A unified experiment design framework for detection and identification in closed-loop performance diagnosis

This paper presents a least-costly experiment design framework for closed-loop performance diagnosis using prediction error identification. The performance diagnosis methodology consists in verifying whether an identified model of the true system lies in a performance-related region of interest. The...

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Bibliographic Details
Main Authors: Mesbah, A., Bombois, X., Forgione, M., Ludlage, J. H. A., Moden, P. E., Hjalmarsson, H., Van den Hof, P. M. J.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Bayesian methods
Closed loop systems
Degradation
Vectors
White noise
Online Access:Request full text
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Summary:This paper presents a least-costly experiment design framework for closed-loop performance diagnosis using prediction error identification. The performance diagnosis methodology consists in verifying whether an identified model of the true system lies in a performance-related region of interest. The experiment design framework minimizes the overall excitation cost incurred for detecting the cause of the performance drop and re-identifying the system dynamics when the degraded performance is due to control-relevant system changes. The optimal design of excitation signals is performed for a desired detection rate and a pre-specified level of accuracy required for the re-identified model.
ISSN:0191-2216
DOI:10.1109/CDC.2012.6426717