Evolutionary design of discrete controllers for hybrid mechatronic systems

Abstract

This paper investigates the issue of evolutionary design of controllers for hybrid mechatronic systems. Finite State Automaton (FSA) is selected as the representation for a discrete controller due to its interpretability, fast execution speed and natural extension to a statechart, which is very popular in industrial applications. A case study of a two-tank system is used to demonstrate that the proposed evolutionary approach can lead to a successful design of an FSA controller for the hybrid mechatronic system, represented by a hybrid bond graph. Generalisation of the evolved FSA controller to unknown control targets is also tested. Further, a comparison with another type of controller, a lookahead controller, is conducted, with advantages and disadvantages of each discussed. The comparison sheds light on which type of controller representation is a better choice to use in various stages of the evolutionary design of controllers for hybrid mechatronic systems. Finally, some important future research directions are pointed out, leading to the major work of the succeeding part of the research.

Keywords:

• evolutionary computation
• hybrid systems

Acknowledgements

This work was supported in part by the Leading Talent Project of Guangdong Province, in part by the National Science Foundation under Cooperative Agreement DBI-0939454, in part by the Chinese National High-Tech 863 Project under Grant 2013AA100305 and in part by the National Natural Science Foundation of China under Grant 61175073 and 61174090.

Additional information

Notes on contributors

Jean-Francois Dupuis

Jean-François Dupuis received a BEng degree in Mechanical Engineering and a MS in Electrical Engineering from Laval University, Québec, Canada in 2003 and 2007, respectively, and a PhD degree in Mechanical Engineering from the Technical University of Denmark in 2011.

Zhun Fan

Zhun Fan received the BSc and MSc degrees in Control Engineering from Huazhong University of Science and Technology, Wuhan, China, in 1995 and 2000, respectively, and the PhD degree in electrical engineering from the Michigan State University, USA, in 2004.From 2004 to 2007, he was an Assistant Professor at the Technical University of Denmark. From 2007 to 2011, he was an Associate Professor at the Technical University of Denmark. He is currently a Professor and Head of the Department of Electrical Engineering at Shantou University, Guangdong, China.Dr. Fan has been principle investigator of various projects sponsored by Danish Research Agency of Science Technology and Innovation. His research is also supported by National Science Foundation, USA, and National Natural Science Foundation of China. His major research interests include evolutionary computation, intelligent control and robotic systems, robot vision and cognition, MEMS, design automation and optimisation, intelligent power system and transportation system, etc.Dr. Fan is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE). He is also a member of ACM and ASME.

Erik Goodman

Erik D. Goodman is PI and Director of the BEACON Center for the Study of Evolution in Action, an NSF Science and Technology Center headquartered at Michigan State University and funded beginning in 2010. His research centres on application of evolutionary principles to solution of engineering design problems. He received the PhD in computer and communication sciences from the University of Michigan, Ann Arbor, in 1971. He became Asst. Prof. of Electrical Engineering and Systems Science in 1972, Assoc. Prof. in 1978 and Prof. in 1984, all at Michigan State University, where he also holds appointments in Mechanical Engineering and in Computer Science and Engineering. He directed the Case Center for Computer-Aided Engineering and Manufacturing from 1983 to 2002, and MSU’s Manufacturing Research Consortium from 1993 to 2003. He has co-directed MSU’s Genetic Algorithms Research and Applications Group (GARAGe) since its founding in 1993. He is co-founder and vice president of Red Cedar Technology, Inc., a firm that develops design optimisation software for use in industry. He was chosen Michigan Distinguished Professor of the Year, 2009, by the Presidents Council, State Universities of Michigan.Prof. Goodman was Chair of the Executive Board and a Senior Fellow of the International Society for Genetic and Evolutionary Computation, 2003–2005. He was founding chair of the ACM’s Special Interest Group on Genetic and Evolutionary Computation (SIGEVO), serving from 2005 to 2007.