assuming the exceedances to be exponentially distributed. For moderately long-tailed exceedance distributions and small to moderate sample sizes it is found, by comparing mean...... square errors of the T-year event estimators, that the exponential distribution is preferable to the correct generalized Pareto distribution despite the introduced model error and despite a possible rejection of the exponential hypothesis by a test of significance. For moderately short-tailed exceedance...
International Nuclear Information System (INIS)
Yock, Adam D.; Kudchadker, Rajat J.; Rao, Arvind; Dong, Lei; Beadle, Beth M.; Garden, Adam S.; Court, Laurence E.
2014-01-01
Purpose: The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Methods: Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. Results: In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: −11.6%–23.8%) and 14.6% (range: −7.3%–27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: −6.8%–40.3%) and 13.1% (range: −1.5%–52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: −11.1%–20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. Conclusions: A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography
Genetic variation in ABCA1 predicts ischemic heart disease in the general population
DEFF Research Database (Denmark)
Frikke-Schmidt, Ruth; Nordestgaard, BG; Jensen, Gorm B
2008-01-01
We tested the hypothesis that 6 nonsynonymous single nucleotide polymorphisms (SNPs) in ATP-Binding-Cassette transporter A1 (ABCA1) affect risk of ischemic heart disease (IHD) in the general population.......We tested the hypothesis that 6 nonsynonymous single nucleotide polymorphisms (SNPs) in ATP-Binding-Cassette transporter A1 (ABCA1) affect risk of ischemic heart disease (IHD) in the general population....
Factors predicting team climate, and its relationship with quality of care in general practice
Goh, Teik T; Eccles, Martin P; Steen, Nick
2009-01-01
Abstract Background Quality of care in general practice may be affected by the team climate perceived by its health and non-health professionals. Better team working is thought to lead to higher effectiveness and quality of care. However, there is limited evidence available on what affects team functioning and its relationship with quality of care in general practice. This study aimed to explore individual and practice factors that were associated with team climate, and to explore the relatio...
Prediction in Partial Duration Series With Generalized Pareto-Distributed Exceedances
DEFF Research Database (Denmark)
Rosbjerg, Dan; Madsen, Henrik; Rasmussen, Peter Funder
1992-01-01
As a generalization of the common assumption of exponential distribution of the exceedances in Partial duration series the generalized Pareto distribution has been adopted. Estimators for the parameters are presented using estimation by both method of moments and probability-weighted moments...... distributions (with physically justified upper limit) the correct exceedance distribution should be applied despite a possible acceptance of the exponential assumption by a test of significance....
Model Predictive Control for an Industrial SAG Mill
DEFF Research Database (Denmark)
Ohan, Valeriu; Steinke, Florian; Metzger, Michael
2012-01-01
identication. When applied to MIMO systems we call this controller a MIMO-ARX based MPC. We use an industrial Semi-Autogenous Grinding (SAG) mill to illustrate the performance of this controller. SAG mills are the primary units in a grinding chain and also the most power consuming units. Therefore, improved...... control of SAG mills has the potential to signicantly improve eciency and reduce the specic energy consumption for mineral processes. Grinding circuits involving SAG mills are multivariate processes. Commissioning of a control system based on a classical single-loop controllers with logic is time...
Zhang, Jianming
2017-03-01
An improved proportional-integral-derivative (PID) controller based on predictive functional control (PFC) is proposed and tested on the chamber pressure in an industrial coke furnace. The proposed design is motivated by the fact that PID controllers for industrial processes with time delay may not achieve the desired control performance because of the unavoidable model/plant mismatches, while model predictive control (MPC) is suitable for such situations. In this paper, PID control and PFC algorithm are combined to form a new PID controller that has the basic characteristic of PFC algorithm and at the same time, the simple structure of traditional PID controller. The proposed controller was tested in terms of set-point tracking and disturbance rejection, where the obtained results showed that the proposed controller had the better ensemble performance compared with traditional PID controllers. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Validation of a predictive model for smart control of electrical energy storage
Homan, Bart; van Leeuwen, Richard Pieter; Smit, Gerardus Johannes Maria; Zhu, Lei; de Wit, Jan B.
