The CONTSID Toolbox: A Software Support for Data-based Continuous-time Modelling

Garnier, Hugues; Gilson, Marion; Bastogne, Thierry; Mensler, M.

doi:10.1007/978-1-84800-161-9_9

Cited by 25 publications

(19 citation statements)

References 33 publications

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“…Estimate a black-box LTI state-space model G i , i = 1, in the initial operating point (p act = p min ) by using any dedicated identification method [16,10,9,18]. …”

Section: Selection Algorithmmentioning

confidence: 99%

“…Second, as far as β is concerned, as can be seen from the results obtained in Section 5, the admissible choice of this hyper-parameter depends on the noise involved in the local I/O data sequences 1 In this context, by best selection, it is meant that the final set of working points is the best in terms of the user-defined β which is a maximal allowable distance, in the nu-gap metric sense [33], between each local model. (9) and…”

Section: Selection Algorithmmentioning

confidence: 99%

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An H∞-norm-based approach for operating point selection and LPV model identification from local experiments

Vizer

Mercère

2014

Period. Polytech. Elec. Eng. Comp. Sci.

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When the identification of linear parameter-varying (LPV) models from local experiments is considered, the question of the necessary number of local operating points as well as the problem of the efficient interpolation of the locally-estimated linear Keywords linear parameter-varying model · linear fractional representation · H ∞ -norm · non-smooth optimization · nu-gap metric IntroductionNowadays, the extensive demand for reliable models of nonlinear and/or time-varying systems requires the development of special model structures in which the non-linear behavior is broken down into several local models [16]. Among all the multi-model structures available in the literature, a particular attention has been paid to the linear parameter-varying (LPV) models during the last two decades (see [28, Chapter 1] for a historical review of LPV modeling and identification). This interest can be mainly explained by the following reasons. First, an LPV model can be seen as a combination of local models with parameters evolving as a function of measurable variables (called the scheduling variables) which can be related to the different operating points of the system. By this way, the model structure is close to the standard linear time-invariant (LTI) one but with a structural flexibility able to cope with time-varying, even non-linear behaviors. Second, the development of LPV models is linked to control engineering, where a controller must be designed in order to guarantee a suitable closed-loop performance for a given plant in different operating conditions. A well-known example of controller design technique using this "divide and conquer" basic idea is the gain scheduling approach [25]. A wide body of LPV controller design techniques is now available for this problem, which can be solved reliably, provided that a suitable model in a parameter-dependent form has been derived. LPV model identification methods based on I/O data [7]can be divided into two sub-classes generally called the global approach and the local approach, respectively. On the one hand, the global approach assumes that one global experiment can be performed during which the control inputs, as well as the scheduling variables, are both excited (see among others e.g., [15,5,32,14,31] and the references therein). By this way, all the nonlinearities of the system are excited at the same time by passing through a large number of operating points. On the other hand, local methods are based on a multi-step procedure where• local experiments are performed in which the operating points (corresponding to fixed values of the scheduling

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“…Estimate a black-box LTI state-space model G i , i = 1, in the initial operating point (p act = p min ) by using any dedicated identification method [16,10,9,18]. …”

Section: Selection Algorithmmentioning

confidence: 99%

Section: Selection Algorithmmentioning

confidence: 99%

An H∞-norm-based approach for operating point selection and LPV model identification from local experiments

Vizer

Mercère

2014

Period. Polytech. Elec. Eng. Comp. Sci.

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“…Solving for D 12 gives an expression for the ideal decoupler, ) ( ) ( ) ( 11 12 12 s P s P s D − = (13) b. multivariable decentralized PID controller…”

Section: Decentralized Pid Controller a Decoupling Control Systemsmentioning

confidence: 99%

“…In this synthesis, a multivariable transfer function is identified using the numeric Direct Continuous-Time Identification (DCTI) approach [12]. This technique has attracted an increasing attention of several researchers in the last few years [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Continuous Time Identification and Decentralized Pid Controller of an Aerothermic Process

Ramzi¹,

Youlal²,

Haloua

2012

International Journal on Smart Sensing and Intelligent Systems

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“…In the synthesis of the decentralized PI-D controller, a multivariable transfer function is identified using the numeric Direct Continuous-Time Identification (DCTI) approach [7,[20][21]. This technique has attracted an increasing attention of several researchers in the last few years [22][23][24][25][26].…”

mentioning

confidence: 99%

Decentralized PI-D Controller Applied to an Aerothermic Process

Ramzi¹,

Bennis

Haloua

et al. 2012

International Journal on Smart Sensing and Intelligent Systems

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The CONTSID Toolbox: A Software Support for Data-based Continuous-time Modelling

Cited by 25 publications

References 33 publications

An H∞-norm-based approach for operating point selection and LPV model identification from local experiments

An H∞-norm-based approach for operating point selection and LPV model identification from local experiments

Continuous Time Identification and Decentralized Pid Controller of an Aerothermic Process

Decentralized PI-D Controller Applied to an Aerothermic Process

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