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Predictive Controller (predictive + controller)
Kinds of Predictive Controller Selected AbstractsEnhanced Performance Assessment of Subspace Model-Based Predictive Controller with Parameters TuningTHE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 4 2007Qiang Zhang Abstract This study focuses on performance assessment of model predictive control. An MPC-achievable benchmark for the unconstrained case is proposed based on closed-loop subspace identification. Two performance measures can be constructed to evaluate the potential benefit to update the new identified model. Potential benefit by tuning the parameter can be found from trade-off curves. Effect of constraints imposed on process variables can be evaluated by the installed controller benchmark. The MPC-achievable benchmark for the constrained case can be estimated via closed-loop simulation provided that constraints are known. Simulation of an industrial example was done using the proposed method. Cette étude porte sur l'évaluation de la performance du contrôle prédictif par modèles (MPC). On propose un banc d'essai adapté au MPC pour le cas non contraint en se basant sur l'identification de sous-espaces en boucle fermée. Deux mesures de performance sont élaborées pour évaluer l'avantage potentiel de mettre à jour le nouveau modèle identifié. L'avantage potentiel par réglage du paramètre peut s'obtenir à partir des courbes de compromis. L'effet des contraintes imposé sur les variables de procédé peut être évalué par le banc d'essai de contrôleur installé. Le banc d'essai adapté au MPC pour le cas contraint peut être estimé par la simulation en boucle fermée dans la mesure où les contraintes sont connues. On a réalisé la simulation d'un exemple industriel à l'aide de la méthode proposée. [source] Continuous glucose monitoring and closed-loop systemsDIABETIC MEDICINE, Issue 1 2006R. Hovorka Abstract Background The last two decades have witnessed unprecedented technological progress in the development of continuous glucose sensors, resulting in the first generation of commercial glucose monitors. This has fuelled the development of prototypes of a closed-loop system based on the combination of a continuous monitor, a control algorithm, and an insulin pump. Method A review of electromechanical closed-loop approaches is presented. This is followed by a review of existing prototypes and associated glucose sensors. A literature review was undertaken from 1960 to 2004. Results Two main approaches exist. The extracorporeal s.c.,s.c. approach employs subcutaneous glucose monitoring and subcutaneous insulin delivery. The implantable i.v.,i.p. approach adopts intravenous sampling and intraperitoneal insulin delivery. Feasibility of both solutions has been demonstrated in small-scale laboratory studies using either the classical proportional,integral,derivative controller or a model predictive controller. Performance in the home setting has yet to be demonstrated. Conclusions The glucose monitor remains the main limiting factor in the development of a commercially viable closed-loop system, as presently available monitors fail to demonstrate satisfactory characteristics in terms of reliability and/or accuracy. Regulatory issues are the second limiting factor. Closed-loop systems are likely to be used first by health-care professionals in controlled environments such as intensive care units. [source] Output power leveling of wind turbine generators using pitch angle control for all operating regions in wind farmELECTRICAL ENGINEERING IN JAPAN, Issue 4 2007Tomonobu Senjyu Abstract Effective utilization of renewable energies such as wind energy instead of fossil fuels is desirable. Wind energy is not constant and windmill output is proportional to the cube of the wind speed, which causes the generated power of wind turbine generators (WTGs) to fluctuate. In order to reduce the output power fluctuation of wind farms, this paper presents an output power leveling control strategy for a wind farm based on both the average wind farm output power and the standard deviation of the wind farm output power, a cooperative control strategy for WTGs, and pitch angle control using a generalized predictive controller (GPC) in all WTG operating regions. Simulation results using an actual detailed model for wind farm systems show the effectiveness of the proposed method. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 158(4): 31, 41, 2007; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/eej.20448 [source] Application of model-free LQG subspace predictive control to TCP congestion controlINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 6 2008Belinda A. Chiera Abstract We investigate the application of a model-free linear quadratic Gaussian (LQG) subspace-based predictive controller to Internet congestion control. Specifically, we consider a classically designed LQG linear congestion controller with a non-standard performance index and determine whether a model-free controller is a viable alternative in this instance. We employ the model-free subspace predictive controller methodology which we customize for end-to-end transmission control protocol (TCP) congestion control. A series of network simulations support the use of the more easily implementable model-free controller over its classical analogue. We further demonstrate