A recursive approach for set-membership EIV identification of LTV systems with bounded variation

Cerone, V.; Fosson, Sophie M.; Regruto, D.; Abdalla, Talal

doi:10.1109/cdc42340.2020.9304379

Cited by 2 publications

(10 citation statements)

References 15 publications

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“…First let us introduce the time-varying feasible controller set (FCS), which is inspired by the feasible parameter set (FPS) for LTV systems proposed in [26] and the FCS definition for LTI controllers presented in [23]. The time-varying FCS, for the case of LTV systems, is defined as follows.…”

Section: Adaptive Ddc For Ltv Systems In the Set-membership Frameworkmentioning

confidence: 99%

Adaptive Data-Driven Control for Linear Time Varying Systems

Abdalla

2021

Machines

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In this paper, we propose an adaptive data-driven control approach for linear time varying systems, affected by bounded measurement noise. The plant to be controlled is assumed to be unknown, and no information in regard to its time varying behaviour is exploited. First, using set-membership identification techniques, we formulate the controller design problem through a model-matching scheme, i.e., designing a controller such that the closed-loop behaviour matches that of a given reference model. The problem is then reformulated as to derive a controller that corresponds to the minimum variation bounding its parameters. Finally, a convex relaxation approach is proposed to solve the formulated controller design problem by means of linear programming. The effectiveness of the proposed scheme is demonstrated by means of two simulation examples.

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Section: Adaptive Ddc For Ltv Systems In the Set-membership Frameworkmentioning

confidence: 99%

Adaptive Data-Driven Control for Linear Time Varying Systems

Abdalla

2021

Machines

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“…According to the problem formulation presented in [11], the FPS, at a generic time instant t, is defined as,…”

Section: Problem Formulationmentioning

confidence: 99%

“…In this section we briefly review the approach proposed in our previous contribution [11] for the computation of the solution to problems ( 13) and ( 14).…”

Section: Problem Formulationmentioning

confidence: 99%

“…All the aforementioned papers formulate the identification problem with reference to the equation error structure. To the best of the authors' knowledge, the first attempt to address the problem of recursive SM identification for LTV systems in the errors-in-variables (EIV) framework, i.e., when both the input and the output measurements are affected by noise, has been presented in our previous contribution [11]. In this paper, we show that the parameter uncertainty intervals (PUIs) can be exactly computed at each time iteration by solving a set of simple linear programming problems, provided that the sign of the parameters are a-priori known.…”

Section: Introductionmentioning

confidence: 99%

“…Section 2 is devoted to the problem formulation. In Section 3, we briefly review the results of our previous contribution [11] in order to highlight the mathematical structure of the problem. The novel convex relaxation approach, based on McCormick envelopes, is presented in Section 4.…”

Section: Introductionmentioning

confidence: 99%

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A convex optimization approach to online set-membership EIV identification of LTV systems

Fosson¹,

Regruto²,

Abdalla³

et al. 2021

Preprint

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This paper addresses the problem of recursive set-membership identification for linear time varying (LTV) systems when both input and output measurements are affected by bounded additive noise. First we formulate the problem of online computation of the parameter uncertainty intervals (PUIs) in terms of nonconvex polynomial optimization. Then, we propose a convex relaxation approach based on McCormick envelopes to solve the formulated problem to the global optimum by means of linear programming. The effectiveness of the proposed identification scheme is demonstrated by means of two simulation examples.

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A recursive approach for set-membership EIV identification of LTV systems with bounded variation

Cited by 2 publications

References 15 publications

Adaptive Data-Driven Control for Linear Time Varying Systems

Adaptive Data-Driven Control for Linear Time Varying Systems

A convex optimization approach to online set-membership EIV identification of LTV systems

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