Robust Adaptive Learning Control for a Class of Non-affine Nonlinear Systems
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.
Cite
@article{arxiv.2602.00968,
title = {Robust Adaptive Learning Control for a Class of Non-affine Nonlinear Systems},
author = {Shuai Gao and Dong Shen and Abdelhamid Tayebi},
journal= {arXiv preprint arXiv:2602.00968},
year = {2026}
}