2016-01-01
The purpose of this paper is to investigate the applicability of a relatively simple model which is based on energy conservation for model predictions as part of smart control of thermal and electric storage. The paper reviews commonly used predictive models. Model predictions of charging and
Hughes, Gethin; Desantis, Andrea; Waszak, Florian
2013-01-01
Sensory processing of action effects has been shown to differ from that of externally triggered stimuli, with respect both to the perceived timing of their occurrence (intentional binding) and to their intensity (sensory attenuation). These phenomena are normally attributed to forward action models, such that when action prediction is consistent…
2014-07-01
Macmillan & Creelman , 2005). This is a quite high degree of discriminability and it means that when the decision model predicts a probability of...ROC analysis. Pattern Recognition Letters, 27(8), 861-874. Retrieved from Google Scholar. Macmillan, N. A., & Creelman , C. D. (2005). Detection
Leading-Edge Noise Prediction of General Airfoil Profiles with Spanwise-Varying Inflow Conditions
Miotto, Renato Fuzaro; Wolf, William Roberto; De Santana, Leandro Dantas
2018-01-01
This paper presents a study of the leading-edge noise radiated by an airfoil undergoing a turbulent inflow. The noise prediction of generic airfoil profiles subjected to spanwise-varying inflow conditions is performed with the support of Amiet’s theory and the inverse strip technique. In the
Leading-Edge Noise Prediction of General Airfoil Profiles with Spanwise-Varying Inflow Conditions
Miotto, Renato Fuzaro; Wolf, William Roberto; De Santana, Leandro Dantas
This paper presents a study of the leading-edge noise radiated by an airfoil undergoing a turbulent inflow. The noise prediction of generic airfoil profiles subjected to spanwise-varying inflow conditions is performed with the support of Amiet’s theory and the inverse strip technique. In the
Mata, Jutta; Silva, Marlene N; Vieira, Paulo N; Carraça, Eliana V; Andrade, Ana M; Coutinho, Sílvia R; Sardinha, Luis B; Teixeira, Pedro J
2009-11-01
Successful weight management relies on at least two health behaviors, eating and exercise. However, little is known about their interaction on a motivational and behavioral level. Based on the Hierarchical Model of Motivation the authors examined whether exercise-specific motivation can transfer to eating regulation during a lifestyle weight control program. The authors further investigated whether general, treatment-related, and exercise motivation underlie the relation between increased exercise and improved eating regulation. Overweight/obese women participated in a 1-year randomized controlled trial (N = 239). The intervention focused on promoting physical activity and internal motivation for exercise and weight loss, following Self-Determination Theory. The control group received general health education. General and exercise specific self-determination, eating self-regulation variables, and physical activity behavior. General self-determination and more autonomous exercise motivation predicted eating self-regulation over 12 months. Additionally, general and exercise self-determination fully mediated the relation between physical activity and eating self-regulation. Increased general self-determination and exercise motivation seem to facilitate improvements in eating self-regulation during weight control in women. These motivational mechanisms also underlie the relationship between improvements in exercise behavior and eating regulation. PsycINFO Database Record (c) 2009 APA, all rights reserved.
Patterson, Fiona; Lievens, Filip; Kerrin, Máire; Munro, Neil; Irish, Bill
2013-11-01
The selection methodology for UK general practice is designed to accommodate several thousand applicants per year and targets six core attributes identified in a multi-method job-analysis study To evaluate the predictive validity of selection methods for entry into postgraduate training, comprising a clinical problem-solving test, a situational judgement test, and a selection centre. A three-part longitudinal predictive validity study of selection into training for UK general practice. In sample 1, participants were junior doctors applying for training in general practice (n = 6824). In sample 2, participants were GP registrars 1 year into training (n = 196). In sample 3, participants were GP registrars sitting the licensing examination after 3 years, at the end of training (n = 2292). The outcome measures include: assessor ratings of performance in a selection centre comprising job simulation exercises (sample 1); supervisor ratings of trainee job performance 1 year into training (sample 2); and licensing examination results, including an applied knowledge examination and a 12-station clinical skills objective structured clinical examination (OSCE; sample 3). Performance ratings at selection predicted subsequent supervisor ratings of job performance 1 year later. Selection results also significantly predicted performance on both the clinical skills OSCE and applied knowledge examination for licensing at the end of training. In combination, these longitudinal findings provide good evidence of the predictive validity of the selection methods, and are the first reported for entry into postgraduate training. Results show that the best predictor of work performance and training outcomes is a combination of a clinical problem-solving test, a situational judgement test, and a selection centre. Implications for selection methods for all postgraduate specialties are considered.
Poteat, V Paul; DiGiovanni, Craig D; Scheer, Jillian R
2013-03-01
As a form of bias-based harassment, homophobic behavior remains prominent in schools. Yet, little attention has been given to factors that underlie it, aside from bullying and sexual prejudice. Thus, we examined multiple domain general (empathy, perspective-taking, classroom respect norms) and sexual orientation-specific factors (sexual orientation identity importance, number of sexual minority friends, parents' sexual minority attitudes, media messages). We documented support for a model in which these sets of factors converged to predict homophobic behavior, mediated through bullying and prejudice, among 581 students in grades 9-12 (55 % female). The structural equation model indicated that, with the exception of media messages, these additional factors predicted levels of prejudice and bullying, which in turn predicted the likelihood of students to engage in homophobic behavior. These findings highlight the importance of addressing multiple interrelated factors in efforts to reduce bullying, prejudice, and discrimination among youth.
