Facing undermodelling in Sign-Perturbed-Sums system identification

Carè, Algo; Campi, Marco C.; Csáji, Balázs Cs.; Weyer, Erik

doi:10.1016/j.sysconle.2021.104936

Cited by 11 publications

(4 citation statements)

References 30 publications

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“…The interested reader is referred to [6,9,12,2] for more details on SPS and its theoretical and computational aspects, and to [3] for an overview of related methods.…”

Section: Consider a System In Linear Regression Formmentioning

confidence: 99%

A simple condition for the boundedness of Sign-Perturbed-Sums (SPS) confidence regions

Carè

2022

Automatica

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“…The interested reader is referred to [6,9,12,2] for more details on SPS and its theoretical and computational aspects, and to [3] for an overview of related methods.…”

Section: Consider a System In Linear Regression Formmentioning

confidence: 99%

A simple condition for the boundedness of Sign-Perturbed-Sums (SPS) confidence regions

Carè

2022

Automatica

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“…Several generalizations of SPS were suggested, such as Data Perturbation (DP) methods [23] which can use other perturbations, not only sign changes; and UD-SPS that is able to detect undermodelling, in case of FIR systems [24].…”

Section: Introductionmentioning

confidence: 99%

Non-Asymptotic State-Space Identification of Closed-Loop Stochastic Linear Systems using Instrumental Variables

Szentpéteri¹,

Csáji²

2023

Preprint

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The paper suggests a generalization of the Sign-Perturbed Sums (SPS) finite sample system identification method for the identification of closed-loop observable stochastic linear systems in state space form. The solution builds on the theory of matrix-variate regression and instrumental variable methods to construct distribution-free confidence regions for the state space matrices. Both direct and indirect identification are studied, and the exactness as well as the strong consistency of the construction are proved. Furthermore, a new, computationally efficient ellipsoidal outer-approximation algorithm for the confidence regions is proposed. The new construction results in a semidefinite optimization problem which has an order-of-magnitude smaller number of constraints, as if one applied the ellipsoidal outer-approximation after vectorization. The effectiveness of the approach is also demonstrated empirically via a series of numerical experiments.

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“…Set membership identification can be not only applied in MC, but also in stochastic adaptive control, where a learning theory -kernel is introduced to achieve the approximation for nonlinear function or system. Based on the bounded noise, many parameters are also included in known intervals in prior, then robust optimal control with adjustable uncertainty sets are studied in [12], where robust optimization is introduced to consider uncertain noise and uncertain parameter simultaneously. To solve the expectation operation with dependence on the uncertainty, sample size of random convex programs is considered to replace the expectation by finite sum.…”

Section: Introductionmentioning

confidence: 99%

Direct Data Driven Model Reference Control for Flight Simulation Table

Wang

Ramirez‐Mendoza

2022

IEEE Access

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We present a direct data driven model reference control framework for flight simulation table from both the theoretical analysis and engineering application. Sate of the art direct driven model reference control designs the unknown controller based on the input-output data and guarantees the actual model converge to the reference model, under a case of no any priori knowledge for the unknown plant. We improve this direct data driven model reference control strategy by considering its stability validation and synthesis analysis for nonlinear controller. The resulting direct data driven model reference control scheme can be implemented to ensure flight simulation table rotate accuracy, resulting in improved performance index. As the main technical contribution, we show that the proposed direct data driven model reference control framework also ensures closed loop stability and suitable extension for nonlinear controller.INDEX TERMS Model reference control, direct data driven, flight simulation table, synthesis analysis. RICARDO A. RAMIREZ-MENDOZA received the Ph.D. degree from INPG, France, in 1997. He is currently a Professor in mechatronicsmechanics and the Dean of Research with the School of Engineering and Science, Tecnologico de Monterrey. He is the coauthor of three books, more than 80 papers in top journals and more than 200 international conference papers and more than 1,000 citations. He has worked as an Expert consulting for different industries. He is also a Certified Reviewer for Regional Development Projects. His research interest includes applications of advanced control to automotive systems.

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Facing undermodelling in Sign-Perturbed-Sums system identification

Cited by 11 publications

References 30 publications

A simple condition for the boundedness of Sign-Perturbed-Sums (SPS) confidence regions

A simple condition for the boundedness of Sign-Perturbed-Sums (SPS) confidence regions

Non-Asymptotic State-Space Identification of Closed-Loop Stochastic Linear Systems using Instrumental Variables

Direct Data Driven Model Reference Control for Flight Simulation Table

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