Prediction-error identification of LPV systems : a nonparametric Gaussian regression approach: A nonparametric Gaussian regression approach

M.A.H. Darwish, P.B. Cox, I. Proimadis, G. Pillonetto, R. Toth

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We consider networked control systems (NCSs) composed of a linear plant and a linear controller interconnected by packet-based communication channels with communication constraints. We are interested in the setup where direct-feedthrough terms are present in the plant and/or in the controller, a case that is largely ignored in the literature due to its inherent complexity and counterintuitive results in the analysis despite its relevance for important classes of controllers including Proportional–Integral (PI) regulators. This setup calls for a novel stability analysis, for which we take a renewed look at the concept of uniformly globally exponentially stable (UGES) scheduling protocols that turned out to be instrumental in earlier approaches. We provide a generalization of the UGES property, called -UGES with being the direct-feedthrough matrices of the plant/controller, respectively, and we present generic conditions on these direct-feedthrough terms such that the classical UGES property of scheduling protocols implies -UGES. This allows us to derive conditions leading to a maximally allowable transmission interval (MATI) such that stability of the overall NCS is guaranteed. In addition, it is shown that it is possible to get more tailored results for the well-known sampled-data (SD), round-robin (RR), and try-once-discard (TOD) protocols leading to less conservative conditions on the direct-feedthrough terms than the generic ones. We also introduce new -UGES scheduling protocols, designed to handle the direct-feedthrough terms in a more effective way than existing protocols. Our results are illustrated using the example of a batch reactor.
Originele taal-2Engels
Pagina's (van-tot)92-103
Aantal pagina's12
TijdschriftAutomatica
Volume97
DOI's
StatusGepubliceerd - nov 2018

Vingerafdruk

Controllers
Networked control systems
Scheduling
Batch reactors
Communication

Citeer dit

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title = "Prediction-error identification of LPV systems : a nonparametric Gaussian regression approach: A nonparametric Gaussian regression approach",
abstract = "We consider networked control systems (NCSs) composed of a linear plant and a linear controller interconnected by packet-based communication channels with communication constraints. We are interested in the setup where direct-feedthrough terms are present in the plant and/or in the controller, a case that is largely ignored in the literature due to its inherent complexity and counterintuitive results in the analysis despite its relevance for important classes of controllers including Proportional–Integral (PI) regulators. This setup calls for a novel stability analysis, for which we take a renewed look at the concept of uniformly globally exponentially stable (UGES) scheduling protocols that turned out to be instrumental in earlier approaches. We provide a generalization of the UGES property, called -UGES with being the direct-feedthrough matrices of the plant/controller, respectively, and we present generic conditions on these direct-feedthrough terms such that the classical UGES property of scheduling protocols implies -UGES. This allows us to derive conditions leading to a maximally allowable transmission interval (MATI) such that stability of the overall NCS is guaranteed. In addition, it is shown that it is possible to get more tailored results for the well-known sampled-data (SD), round-robin (RR), and try-once-discard (TOD) protocols leading to less conservative conditions on the direct-feedthrough terms than the generic ones. We also introduce new -UGES scheduling protocols, designed to handle the direct-feedthrough terms in a more effective way than existing protocols. Our results are illustrated using the example of a batch reactor.",
keywords = "Bayesian identification, Box–Jenkins models, Gaussian processes, Linear parameter-varying systems, Machine learning, Prediction-error identification, Regularization, Reproducing kernel Hilbert space, System identification",
author = "M.A.H. Darwish and P.B. Cox and I. Proimadis and G. Pillonetto and R. Toth",
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Prediction-error identification of LPV systems : a nonparametric Gaussian regression approach : A nonparametric Gaussian regression approach. / Darwish, M.A.H.; Cox, P.B.; Proimadis, I.; Pillonetto, G.; Toth, R.

In: Automatica, Vol. 97, 11.2018, blz. 92-103.

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

TY - JOUR

T1 - Prediction-error identification of LPV systems : a nonparametric Gaussian regression approach

T2 - A nonparametric Gaussian regression approach

AU - Darwish, M.A.H.

AU - Cox, P.B.

AU - Proimadis, I.

AU - Pillonetto, G.

AU - Toth, R.

PY - 2018/11

Y1 - 2018/11

N2 - We consider networked control systems (NCSs) composed of a linear plant and a linear controller interconnected by packet-based communication channels with communication constraints. We are interested in the setup where direct-feedthrough terms are present in the plant and/or in the controller, a case that is largely ignored in the literature due to its inherent complexity and counterintuitive results in the analysis despite its relevance for important classes of controllers including Proportional–Integral (PI) regulators. This setup calls for a novel stability analysis, for which we take a renewed look at the concept of uniformly globally exponentially stable (UGES) scheduling protocols that turned out to be instrumental in earlier approaches. We provide a generalization of the UGES property, called -UGES with being the direct-feedthrough matrices of the plant/controller, respectively, and we present generic conditions on these direct-feedthrough terms such that the classical UGES property of scheduling protocols implies -UGES. This allows us to derive conditions leading to a maximally allowable transmission interval (MATI) such that stability of the overall NCS is guaranteed. In addition, it is shown that it is possible to get more tailored results for the well-known sampled-data (SD), round-robin (RR), and try-once-discard (TOD) protocols leading to less conservative conditions on the direct-feedthrough terms than the generic ones. We also introduce new -UGES scheduling protocols, designed to handle the direct-feedthrough terms in a more effective way than existing protocols. Our results are illustrated using the example of a batch reactor.

AB - We consider networked control systems (NCSs) composed of a linear plant and a linear controller interconnected by packet-based communication channels with communication constraints. We are interested in the setup where direct-feedthrough terms are present in the plant and/or in the controller, a case that is largely ignored in the literature due to its inherent complexity and counterintuitive results in the analysis despite its relevance for important classes of controllers including Proportional–Integral (PI) regulators. This setup calls for a novel stability analysis, for which we take a renewed look at the concept of uniformly globally exponentially stable (UGES) scheduling protocols that turned out to be instrumental in earlier approaches. We provide a generalization of the UGES property, called -UGES with being the direct-feedthrough matrices of the plant/controller, respectively, and we present generic conditions on these direct-feedthrough terms such that the classical UGES property of scheduling protocols implies -UGES. This allows us to derive conditions leading to a maximally allowable transmission interval (MATI) such that stability of the overall NCS is guaranteed. In addition, it is shown that it is possible to get more tailored results for the well-known sampled-data (SD), round-robin (RR), and try-once-discard (TOD) protocols leading to less conservative conditions on the direct-feedthrough terms than the generic ones. We also introduce new -UGES scheduling protocols, designed to handle the direct-feedthrough terms in a more effective way than existing protocols. Our results are illustrated using the example of a batch reactor.

KW - Bayesian identification

KW - Box–Jenkins models

KW - Gaussian processes

KW - Linear parameter-varying systems

KW - Machine learning

KW - Prediction-error identification

KW - Regularization

KW - Reproducing kernel Hilbert space

KW - System identification

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U2 - 10.1016/j.automatica.2018.07.032

DO - 10.1016/j.automatica.2018.07.032

M3 - Article

AN - SCOPUS:85051671384

VL - 97

SP - 92

EP - 103

JO - Automatica

JF - Automatica

SN - 0005-1098

ER -