Adaptive B-Spline Based Neuro-Fuzzy Control for Full Car Active Suspension System

The main role of a car suspension system is to improve the ride comfort and road holding. The traditional spring-damper suspension is currently being replaced by either a semi-active or active suspension system, because, passive suspension system cannot give better ride comfort and handling property. In order to improve the capability of active suspension systems, a robust Adaptive B-spline Neuro-fuzzy (ABNF) based control strategies are used to give better road handling and passenger comfort. In this paper, different orders of B-spline membership functions are used in the proposed ABNFs control strategies. The shape of B-spline membership functions can be adjusted self-adaptively by changing control points during learning process. The order of the B-spline membership functions gives a structure for choosing the shape of the fuzzy sets. The update parameters of ABNFs are trained by gradient descent based technique during the learning process. The ABNFs control techniques are successfully applied to full car suspension model which reduces the seat heave pitch and roll displacement, suspension travel and wheel displacement of the vehicle. The performance index of the proposed techniques is taken on the basis of seat, heave, pitch and roll of the vehicle. Simulation is based on the full car mathematical model by using MATLAB/SIMULINK. The simulation results show that the ABNFs control techniques give better results than passive and semi-active suspension systems.