Abstract
Multiobjective optimization strategy so-called Physical Programming allows controller designers a flexible way to express design preferences with a ’physical’ sense. For each objective (settling time, overshoot, disturbance rejection, etc.) preferences are established through categories as desirable, tolerable, unacceptable, etc. assigned to numerical ranges. The problem is translated into a unique objective optimization but normally as a multimodal problem. This work shows how to convert a robust control design problem into a multiobjective optimization problem and to solve it by Physical Programming and Genetic Algorithms. An application to the American Control Conference (ACC) Robust Control Benchmark is presented and compared with other known solutions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Back, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York (1996)
Baker, J.E.: Reducing Bias and Inefficiency in the Selection Algorithms. In: Grefenstette, J.J. (ed.) Proceedings of the Second International Conference on Genetic Algorithms, pp. 14–21. Lawrence Erlbaum Associates, Hillsdale (1987)
Coello, C.A.C., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary algorithms for solving multi-objetive problems. Kluwer Academic Publishers, Dordrecht (2002)
Goldberg, D.E.: Genetic Algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)
Holland, J.H.: Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor (1975)
Martínez, M.A., Sanchis, J., Blasco, X.: Algoritmos genéticos en physical programming. applicación al benchmark de control robusto de la acc Revista Iberoamericana de Automática e Informática Industrial, Submitted (2005)
Messac, A.: Physical programming: effective optimitation for computational design. AIAA Journal 34(1), 149–158 (1996)
Messac, A., Wilson, B.H.: Physical programming for computational control. AIAA Journal 36(1), 219–226 (1999)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Series Artificial Intelligence. Springer, Heidelberg (1996)
Miettinen, K.M.: Nonlinear multiobjective optimization. Kluwer Academic Publishers, Dordrecht (1998)
Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive models for the breeder genetic algorithm. i. continuous parameter optimization. Evolutionary Computation 1(1), 25–49 (1993)
Stengel, R., Marrison, C.: Robustness of solutions to a benchmark control problem. Journal of Guidance, Control and Dynamics 15(5), 1060–1067 (1992)
Wie, B., Bernstein, D.: A benchmark problem for robust control design. In: Proceedings of the American Control Conference, San Diego, CA, pp. 961–962 (1990)
Wie, B., Bernstein, D.: Benchmark problems for robust control design. Journal of Guidance, Control and Dynamics 15(5), 1057–1059 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Marínez, M.A., Sanchis, J., Blasco, X. (2005). Genetic Algorithms for Multiobjective Controller Design. In: Mira, J., Álvarez, J.R. (eds) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. IWINAC 2005. Lecture Notes in Computer Science, vol 3562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499305_25
Download citation
DOI: https://doi.org/10.1007/11499305_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26319-7
Online ISBN: 978-3-540-31673-2
eBook Packages: Computer ScienceComputer Science (R0)