Abstract
Perturbation analysis (PA) is a systematic methodology for estimating the sensitivities (gradients) of performance measures in discrete event systems (DES) with respect to various model or control parameters of interest. PA takes advantage of the special structure of DES sample realizations and is based entirely on observable system data. In particular, it does not require knowledge of the stochastic characterizations of the random processes involved and is simple to implement in a nonintrusive manner. PA estimators, therefore, enable implementations for real-time control in addition to off-line optimization. This entry presents the main ideas and statistical properties of PA techniques for both DES and their recent generalizations to stochastic hybrid systems (SHS), especially for the simplest class of sensitivity estimators known as infinitesimal perturbation analysis (IPA).
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Wardi, Y., Cassandras, C.G. (2020). Perturbation Analysis of Discrete Event Systems. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_58-2
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DOI: https://doi.org/10.1007/978-1-4471-5102-9_58-2
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Latest
Perturbation Analysis of Discrete Event Systems- Published:
- 20 November 2019
DOI: https://doi.org/10.1007/978-1-4471-5102-9_58-2
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Original
Perturbation Analysis of Discrete Event Systems- Published:
- 07 February 2014
DOI: https://doi.org/10.1007/978-1-4471-5102-9_58-1