Výsledky vyhledávání - "PB Pepijn Cox"

LPVcore: MATLAB Toolbox for LPV Modelling, Identification and Control

Autor: Roland Tóth, Pascal den Boef, PB Pepijn Cox

Publikováno v: Proc. of the 19th IFAC Symposium System Identification: learning models for decision and control

This paper describes the LPVcore software package for MATLAB developed to model, simulate, estimate and control systems via linear parameter-varying (LPV) input-output (IO), state-space (SS) and linear fractional (LFR) representations. In the LPVcore

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Analysing Multibeam, Cooperative, Ground Based Radar in a Bistatic Configuration

Autor: Wim L. van Rossum, PB Pepijn Cox

Recent advances in digital beam forming for phased arrays in combination with digital signal processing should enable the development of multibeam radar in a bistatic configuration. In the bistatic setting, the pulse travelling outward from the trans

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

Autor: PB Pepijn Cox, Mohamed Abdelmonim Hassan Darwish, Gianluigi Pillonetto, Roland Tóth, Ioannis Proimadis

Publikováno v: Automatica. 97:92-103

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 pr

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https://doi.org/10.1016/j.automatica.2018.07.032

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Affine Parameter-Dependent Lyapunov Functions for LPV Systems With Affine Dependence

Autor: Roland Tóth, Siep Weiland, PB Pepijn Cox

Publikováno v: IEEE Transactions on Automatic Control, 63(11):8334291, 3865-3872. Institute of Electrical and Electronics Engineers
IEEE Transactions on Automatic Control

This paper deals with the certification problem for robust quadratic stability, robust state convergence, and robust quadratic performance of linear systems that exhibit bounded rates of variation in their parameters. We consider both continuous-time

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https://doi.org/10.1109/tac.2018.2824982

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Towards efficient maximum likelihood estimation of LPV-SS models

Autor: Mihaly Petreczky, Roland Tóth, PB Pepijn Cox

Publikováno v: Automatica
Automatica, Elsevier, 2018, 97, pp.392-403. ⟨10.1016/j.automatica.2018.08.021⟩
Automatica, 2018, 97, pp.392-403. ⟨10.1016/j.automatica.2018.08.021⟩
Automatica, 97, 392-403. Elsevier

How to efficiently identify multiple-input multiple-output (MIMO) linear parameter-varying (LPV) discrete-time state-space (SS) models with affine dependence on the scheduling variable still remains an open question, as identification methods propose

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https://hal.archives-ouvertes.fr/hal-01931160

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LPV state-space identification via IO methods and efficient model order reduction in comparison with subspace methods

Autor: Erik Schulz, Herbert Werner, Roland Tóth, PB Pepijn Cox

Publikováno v: CDC
2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, 2018-January, 3575-3581

In this paper, we introduce a procedure for global identification of linear parameter-varying (LPV) discrete-time state-space (SS) models with a static, affine dependency structure in a computationally efficient way. The aim is to develop off-the-she

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https://doi.org/10.1109/CDC.2017.8264184

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Estimation of LPV-SS Models with Static Dependency using Correlation Analysis**This work was supported by the Netherlands Organization for Scienti_c Research (NWO, grant no.: 639.021.127). This work was partially supported by the ESTIREZ project of Region Nord-Pas de Calais, France

Autor: Roland Tóth, Mihaly Petreczky, PB Pepijn Cox

Publikováno v: IFAC-PapersOnLine. 48:91-96

Many global identification approaches described in the literature for estimating linear parameter-varying (LPV) discrete-time state-space (SS) models with affine dependence on the scheduling parameter suffer heavily from the curse of dimensionality m

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https://doi.org/10.1016/j.ifacol.2015.11.119

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Bayesian identification of LPV Box-Jenkins models

Autor: Mohamed Abdelmonim Hassan Darwish, Gianluigi Pillonetto, PB Pepijn Cox, Roland Tóth

Publikováno v: CDC
54th IEEE Conference on Decision and Control (CDC 2015), 15-18 December 2015, Osaka, Japan, 66-71
STARTPAGE=66;ENDPAGE=71;TITLE=54th IEEE Conference on Decision and Control (CDC 2015), 15-18 December 2015, Osaka, Japan

In this paper, we introduce a nonparametric approach in a Bayesian setting to efficiently estimate, both in the stochastic and computational sense, linear parameter-varying (LPV) input-output models under general noise conditions of Box-Jenkins (BJ)

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https://doi.org/10.1109/cdc.2015.7402087

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CVA identification of nonlinear systems with LPV state-space models of affine dependence

Autor: PB Pepijn Cox, Wallace E. Larimore, Roland Tóth

Publikováno v: Proceedings of the American Control Conference (ACC), 1-3 July 2015, Chicago, Illinois, 831-837
STARTPAGE=831;ENDPAGE=837;TITLE=Proceedings of the American Control Conference (ACC), 1-3 July 2015, Chicago, Illinois
ACC

This paper discusses an improvement on the extension of linear subspace methods (originally developed in the Linear Time-Invariant (LTI) context) to the identification of Linear Parameter-Varying (LPV) and state-affine nonlinear system models. This i

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https://research.tue.nl/nl/publications/ebe0812a-d74d-4cf3-8588-42486c1df99b

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Linear parameter-varying subspace identification: A unified framework

Autor: PB Pepijn Cox, Roland Tóth

Publikováno v: Automatica, 123:109296. Elsevier

In this paper, we establish a unified framework for subspace identification (SID) of linear parameter-varying (LPV) systems to estimate LPV state–space (SS) models in innovation form. This framework enables us to derive novel LPV SID schemes that a

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