Iterative learning tracking control for a class of MIMO nonlinear time-varying systems

Abstract

Tracking control nonlinear systems with model-less controllers have attracted extensive attention in the research community. One frequently applied model-less controller technique is iterative learning tracking control. However, this technique has mostly been applied for controlling time-invariant systems, where the dynamic behaviour of the system would not change over time. In this article, the authors demonstrate a variable gain iterative learning control (VGILC) technique that is capable of continuously adjusting and effectively controlling multi-input and multi-output (MIMO) time-varying nonlinear systems. VGILC incorporates variable gain into the PD-type updating law to accelerate convergence speed. The convergence condition of the new learning law is presented and proved. The proposed approach improves the output tracking performance within a few trials. The effectiveness of the presented approach is demonstrated using modified robotic manipulators systems and its validity is proven.