A new kind of nonlinear model predictive iterative learning control

Abstract

A nonlinear model predictive controller based on iterative learning control (NMPILC) is proposed. The nonlinear plant dynamic is described by a fuzzy model which contains local liner models. Based on this model, model predictive control algorithm that utilizes past data along with real-time measurements is devised. This algorithm is developed to address the learning rate for a class of repetitive system with non-repetitive disturbances. The iterative learning control law is given. It is shown that the control performance of the proposed NMPILC can be greatly improved by using this on-linear model predictive iterative learning control algorithm.