Predictive Models and Tools for Assessing Chemicals under the Toxic Substances Control Act (TSCA)
EPA has developed databases and predictive models to help evaluate the hazard, exposure, and risk of chemicals released to the environment and how workers, the general public, and the environment may be exposed to and affected by them.
PID and predictive control of electrical drives and power converters using MATLAB/Simulink
Wang, Liuping; Yoo, Dae; Gan, Lu; Ng, Ki
2015-01-01
A timely introduction to current research on PID and predictive control by one of the leading authors on the subject PID and Predictive Control of Electric Drives and Power Supplies using MATLAB/Simulink examines the classical control system strategies, such as PID control, feed-forward control and cascade control, which are widely used in current practice. The authors share their experiences in actual design and implementation of the control systems on laboratory test-beds, taking the reader from the fundamentals through to more sophisticated design and analysis. The book contains secti
A Building Model Framework for a Genetic Algorithm Multi-objective Model Predictive Control
DEFF Research Database (Denmark)
Arendt, Krzysztof; Ionesi, Ana; Jradi, Muhyiddine
2016-01-01
Model Predictive Control (MPC) of building systems is a promising approach to optimize building energy performance. In contrast to traditional control strategies which are reactive in nature, MPC optimizes the utilization of resources based on the predicted effects. It has been shown that energy ...
Saltik, M.B.; Özkan, L.; Ludlage, J.H.A.; Weiland, S.; Van den Hof, P.M.J.
2018-01-01
In this paper, we discuss the model predictive control algorithms that are tailored for uncertain systems. Robustness notions with respect to both deterministic (or set based) and stochastic uncertainties are discussed and contributions are reviewed in the model predictive control literature. We
Directory of Open Access Journals (Sweden)
Lorena Ruiz-González
2015-12-01
Full Text Available Abstract The aim of this study was to test the predictive power of dispositional orientations, general self-efficacy and self-determined motivation on fun and boredom in physical education classes, with a sample of 459 adolescents between 13 and 18 with a mean age of 15 years (SD = 0.88. The adolescents responded to four Likert scales: Perceptions of Success Questionnaire, General Self-Efficacy Scale, Sport Motivation Scale and Intrinsic Satisfaction Questionnaire in Sport. The results showed the structural regression model showed that task orientation and general self-efficacy positively predicted self-determined motivation and this in turn positively predicted more fun and less boredom in physical education classes. Consequently, the promotion of an educational task-oriented environment where learners perceive their progress and make them feel more competent, will allow them to overcome the intrinsically motivated tasks, and therefore they will have more fun. Pedagogical implications for less boredom and more fun in physical education classes are discussed.
International Nuclear Information System (INIS)
Metz, C.E.; Starr, S.J.; Lusted, L.B.
1975-01-01
The theories of decision processes and signal detection provide a framework for the evaluation of observer performance. Some radiologic procedures involve a search for multiple similar lesions, as in gallstone or pneumoconiosis examinations. A model is presented which attempts to predict, from the conventional receiver operating characteristic (ROC) curve describing the detectability of a single visual signal in a radiograph, observer performance in an experiment requiring detection of more than one such signal. An experiment is described which tests the validity of this model for the case of detecting the presence of zero, one, or two low-contrast radiographic images of a two-mm.-diameter lucite bead embedded in radiographic mottle. Results from six observers, including three radiologists, confirm the validity of the model and suggest that human observer performance for relatively complex detection tasks can be predicted from the results of simpler experiments
Just-In-Time predictive control for a two-wheeled robot
Nakpong, Nuttapun; Yamamoto, Shigeru
2012-01-01
In this paper, we introduce the use of Just-In-Time predictive control to enhance the stability of a two-wheeled robot. Just-In-Time predictive control uses a database which includes a huge amounts of input-output data of the two-wheeled robot and predicts its future movements based on a Just-In-Time algorithm. © 2012 IEEE.
Application of Predictive Control in District Heating Systems
DEFF Research Database (Denmark)
Palsson, Olafur Petur; Madsen, Henrik; Søgaard, Henning Tangen
1993-01-01
In district heating systems, and in particular if the heat production cakes place at a combined heat and power (CHP) plant, a reasonable control strategy is to keep the supply temperature from the district heating plant as low as possible. However, the control is subject to some restrictions, for...
Robust, Optimal, Predictive, and Integrated Road Traffic Control : Research proposal
Van de Weg, G.S.; Hegyi, A.; Hoogendoorn, S.P.
2014-01-01
The development of control strategies for traffic lights, ramp metering installations, and variable speed limits to improve the throughput of road traffic networks can contribute to a more efficient use of road networks. In this project, a hierarchical controller will be developed for the
Self-organization of critical behavior in controlled general queueing models
International Nuclear Information System (INIS)
Blanchard, Ph.; Hongler, M.-O.
