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Electrical Engineering and Systems Science > Systems and Control

arXiv:2204.00073 (eess)
[Submitted on 31 Mar 2022]

Title:On-line Estimation of Stability and Passivity Metrics

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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.
Comments:Submitted to the CDC 2022
Subjects:Systems and Control (eess.SY); Optimization and Control (math.OC)
Cite as:arXiv:2204.00073 [eess.SY]
 (orarXiv:2204.00073v1 [eess.SY] for this version)
 https://doi.org/10.48550/arXiv.2204.00073
arXiv-issued DOI via DataCite

Submission history

From: Shirantha Welikala [view email]
[v1] Thu, 31 Mar 2022 20:23:40 UTC (891 KB)
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