Vehicle Suspension Control Using Recurrent Neurofuzzy Wavelet Network

Abstract

The main aim of this paper is to design the controller for a vehicle suspension system to reduce the uneasiness felt by passengers which arises from road disturbances and to increase the road holding related with the movements of pitch and roll of the vehicle. This demands an accurate and quick adaptive controller to obtain such control objectives, because, the passive suspension system and semi‐active suspension cannot perform better. Therefore, an adaptive Recurrent Fuzzy Wavelet Neural Network (RFWNN) based active suspension systems are designed to give better ride comfort and vehicle stability. The proposed adaptive RFWNN model combines the traditional TSK fuzzy model and the wavelet neural networks. The RFWNN controller is highly nonlinear and robust to meet the control objectives and can handle the nonlinearities faster than other conventional controllers. An online learning algorithm, which consists of parameter learning, is also presented. The learning parameters are based on the steepest‐descent method, to train the proposed RFWNN. The proposed approach is used to minimize the vibrations of seat, heave pitch and roll of the vehicle when traveling on rough road. The performance of the proposed RFWNN control strategy is being assessed by comparing with passive and semi‐active suspension systems.