Enhanced Robust Tracking Control: An Online Learning Approach

TL;DR

This paper introduces an online learning method integrated with contraction-based control to improve the robustness of nonlinear system tracking under unknown disturbances, demonstrated through space robot and PVTOL simulations.

Contribution

It proposes a novel online disturbance learning scheme embedded in contraction control, enhancing tracking robustness for nonlinear systems.

Findings

Effective disturbance compensation demonstrated in simulations

Improved tracking accuracy under unknown disturbances

Plug-and-play online learning module enhances existing controllers

Abstract

This work focuses the tracking control problem for nonlinear systems subjected to unknown external disturbances. Inspired by contraction theory, a neural network-dirven CCM synthesis is adopted to obtain a feedback controller that could track any feasible trajectory. Based on the observation that the system states under continuous control input inherently contain embedded information about unknown external disturbances, we propose an online learning scheme that captures the disturbances dyanmics from online historical data and embeds the compensation within the CCM controller. The proposed scheme operates as a plug-and-play module that intrinsically enhances the tracking performance of CCM synthesis. The numerical simulations on tethered space robot and PVTOL demonstrate the effectiveness of proposed scheme. The source code of the proposed online learning scheme can be found at https://github.com/NPU-RCIR/Online_CCM.git.