Research On Parameter Optimization Of Semi-active Air Suspension System Using Improved Particle Swarm Optimization Algorithm

Vehicle semi-active air suspension system is a nonlinear complex system working in various conditions.How to improve the performance of a semi-active air suspension system by parameter optimization is a hot issue in academic circles.Swarm Intelligent optimization algorithms,including PSO(Particle Swarm Optimization)algorithm,have the advantages of simple principle and fast convergence,but they also have some problems,such as too early convergence and easy to fall into local optimization,which make them difficult to be directly used in the parameter optimization of semi-active suspension system.Aiming at the above problems,this paper studies the parameter optimization of vehicle 7-degree-of-freedom semiactive air suspension system based on an improved particle swarm optimization algorithm.The main research contents of this paper are as follows:Firstly,in order to obtain the mechanical characteristics of the membrane air spring,the finite element analysis of the membrane air spring was carried out by Abaqus.The fitting force-compression curves under several initial pressure conditions were obtained.The interpolation reaction-compression amount curves at the initial pressure that is not obtained by finite element analysis are predicted by interpolating.Based on the finite element analysis results,the quadratic fitting equation of the initial mass-initial pressure curve is obtained.Secondly,in order to carry out the dynamic analysis of the vehicle semi-active air suspension,the mathematical model of vehicle 7-degree-of-freedom semi-active air suspension system of the vehicle is established.Based on the speed limit requirements of China's roads and the quality of roads on different levels,the vehicle's straight driving conditions are divided into three types: high-speed road conditions,medium-speed road conditions,and low-speed road conditions.The analytic hierarchy process is used to determine the weighting values for different straight road conditions.Thirdly,in order to obtain a better algorithm that can be used for dynamic optimization of air suspension parameters,the advantages of the CSO algorithm are integrated into the PSO algorithm to obtain the IPSO algorithm.Five test functions are chosen to test the performance of the IPSO algorithm.As for the Ackley function,the optimal value,the worst value,the mean value in the optimization results of the IPSO algorithm are better than the PSO algorithm's and the CSO algorithm's counterparts,and the standard deviation of IPO algorithm's result is zero,which means better robustness.The results of the test show that the IPO algorithm has a success rate of 100% for the other four test functions.Finally,in order to carry out the simulation experiment of vehicle suspension system's parameter optimization,combined with the results of finite element analysis of air spring,the simulation model of the vehicle's 7-degree-of-freedom semi-active air suspension system is established.In the MATLAB/Simulink environment,the IPSO algorithm is used to optimize the initial pressure combination of the air spring of the vehicle semi-active air suspension system.The optimal initial pressure combinations under different road conditions are obtained.Based on the initial mass-initial pressure fitting curves of air spring and the optimal initial pressure combinations,the optimal air mass combinations under three road conditions are calculated.The simulation results show that compared with the CSO algorithm and the PSO algorithm,the overall performance of the vehicle suspension system optimized by the IPSO algorithm is improved by 34.32% ~ 65.47%.Compared with the passive air suspension system,the vertical acceleration of the vehicle body's mass center optimized by the IPSO algorithm is improved by 35.98%,31.74% and 27.17% respectively.