Artificial Delay Based ARC of a Class of Uncertain EL Systems with Only Position Feedback

TL;DR

This paper introduces a novel adaptive-robust control method called TARC for uncertain Euler-Lagrange systems, which only uses position feedback and estimates velocities and accelerations from past data, ensuring robustness without full system knowledge.

Contribution

The paper proposes TARC, a new adaptive-robust control strategy that does not require complete system models or known uncertainty bounds, and introduces a stability framework considering time delay and estimation.

Findings

TARC effectively handles parametric uncertainties in EL systems.

The method avoids explicit velocity and acceleration measurements.

Stability criteria guide gain and sampling interval selection.

Abstract

In this paper, the tracking control problem of an Euler-Lagrange system is addressed with regard to parametric uncertainties, and an adaptive-robust control strategy, christened Time-Delayed Adaptive Robust Control (TARC), is presented. TARC approximates the unknown dynamics through the time-delayed estimation, and the adaptive-robust control provides robustness against the approximation error. The novel adaptation law of TARC, in contrast to the conventional adaptive-robust control methodologies, requires neither complete model of the system nor any knowledge of predefined uncertainty bounds to compute the switching gain, and circumvents the over- and underestimation problems of the switching gain. Moreover, TARC only utilizes position feedback and approximates the velocity and acceleration terms from the past position data. The adopted state-derivatives estimation method in TARC avoids any explicit requirement of external low pass filters for the removal of measurement noise. A new stability notion in continuous-time domain is proposed considering the time delay, adaptive law, and state-derivatives estimation which in turn provides a selection criterion for gains and sampling interval of the controller.