Extended Laguerre basis function based iterative learning control for non-minimum phase systems
非最小相位系统的扩展Laguerre基函数迭代学习控制

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.