Enhanced model-free adaptive iterative learning control with load disturbance and data dropout

Abstract

In this paper, an enhanced model-free adaptive iterative learning control (EMFAILC) method is proposed, which is applied for a class of nonlinear discrete-time systems with load disturbance and random data dropout. This method is a data-driven control strategy and only the I/O data are required for the controller design. Data are lost at every time instance and iteration instance independently, which allows successive data dropout both in time and iterative axes. By compensating the missing data, the proposed EMFAILC algorithm can track the desired time-varying trajectory. The convergence and effectiveness of the proposed approach are verified by both the rigorous mathematical analysis and the simulation results.