On-line Estimation of Stability and Passivity Metrics

TL;DR

This paper introduces a novel on-line method for estimating key stability and passivity metrics of nonlinear systems using input-output data, overcoming limitations of existing approaches by leveraging fractional function optimization.

Contribution

It develops a new theoretical framework for on-line estimation of system indices based on fractional optimization, improving efficiency and accuracy over prior averaging-based methods.

Findings

Proposed method accurately estimates system indices in real-time.

Numerical examples demonstrate improved efficiency and robustness.

Compared to existing methods, the new approach reduces computational complexity.

Abstract

We consider the problem of on-line evaluation of critical characteristic parameters such as the L_2-gain (L2G), input feedforward passivity index (IFP) and output feedback passivity index (OFP) of non-linear systems using their input-output data. Typically, having an accurate measure of such "system indices" enables the application of systematic control design techniques. Moreover, if such system indices can efficiently be evaluated on-line, they can be exploited to device intelligent controller reconfiguration and fault-tolerant control techniques. However, the existing estimation methods of such system indices (i.e., L2G, IFP and OFP) are predominantly off-line, computationally inefficient, and require a large amount of actual or synthetically generated input-output trajectory data under some specific initial/terminal conditions. On the other hand, the existing on-line estimation methods take an averaging-based approach, which may be sub-optimal, computationally inefficient and susceptible to estimate saturation. In this paper, to overcome these challenges (in the on-line estimation of system indices), we establish and exploit several interesting theoretical results on a particular class of fractional function optimization problems. For comparison purposes, the details of an existing averaging-based approach are provided for the same on-line estimation problem. Finally, several numerical examples are discussed to demonstrate the proposed on-line estimation approach and to highlight our contributions.