Abstract
Multi-Objective Evolutionary Algorithms (MOEA) have been succesfully applied to solve control problems. However, many improvements are still to be accomplished. In this paper a new approach is proposed: the Multi-Objective Pole Placement with Evolutionary Algorithms (MOPPEA). The design method is based upon using complex-valued chromosomes that contain information about closed-loop poles, which are then placed through an output feedback controller. Specific cross-over and mutation operators were implemented in simple but efficient ways. The performance is tested on a mixed multi-objective \(\mathcal{H}_{2}\)/\(\mathcal{H}_{\infty }\) control problem.
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References
Tan, K.C., Khor, E.F., Lee, T.H.: Multiobjective Evolutionary Algorithms and Applications. Springer, Heidelberg (2005)
Fleming, P.J., Purshouse, R.C.: Evolutionary algorithms in control systems engineering: a survey. Control Engineering Practice 10, 1223–1241 (2002)
Fonseca, C.: Multiobjective Genetic Algorithms with Application to Control Engineering Problems. PhD thesis, Departement of Automatic Control and Systems Engineering. University of Sheffield (1995)
Whidborne, J.F., Gu, D.W., Postlethwaite, I.: Algorithms for the method of inequalities: A comparative study. In: American Control Conference, Washington (1995)
Herreros, A.: Diseño de controladores robustos multiobjetivo por medio de algoritmos geneticos. Tesis Doctoral, Departamento de Ingenieria de Sistemas y Automatica.Universidad de Valladolid, Espana (2000)
Zakian, V.: Control systems design: a new framework. Springer, London (2005)
Molina-Cristobal, A., Griffin, I.A., Fleming, P.J., Owens, D.H.: Linear matrix inequalities and evolutionary computation. International Journal of Systems Science 37(8), 513–522 (2006)
Ho, S.-J., Ho, S.-Y., Hung, M.-H., Shu, L.-S., Huang, H.-L.: Designing Structure-Specified Mixed H2/Hinf Optimal Controllers Using an Intelligent Genetic Algorithm. IEEE Transactions on Control System Technology 13(6), 1119–1124 (2005)
Osyczka, A.: Multicriteria Optimization for Engineering Design. Academic Press, Cambridge (1985)
Kautsky, J., Nichols, N.K.: Robust Pole Assignment in Linear State Feedback. Int. J. Control 41, 1129–1155 (1985)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms on Test Functions of Different Difficulty. In: Wu, A.S. (ed.) Proceedings of the 1999 Genetic and Evolutionary Computation Conference. Workshop Program, Orlando, Florida, pp. 121–122 (1999)
Laub, A.J., Wette, M.: Algorithms and Software for Pole Assignment and Observers. UCRL-15646 Rev. 1, EE Dept., Univ. of Calif., Santa Barbara, CA (1984)
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Sánchez, G., Villasana, M., Strefezza, M. (2007). Multi-objective Pole Placement with Evolutionary Algorithms. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol 4403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70928-2_33
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DOI: https://doi.org/10.1007/978-3-540-70928-2_33
Publisher Name: Springer, Berlin, Heidelberg
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