Abstract
For the lack of artificial experience in weighted matrix Q and R in LQR optimal control algorithm of suspension, this paper proposed an optimal control strategy based on improved particle swarm optimization for semi-active suspension system. The paper mainly established a quarter vehicle semi-active suspension system model in MatLab, and wrote the S-function of the optimal controller. In addition, this article optimized weighted coefficient matrix Q of the state variable and the weighted coefficient matrix R of the control variable in the linear quadratic regulator (LQR) [1] by utilizing the improved particle swarm optimization. The simulation results showed that the semi-active suspension system which based on the improved particle swarm optimization (IPSO) had better ride comfort and smoothness.
Highlights
As a connecting device between the vehicle’s body and wheel, the suspension can bear the weight of the body and isolate the impact and vibration of the vehicle body from the ground
In order to solve this problem, this paper proposed the optimal control strategy to improved particle swarm optimization (IPSO)
IPSO was a new type of optimization calculation technology which derived from the study of the predation behavior of flock birds
Summary
As a connecting device between the vehicle’s body and wheel, the suspension can bear the weight of the body and isolate the impact and vibration of the vehicle body from the ground. The semi-active suspension system can adjust the suspension stiffness or damping coefficient according to the different driving conditions. It overcomes the technical defects of the passive suspension system and holds the advantage of low cost. LQR theory is a state space design method developed in modern control theory, which was the earliest and most proved theory. This theory got the optimal control law of state linear feedback, which is easy to form closed loop optimal control. IPSO was a new type of optimization calculation technology which derived from the study of the predation behavior of flock birds. This paper was focused on two models, that was established the semi-active suspension system model and integral white noise road input model in MatLab, and applied the improved particle swarm optimization to optimize the weighting coefficient of the optimal controller
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