that the model-free controller provides increased stability under transient network conditions when compared with the first feedback congestion controller, TCP Vegas. Copyright © 2007 John Wiley & Sons, Ltd. [source] Hybrid adaptive predictive control for a dynamic pickup and delivery problem including traffic congestionINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 2 2008Cristián E. Cortés Abstract This paper presents a hybrid adaptive predictive control approach to incorporate future information regarding unknown demand and expected traffic conditions, in the context of a dynamic pickup and delivery problem with fixed fleet size. As the routing problem is dynamic, several stochastic effects have to be considered within the analytical expression of the dispatcher assignment decision objective function. This paper is focused on two issues: one is the extra cost associated with potential rerouting arising from unknown requests in the future, and the other is the potential uncertainty in travel time coming from non-recurrent traffic congestion from unexpected incidents. These effects are incorporated explicitly in the objective function of the hybrid predictive controller. In fact, the proposed predictive control strategy is based on a multivariable model that includes both discrete/integer and continuous variables. The vehicle load and the sequence of stops correspond to the discrete/integer variable, adding the vehicle position as an indicator of the traffic congestion conditions. The strategy is analyzed under two scenarios. The first one considers a predictable congestion obtained using historical data (off-line method) requiring a predictive model of velocities distributed over zones. The second scenario that accepts unpredictable congestion events generates a more complex problem that is managed by using both fault detection and isolation and fuzzy fault-tolerant control approaches. Results validating these approaches are presented through a simulated numerical example. Copyright © 2007 John Wiley & Sons, Ltd. [source] Plant-wide control of a hybrid processINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 2 2008C. de Prada Abstract This paper deals with the model predictive control (MPC) of an industrial hybrid process where continuous and batch units operate jointly: the crystallization section of a sugar factory. The paper describes a plant-wide predictive controller that takes into account, both, continuous objectives and manipulated variables, as well as those related to the discrete operation and logic of the batch units. The MPC is formulated with time events, so that a more efficient NLP optimization technique, instead of MINLP, could be applied. Adaptation is provided by model updating and error estimation. Results of the controller operation in an industrial simulator are provided. Copyright © 2007 John Wiley & Sons, Ltd. [source] Adaptive model predictive control for co-ordination of compression and friction brakes in heavy duty vehiclesINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2006Ardalan Vahidi Abstract In this paper, an adaptive model predictive control scheme is designed for speed control of heavy vehicles. The controller co-ordinates use of compression brakes and friction brakes on downhill slopes. Moreover, the model predictive controller takes the actuator constraints into account. A recursive least square scheme with forgetting is used in parallel with the controller to update the estimates of vehicle mass and road grade. The adaptation improved the model predictive controller. Also online estimation of the road grade enhanced the closed-loop performance further by contributing through feedforward control. Simulations of realistic driving scenarios with a validated longitudinal vehicle model are used throughout this paper to illustrate the benefits of co-ordinating the two braking mechanisms and influence of unknown vehicle mass and road grade. Copyright © 2006 John Wiley & Sons, Ltd. [source] Integrating fault detection and isolation with model predictive controlINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2005Barry Lennox Abstract This paper illustrates how the application of partial least squares (PLS) can be extended to provide an integrated solution to fault detection and isolation, inferential estimation and model predictive control. It is shown that if PLS is used to identify a dynamic model of a plant then the latent variables of the model can identify the suitability of using this model under current conditions. This functionality enables automated model switching in piecewise linear systems. A further advantage of the proposed technique is that the inner structure of the model can be used to provide fault detection and isolation capabilities. By extending the approach to control systems and integrating a dynamic model, identified using the PLS algorithm, within a model predictive controller, similar benefits, such as automatic model selection can be achieved for the control system. The proposed approach is illustrated through its application to the Tennessee Eastman challenge process. Copyright © 2004 John Wiley & Sons, Ltd. [source] Model predictive control for networked control systemsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 9 2009Jing Wu Abstract This paper investigates the problem of model predictive control for a class of networked