Abstract:

 An active control method for the vehicle nonlinear suspension was proposed based on the multimodel predictive control to avoid the suspension breakdown when the vehicle passes through the rough road. A 1/4 nonlinear suspension model was established with a linear piece wise affine (PWA) model related to the active force and the body displacement by using data clustering and parameter identification based on the improved particle swarm algorithm. The rolling time domain optimal control problem of the semi-active suspension was investigated based on the multi-model predictive control theory to obtain the optimal control signals. The simulation of random road and sinusoidal road surface was carried out in Matlab/Simulink. The results show that the adoption of multi-model control could make the vehicle maintain stable body posture when driving through the rough road, and the suspension dynamic travel is well controlled at the same time, which reduces the probability of suspension breakdown. 

Key words:  , vehicle ride comfort , vehicle suspension ,  particle swarm optimization , predictive control , parameter identification , nonlinear suspension ,  linear piece wise affine