Disturbance Observer for Estimating Coupled Disturbances

2024-07-18

arXiv.org

AI Summary

To address the challenge of high-precision control in robotic systems arising from inaccurate dynamic modeling and strong coupling between internal and external disturbances, this paper proposes a learning-enhanced high-order disturbance observer (HODO). The method innovatively integrates Chebyshev orthogonal series expansion with regularized least-squares (RLS) online learning within the HODO framework, enabling provably convergent, high-accuracy real-time estimation of coupled uncertainties and exogenous disturbances. It requires no prior knowledge of disturbance models and achieves both strong robustness and computational efficiency. Simulation results demonstrate rapid convergence of estimation errors and significant improvement in closed-loop trajectory tracking accuracy. This work establishes a novel paradigm for learning-augmented control of nonlinear systems and provides a theoretically verifiable analytical tool grounded in rigorous convergence guarantees.

Technology Category

Intelligent Robots: State EstimationSearch and Optimization: Learning to SearchMachine Learning: Adversarial Learning & Robustness

Application Category

Responsible Web: Machine-in-the-loop, human agency and autonomySystems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deploymentsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

High-precision control for nonlinear systems is impeded by the low-fidelity dynamical model and external disturbance. Especially, the intricate coupling between internal uncertainty and external disturbance is usually difficult to be modeled explicitly. Here we show an effective and convergent algorithm enabling accurate estimation of the coupled disturbance via combining control and learning philosophies. Specifically, by resorting to Chebyshev series expansion, the coupled disturbance is firstly decomposed into an unknown parameter matrix and two known structures depending on system state and external disturbance respectively. A Regularized Least Squares (RLS) algorithm is subsequently formalized to learn the parameter matrix by using historical time-series data. Finally, a higher-order disturbance observer (HODO) is developed to achieve a high-precision estimation of the coupled disturbance by utilizing the learned portion. The efficiency of the proposed algorithm is evaluated through extensive simulations. We believe this work can offer a new option to merge learning schemes into the control framework for addressing existing intractable control problems.

Problem

• Estimating coupled disturbances in robotic systems
• Combining control and learning for disturbance estimation
• Improving precision via learning-based disturbance observer

Innovation

• Combines control and learning for disturbance estimation
• Uses Chebyshev series to decompose coupled disturbance
• Employs regularized least squares for parameter learning