Model-Free Adaptive Optimal Iterative Learning Control for a Class of Nonlinear Discrete-Time Systems

on a novel dynamical linearized technique and a new concept called pseudo-partial derivative (PPD), a model free adaptive optimal iterative learning control method (MFAOILC) is presented for a general nonlinear discrete-time system. The main contribution of this work lies in that the controller design and analysis only depends on the measured input and output data without requiring any priori knowledge of the plant model and the initial state can be variable randomly along the iteration axis. The convergence is shown with rigorous analysis, and the simulation and application examples are provided to demonstrate the availability and effectiveness of the proposed algorithm further.

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