Sensor-Noise Mitigation in Extremum Seeking Control Using Adaptive Numerical Differentiation

TL;DR

This paper introduces ESC/AISE, a novel approach that replaces traditional high-pass filters with adaptive input and state estimation to enhance extremum-seeking control performance amid sensor noise.

Contribution

It develops a new ESC method utilizing AISE for numerical differentiation, reducing noise sensitivity and improving robustness over existing techniques.

Findings

ESC/AISE outperforms traditional ESC in noisy environments

Numerical examples demonstrate improved accuracy and stability

The method effectively mitigates sensor noise effects

Abstract

Extremum-seeking control (ESC) is widely used to optimize performance when the system dynamics are uncertain. However, sensitivity to sensor noise is a crucial issue in ESC implementation due to the use of high-pass filters or gradient estimators. To reduce the sensitivity of ESC to noise, this paper investigates the use of the recently developed adaptive input and state estimation (AISE) technique for numerical differentiation. In particular, this paper develops extremum-seeking control with adaptive input and state estimation (ESC/AISE), where AISE replaces the high-pass filter of ESC to improve performance under sensor noise. The effectiveness of ESC/AISE is illustrated via numerical examples.