车辆主动悬架系统LQR的多种群遗传优化算法

Multi-population Genetic Optimization of LQR for Vehicle Active Suspension System

车辆主动悬架系统的线性二次型调节器(LQR)因结构简单、易于实现而得到广泛应用,但其目标函数最优参数确定的问题还没有公认的解决方法。为此,利用遗传算法的群体搜索特性,提出一种基于多种群遗传算法的LQR参数优化策略。仿真结果表明:与传统遗传算法优化的LQR及无优化LQR相比,多种群遗传算法在悬架动行程、轮胎动位移和车身垂向加速度3个指标的平均性能上,分别提高了2.26%和33.55%,增加汽车的运行平顺性、操纵稳定性和乘坐舒适性。

Linear quadratic optimal(LQR)control of vehicle active suspension system has been widely used because of its simple structure and easy realization,but there is no recognized solution to the problem of determining the optimal parameters of its objective function.Therefore,aiming at the problem of determining the optimal parameters of LQR controller for active suspension system,a LQR controller parameter optimization strategy based on multi population genetic algorithm is proposed by using the population search characteristics of genetic algorithm,in order to obtain the optimal values of parameters.The results show that,compared with the traditional genetic algorithm optimization and no optimization,the average performance of the multi population genetic algorithm is improved by 2.26%and 33.55%respectively in the three indexes of body vertical acceleration,suspension dynamic travel and tire dynamic displacement.The results show that the optimal parameters of LQR controller of active suspension system obtained by multi population genetic algorithm have positive significance for improving the ride comfort,handling stability and riding comfort of vehicles.