Regular Papers

International Journal of Control, Automation and Systems 2006; 4(6): 669-681

© The International Journal of Control, Automation, and Systems

Adaptive Predictive Control using Multiple Models, Switching and Tuning

Leonardo Giovanini, Andrzej W. Ordys, and Michael J. Grimble

University of Strathclyde, UK

Abstract

In this work, a new method of design adaptive controllers for SISO systems based on multiple models and switching is presented. The controller selects the model from a given set, according to a switching rule based on output prediction errors. The goal is to design, at each sample instant, a predictive control law that ensures the robust stability of the closed-loop system and achieves the best performance for the current operating point. At each sample the proposed control scheme identifies a set of linear models that best characterizes the dynamics of the current operating region. Then, it carries out an automatic reconfiguration of the controller to achieve the best possible performance whilst providing a guarantee of robust closed-loop stability. The results are illustrated by simulations a nonlinear continuous and stirred tank reactor.

Keywords Adaptive control, infinite controller cover set, multiple models, multi-objective optimization, predictive control.

Article

Regular Papers

International Journal of Control, Automation and Systems 2006; 4(6): 669-681

Published online December 1, 2006

Copyright © The International Journal of Control, Automation, and Systems.

Adaptive Predictive Control using Multiple Models, Switching and Tuning

Leonardo Giovanini, Andrzej W. Ordys, and Michael J. Grimble

University of Strathclyde, UK

Abstract

In this work, a new method of design adaptive controllers for SISO systems based on multiple models and switching is presented. The controller selects the model from a given set, according to a switching rule based on output prediction errors. The goal is to design, at each sample instant, a predictive control law that ensures the robust stability of the closed-loop system and achieves the best performance for the current operating point. At each sample the proposed control scheme identifies a set of linear models that best characterizes the dynamics of the current operating region. Then, it carries out an automatic reconfiguration of the controller to achieve the best possible performance whilst providing a guarantee of robust closed-loop stability. The results are illustrated by simulations a nonlinear continuous and stirred tank reactor.

Keywords: Adaptive control, infinite controller cover set, multiple models, multi-objective optimization, predictive control.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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