Robust point-to-point iterative learning control for constrained systems: A minimum energy approach

Abstract

Iterative learning control (ILC) is a high performance control scenario that is widely applied to systems that repeat a given task or operation defined over a finite duration, and has been introduced to point-to-point motion tasks in existing work. However, its design degree of freedom has not been fully utilized to optimize performance beyond tracking accuracy in constrained conditions. The framework of point-to-point ILC in this article is extended within discrete linear time-invariant (LTI) system, so as to take the tracking time instants of desired positions as changing variables. Therefore, it is possible to achieve the objective of minimizing energy while maintaining the required tracking accuracy. The multiobjective optimization problem is divided into two sub-problems, which are solved with an iterative algorithm composed of norm-optimal ILC approach as well as the coordinate descend method. Furthermore, the impact of model uncertainty on algorithm performance is also considered, and the iterative algorithm is further extended to capture constrained systems. The algorithm is robust to the model uncertainty and has a certain robustness to output disturbances. Finally, the validity of the proposed algorithm is verified by a twin rotor aerodynamic system (TRAS) model.