基于混沌粒子群的主动悬架LQG控制研究  

Research on LQG Control of Active Suspension Based on Chaotic Particle Swarm Optimization

作　　者：陈晨[1] 程吉鹏 许家楠 刘源 李阳康 CHEN Chen;CHENG Jipeng;XU Jianan;LIU Yuan;LI Yangkang(College of Mechanical Engineering,Shaanxi Polytechnic Institute,Xianyang 712000,China;College of Mechanical Engineering,Xi'an University of Science and Technology,Xi'an 710054,China;Xi'an Tedian Intelligent Charging Technology Company Limited,Xi'an 710075,China)

机构地区：[1]陕西工业职业技术学院汽车工程学院,陕西咸阳712000 [2]西安科技大学机械工程学院,陕西西安710054 [3]西安特来电智能充电科技有限公司,陕西西安710075

出　　处：《汽车实用技术》2023年第23期56-62,共7页Automobile Applied Technology

基　　金：陕西工业职业技术学院院级科研计划项目(2022YKYB-026)。

摘　　要：针对主动悬架线性二次型调节器(LQG)控制算法存在权重系数依靠经验来确定的不足,采用了粒子群优化算法对LQG算法参数进行离线优化,但传统粒子群算法存在易早熟收敛陷入局部最优的缺点,鲁棒性较差,因此提出了混沌粒子群优化算法。该算法首先利用混沌映射遍历性、随机性的特点,对粒子进行初始化,随后引入动态惯性权重系数及学习因子,可以权衡全局搜索能力和局部搜索能力。通过MATLAB对所设计的优化算法及悬架特性进行仿真,并与传统粒子群算法进行对比分析,结果表明,改进后的混沌粒子群优化收敛速度更快、收敛精度更好,且悬架车身加速度、悬架动挠度与轮胎动载荷均方根值分别降低了5.8%、0.8%、6.0%,验证了所提方法的可行性与有效性。In response to the shortcomings of relying on experience to determine the weight coefficients in the active suspension linear quadratic gaussian(LQG)control algorithm,a particle swarm optimization algorithm is used to optimize the parameters of the LQG algorithm offline.However,traditional particle swarm optimization algorithms have the disadvantage of being prone to premature convergence and falling into local optima,resulting in poor robustness.Therefore,a chaotic particle swarm optimization algorithm is proposed.This algorithm first utilizes the ergodicity and randomness of chaotic maps to initialize particles,and then introduces dynamic inertia weight coefficients and learning factors to balance global and local search capabilities.The optimization algorithm and suspension characteristics are simulated using MATLAB,and compared with traditional particle swarm optimization.The results show that the improved chaotic particle swarm optimization had faster convergence speed and better convergence accuracy,and the suspension body acceleration,suspen-sion dynamic deflection,and wheel tire dynamic load root mean square values are reduced by 5.8%,0.8%,and 6.0%,respectively,verifying the feasibility and effectiveness of the proposed method.

关 键 词：主动悬架 主动控制 粒子群算法 混沌映射 

分 类 号：TH134[机械工程—机械制造及自动化]