2004-01-01
We consider general queueing models of the (G/G/1) type with service times controlled by the busy period. For feedback control mechanisms driving the system to very high traffic load, it is shown the busy period probability density exhibits a generic -((3)/(2)) power law which is a typical mean field behavior of SOC models
A Comparison of Item Exposure Control Procedures with the Generalized Partial Credit Model
Sanchez, Edgar Isaac
2008-01-01
To enhance test security of high stakes tests, it is vital to understand the way various exposure control strategies function under various IRT models. To that end the present dissertation focused on the performance of several exposure control strategies under the generalized partial credit model with an item pool of 100 and 200 items. These…
Self-organization of critical behavior in controlled general queueing models
Blanchard, Ph.; Hongler, M.-O.
2004-03-01
We consider general queueing models of the (G/G/1) type with service times controlled by the busy period. For feedback control mechanisms driving the system to very high traffic load, it is shown the busy period probability density exhibits a generic - {3}/{2} power law which is a typical mean field behavior of SOC models.
Nonlinear model predictive control for chemical looping process
Joshi, Abhinaya; Lei, Hao; Lou, Xinsheng
2017-08-22
A control system for optimizing a chemical looping ("CL") plant includes a reduced order mathematical model ("ROM") that is designed by eliminating mathematical terms that have minimal effect on the outcome. A non-linear optimizer provides various inputs to the ROM and monitors the outputs to determine the optimum inputs that are then provided to the CL plant. An estimator estimates the values of various internal state variables of the CL plant. The system has one structure adapted to control a CL plant that only provides pressure measurements in the CL loops A and B, a second structure adapted to a CL plant that provides pressure measurements and solid levels in both loops A, and B, and a third structure adapted to control a CL plant that provides full information on internal state variables. A final structure provides a neural network NMPC controller to control operation of loops A and B.
SIMULATION AND PREDICTION OF THE PROCESS BASED ON THE GENERAL LOGISTIC MAPPING
Directory of Open Access Journals (Sweden)
V. V. Skalozub
2013-11-01
Full Text Available Purpose. The aim of the research is to build a model of the generalzed logistic mapping and assessment of the possibilities of its use for the formation of the mathematical description, as well as operational forecasts of parameters of complex dynamic processes described by the time series. Methodology. The research results are obtained on the basis of mathematical modeling and simulation of nonlinear systems using the tools of chaotic dynamics. Findings. A model of the generalized logistic mapping, which is used to interpret the characteristics of dynamic processes was proposed. We consider some examples of representations of processes based on enhanced logistic mapping varying the values of model parameters. The procedures of modeling and interpretation of the data on the investigated processes, represented by the time series, as well as the operational forecasting of parameters using the generalized model of logistic mapping were proposed. Originality. The paper proposes an improved mathematical model, generalized logistic mapping, designed for the study of nonlinear discrete dynamic processes. Practical value. The carried out research using the generalized logistic mapping of railway transport processes, in particular, according to assessment of the parameters of traffic volumes, indicate the great potential of its application in practice for solving problems of analysis, modeling and forecasting complex nonlinear discrete dynamical processes. The proposed model can be used, taking into account the conditions of uncertainty, irregularity, the manifestations of the chaotic nature of the technical, economic and other processes, including the railway ones.
International Nuclear Information System (INIS)
Rammant, J.P.; Van Laethem, L.; Backx, E.
1977-01-01
The effect of steel fibre reinforcement on the mechanical behavior of concrete reactor structures is studied. It is shown that this material leads to a higher safety factor for highly stressed concrete structures like prestressed concrete pressure vessels. The reinforcement of concrete with short steel fibres results clearly in a fundamental change of the material properties. The study comprises basic experiments, the elaboration of an expression of the material laws, the development of a general computer program and the comparison of computational results with more elaborate experiments. Basic experimental work is conducted to determine the material characteristics of the fibre reinforced concrete. It is shown how the fibre reinforcement mechanism is translated into mathematical formulae by expressing the principal characteristics as matrix relationships. These relationships describe the elasto-plastic behavior and the cracked behavior. Probabilistic principles are used to express to fibre efficiency, such that a general stress-strain relationship is incorporated in a subsequent computer program. A general finite element program is developed which includes the new matrix relationships, the pull-out of fibres and the general stress-strain equations. A nonlinear calculation method gives the propagation of the distributed cracks with increasing load untill failure of the structure. Similarly, thermal cycling conditions are accounted for. For example the crack propagation in a fibre reinforced beam was measured by the photostress coating technique: the comparison with the computed crack propagation reveals an excellent agreement. Other comparative studies on simple structural parts are also reported
Factors predicting team climate, and its relationship with quality of care in general practice
Directory of Open Access Journals (Sweden)
Eccles Martin P
2009-08-01
Full Text Available Abstract Background Quality of care in general practice may be affected by the team climate perceived by its health and non-health professionals. Better team working is thought to lead to higher effectiveness and quality of care. However, there is limited evidence available on what affects team functioning and its relationship with quality of care in general practice. This study aimed to explore individual and practice factors that were associated with team climate, and to explore the relationship between team climate and quality of care. Methods Cross sectional survey of a convenience sample of 14 general practices and their staff in South Tyneside in the northeast of England. Team climate was measured using the short version of Team Climate Inventory (TCI questionnaire. Practice characteristics were collected during a structured interview with practice managers. Quality was measured using the practice Quality and Outcome Framework (QOF scores. Results General Practitioners (GP had a higher team climate scores compared to other professionals. Individual's gender and tenure, and number of GPs in the practice were significantly predictors of a higher team climate. There was no significant correlation between mean practice team climate scores (or subscales with QOF scores. Conclusion The absence of a relationship between a measure of team climate and quality of care in this exploratory study may be due to a number of methodological problems. Further research is required to explore how to best measure team functioning and its relationship with quality of care.
