Integral Concurrent Learning: Adaptive Control with Parameter Convergence without PE or State Derivatives

TL;DR

This paper introduces an integral concurrent learning method for adaptive control that guarantees parameter convergence without needing state derivatives or persistent excitation, improving robustness to noise.

Contribution

The paper proposes a new integral concurrent learning approach that eliminates the need for derivative estimation while ensuring parameter convergence.

Findings

Improved robustness to noise demonstrated in simulations

Parameter convergence achieved without persistent excitation

Elimination of state derivative estimation requirement

Abstract

Concurrent learning is a recently developed adaptive update scheme that can be used to guarantee parameter convergence without requiring persistent excitation. However, this technique requires knowledge of state derivatives, which are usually not directly sensed and therefore must be estimated. A novel integral concurrent learning method is developed in this paper that removes the need to estimate state derivatives while maintaining parameter convergence properties. A Monte Carlo simulation illustrates improved robustness to noise compared to the traditional derivative formulation.