Semi-active Suspension Control with PSO Tuned LQR Controller Based on MR Damper

Abstract

As the Linear Quadratic Regulator (LQR) approach is applied extensively in the system control of automobile suspension, the accuracy improvement of the weighting Q and R matrices is getting concern. The Particle Swarm Optimization (PSO) algorithm is being introduced to identify parameters and optimize matrix Q and R in order to fix the insufficiency of these experienced values because of the fast convergence and a more accurate solution. In this article, a quarter car model and a Bouc-Wen-based magnetorheological (MR) damper model are developed to combine the control of PSO identification and PSO-LQR controller in the semi-active suspension system. The MR damper was performed with an experimental test for running identification using experimental data as input into the Bouc-Wen model to obtain six unknown parameters, where the parameters were estimated with the PSO algorithm. Since the numerical model has been done with all parameters clear, the need for damping force from suspension is obtained by means of running the model using an input current. In the employment of PSO for damper model and vehicle control, the dual applications succeeded in verifying the feasibility of parameter identification in the MR damper and successfully tuned the LQR controller in the semi-active suspension, which decreases the vehicle body acceleration and displacement so that the improvement of ride comfort and drive stability achieved.