Platsidou, Maria; Agaliotis, Ioannis
2017-01-01
The role of empathy in the teaching profession has been vastly investigated in relation to its effect on students, but research on how teachers' empathy affects their own well-being at work is limited. This study investigated empathy and instructional assignment-related stress factors of primary school teachers serving in general or special…
Factors predicting team climate, and its relationship with quality of care in general practice.
Goh, Teik T; Eccles, Martin P; Steen, Nick
2009-08-04
Quality of care in general practice may be affected by the team climate perceived by its health and non-health professionals. Better team working is thought to lead to higher effectiveness and quality of care. However, there is limited evidence available on what affects team functioning and its relationship with quality of care in general practice. This study aimed to explore individual and practice factors that were associated with team climate, and to explore the relationship between team climate and quality of care. Cross sectional survey of a convenience sample of 14 general practices and their staff in South Tyneside in the northeast of England. Team climate was measured using the short version of Team Climate Inventory (TCI) questionnaire. Practice characteristics were collected during a structured interview with practice managers. Quality was measured using the practice Quality and Outcome Framework (QOF) scores. General Practitioners (GP) had a higher team climate scores compared to other professionals. Individual's gender and tenure, and number of GPs in the practice were significantly predictors of a higher team climate. There was no significant correlation between mean practice team climate scores (or subscales) with QOF scores. The absence of a relationship between a measure of team climate and quality of care in this exploratory study may be due to a number of methodological problems. Further research is required to explore how to best measure team functioning and its relationship with quality of care.
PREDICTIVE VALUE OF GENERAL MOVEMENTS IN ASPHYXIATED FULL-TERM INFANTS
PRECHTL, HFR; FERRARI, F; CIONI, G
1993-01-01
The developmental course of spontaneous motility was investigated in a group of 26 fullterm infants, affected by mild to severe hypoxic-ischaemic encephalopathy. Serial 1-h videorecordings were carried out from birth to 15-22 weeks and a quality assessment of general movements (GMs) was made from a
Cooper, Cameron I.; Pearson, Paul T.
2012-01-01
In higher education, many high-enrollment introductory courses have evolved into "gatekeeper" courses due to their high failure rates. These courses prevent many students from attaining their educational goals and often become graduation roadblocks. At the authors' home institution, general chemistry has become a gatekeeper course in which…
Lonigan, Christopher J; Spiegel, Jamie A; Goodrich, J Marc; Morris, Brittany M; Osborne, Colleen M; Lerner, Matthew D; Phillips, Beth M
2017-11-01
Findings from prior research have consistently indicated significant associations between self-regulation and externalizing behaviors. Significant associations have also been reported between children's language skills and both externalizing behaviors and self-regulation. Few studies to date, however, have examined these relations longitudinally, simultaneously, or with respect to unique clusters of externalizing problems. The current study examined the influence of preschool self-regulation on general and specific externalizing behavior problems in early elementary school and whether these relations were independent of associations between language, self-regulation, and externalizing behaviors in a sample of 815 children (44% female). Additionally, given a general pattern of sex differences in the presentations of externalizing behavior problems, self-regulation, and language skills, sex differences for these associations were examined. Results indicated unique relations of preschool self-regulation and language with both general externalizing behavior problems and specific problems of inattention. In general, self-regulation was a stronger longitudinal correlate of externalizing behavior for boys than it was for girls, and language was a stronger longitudinal predictor of hyperactive/impulsive behavior for girls than it was for boys.
