Neural Network-Augmented Iterative Learning Control for Friction Compensation of Motion Control Systems with Varying Disturbances

TL;DR

This paper introduces a combined neural network and iterative learning control approach to improve motion system accuracy by compensating for friction and disturbances, demonstrating practical real-time application and enhanced tracking performance.

Contribution

It presents a simplified neural network-augmented ILC method that adapts to varying friction and disturbances, improving trajectory tracking in motion control systems.

Findings

Effective friction compensation demonstrated in experiments

Improved tracking accuracy across multiple tasks

Simplified online training for real-time implementation

Abstract

This paper proposes a robust control strategy that integrates Iterative Learning Control (ILC) with a simple lateral neural network to enhance the trajectory tracking performance of a linear Lorentz force actuator under friction and model uncertainties. The ILC compensates for nonlinear friction effects, while the neural network estimates the nonlinear ILC effort for varying reference commands. By dynamically adjusting the ILC effort, the method adapts to time-varying friction, reduces errors at reference changes, and accelerates convergence. Compared to previous approaches using complex neural networks, this method simplifies online training and implementation, making it practical for real-time applications. Experimental results confirm its effectiveness in achieving precise tracking across multiple tasks with different reference trajectories.