Iterative learning control for discrete-time nonlinear systems based on adaptive tuning of 2D learning gain

A novel data-driven iterative learning control (ILC) based on the adaptive tuning of the 2D learning gain, is proposed in this paper for a class of general discrete-time nonlinear SISO systems. Based on an equivalent compact form dynamic linearization data model of the controlled nonlinear system in the iteration domain, an iterative learning law is formulated by using a recursive search algorithm to adaptively tune the 2D learning gain with only the requirement of the measured I/O data of the controlled nonlinear system. The theoretical analysis shows that the proposed ILC guarantees convergence of the tracking error. The effectiveness of the proposed ILC is validated by simulations on a complex unknown nonlinear system with time-varying structure, order and parameters.