Abstract
In this paper, a novel method for the digital two-Degrees-Of-Freedom (2DOF) controller design, called canonical RST structure, is proposed and successfully implemented based on a Multi-Objective Particle Swarm Optimization (MOPSO) approach. This is a polynomial control structure allowing independently the regulation and the tracking of discrete-time systems. An application to the variable speed control of an electrical DC Drive is investigated. The RST design and tuning problem is formulated as a multi-objective optimization problem. The proposed MOPSO algorithm which is based on the Pareto dominance is used to identify the non-dominated solutions. This approach used the leader selection strategy that is called a geographically-based system. In addition, the adaptive grid method is used to produce well-distributed Pareto fronts in the multi-objective formalism. The well known NSGA-II and the proposed MOPSO algorithms are evaluated and compared with each other in terms of several performance metrics in order to show the superiority and the effectiveness of the proposed method. Simulation results demonstrate the advantages of the MOPSO-tuned RST control structure in terms of performance and robustness.
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Recommended by Associate Editor DaeEun Kim under the direction of Editor Euntai Kim.
Riadh Madiouni was born in Tunisia in 1987. He graduated from the Faculty of Sciences of Bizerte in Computer Sciences, in 2012. He received his master’s degree in Automatic and Signal Processing from the National School of Engineers of Tunis (ENIT), in 2012 and is currently preparing a PhD Thesis in the LARA and LiSSi (University of Paris-Est) laboratories, since 2012. His research interests are in multi-objective particle swarm optimization and robust control design.
Soufiene Bouallègue was born in 1982 in Nafta, Tunisia. He graduated from the National School of Engineers of Tunis (ENIT) in 2006 and received the PhD degree in Electrical Engineering in 2010. He is currently an Associate Professor of Electrical Engineering at the High Institute of Industrial Systems of Gabés (ISSIG). His research interests are in the area of meta-heuristics optimization, intelligent control, robotics, renewable energies, and digital control applications.
Joseph Haggège was born in 1975 in Tunis, Tunisia. He graduated from National School of Engineers of Tunis in 1998. He received his PhD degree in Electrical Engineering 2003 and the Habilitation in 2010. He is currently a Senior Lecturer at the National School of Engineers of Tunis (ENIT). His research interests are in the area of meta-heuristics optimization, embedded systems and robust digital control.
Patrick Siarry was born in France in 1952. He received his PhD degree from the University Paris 6, in 1986, and the Doctorate of Sciences (Habilitation) from the University Paris 11, in 1994. He was first involved in the development of analog and digital models of nuclear power plants at Electricité de France (E.D.F.). Since 1995 he is a Professor in automatics and informatics. His main research interests are computer-aided design of electronic circuits, and the applications of new stochastic global optimization heuristics to various engineering fields. He is also interested in the fitting of process models to experimental data, the learning of fuzzy rule bases, and of neural networks.
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Madiouni, R., Bouallègue, S., Haggège, J. et al. Robust RST control design based on Multi-Objective Particle Swarm Optimization approach. Int. J. Control Autom. Syst. 14, 1607–1617 (2016). https://doi.org/10.1007/s12555-015-0173-1
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DOI: https://doi.org/10.1007/s12555-015-0173-1