A multivariable multiobjective predictive controller

Faten Ben Aicha; Faouzi Bouani; Mekki Ksouri

International Journal of Applied Mathematics and Computer Science (2013)

  • Volume: 23, Issue: 1, page 35-45
  • ISSN: 1641-876X

Abstract

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Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MIMO system). First, we adopt the Weighted Sum Method (WSM), which is an aggregative method combined with a Genetic Algorithm (GA) used to minimize a single criterion generated by the WSM. Second, we use the NonDominated Sorting Genetic Algorithm II (NSGA-II) as a Pareto method and we compare the results of both the methods. The performance of the two strategies in the adjustment of multivariable predictive control is illustrated by a simulation example. The simulation results confirm that a multiobjective, Pareto-based GA search yields a better performance than a single objective GA.

How to cite

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Faten Ben Aicha, Faouzi Bouani, and Mekki Ksouri. "A multivariable multiobjective predictive controller." International Journal of Applied Mathematics and Computer Science 23.1 (2013): 35-45. <http://eudml.org/doc/251312>.

@article{FatenBenAicha2013,
abstract = {Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MIMO system). First, we adopt the Weighted Sum Method (WSM), which is an aggregative method combined with a Genetic Algorithm (GA) used to minimize a single criterion generated by the WSM. Second, we use the NonDominated Sorting Genetic Algorithm II (NSGA-II) as a Pareto method and we compare the results of both the methods. The performance of the two strategies in the adjustment of multivariable predictive control is illustrated by a simulation example. The simulation results confirm that a multiobjective, Pareto-based GA search yields a better performance than a single objective GA.},
author = {Faten Ben Aicha, Faouzi Bouani, Mekki Ksouri},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {closed loop performance; coupled multivariable system; generalized predictive control; multiobjective optimization; weighted sum method; NSGA-II; non-dominated sorting genetic algorithm II (NSGA-II)},
language = {eng},
number = {1},
pages = {35-45},
title = {A multivariable multiobjective predictive controller},
url = {http://eudml.org/doc/251312},
volume = {23},
year = {2013},
}

TY - JOUR
AU - Faten Ben Aicha
AU - Faouzi Bouani
AU - Mekki Ksouri
TI - A multivariable multiobjective predictive controller
JO - International Journal of Applied Mathematics and Computer Science
PY - 2013
VL - 23
IS - 1
SP - 35
EP - 45
AB - Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MIMO system). First, we adopt the Weighted Sum Method (WSM), which is an aggregative method combined with a Genetic Algorithm (GA) used to minimize a single criterion generated by the WSM. Second, we use the NonDominated Sorting Genetic Algorithm II (NSGA-II) as a Pareto method and we compare the results of both the methods. The performance of the two strategies in the adjustment of multivariable predictive control is illustrated by a simulation example. The simulation results confirm that a multiobjective, Pareto-based GA search yields a better performance than a single objective GA.
LA - eng
KW - closed loop performance; coupled multivariable system; generalized predictive control; multiobjective optimization; weighted sum method; NSGA-II; non-dominated sorting genetic algorithm II (NSGA-II)
UR - http://eudml.org/doc/251312
ER -

References

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