Study on Neural Networks Control Algorithms for Automotive Adaptive Suspension Systems

The semi-active suspension, which consists of passive spring and active shock absorber in the light of different road conditions and automobile running conditions, is the most popular automotive suspension because active suspension is complicated in structure and passive suspension cannot meet the demands of various road conditions and automobile running conditions. In this paper, a neurofuzzy adaptive control controller via modeling of recurrent neural networks of automotive suspension is presented. The modeling of neural networks has identified automotive suspension dynamic parameters and provided learning signals to neurofuzzy adaptive control controller. In order to verify control results, a mini-bus fitted with magnetorheological fluid shock absorber and neurofuzzy control system based on DSP microprocessor has been experimented with various velocity and road surfaces. The control results have been compared with those of open loop passive suspension system. These results show that neural control algorithm exhibits good performance to reduction of mini-bus vibration