control systems. Both sensor-to-controller and controller-to-actuator delays are considered and described by Markovian chains. The resulting closed-loop systems are written as jump linear systems with two modes. The control scheme is characterized as a constrained delay-dependent optimization problem of the worst-case quadratic cost over an infinite horizon at each sampling instant. A linear matrix inequality approach for the controller synthesis is developed. It is shown that the proposed state feedback model predictive controller guarantees the stochastic stability of the closed-loop system. Copyright © 2008 John Wiley & Sons, Ltd. [source] Predictive control of parabolic PDEs with state and control constraintsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 16 2006Stevan Dubljevic Abstract This work focuses on predictive control of linear parabolic partial differential equations (PDEs) with state and control constraints. Initially, the PDE is written as an infinite-dimensional system in an appropriate Hilbert space. Next, modal decomposition techniques are used to derive a finite-dimensional system that captures the dominant dynamics of the infinite-dimensional system, and express the infinite-dimensional state constraints in terms of the finite-dimensional system state constraints. A number of model predictive control (MPC) formulations, designed on the basis of different finite-dimensional approximations, are then presented and compared. The closed-loop stability properties of the infinite-dimensional system under the low order MPC controller designs are analysed, and sufficient conditions that guarantee stabilization and state constraint satisfaction for the infinite-dimensional system under the reduced order MPC formulations are derived. Other formulations are also presented which differ in the way the evolution of the fast eigenmodes is accounted for in the performance objective and state constraints. The impact of these differences on the ability of the predictive controller to enforce closed-loop stability and state constraints satisfaction in the infinite-dimensional system is analysed. Finally, the MPC formulations are applied through simulations to the problem of stabilizing the spatially-uniform unstable steady-state of a linear parabolic PDE subject to state and control constraints. Copyright © 2006 John Wiley & Sons, Ltd. [source] Fault-tolerant control of nonlinear processes: performance-based reconfiguration and robustnessINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 3 2006Prashant Mhaskar Abstract This work considers the problem of control system/actuator failures in nonlinear processes subject to input constraints and presents two approaches for fault-tolerant control that focus on incorporating performance and robustness considerations, respectively. In both approaches, first a family of candidate control configurations, characterized by different manipulated inputs, is identified for the process under consideration. Performance considerations are first incorporated via the design of a Lyapunov-based predictive controller that enforces closed-loop stability from an explicitly characterized set of initial conditions (computed using an auxiliary Lyapunov-based nonlinear controller). A hierarchical switching policy is derived, that uses stability considerations (evaluated via the presence of the state in the stability region of a control configuration) to ascertain the suitability of a candidate backup configuration and then performance considerations are again considered in choosing between the suitable backup configurations. Next, we consider the problem of implementing fault-tolerant control to nonlinear processes subject to input constraints and uncertainty. To this end, we first design a robust hybrid predictive controller for each candidate control configuration that guarantees stability from an explicitly characterized set of initial conditions, subject to uncertainty and constraints. A switching policy is then derived to orchestrate the activation/deactivation of the constituent control configurations. Finally, simulation studies are presented to demonstrate the implementation and evaluate the effectiveness of the proposed fault-tolerant control method. Copyright © 2005 John Wiley & Sons, Ltd. [source] Sequential and iterative architectures for distributed model predictive control of nonlinear process systemsAICHE JOURNAL, Issue 8 2010Jinfeng Liu Abstract In this work, we focus on distributed model predictive control of large scale nonlinear process systems in which several distinct sets of manipulated inputs are used to regulate the process. For each set of manipulated inputs, a different model predictive controller is used to compute the control actions, which is able to communicate with the rest of the controllers in making its decisions. Under the assumption that feedback of the state of the process is available to all the distributed controllers at each sampling time and a model of the plant is available, we propose two different distributed model predictive control architectures. In the first architecture, the distributed controllers use a one-directional communication strategy, are evaluated in sequence and each controller is evaluated only once at each sampling time; in the second architecture, the distributed controllers utilize a bi-directional communication