A Method to Speed Up Convergence of Iterative Learning Control for High Precision Repetitive Motions

TL;DR

This paper proposes a method to accelerate the convergence of Iterative Learning Control (ILC) for high-precision repetitive motions by using a model-based initial guess to reduce hardware iterations, demonstrated through numerical simulations.

Contribution

It introduces a numerical approach to determine the optimal number of model-based iterations before hardware ILC, improving convergence speed in high-precision control tasks.

Findings

Numerical simulations show faster convergence with the proposed method.

The approach reduces the number of hardware iterations needed.

Effective in high-precision repetitive motion control scenarios.

Abstract

Various spacecraft have sensors that repeatedly perform a prescribed scanning maneuver, and one may want high precision. Iterative Learning Control (ILC) records previous run tracking error, adjusts the next run command, aiming for zero tracking error in the real world, not our model of the world. In response to a command, feedback control systems perform a convolution sum over all commands given since time zero, with a weighting factor getting smaller going further back in time. ILC learns to eliminate this error through iterations in hardware. It aims to find that command that causes the real world system to actually follow the desired command. The topic of this paper considers the possibility of learning to make our model of the world produce less error. This can be done easily and quickly numerically, and the result then used as a starting point for the ILC iterations performed in hardware. The point is to reduce the number of hardware iteration, converging more quickly to closely tracking the desired trajectory in the world. How to decide how many iterations with our model to make before switching to hardware iterations is presented, with numerical simulations performed to illustrate the approach.