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Neural Network Control for a Semi-Active Vehicle Suspension with a Magnetorheological Damper

Key Laboratory of Smart Materials and Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, China, Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China

H. Y. Hu

Key Laboratory of Smart Materials and Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, China

J. Q. Yi

Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Semi-active vehicle suspension with magnetorheological dampers is a promising technology for improving the ride comfort of a ground vehicle. However, the magnetorheological damper always exhibits nonlinear hysteresis between its output force and relative velocity, and additional nonlinear stiffness owing to the state transition from liquid to semi-solid or solid, so that the semi-active suspension with magnetorheological dampers features nonlinearity by nature. To control such nonlinear dynamic systems subject to random road roughness, in this paper we present a neural network control, which includes an error back propagation algorithm with quadratic momentum of the multilayer forward neural networks. Both the low frequency of road-induced vibration of the vehicle body and the fast response of the magnetorheological damper enable the neural network control to work effectively on-line. The numerical simulations and an experiment for a quarter-car model indicate that the semi-active suspension with a magnetorheological damper and neural network control is superior to the passive suspensions in a range of low frequency.

Key Words: Semi-active control • neural networks • magnetorheological damper • vehicle suspension • vibration isolation

Journal of Vibration and Control, Vol. 10, No. 3, 461-471 (2004)
DOI: 10.1177/1077546304038968


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