Modeling generalized interline power-flow controller (GIPFC using 48-pulse voltage source converters
Directory of Open Access Journals (Sweden)
Amir Ghorbani
2018-05-01
Full Text Available Generalized interline power-flow controller (GIPFC is one of the voltage-source controller (VSC-based flexible AC transmission system (FACTS controllers that can independently regulate the power-flow over each transmission line of a multiline system. This paper presents the modeling and performance analysis of GIPFC based on 48-pulsed voltage-source converters. This paper deals with a cascaded multilevel converter model, which is a 48-pulse (three levels voltage source converter. The voltage source converter described in this paper is a harmonic neutralized, 48-pulse GTO converter. The GIPFC controller is based on d-q orthogonal coordinates. The algorithm is verified using simulations in MATLAB/Simulink environment. Comparisons between unified power flow controller (UPFC and GIPFC are also included. Keywords: Generalized interline power-flow controller (GIPFC, Voltage source converter (VCS, 48-pulse GTO converter
Badre, David; Wagner, Anthony D
2004-02-05
Prefrontal cortex (PFC) supports flexible behavior by mediating cognitive control, though the elemental forms of control supported by PFC remain a central debate. Dorsolateral PFC (DLPFC) is thought to guide response selection under conditions of response conflict or, alternatively, may refresh recently active representations within working memory. Lateral frontopolar cortex (FPC) may also adjudicate response conflict, though others propose that FPC supports higher order control processes such as subgoaling and integration. Anterior cingulate cortex (ACC) is hypothesized to upregulate response selection by detecting response conflict; it remains unclear whether ACC functions generalize beyond monitoring response conflict. The present fMRI experiment directly tested these competing theories regarding the functional roles of DLPFC, FPC, and ACC. Results reveal dissociable control processes in PFC, with mid-DLPFC selectively mediating resolution of response conflict and FPC further mediating subgoaling/integration. ACC demonstrated a broad sensitivity to control demands, suggesting a generalized role in modulating cognitive control.
Gain-Scheduled Model Predictive Control of Wind Turbines using Laguerre Functions
DEFF Research Database (Denmark)
Adegas, Fabiano Daher; Wisniewski, Rafal; Larsen, Lars Finn Sloth
2014-01-01
This paper presents a systematic approach to design gain-scheduled predictive controllers for wind turbines. The predictive control law is based on Laguerre functions to parameterize control signals and a parameter-dependent cost function that is analytically determined from turbine data....... These properties facilitate the design of speed controllers by placement of the closed-loop poles (when constraints are not active) and systematic adaptation towards changes in the operating point. Vibration control of undamped modes is achieved by imposing a certain degree of stability to the closed-loop system....... The approach can be utilized to the design of new controllers and to represent existing gain-scheduled controllers as predictive controllers. The numerical example and simulations illustrate the design of a speed controller augmented with active damping of the tower fore-aft displacement....
Stability of a neural predictive controller scheme on a neural model
DEFF Research Database (Denmark)
Luther, Jim Benjamin; Sørensen, Paul Haase
2009-01-01
In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue....... The resulting controller is tested on a nonlinear pneumatic servo system.......In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue...... has not been addressed specifically for these controllers. On the other hand a number of results concerning the stability of receding horizon controllers on a nonlinear system exist. In this paper we present a proof of stability for a predictive controller controlling a neural network model...
Predictive Duty Cycle Control of Three-Phase Active-Front-End Rectifiers
DEFF Research Database (Denmark)
Song, Zhanfeng; Tian, Yanjun; Chen, Wei
2016-01-01
This paper proposed an on-line optimizing duty cycle control approach for three-phase active-front-end rectifiers, aiming to obtain the optimal control actions under different operating conditions. Similar to finite control set model predictive control strategy, a cost function previously...
Model Predictive Control for the acquisition queue and related queueing networks
Leeuwaarden, van J.S.H.; Lefeber, A.A.J.; Nazarathy, J.; Rooda, J.E.
2010-01-01
Model Predictive Control (MPC) is a well established method in control theory and engineering practice. It is often the method of choice for systems that need to be controlled in view of constraints. The main idea of MPC is to solve an optimization problem over a given time horizon at each control
A Wind Tunnel Model to Explore Unsteady Circulation Control for General Aviation Applications
Cagle, Christopher M.; Jones, Gregory S.
2002-01-01
Circulation Control airfoils have been demonstrated to provide substantial improvements in lift over conventional airfoils. The General Aviation Circular Control model is an attempt to address some of the concerns of this technique. The primary focus is to substantially reduce the amount of air mass flow by implementing unsteady flow. This paper describes a wind tunnel model that implements unsteady circulation control by pulsing internal pneumatic valves and details some preliminary results from the first test entry.
Recent Advances in Explicit Multiparametric Nonlinear Model Predictive Control
Domínguez, Luis F.; Pistikopoulos, Efstratios N.
2011-01-01
are derived. The performance of the explicit controllers are then tested and compared in a simulation example involving the operation of a continuous stirred-tank reactor (CSTR). © 2010 American Chemical Society.
