Algebraic switching time identification for a class of linear hybrid systems

Tian, Yang; Floquet, Thierry; Belkoura, Lotfi; Perruquetti, Wilfrid

doi:10.1016/j.nahs.2010.07.003

Cited by 31 publications

(6 citation statements)

References 41 publications

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“…In this paper, we study the systems whose hybrid time trajectories satisfy the minimal dwell time definition. Moreover, it is assumed that the dwell time is sufficiently large, or it is possible to estimate it (see, e.g., [49] for the algebraic estimation of the switching times for LSS).…”

Section: Definition 7 ([2])mentioning

confidence: 99%

Non‐minimum phase switched systems: HOSM‐based fault detection and fault identification via Volterra integral equation

Ríos

Efimov

Dávila

et al. 2013

Adaptive Control & Signal

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In this paper, the problem of continuous and discrete state estimation for a class of linear switched systems with additive faults is studied. The class of systems under study can contain non-minimum phase zeroes in some of their 'operating modes'. The conditions for exact reconstruction of the discrete state are given using structural properties of the switched system. The state space is decomposed into the strongly observable part, the non-strongly observable part, and the unobservable part, to analyze the effect of the unknown inputs. State observers based on high-order sliding mode to exactly estimate the strongly observable part and Luenberger-like observers to estimate the remaining parts are proposed. For the case when the exact estimation of the state cannot be achieved, the ultimate bounds on the estimation errors are provided. The proposed strategy includes a high-order sliding-mode-based fault detection and a fault identification scheme via the solution of a Volterra integral equation. The feasibility of the proposed method is illustrated by simulations.

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Section: Definition 7 ([2])mentioning

confidence: 99%

Non‐minimum phase switched systems: HOSM‐based fault detection and fault identification via Volterra integral equation

Ríos

Efimov

Dávila

et al. 2013

Adaptive Control & Signal

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“…The principal of these systems consists in decomposing the regression domain of the system into regions, then, an ARX sub-model is assigned to every one. There exist numerous approaches in the literature for the identification of PWA models Tian et al (2011), Ferrari-Trecate et al (2003, Bemporad et al (2003), Juloski et al (2005), Bemporad et al (2005). We have advocated the use of clustering based approach Lassoued and Abderrahim (2014c), which is based on the fact that process has local affine behaviours.…”

Section: Introductionmentioning

confidence: 99%

Extension of the clustering identification by extending the Density Based Spatial Clustering of Applications with Noise approach to Multi-Input Multi-Output Piece Wise Affine systems: Application to an industrial robot

Lassoued,

Abderrahim

2023

CEAI

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In this paper the problem of clustering based identification of a Multi-Input Multi-Output (MIMO) PieceWise Affine systems (PWA) is considered. This approach, originally designed for systems with a Multiple-Input Single-Output (MISO) structure, is carried out by three main steps which are data clustering, parameters vectors estimation and regions computing. Data clustering is the most important step because the two other steps depend on the results given by the used clustering algorithm. In case of MIMO PWA systems, we should cluster matrices of parameters which are considered as high dimensional data. However, most of the conventional clustering algorithms do not work well in terms of effectiveness and efficiency since the similarity assessment which is based on the distances between objects is fruitless in high dimension space. Therefore, we propose an extension of the DBSCAN (Density Based Spatial Clustering of Applications with Noise) clustering approach for the identification of MIMO PWA systems. The simulation results presented in this paper prove the performance of the suggested approach. An application of the proposed approach to an industrial robot manipulator is then presented in order to validate the simulation results.

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“…To reduce this complexity, all existing approaches assume that the sub-model orders are known a priori as well as the number of sub-models for some methods. Several solutions have been presented in the literature for the identification of PWARX models such as the algebraic solution (Tian et al, 2011), the classification solution (Ferrari-Trecate et al, 2003), the greedy solution (Bemporad et al, 2003), the Bayesian solution (Juloski et al, 2005), the bounded error solution (Bemporad et al, 2005), the sparse optimization solution (Bako, 2011; Mattsson et al, 2016), and so forth. Each approach has its advantages and its drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Identification and control of nonlinear systems using PieceWise Auto-Regressive eXogenous models

Lassoued

Abderrahim

2019

Transactions of the Institute of Measurement and Control

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In this paper, we consider the problems of nonlinear system representation and control. In fact, we propose a solution based on PieceWise Auto-Regressive eXogenous (PWARX) models since these models are able to approximate any nonlinear behaviour with arbitrary precision. Moreover, the identification and control approaches of linear systems can be extended to these models because the parameters of each sub-model are linearly related to the output. The proposed solution is based on two steps. The first allows to represent the nonlinear system by a PWARX model using the identification approach. The second consists in designing a controller for each sub-model using the pole placement strategy. Simulation and experimental results are presented to illustrate the performance of the proposed approach.

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Algebraic switching time identification for a class of linear hybrid systems

Cited by 31 publications

References 41 publications

Non‐minimum phase switched systems: HOSM‐based fault detection and fault identification via Volterra integral equation

Non‐minimum phase switched systems: HOSM‐based fault detection and fault identification via Volterra integral equation

Extension of the clustering identification by extending the Density Based Spatial Clustering of Applications with Noise approach to Multi-Input Multi-Output Piece Wise Affine systems: Application to an industrial robot

Identification and control of nonlinear systems using PieceWise Auto-Regressive eXogenous models

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