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Jaimes A.L., Zapotecas Martinez S., C.A. C. Coello, Chapter 1 An Introduction To Multi-Objective Optimization Techniques, In: Book Title Editor: Editor Name, pp. 1-26, 2009 Nova Science Publishers, Inc.

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Article

Controllers Tuning through Multi-objective Non-Dominated Sorting Genetic Algorithms

1Department of Computer Science, University of Sciences and Technology USTO’MB, El Mnouar, Algeria


World Journal Control Science and Engineering. 2013, Vol. 1 No. 1, 15-24
DOI: 10.12691/wjcse-1-1-3
Copyright © 2013 Science and Education Publishing

Cite this paper:
CHOURAQUI Samira, BENZATER Habiba. Controllers Tuning through Multi-objective Non-Dominated Sorting Genetic Algorithms. World Journal Control Science and Engineering. 2013; 1(1):15-24. doi: 10.12691/wjcse-1-1-3.

Correspondence to: CHOURAQUI  Samira, Department of Computer Science, University of Sciences and Technology USTO’MB, El Mnouar, Algeria. Email: s_chouraqui@yahoo.fr

Abstract

In control system design there are often a number of design objectives to be considered. The objectives are sometimes connecting and no design exists which can be considered best with respect to all objectives. Hence, there is an inevitable tradeoff between design objectives, for example, between and output performance objective and stability robustness. These considerations have led to the study of multi objective optimization methods for control systems. In this paper a multi-objective Non-Dominated sorting genetic Algorithms NSGA-II is used to tuning of Proportional Derivative (PD) controller of a six freedom arm manipulator PUMA560. The NSGAII algorithm searches for the controller PD gains so that the six values of Integral Absolute Error (IAE) in joint space are minimized. Simulation numerical results of multivariable PD control and convergence of the NSGA-II are presented and discussed.

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