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Cooperative body–brain coevolutionary synthesis of mechatronic systems

Published online by Cambridge University Press:  12 June 2008

Jiachuan Wang ,
Zhun Fan ,
Janis P. Terpenny  and
Erik D. Goodman
Jiachuan Wang
Affiliation:
Systems Department, United Technologies Research Center, East Hartford, Connecticut, USA
Zhun Fan
Affiliation:
Department of Mechanical Engineering, Technical University of Denmark, Lyngby, Denmark
Janis P. Terpenny
Affiliation:
Department of Engineering Education, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
Erik D. Goodman
Affiliation:
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan, USA
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Abstract

To support the concurrent design processes of mechatronic subsystems, unified mechatronics modeling and cooperative body–brain coevolutionary synthesis are developed. In this paper, both body-passive physical systems and brain-active control systems can be represented using the bond graph paradigm. Bond graphs are combined with genetic programming to evolve low-level building blocks into systems with high-level functionalities including both topological configurations and parameter settings. Design spaces of coadapted mechatronic subsystems are automatically explored in parallel for overall design optimality. A quarter-car suspension system case study is provided. Compared with conventional design methods, semiactive suspension designs with more creativity and flexibility are achieved through this approach.

Keywords

Bond GraphsCooperative Coevolutionary SynthesisGenetic ProgrammingMechatronics
Type
Research Article
Information
AI EDAM , Volume 22 , Issue 3 , August 2008 , pp. 219 - 234
Copyright
Copyright © Cambridge University Press 2008

