Fractional Barrier Lyapunov Functions with Application to Learning Control

TL;DR

This paper introduces fractional barrier Lyapunov functions for learning control, demonstrating their advantages over traditional functions in error-constrained control schemes with uncertainties, ensuring boundedness and robust tracking performance.

Contribution

It develops fractional barrier Lyapunov functions for learning control, providing theoretical guarantees and robust modifications for improved error constraint handling.

Findings

Fractional barrier Lyapunov functions enhance learning control performance.

Theoretical guarantees for existence and convergence are established.

Robust techniques improve tracking in the presence of residuals.

Abstract

Barrier Lyapunov functions are suitable for learning control designs, due to their feature of finite duration tracking. This paper presents fractional barrier Lyapunov functions, provided and compared with the conventional ones in the error-constraint learning control designs. Two error models are adopted and the desired compensation control approach is applied for a non-parametric design, allowing two kinds of uncertainties involved in the error dynamics. Theoretical results about existence of the solution and convergence of the learning control schemes are presented. It is shown that fully-saturated learning algorithms play important role in assuring boundedness of the estimates, by which the error constraint objective can be achieved. Moreover, the robust technique is developed through modifying the discontinuous action involved in the learning control scheme that yields the expected tracking performance in the presence of residual.