Lorbeer, Roberto; Ittermann, Till; Völzke, Henry; Gläser, Sven; Ewert, Ralf; Felix, Stephan B; Dörr, Marcus
2015-07-01
Cutoff values for increased exercise blood pressure (BP) are not established in hypertension guidelines. The aim of the study was to assess optimal cutoff values for increased exercise BP to predict incident hypertension. Data of 661 normotensive participants (386 women) aged 25-77 years from the Study of Health in Pomerania (SHIP-1) with a 5-year follow-up were used. Exercise BP was measured at a submaximal level of 100 W and at maximum level of a symptom-limited cycle ergometry test. Cutoff values for increased exercise BP were defined at the maximum sum of sensitivity and specificity for the prediction of incident hypertension. The area under the receiver-operating characteristic curve (AUC) and net reclassification index (NRI) were calculated to investigate whether increased exercise BP adds predictive value for incident hypertension beyond established cardiovascular risk factors. In men, values of 160 mmHg (100 W level; AUC = 0.7837; NRI = 0.534, P AUC = 0.7677; NRI = 0.340, P = 0.003) were detected as optimal cutoff values for the definition of increased exercise SBP. A value of 190 mmHg (AUC = 0.8347; NRI = 0.519, P < 0.001) showed relevance for the definition of increased exercise SBP in women at the maximum level. According to our analyses, 190 and 210 mmHg are clinically relevant cutoff values for increased exercise SBP at the maximum exercise level of cycle ergometry test for women and men, respectively. In addition, for men, our analyses provided a cutoff value of 160 mmHg for increased exercise SBP at the 100 W level.
Multi-Objective Nonlinear Model Predictive Control: Lexicographic Method
Zheng, Tao; Wu, Gang; Liu, Guang-Hong; Ling, Qing
2010-01-01
In this chapter, to avoid the disadvantages of weight coefficients in multi-objective dynamic optimization, lexicographic (completely stratified) and partially stratified frameworks of multi-objective controller are proposed. The lexicographic framework is absolutely prioritydriven and the partially stratified framework is a modification of it, they both can solve the multi-objective control problem with the concept of priority for objectiveâ€™s relative importance, while the latter one is mo...
Wind tunnel testing to predict control room atmospheric dispersion factors
International Nuclear Information System (INIS)
Holmquist, L.J.; Harden, P.A.; Muraida, J.E.
1993-01-01
Recent concerns at Palisades about control room habitability in the event of a loss-of-coolant accident have led to an extensive effort to increase control room habitability margin. The heating, ventilating and air-conditioning (HVAC) system servicing the control room has the potential for unfiltered in-leakage through its normal outside air intake louvered isolation dampers during emergency mode. The current limiting control room habitability analysis allows for 1.2 x 10 -2 m 3 /s (25 ft 3 /min) unfiltered in-leakage into the control room envelope. This leakage value was not thought to be achievable with the existing as-built configuration. Repairing the system was considered as a potential solution; however, this would be costly and could negatively affect plant operation. In addition, the system would still be required to meet the low specified unfiltered in-leakage. A second approach to this problem was to determine the atmospheric dispersion factors (x/Q's) through a wind tunnel test using a scale model of Palisades. The results of the wind tunnel testing could yield more realistic x/Q's for control room habitability than previously employed methods. Palisades selected the wind tunnel study option based on its ease of implementation, realistic results, and low cost. More importantly, the results of the study could increase the allowable unfiltered in-leakage
Do depressive traits and hostility predict age-related decline in general intelligence?
DEFF Research Database (Denmark)
Mortensen, Erik Lykke; Barefoot, John Calvin; Avlund, Kirsten
2012-01-01
on decline in general intelligence over a 30-year period. The Minnesota Multiphasic Personality Inventory was administered at a 50-year baseline exam, and from this inventory the Obvious Depression Scale and an abbreviated version of the Cook-Medley Hostility Scale were derived. At the 50-year baseline...... and at the 60-, 70-, and 80-year followups the full version of Wechsler's Adult Intelligence Scale (WAIS) was administered to 673, 513, 136, and 184 participants. Mixed effects statistical models were used to evaluate both the effect of the personality scores on level of intelligence and the interaction between...... the personality scores and the time since followup. Analyses were adjusted for demographic background and a wide range of lifestyle factors. Both obvious depression and hostility were negatively associated with level of intelligence, but personality scores did not influence rate of decline in general intelligence....
DEFF Research Database (Denmark)
Hildebrandt, Per
2009-01-01
(General Practitioner) setting as in the acute setting. Supporting this use is a very strong prognostic value of the natriuretic peptides. This has been shown in as well heart failure as acute coronary syndromes, but also in the general population and in high-risk groups as patients with diabetes......, hypertension and coronary artery disease. This has of course raised interest for the use of the natriuretic peptides as a risk marker and for screening for heart failure with reduced systolic function in these populations. In symptomatic persons and in high risk populations, the natriuretic peptides have...... demonstrated a high sensitivity for ruling out the disease, if the right decision limits are choosen. Thus the number of normal echocardiographies can be reduced. More recently, the use in screening asymptomatic persons for left ventricular systolic dysfunction has gained more interest. In the unselected...
