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Adaptive Fuzzy-Neural Network Control for Magneto-Rheological Suspension


Yu Miao, Dong Xiao-min, Liao Chang-rong, Chen Wei-min


Vol. 6  No. 10  pp. 66-71


Since Magneto-rheological (MR) suspension has nonlinearity and time-delay, the application of linear feedback strategy has been limited. This paper addresses the problem of control of MR suspension with time-delay when transient dynamics are present. An adaptive fuzzy-neural network control (FNNC) scheme for the transient course is proposed using fuzzy logic control and artificial neural network methodologies. To attenuate the adverse effects of time-delay on control performance, a prediction neural network (PNN) is established. Then, through a numerical example of a quarter car model and a real road test with a bump input, the comparison is made between passive suspension and semi-active suspension. The results show that the MR vehicle with FNNC strategy can depress the peak acceleration and shorten the setting time, and the effect of time-delay can be attenuated. The results of road test with the similarity of numerical study verify the feasibility of the control strategy