Abstract
The performance of vehicle body vibration and ride comfort of active or semi-active suspension with proper control is better than that with passive suspension. It is important to use simple, reliable, effective and low-cost optimal control methods to control the vehicle suspension. An important issue in the optimal control of vehicle semi-active suspension is to determine the weighting coefficient reasonably. This paper established a whole-car model of semi-active suspension systems with 7 degrees of freedom in Matlab, built optimum control system with system function, and then optimized the weight of control system by Particle Swarm Optimization (PSO) [1]. The results show that under different road input, the seven-degree-of-freedom control model for the whole vehicle controlled by particle swarm optimization algorithm can obtain better control effect, and effectively improve the comprehensive performance of the semi-active suspension system, both the vehicle's ride comfort and handling stability.
Highlights
The suspension system is an important part of the vehicle, and its performance plays a decisive role in the ride comfort, operation, and stability
The passive vehicle suspension system was firstly presented by Olley in 1930s, which was generally composed of the stiffness-damping system
In order to make full use of the advantages of passive and active suspension force control and its practical application, a force control strategy for vehicle semi-active suspension system based on Particle Swarm Optimization (PSO) optimization is proposed
Summary
The suspension system is an important part of the vehicle, and its performance plays a decisive role in the ride comfort, operation, and stability. In the previous studies of active suspension control, PID control is often used as it has a simple principle and strong adaptability It is not very effective in suspension systems with uncertain parameters. The system adopting the neural network control method needs to provide many labeled samples and consumes a lot of computing power These control algorithms are only applied to a quarter of the car model, and are not used in the simulation application of the 7-degree-of-freedom vehicle control model. In order to make full use of the advantages of passive and active suspension force control and its practical application, a force control strategy for vehicle semi-active suspension system based on PSO optimization is proposed. In the design of vehicle semi-active suspension, the optimal controller with different objective functions can be determined according to different performance requirements. Under the vibration response of driving, the particle swarm algorithm is used to calculate the weight coefficient of each evaluation index in the optimal controller under different working conditions to meet the requirements of ride comfort and handling stability under different road grades
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