Online Data-driven Stabilization of Switched Linear Systems

Rotulo, Monica; Persis, Claudio De; Tesi, Pietro

doi:10.23919/ecc54610.2021.9654997

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…The proof of the result was first presented in [37] and we report it here to make the paper as self-contained as possible. Without loss of generality, we consider k = 0.…”

Section: A Appendixmentioning

confidence: 90%

Online learning of data-driven controllers for unknown switched linear systems

Rotulo¹,

Persis²,

Tesi³

2021

Preprint

Self Cite

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Motivated by the goal of learning controllers for complex systems whose dynamics change over time, we consider the problem of designing control laws for systems that switch among a finite set of unknown discrete-time linear subsystems under unknown switching signals. To this end, we propose a method that uses data to directly design a control mechanism without any explicit identification step. Our approach is online, meaning that the data are collected over time while the system is evolving in closed-loop, and are directly used to iteratively update the controller. A major benefit of the proposed online implementation is therefore the ability of the controller to automatically adjust to changes in the operating mode of the system. We show that the proposed control mechanism guarantees stability of the closed-loop switched linear system provided that the switching is slow enough. Effectiveness of the proposed design technique is illustrated for two aerospace applications.

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“…The proof of the result was first presented in [37] and we report it here to make the paper as self-contained as possible. Without loss of generality, we consider k = 0.…”

Section: A Appendixmentioning

confidence: 90%

Online learning of data-driven controllers for unknown switched linear systems

Rotulo¹,

Persis²,

Tesi³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, matrices {X j } j∈K are persistently excited. The reverse implication is direct by noting that if {X j } j∈K are assumed to be persistently excited, then matrices Y j are full row rank because of the structure of Y j in (21). ▪ Assumption 1.…”

Section: Measurement Noise and Modelling Of Uncertaintiesmentioning

confidence: 99%

Linear matrix inequality relaxations and its application to data‐driven control design for switched affine systems

Seuret

Albea-Sánchez

Gordillo

2023

Intl J Robust & Nonlinear

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The problem of data-driven control is addressed here in the context of switched affine systems. This class of nonlinear systems is of particular importance when controlling many types of applications in electronic, biology, medicine and so forth. Still in the view of practical applications, providing an accurate model for this class of systems can be a hard task, and it might be more relevant to work on data issued from some trajectories obtained from experiments and to deploy a new branch of tools to stabilize the systems that are compatible with the processed data. Following the recent concept of data-driven control design, this paper first presents a generic equivalence lemma that shows a matrix constraint based on data, instead of the system parameter. Then, following the concept of robust hybrid limit cycles for uncertain switched affine systems, robust model-based and then data-driven control laws are designed based on a Lyapunov approach. The proposed results are then illustrated and evaluated on an academic example.

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“…Therefore, matrices { } ∈ are persistently excited. The reverse implication is direct by noting that if { } ∈ are assumed to be persistently excited, then matrices are full row rank because of the structure of in (21).…”

Section: Measurement Noise and Modelling Of Uncertaintiesmentioning

confidence: 99%

“…In the context of data-driven methods for switched systems (affine or not), the authors of 20,21 provided the first attempts on switched systems (without affine terms). However, there are only a few works considering data-driven control design for switched affine systems, which is the objective of this paper.…”

Section: Introductionmentioning

confidence: 99%

LMI relaxations and its application to data-driven control design for switched affine systems

Seuret¹,

Albea-Sánchez²,

Gordillo³

2023

Preprint

View full text Add to dashboard Cite

The problem of data-driven control is addressed here in the context of switched affine systems. This class of nonlinear systems is of particular importance when controlling many types of applications in electronic, biology, medicine, etc. Still in the view of practical applications, providing an accurate model for this class of systems can be a hard task, and it might be more relevant to work on data issued from some trajectories obtained from experiments and to deploy a new branch of tools to stabilize the systems that are compatible with the processed data. Following the recent concept of data-driven control design, this paper first presents a generic equivalence lemma that shows a matrix constraint based on data, instead of the system parameter. Then, following the concept of robust hybrid limit cycles for uncertain switched affine systems, robust model-based and then data-driven control laws are designed based on a Lyapunov approach. The proposed results are then illustrated and evaluated on an academic example.

show abstract

Online Data-driven Stabilization of Switched Linear Systems

Cited by 6 publications

References 25 publications

Online learning of data-driven controllers for unknown switched linear systems

Online learning of data-driven controllers for unknown switched linear systems

Linear matrix inequality relaxations and its application to data‐driven control design for switched affine systems

LMI relaxations and its application to data-driven control design for switched affine systems

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