strategy, are evaluated in parallel and iterate to improve closed-loop performance. In the design of the distributed model predictive controllers, Lyapunov-based model predictive control techniques are used. To ensure the stability of the closed-loop system, each model predictive controller in both architectures incorporates a stability constraint which is based on a suitable Lyapunov-based controller. We prove that the proposed distributed model predictive control architectures enforce practical stability in the closed-loop system and optimal performance. The theoretical results are illustrated through a catalytic alkylation of benzene process example. © 2010 American Institute of Chemical Engineers AIChE J, 2010 [source] Enhanced stability regions for model predictive control of nonlinear process systemsAICHE JOURNAL, Issue 6 2008Maaz Mahmood Abstract The problem of predictive control of nonlinear process systems subject to input constraints is considered. The key idea in the proposed approach is to use control-law independent characterization of the process dynamics subject to constraints via model predicative controllers to expand on the set of initial conditions for which closed,loop stability can be achieved. An application of this idea is presented to the case of linear process systems for which characterizations of the null controllable region (the set of initial conditions from where closed,loop stability can be achieved subject to input constraints) are available, but not practically implementable control laws that achieve stability from the entire null controllable region. A predictive controller is designed that achieves closed,loop stability for every initial condition in the null controllable region. For nonlinear process systems, while the characterization of the null controllable region remains an open problem, the set of initial conditions for which a (given) Lyapunov function can be made to decay is analytically computed. Constraints are formulated requiring the process to evolve within the region from where continued decay of the Lyapunov function value is achievable and incorporated in the predictive control design, thereby expanding on the set of initial conditions from where closed,loop stability can be achieved. The proposed method is illustrated using a chemical reactor example, and the robustness with respect to parametric uncertainty and disturbances demonstrated via application to a styrene polymerization process. © 2008 American Institute of Chemical Engineers AIChE J, 2008 [source] Online estimation and control of polymer quality in a copolymerization reactorAICHE JOURNAL, Issue 5 2002Myung-June Park The validity of an online state estimator for a semi-batch MMA/MA solution copolymerization reactor was established using online densitometer and viscometer. Using the conventional extended Kalman filter (EKF) as the state estimator, the experiment was conducted under both isothermal and nonisothermal conditions for application to the control of copolymer properties. Further analysis was made by using ofline measurement data for the mol fraction of MMA in the remaining monomers and the solid content. The EKF was found to provide a good estimate for the state of the copolymerization system. A model predictive controller was designed and implemented to obtain copolymers with uniform copolymer composition and the desired weight average molecular weight by adopting the feed flow rate of MMA and the reaction temperature as control inputs. The controller was proven effective with a satisfactory performance for the control of polymer properties in the semi-batch copolymerization reactor. [source] Transition from Batch to Continuous Operation in Bio-Reactors: A Model Predictive Control Approach and ApplicationTHE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 4 2007Prashant Mhaskar Abstract This work considers the problem of determining the transition of ethanol-producing bio-reactors from batch to continuous operation and subsequent control subject to constraints and performance considerations. To this end, a Lyapunov-based non-linear model predictive controller is utilized that stabilizes the bio-reactor under continuous mode of operation. The key idea in the predictive controller is the formulation of appropriate stability constraints that allow an explicit characterization of the set of initial conditions from where feasibility of the optimization problem and hence closed-loop stability is guaranteed. Additional constraints are incorporated in the predictive control design to expand on the set of initial conditions that can be stabilized by control designs that only require the value of the Lyapunov function to decay. Then, the explicit characterization of the set of stabilizable initial conditions is used in determining the appropriate time for which the reactor must be run in batch mode. Specifically, the predictive control approach is utilized in determining the appropriate batch length that achieves stabilizable values of the state variables at the end of the batch. Application of the proposed method to the ethanol production process using Zymomonas mobilis as the ethanol producing micro-organism demonstrates the effectiveness of the proposed model predictive control strategy in stabilizing the bio-reactor. Ce travail porte sur le problème de la détermination de la transition entre le fonctionnement