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References

REFERENCES

Bentley, P.J. (1999). Evolutionary Design by Computers. San Mateo, CA: Morgan Kaufmann.Google Scholar
Broenink, J.F. (1999). Introduction to physical systems modelling with bond graphs. Accessed at http://www.ce.utwente.nl/bnk/papers/BondGraphsV2.pdfGoogle Scholar
Campbell, M. (2000). The A-Design invention machine: a means of automating and investigating conceptual design. PhD Thesis. Carnegie Mellon University, Department of Mechanical Engineering.Google Scholar
Chalasani, R.M. (1986). Ride performance potential of active suspension systems—part I: simplifies analysis based on a quarter-car model. Proc. 1986 ASME Winter Annual Meeting, Los Angeles.Google Scholar
Fan, Z. (2004). Design automation of mechatronic systems using evolutionary computation and bond graph. PhD Thesis. Michigan State University.Google Scholar
Gagné, C., & Parizeau, M. (2002). Open BEAGLE: a new versatile C++ framework for evolutionary computations. Genetic and Evolutionary Computation Conf. Late-Breaking Papers, pp. 161–168. Accessed at http://www.gel.ulaval.ca/~beagle.Google Scholar
Gagné, C., Parizeau, M., & Dubreuil, M. (2003). A robust master–slave distribution architecture for evolutionary computations. 2003 Genetic and Evolutionary Computation Conf. Late-Breaking Papers.CrossRefGoogle Scholar
Gawthrop, P.J. (1995). Physical model-based control: a bond graph approach. Journal of the Franklin Institute 332B(3), 285–305.CrossRefGoogle Scholar
Genetic programming official website. (2008). Accessed at www.genetic-programming.orgGoogle Scholar
Goodman, E.D., Seo, K., Rosenberg, R.C., Fan, Z., Hu, J., & Zhang, B. (2002). Automated design methodology for mechatronic systems using bond graphs and genetic programming. Proc. 2002 NSF Design, Service and Manufacturing Grantees and Research Conf., San Juan, Puerto Rico, January 7–10.Google Scholar
Harman, W.W., & Lytle, D.W. (1962). Electrical and Mechanical Networks. New York: McGraw–Hill.Google Scholar
Hogan, N. (1985). Impedance control: an approach to manipulation. ASME Journal of Dynamic Systems, Measurement, and Control 107, 1–24.CrossRefGoogle Scholar
Holland, J. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press.Google Scholar
Hornby, G.S., & Pollack, J.B. (2001). Body–brain co-evolution using L-systems as a generative encoding. Genetic and Evolutionary Computation Conf., San Francisco.Google Scholar
Isermann, R. (2003). Mechatronic design approach. In The Mechatronics Handbook (Bishop, R.H., Ed.), Boca Raton, FL: CRC Press.Google Scholar
Karnopp, D.C. (1995). Actively controlled systems: an ideal application area for bond graph modeling. 1995 Int. Conf. Bond Graph Modeling.Google Scholar
Karnopp, D.C., Margolis, D.L., & Rosenberg, R.C. (2000). System Dynamics: A Unified Approach, 3rd ed. New York: Wiley.Google Scholar
Koza, J.R. (1992). Genetic Programming. Cambridge, MA: MIT Press.Google Scholar
Koza, J.R. (1999). Genetic Programming III Cambridge, MA: MIT Press.Google Scholar
Koza, J.R., Keane, M.A., Yu, J., Bennett, F.H. III, & Mydlowec, W. (2000). Automatic creation of human-competitive programs and controllers by means of genetic programming. Genetic Programming and Evolvable Machines 1, 121–164.CrossRefGoogle Scholar
Lipson, H., Antonsson, E.K., & Koza, J.R. (Eds.). (2003). Computational synthesis: from basic building blocks to high level functionality. AAAI Symp., Stanford, CA, March 24–26.Google Scholar
Lipson, H., & Pollack, J.B. (2000). Automated design and manufacture of artificial lifeforms. Nature 406, 974–978.CrossRefGoogle ScholarPubMed
Lund, H.H. (2003). Co-evolving control and morphology with LEGO Robots. In Morpho-Functional Machines (Hara, F., & Pfelifer, R., Eds.). Heidelberg: Springer–Verlag.Google Scholar
Newcomb, R.W. (1966). Linear Multiport Synthesis New York: McGraw–Hill.Google Scholar
Paynter, H.M. (1961). Analysis and Design of Engineering Systems Cambridge, MA: MIT Press.Google Scholar
Pollack, J.B., Lipson, H., Funes, P., & Hornby, G. (2001). Three generations of coevolutionary robotics. Artificial Life 7, 215–223.CrossRefGoogle Scholar
Potter, M.A., & DeJong, K.A. (2000). Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1–29.CrossRefGoogle ScholarPubMed
Preumont, A. (2002). Vibration Control of Active Structures New York: Kluwer Academic.Google Scholar
Redfield, R.C., & Krishnan, S. (1993). Dynamic system synthesis with a bond graph approach: part I—synthesis of one-port impedances. Journal of Dynamic Systems, Measurement, and Control 115, 357–363.CrossRefGoogle Scholar
Rosenberg, R.C., & Karnopp, D.C. (1983). Introduction to Physical System Dynamics New York: McGraw–Hill.Google Scholar
Sharon, A., Hogan, N., & Hardt, D.E. (1991). Controller design in the physical domain. Journal of the Franklin Institute, 328(5/6) 697–721.CrossRefGoogle Scholar
Smith, M.C. (1995). Achievable dynamic response for automotive active suspension. Vehicle System Dynamics 24, 1–33.CrossRefGoogle Scholar
Smith, M.C., & Walker, G.W. (2000). Performance limitations and constraints for active and passive suspensions: a mechanical multi-port approach. Vehicle System Dynamics 33, 137–168.CrossRefGoogle Scholar
Wang, F. (2001). Design and synthesis of active and passive vehicle suspensions. PhD Thesis. University of Cambridge, Department of Engineering.Google Scholar
Wang, J., Fan, Z., Terpenny, J.P., & Goodman, E.D. (2005). Knowledge interaction with genetic programming in mechatronic systems design using bond graphs. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 35(2), 172–182.CrossRefGoogle Scholar
Wang, J., & Terpenny, J.P. (2003). Integrated active and passive mechatronic system design using bond graphs and genetic programming. 2003 Genetic and Evolutionary Computation Conf. Late-Breaking Papers, Chicago, July 12–16.Google Scholar
Wiegand, R.P., Liles, W.C., & DeJong, K.A. (2001). An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. Genetic and Evolutionary Computation Conf., pp. 1235–1245.Google Scholar
Yeh, T.J. (2002). Controller synthesis for cascade systems using bond graphs. International Journal of Systems Science 33(4), 1161–1177.CrossRefGoogle Scholar