DocumentCode :
2685573
Title :
Dynamic vehicle roll control using reinforcement learning
Author :
Frost, G.P. ; Howell, M.N. ; Gordon, T.J. ; Wu, Q.H.
Author_Institution :
Dept. of Aeronaut. & Automotive Eng. & Transp. Studies, Loughborough Univ. of Technol., UK
Volume :
2
fYear :
1996
fDate :
2-5 Sept. 1996
Firstpage :
1107
Abstract :
A reinforcement learning strategy is applied to the problem of the dynamic roll control of a full-body vehicle system fitted with semi-active suspension under digital control. The simulation model used in this study is based upon realistic vehicle hardware. Prior engineering knowledge of the non-linear actuation system is used to develop a control structure. Parameters in this structure are then obtained using continuous action reinforcement learning automata (CARLA), an extension of the interconnected learning automata methodology. No model-based information is used in the controller synthesis.
Keywords :
automobiles; digital control; intelligent control; learning (artificial intelligence); learning automata; learning systems; multivariable control systems; nonlinear control systems; continuous action reinforcement learning automata; controller synthesis; digital control; dynamic vehicle roll control; full-body vehicle system; interconnected learning automata; nonlinear actuation system; semi-active suspension;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Control '96, UKACC International Conference on (Conf. Publ. No. 427)
ISSN :
0537-9989
Print_ISBN :
0-85296-668-7
Type :
conf
DOI :
10.1049/cp:19960708
Filename :
656190
Link To Document :
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