End-to-End ILC for Repetitive Untrackable Tasks: A Cooperative Game Perspective
For control systems facing untrackable trajectories, this work provides a new cooperative game framework that improves performance over standard ILC.
This paper proposes an end-to-end iterative learning control (ILC) design for repetitive untrackable tasks, where both the reference input and ILC feedforward input are updated trial-to-trial. From a cooperative game perspective, it shows that the two-player approach achieves lower cost than one-player norm optimal ILC, verified by a numerical example.
An inherent assumption of perfect tracking in iterative learning control (ILC) is that there exists an ILC input such that the generated output can track the desired trajectory reference. This assumption may fail in practice, which gives rise to desired but untrackable tasks. This paper gives an end-to-end ILC design for repetitive untrackable tasks in closed-loop systems. The reference input is trial-to-trial updated together with the ILC feedforward input based on the measurement data. This two-player behavior of the closed-loop ILC system is investigated from a cooperative game perspective. A sufficient condition for the two-player end-to-end ILC to have a lower cost than the one-player norm optimal ILC (NOILC) is discovered. Finally, a numerical example is given to verify the effectiveness of the developed method.