Directory of Open Access Journals (Sweden)
Niels Hadrup
Full Text Available Humans are concomitantly exposed to numerous chemicals. An infinite number of combinations and doses thereof can be imagined. For toxicological risk assessment the mathematical prediction of mixture effects, using knowledge on single chemicals, is therefore desirable. We investigated pros and cons of the concentration addition (CA, independent action (IA and generalized concentration addition (GCA models. First we measured effects of single chemicals and mixtures thereof on steroid synthesis in H295R cells. Then single chemical data were applied to the models; predictions of mixture effects were calculated and compared to the experimental mixture data. Mixture 1 contained environmental chemicals adjusted in ratio according to human exposure levels. Mixture 2 was a potency adjusted mixture containing five pesticides. Prediction of testosterone effects coincided with the experimental Mixture 1 data. In contrast, antagonism was observed for effects of Mixture 2 on this hormone. The mixtures contained chemicals exerting only limited maximal effects. This hampered prediction by the CA and IA models, whereas the GCA model could be used to predict a full dose response curve. Regarding effects on progesterone and estradiol, some chemicals were having stimulatory effects whereas others had inhibitory effects. The three models were not applicable in this situation and no predictions could be performed. Finally, the expected contributions of single chemicals to the mixture effects were calculated. Prochloraz was the predominant but not sole driver of the mixtures, suggesting that one chemical alone was not responsible for the mixture effects. In conclusion, the GCA model seemed to be superior to the CA and IA models for the prediction of testosterone effects. A situation with chemicals exerting opposing effects, for which the models could not be applied, was identified. In addition, the data indicate that in non-potency adjusted mixtures the effects cannot
A residual life prediction model based on the generalized σ -N curved surface
Zongwen AN; Xuezong BAI; Jianxiong GAO
2016-01-01
In order to investigate change rule of the residual life of structure under random repeated load, firstly, starting from the statistic meaning of random repeated load, the joint probability density function of maximum stress and minimum stress is derived based on the characteristics of order statistic (maximum order statistic and minimum order statistic); then, based on the equation of generalized σ -N curved surface, considering the influence of load cycles number on fatigue life, a relation...
Sense and Respond Logistics: Integrating Prediction, Responsiveness, and Control Capabilities
2006-01-01
location. In the current environment, increased ambiguity has diminished this advantage and increased the need for a sense and respond combat...readily be applied to system dynamics prob- lems in business and organization processes. ABMs bring the “natu- ralness” advantage (which allows more...negotiation) as part of eCommerce applications being achieved by 2007. In the general opinion of AgentLink’s respondents, as well as our technology
Cull, R. C.; Eltimsahy, A. H.
1983-01-01
The present investigation is concerned with the formulation of energy management strategies for stand-alone photovoltaic (PV) systems, taking into account a basic control algorithm for a possible predictive, (and adaptive) controller. The control system controls the flow of energy in the system according to the amount of energy available, and predicts the appropriate control set-points based on the energy (insolation) available by using an appropriate system model. Aspects of adaptation to the conditions of the system are also considered. Attention is given to a statistical analysis technique, the analysis inputs, the analysis procedure, and details regarding the basic control algorithm.
Metabolic syndrome and atypical antipsychotics: Possibility of prediction and control.
Franch Pato, Clara M; Molina Rodríguez, Vicente; Franch Valverde, Juan I
Schizophrenia and other psychotic disorders are associated with high morbidity and mortality, due to inherent health factors, genetic factors, and factors related to psychopharmacological treatment. Antipsychotics, like other drugs, have side-effects that can substantially affect the physical health of patients, with substantive differences in the side-effect profile and in the patients in which these side-effects occur. To understand and identify these risk groups could help to prevent the occurrence of the undesired effects. A prospective study, with 24 months follow-up, was conducted in order to analyse the physical health of severe mental patients under maintenance treatment with atypical antipsychotics, as well as to determine any predictive parameters at anthropometric and/or analytical level for good/bad outcome of metabolic syndrome in these patients. There were no significant changes in the physical and biochemical parameters individually analysed throughout the different visits. The baseline abdominal circumference (lambda Wilks P=.013) and baseline HDL-cholesterol levels (lambda Wilks P=.000) were the parameters that seem to be more relevant above the rest of the metabolic syndrome constituents diagnosis criteria as predictors in the long-term. In the search for predictive factors of metabolic syndrome, HDL-cholesterol and abdominal circumference at the time of inclusion were selected, as such that the worst the baseline results were, the higher probability of long-term improvement. Copyright © 2016 SEP y SEPB. Publicado por Elsevier España, S.L.U. All rights reserved.