discontinu et continu pour des bioréacteurs produisant de l'éthanol et sur le contrôle subséquent lorsque ceux-ci sont soumis à des contraintes et des considérations de performance. À cette fin, on utilise un contrôleur prédictif par modèles non linéaires de type Lyapunov qui stabilise le bioréacteur lorsqu'il est en mode de fonctionnement continu. L'idée maîtresse dans le contrôleur prédictif est la formulation de contraintes de stabilité appropriées qui permettent une caractérisation explicite du jeu des conditions initiales à partir de laquelle la faisabilité du problème d'optimisation et donc la stabilité en boucle fermée sont garanties. Des contraintes additionnelles sont introduites dans la conception du contrôle prédictif pour étendre le jeu de conditions initiales qui peuvent être stabilisées par la conception du contrôle qui requiert seulement que la valeur de la fonction de Lyapunov diminue. Ensuite, la caractérisation explicite du jeu des conditions initiales stabilisables est utilisée dans la détermination de la durée de fonctionnement adéquate pour laquelle le réacteur doit fonctionner en mode discontinu. Spécifiquement, on utilise la méthode de contrôle prédictif dans la détermination de la longueur discontinue appropriée qui réalise les valeurs stabilisables des variables d'état à la fin du mode discontinu. L'application de la méthode proposée au procédé de production de l'éthanol utilisant Zymomonas mobilis comme microorganisme produisant de l'éthanol, démontre l'efficacité de la stratégie de contrôle prédictif de modèles proposée pour stabiliser le bioréacteur. [source] Application of two-loop robust control to air-conditioning systems,ASIAN JOURNAL OF CONTROL, Issue 6 2009Gongsheng Huang Abstract This paper presents the design and application of a two-loop robust controller for temperature control in air-conditioning systems. A Takagi-Sugeno fuzzy model with uncertain local models is developed to describe the associated nonlinearities and uncertainties in the operation of the air handling units. Parallel distributed compensation is used to design the global control law. The local control law consists of two loops: an inner-loop integral controller and an outer-loop min-max predictive controller with short prediction horizon. A discounting scheme is developed to weight the contribution of the two loops. Experimental results are presented which show that the proposed strategy can achieve acceptable control performance with a minimum of onsite tuning. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Simple Recurrent Neural Network-Based Adaptive Predictive Control for Nonlinear SystemsASIAN JOURNAL OF CONTROL, Issue 2 2002Xiang Li ABSTRACT Making use of the neural network universal approximation ability, a nonlinear predictive control scheme is studied in this paper. On the basis of a uniform structure of simple recurrent neural networks, a one-step neural predictive controller (OSNPC) is designed. The whole closed-loop system's asymptotic stability and passivity are discussed, and stable conditions for the learning rate are determined based on the Lyapunov stability theory for the whole neural system. The effectiveness of OSNPC is verified via exhaustive simulations. [source] Nonlinear parametric predictive control.ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 6 2009Application to a continuous stirred tank reactor Abstract This paper presents a nonlinear model-based controller based on the ideas of parametric predictive control applied to a continuous stirred tank reactor (CSTR) process unit. Controller design aims at avoiding the complexity of implementation and long computational times associated with conventional NMPC while maintaining the main advantage of taking into account process nonlinearities that are relevant for control. The design of the parametric predictive controller is based on a rather simplified process model having parameters that are instrumental in determining the required changes to the manipulated variables for error reduction. The nonlinear controller is easy to tune and can operate successfully over a wide range of operating conditions. The use of an estimator of unmeasured disturbances and process-model mismatch further enhances the behavior of the controller. Copyright © 2009 Curtin University of Technology and John Wiley & Sons, Ltd. [source] Nonlinear Predictive Control of Fed-Batch Cultures of Escherichia coliCHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 7 2010S. Tebbani Abstract A strategy for controlling a fed-batch Escherichia coli culture is described to maintain the culture at the boundary between oxidative and oxido-fermentative regimes. A nonlinear predictive controller is designed to regulate the acetate concentration, constraining the feed rate to follow an optimal reference profile which maximizes the biomass growth. For the sake of simplicity and efficiency, the original problem is converted into an unconstrained nonlinear programming problem, solved by control vector parameterization techniques. The robustness of the structure is further improved by explicitly including the difference between system and model prediction. A robustness study based on a Monte Carlo approach is used to evaluate the performance of the proposed controller. This control law is finally compared to the generic model control strategy. [source] | |||||||||||||