非最小相位系统的扩展Laguerre基函数迭代学习控制

摘要

针对非最小相位系统的跟踪问题,提出了一种新的基函数迭代学习控制算法.该算法利用新型的非因果Laguerre扩展基函数逼近系统逆传递函数,设计最优迭代学习律使系统输入收敛到系统的稳定逆,保证了控制性能.算法不依赖于系统的先验模型,仅需以基函数信号作为系统输入进行模型辨识,减少了模型不确定性的影响.通过对单连杆柔性机械臂这样的典型非最小相位系统跟踪问题的仿真,验证了该方法的良好效果.%A new iterative learning control (ILC) method based on extended Laguerre basis function is proposed for the non-minimum phase system. The stable inversion which is an optimal and ideal solution for the non-minimum phase system tracking problem is achieved by iteration using this method. An optimal ILC law is designed in the basis function space to ensure the control performance. A priori model is not required in this method because a simple version of system model can be identified in the basis function space. Compared with other model based ILC methods, this method alleviates the influence of the model uncertainty. The effectiveness of the method is verified through a simulation on a single-link flexible manipulator model, which is a typical non-minimum phase system.