Moderated reinforcement learning of active and semi-active vehicle suspension control laws

Frost, G. P. and Gordon, T. J. and Howell, M. N. and Wu, Q. H. (1996) Moderated reinforcement learning of active and semi-active vehicle suspension control laws. Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering, 210 (4). pp. 249-257. ISSN 0959-6518

Full content URL: http://dx.doi.org/10.1243/PIME_PROC_1996_210_464_0...

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Item Type:Article
Item Status:Live Archive

Abstract

This paper is concerned with the application of reinforcement learning to the dynamic ride control of an active vehicle suspension system. The study makes key extensions to earlier simulation work to enable on-line implementation of the learning automation methodology using an actual vehicle. Extensions to the methodology allow safe and continuous learning to take place on the road, using a limited instrumentation set. An important new feature is the use of a moderator to set physical limits on the vehicle states. It is shown that the addition of the moderator has little direct effect on the system's ability to learn, and allows learning to take place continuously even when there are unstable controllers present. The study concludes with the results of an experimental trial using vehicle hardware, where the successful synthesis of a semi-active ride controller is demonstrated.

Keywords:Closed loop control systems, Computer simulation, Control system synthesis, Online systems, Optimal control systems, System stability, Vehicle suspensions, Active vehicle suspension system, Dynamic ride control, Learning automaton methodology, Semi active ride controller, Learning systems, bmjdoi
Subjects:H Engineering > H660 Control Systems
H Engineering > H330 Automotive Engineering
Divisions:College of Science > School of Engineering
ID Code:11691
Deposited On:01 Oct 2013 17:20

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