Abstract: In order to solve the disadvantage that the weight coefficients depend on prior knowledge for the LQG controllerused in the active suspension, an immune particle swarm optimization algorithm is proposed. Firstly, the particle swarm optimization algorithm is used to optimize the parameters and obtain the quasi-optimal parameters as the initial value of online regulation, then the parameters are optimized in real time with the immune particle swarm algorithm. This method is simple, which can maintain the particle diversity and the convergence rate and precocity of PSO in particle swarm and the complexity of the immune algorithm are improved at the same time. The feasibility and validity of the proposed method are verified by simulation.

Key words: active suspension;linear quadratic（LQG） controller, immune particle swarm optimization, particle swarm optimization

摘要： 针对LQG控制器用于主动悬架存在权重系数依靠先验知识来确定的不足，提出了免疫粒子群混合优化算法。该算法首先利用粒子群算法对LQG参数进行离线优化，得到一组准最优LQG参数，将其作为在线调节初始值，然后引入免疫粒子群算法对LQG参数进行在线实时优化。该方法实现简单，在保持粒子多样性的同时也改善了粒子群算法收敛速度慢、早熟以及免疫算法过程繁复冗长。仿真验证了所提方法的可行性与有效性。

关键词: 主动悬架, 线性二次型（LQG）控制器, 免疫粒子群混合优化算法, 粒子群算法