Semiactive suspension system using sky hook controller with particle swarm optimization

Abstract

Skyhook control technique is the most common control algorithm applied in various engineering applications. Also, particle swarm optimization (PSO) is extensively applied in various optimization problems. This paper introduces an investigation into the use of a PSO algorithm to tune the Skyhook controller for a semi-active vehicle suspension system incorporating magneto-rheological (MR) damper to improve the ride comfort and vehicle stability. The proposed suspension system consists of a system controller that determine the desired damping force using a Skyhook controller tuned using PSO, and a continuous state damper controller that estimate the command voltage that is required to track the desired damping force. The PSO technique is applied to solve the nonlinear optimization problem to find the Skyhook controller gains by identifying the optimal problem solution through cooperation and competition among the individuals of a swarm. A mathematical model of a two degree-of-freedom MR-damped vehicle suspension system is derived and simulated using Matlab/SIMULINK software. The proposed PSO Skyhook controlled suspension is compared to the passive suspension systems. System performance criteria are evaluated in both time and frequency domains, in order to quantify the success of the proposed suspension system. The simulated results reflect that the proposed PSO Skyhook controller of the MR-damped vehicle suspension offers a significant improvement in ride comfort and vehicle stability.