Design of a BP neural network PID controller for an air suspension system by considering the stiffness of rubber bellows

Abstract

A rubber bellows has certain creep characteristics, and its role has often been ignored in previous air suspension research. To describe the motion state of a vehicle more accurately during driving, this paper uses amplitude correlation and frequency correlation to describe the mechanical characteristics of the rubber bellows in air spring mechanical characteristics research, establishes an air suspension simulation, and explores the role of the rubber bellows in the suspension. In addition, an adaptive neural network controller is designed to simulate and analyze the Body Acceleration (BA), Sprung Mass Displacement (SMD), Unsprung Mass Displacement (UMD) and Dynamic Wheel Load (DWL) of the suspension. Simulation results show that the rubber bellows has a certain impact on suspension SMD and has a great impact on the performance of the active suspension. The root mean square (RMS) can be increased by 4.55 % but little impact is found on other performance indicators. In addition, compared with the conventional PID control strategy, the adaptive neural network control strategy designed in this paper has better control performance, reducing the RMS of BA and SMD by 27.58 % and 16.67 %, respectively, and improving the control effect by about 4.48 % and 4.17 %, respectively compared with PID. Thus, it can better maintain the stability of the car during driving.