Enhance ride comfort and road handling on active suspension system by PSO-based state-feedback controller

Abstract

Determine the state feedback controller gain in the LQR controller is quite challenging as it requires multiple attempts of trial and error process. To eliminate the trial and error method when selecting the optimal controller parameter, we propose a PSO-based state feedback controller for the active suspension system. It is an intelligent-based method to determine state feedback gain controller by employing optimization technique using Particle Swarm Optimization (PSO). Unlike optimization-based LQR controller which seek the optimum Q and R matrices and then calculate the LQR feedback gain, in this study, the PSO algorithm is used to determine feedback gain controller parameters directly. In addition to the simple and straightforward controller design approach, the proposed controller is designed to obtain the optimum state feedback gain for improving both ride comfort and road handling aspects simultaneously by employing a multi-objective optimization technique. The proposed controller is applied on a quarter-car active suspension model. The controller performance is evaluated using Performance Index value based on the response of the suspension system under different road excitation, i.e. bump road profile and sinusoidal road profile at the frequency range from 1 to 10 Hz. The simulation results showed that the proposed controller improves both ride comfort and road handling successfully.