Abstract: 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, suspension 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.

Key words: Active suspension; Active control; Particle swarm optimization algorithm; Chaotic mapping

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。

关键词: 主动悬架；主动控制；粒子群算法；混沌映射