A Self-Learning Disturbance Observer for Nonlinear Systems in Feedback-Error Learning Scheme

TL;DR

This paper introduces a novel online self-learning disturbance observer (SLDO) that combines type-2 neuro-fuzzy systems, feedback-error learning, and sliding mode control to accurately estimate time-varying disturbances in nonlinear systems.

Contribution

It presents a new SLDO framework integrating T2NFS, feedback-error learning, and SMC, with proven stability and improved disturbance estimation over traditional methods.

Findings

SLDO accurately estimates time-varying disturbances.

The proposed method maintains system stability.

Simulation results outperform basic nonlinear disturbance observer.

Abstract

This paper represents a novel online self-learning disturbance observer (SLDO) by benefiting from the combination of a type-2 neuro-fuzzy structure (T2NFS), feedback-error learning scheme and sliding mode control (SMC) theory. The SLDO is developed within a framework of feedback-error learning scheme in which a conventional estimation law and a T2NFS work in parallel. In this scheme, the latter learns uncertainties and becomes the leading estimator whereas the former provides the learning error to the T2NFS for learning system dynamics. A learning algorithm established on SMC theory is derived for an interval type-2 fuzzy logic system. In addition to the stability of the learning algorithm, the stability of the SLDO and the stability of the overall system are proven in the presence of time-varying disturbances. Thanks to learning process by the T2NFS, the simulation results show that the SLDO is able to estimate time-varying disturbances precisely as distinct from the basic nonlinear disturbance observer (BNDO) so that the controller based on the SLDO ensures robust control performance for systems with time-varying uncertainties, and maintains nominal performance in the absence of uncertainties.