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针对参数不确定的自动引导车的运动控制问题,应用Backstepping方法改计自适应控制器,并运用Lyapunov稳定性理论与Barbalat定理证明了系统的稳定性;同时利用进化规划算法优化控制器参数,通过跟踪微分器对输入信号与虚拟控制信号进行滤波处理并提取微分信号,避免了对虚拟控制信号的解析求导,简化了控制器的设计过程.与传统PID控制的对比仿真结果表明,所提出的自适应控制策略能较好地补偿系统参数摄动的影响,提高了自动引导车的轨迹跟踪性能和鲁棒性.
Aimed at the motion control of auto-guided vehicles with uncertain parameters, the Backstepping method is used to count the adaptive controller. The stability of the system is proved by Lyapunov stability theory and Barbalat’s theorem. At the same time, the parameters of the controller are optimized by using the evolutionary programming algorithm. The tracking differentiator filters the input signal and the virtual control signal and extracts the differential signal, which avoids the analysis and derivation of the virtual control signal and simplifies the design process of the controller.Compared with the traditional PID control, the simulation results show that the proposed Adaptive control strategy can better compensate for the influence of system parameter perturbation and improve the tracking performance and robustness of the auto-guided vehicle.