基于粒子群算法的车队滑模控制器参数优化

Parameter optimization of sliding mode controller for fleet based on particle swarm optimization

为保证车辆驾驶过程中的稳定性和安全性,对智能网联式车队控制器参数的优化和选取展开研究。采用领头车-前车跟随策略进行车辆编队,构建网联式车队纵向控制动力学模型,结合滑模控制器规律,利用切换函数,实现网联式车队车辆自适应控制。在此基础上,利用粒子群优化算法,将滑模控制器4个参数设置为空间粒子,通过搜索个体极值和全局极值动态调整粒子参数,获得滑模控制器的最优参数。采用MATLAB软件进行仿真研究,仿真结果表明,相对于给定的未优化控制器参数,优化后的滑模控制器具有响应快、精度高等特点,能够有效实现网联式车队的稳定控制。

To ensure the stability and safety of the vehicle during driving,the optimization and selection of networked fleet controller parameters was studied.The leader-predecessor following strategy was used for vehicle formation.The longitudinal control dynamics model of networked fleet was constructed.The networked fleet vehicle control strategy was realized by the sliding mode controller law and switching function.Particle swarm optimization algorithm was adopted to set the four parameters of the sliding mode controller space as particles.The particle parameters were adjusted dynamically by seeking the individual parameters and global extreme values to obtain the optimal parameters of the sliding mode controller.The simulation results researched through MATLAB show that networked fleet controller based on particle swarm optimization algorithm which compared with the given unoptimized controller parameters has the characteristics of fast response and high accuracy,and the optimized sliding mode controller can control the networked fleet stably and effectively.