Output-redefinition-based iterative learning control for nonlinear non-minimum phase systems
输出重定义下的非线性非最小相位系统迭代学习控制

An iterative learning control is presented for nonlinear non-minimum phase systems. The systems undertaken are assumed to perform the same tasks repeatedly. Output redefinition is carried out and the zero-dynamics with respect to the redefined output exhibit a stable behavior. Both methodologies of partially saturated and fully saturated learning are adopted in designing the learning controllers. Stability and convergence of the learning systems are established, ensuring the zero-convergence of the tracking error and the boundedness of all variables in the closed-loop. Numerical results are presented for demonstrating the effectiveness of the proposed two learning control methods.