Data-Based Control for Learning Systems With Nonlinear Dynamics: A Case Study

Abstract

This paper focuses on exploiting a data-based control design framework for a class of iterative learning control (ILC) systems with nonlinear dynamics. A test method is first presented for nonlinear ILC systems to generate some helpful output data under some specific test inputs. Then, a data-based P-type ILC updating law is developed by resorting to these input and output data, for which a simple data-based selection condition on the gain matrix is provided to accomplish the perfect tracking objective of ILC systems having the globally Lipschitz nonlinear dynamics. The proposed data-based P-type ILC updating law is applicable for nonlinear ILC systems without the common full rank requirement, where none of the specific model information is utilized. A simulation example is given to illustrate the validity of the data-based ILC design framework for nonlinear ILC systems.