Adaptive Control of Vehicle Active Suspension Based on Neural Network Optimization

Abstract

An adaptive proportional-integral-derivative (PID) control method based on radial basis function neural network optimization (RBF-PID) is designed for a four-degree-of-freedom active suspension model of a 1/2 vehicle. By building a simulation model of the suspension in MATLAB/Simulink, a C-level road white noise random excitation signal is used as the road input, and the front and rear body vertical accelerations are simulated as the feedback of the control loop, respectively. The simulation results show that the proposed RBF-PID control strategy can effectively suppress the front and rear body acceleration and effectively reduce the centroid vertical acceleration, and improve the performance by about 10.3% compared with the traditional PID control suspension and about 31.2% compared with the passive suspension, but the control effect improvement for the angular acceleration of body pitch angle is not obvious.