Practical implementation of skyhook and adaptive active force control to an automotive suspension system

Abstract

The paper focuses on the practical implementation of a new robust control method to an automotive active suspension system using skyhook and adaptive active force control (SANAFC) strategy. The overall control system essentially comprises four feedback control loops to cater for a number of specific taks. Neural networks (NN) with modified adaptive Levenberg-Marquardt (LM) learning algorithms were used to approximate the estimated mass and inverse dynamics of the pneumatic actuator in the AFC loop. A number of experiments were carried out on a physical test rig with hardware-in-the-loop feature that fully incorporates the theoretical elements. The performance of the proposed control method was evaluated and benchmarked to examine the effectiveness of the system in suppressing the vibration effect of the suspension system. It was found that the experimental results demonstrate the superiority of the active suspension system with SANAFC scheme compared to the proportional-integhral-derivative (PID) and passive counterparts.