Robust Adaptive Learning Control for a Class of Non-affine Nonlinear Systems

TL;DR

This paper introduces a robust adaptive learning control method for uncertain non-affine nonlinear systems with high relative degrees, utilizing gradient descent adaptation and state estimation to improve tracking performance in complex, non-repetitive tasks.

Contribution

It presents a novel, rigorously proven control scheme with explicit iterative computation for non-affine nonlinear systems, addressing unknown parameters and unmeasurable states.

Findings

Effective tracking performance demonstrated in simulations

Robustness against system uncertainties validated

Explicit iterative method simplifies implementation

Abstract

We address the tracking problem for a class of uncertain non-affine nonlinear systems with high relative degrees, performing non-repetitive tasks. We propose a rigorously proven, robust adaptive learning control scheme that relies on a gradient descent parameter adaptation law to handle the unknown time-varying parameters of the system, along with a state estimator that estimates the unmeasurable state variables. Furthermore, despite the inherently complex nature of the non-affine system, we provide an explicit iterative computation method to facilitate the implementation of the proposed control scheme. The paper includes a thorough analysis of the performance of the proposed control strategy, and simulation results are presented to demonstrate the effectiveness of the approach.