Research on ride comfort optimization of active suspension system based on model predictive control algorithm

Abstract

Suspension system has been known to make a tremendous contribution to the ride comfort and safety of the car. In order to increase the comfort of the ride with tricky road conditions, this paper introduces a model predictive control (MPC)-based active suspension system. Our active suspension model is quarter-car with the model being the filtered white noise as our road excitation model. The design and implementation of an MPC control algorithm is carried out in MATLAB. ADAMS co-simulation is used to measure performance metrics. It has been found that tire dynamic load, suspension dynamic deflection and the RMS of the sprung mass acceleration can be reduced by 21.16, 32.41 and 42.45 percent respectively with Class C random road excitation, MPC-controlled suspension, which are significant in ride comfort.