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Supervisory predictive control and on-line set-point optimization

Tatjewski P.

International Journal of Applied Mathematics and Computer Science

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2010

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Vol. 20, no 3

483-495

EN

The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers.

2

Rozmyte modele Takagi-Sugeno w układach zintegrowanej regulacji predykcyjnej i optymalizacji punktu pracy

Marusak P.

Pomiary Automatyka Robotyka

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2008

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R. 12, nr 2

571--580

PL

Układy ze zintegrowanymi zadaniami regulacji predykcyjnej i optymalizacji punktu pracy są stosowane w przypadku, gdy zmienność zakłóceń jest porównywalna z dynamiką obiektu sterowania. W strukturach tych, w zadaniu zintegrowanej optymalizacji, są używane dwa modele obiektu: model dynamiczny z problemu optymalizacji algorytmu predykcyjnego oraz zlinearyzowany model statyczny. Zmiana zaproponowana w artykule polega na użyciu rozmytego modelu dynamicznego obiektu zarówno do celów regulacji, jak i optymalizacji punktu pracy. Podejęcie takie, przyczynia się do uproszczenia zadania, a może przynieść rozwiązania bardzo bliskie do otrzymanych w przypadku użycia dwóch modeli. Zosta ło to pokazane na przykładzie układu sterowania nieliniowego reaktora chemicznego z odpowiedzią odwrotną.

XX

Control systems with integrated predictive control and set-point optimization are used in cases when variability of disturbances is comparable with dynamics of the control plant. In these structures, in the optimization problem solved by the integrated predictive controller, two control plant models are used: a dynamic model from optimization problem of a predictive algorithm and linearized steady-state model. The idea proposed in the paper consists in application of only one fuzzy (nonlinear) dynamic control plant model for realization of both tasks: predictive control and set-point optimization. Such an approach gives simplification of the problem and may give results very close to those obtained when two models are used. It is demonstrated using an example of a control system of a nonlinear chemical reactor with inverse response.

3

Actuator fault tolerance in control systems with predictive constrained set-point optimizers

Marusak P. M., Tatjewski P.

International Journal of Applied Mathematics and Computer Science

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2008

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Vol. 18, no 4

539-551

EN

Mechanisms of fault tolerance to actuator faults in a control structure with a predictive constrained set-point optimizer are proposed. The structure considered consists of a basic feedback control layer and a local supervisory set-point optimizer which executes as frequently as the feedback controllers do with the aim to recalculate the set-points both for constraint feasibility and economic performance. The main goal of the presented reconfiguration mechanisms activated in response to an actuator blockade is to continue the operation of the control system with the fault, until it is fixed. This may be even long-term, if additional manipulated variables are available. The mechanisms are relatively simple and consist in the reconfiguration of the model structure and the introduction of appropriate constraints into the optimization problem of the optimizer, thus not affecting the numerical effectiveness. Simulation results of the presented control system for a multivariable plant are provided, illustrating the efficiency of the proposed approach.