Point-to-point iterative learning model predictive control

Abstract

Iterative learning model predictive control (ILMPC) is a technique that combines iterative learning control (ILC) and model predictive control (MPC). The objective is to track a reference trajectory of repetitive processes on a finite time interval while rejecting real-time disturbances. In many repetitive processes, the output is not required to track all the points of a reference trajectory. In this study, we propose a point-to-point ILMPC (PTP ILMPC) technique considering only the desired reference points, and not an entire reference trajectory. In this method, an arbitrary reference trajectory passing through the desired reference values need not be generated. Numerical examples are provided to demonstrate the performances of the suggested approach in terms of PTP tracking, iterative learning, constraint handling, and real-time disturbance rejection.