Adaptive Variable Universe T-S Fuzzy Control for Vehicle Active Suspension
The traditional variable universe fuzzy control relies on expert experience and the expansion factor can not be adjusted adaptively for vehicle active suspension.An adaptive variable universe T-S fuzzy control is proposed to improve vehicle ride comfort.Combining neural network into T-S fuzzy inference system,a first-order T-S fuzzy controller based on an adaptive neuro-fuzzy inference system is established.And the perfect fuzzy rules are generated by the self-learning characteristics of neural network.Then,on the basis of the tradi-tional functional expansion factor,the system error and error change rate are introduced into the expansion factor as dynamic parameters to realize the adaptive adjustment of expansion factor parameters,which can avoid pro-ducing control effect caused by the difficulty in determining the traditional functional expansion factor param-eters.The effectiveness and adaptability of the proposed algorithm are verified by simulation analysis of random and bump roads and scale experiments based on similarity theory under multiple working conditions.The re-search results show that the proposed adaptive variable universe T-S fuzzy control strategy has strong adaptabil-ity,thereby effectively improving the vehicle ride comfort and handling stability under different vehicle speeds and road excitations.
active suspensionvariable universeexpansion factorT-S fuzzy controlneuro fuzzy system
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