Home About us Contact
|
Home About us Contact | ||
|
|
||
Model Predictive Controller (model + predictive_controller)
Selected AbstractsContinuous 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] 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] 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] 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] | |||||||||||||