Tuning of extended state observer with neural network-based control performance assessment

TL;DR

This paper introduces a neural network-based method for tuning extended state observers in control systems, enabling rapid, near-optimal parameter selection based on user-defined performance criteria.

Contribution

It presents a novel NN-based tuning procedure for ESO parameters that quickly achieves near-optimal control performance tailored to specific quality criteria.

Findings

The NN provides accurate performance assessment.

The tuning process is fast, taking seconds.

It achieves near-optimal ESO gains.

Abstract

The extended state observer (ESO) is an inherent element of robust observer-based control systems that allows estimating the impact of disturbance on system dynamics. Proper tuning of ESO parameters is necessary to ensure a good quality of estimated quantities and impacts the overall performance of the robust control structure. In this paper, we propose a neural network (NN) based tuning procedure that allows the user to prioritize between selected quality criteria such as the control and observation errors and the specified features of the control signal. The designed NN provides an accurate assessment of the control system performance and returns a set of ESO parameters that delivers a near-optimal solution to the user-defined cost function. The proposed tuning procedure, using an estimated state from the single closed-loop experiment produces near-optimal ESO gains within seconds.