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Predictive Control Design (predictive + control_design)
Selected AbstractsA two-step predictive control design for input saturated Hammerstein systemsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 7 2006Baocang Ding Abstract The two-step model predictive control is designed for input saturated Hammerstein systems. It first applies the unconstrained linear dynamic subsystem to get the desired intermediate variable, and then obtains the actual control action by solving nonlinear algebraic equation group and desaturation. The stability of the closed-loop system is analysed and its domain of attraction is designed applying semi-global stabilization techniques. The stability conclusions are illustrated with an example. Copyright © 2006 John Wiley & Sons, Ltd. [source] Distributed model predictive control of nonlinear process systemsAICHE JOURNAL, Issue 5 2009Jinfeng Liu Abstract This work focuses on a class of nonlinear control problems that arise when new control systems which may use networked sensors and/or actuators are added to already operating control loops to improve closed-loop performance. In this case, it is desirable to design the pre-existing control system and the new control system in a way such that they coordinate their actions. To address this control problem, a distributed model predictive control method is introduced where both the pre-existing control system and the new control system are designed via Lyapunov-based model predictive control. Working with general nonlinear models of chemical processes and assuming that there exists a Lyapunov-based controller that stabilizes the nominal closed-loop system using only the pre-existing control loops, two separate Lyapunov-based model predictive controllers are designed that coordinate their actions in an efficient fashion. Specifically, the proposed distributed model predictive control design preserves the stability properties of the Lyapunov-based controller, improves the closed-loop performance, and allows handling input constraints. In addition, the proposed distributed control design requires reduced communication between the two distributed controllers since it requires that these controllers communicate only once at each sampling time and is computationally more efficient compared to the corresponding centralized model predictive control design. The theoretical results are illustrated using a chemical process example. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [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] 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] | ||||||||||