Data-driven predictive point-to-point iterative learning control

Integrating the idea of predictive control and point-to-point iterative learning control, this paper presents a data-driven predictive point-to-point iterative learning control scheme for a class of unknown repetitive non-affine nonlinear SISO systems. The tracking task is driven by the optimal control input sequence generated by the proposed algorithm, and the tracking errors at the specified sampling time instants are minimized. The advantages of this scheme are that the structure of the controller and its stability analysis both are based on an equivalent dynamic linearization data model of the nonlinear system, and the proposed scheme does not involve the operation of matrix inversion. Numerical simulations verify the effectiveness of this method.