One-Shot Verification of Dissipativity Properties From Input–Output Data

Römer, Anne; Berberich, Julian; Köhler, Johannes; Allgöwer, Frank

doi:10.1109/lcsys.2019.2917162

Cited by 104 publications

(127 citation statements)

References 12 publications

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“…The papers [29]- [32] deal with data-based controllability and observability analysis. Moreover, the problem of verifying dissipativity on the basis of measured system trajectories has been studied in [33]- [36].…”

Section: Introductionmentioning

confidence: 99%

Data Informativity: A New Perspective on Data-Driven Analysis and Control

Waarde

Eising

Trentelman

et al. 2020

IEEE Trans. Automat. Contr.

312

354

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The use of persistently exciting data has recently been popularized in the context of data-driven analysis and control. Such data have been used to assess system theoretic properties and to construct control laws, without using a system model. Persistency of excitation is a strong condition that also allows unique identification of the underlying dynamical system from the data within a given model class. In this paper, we develop a new framework in order to work with data that are not necessarily persistently exciting. Within this framework, we investigate necessary and sufficient conditions on the informativity of data for several data-driven analysis and control problems. For certain analysis and design problems, our results reveal that persistency of excitation is not necessary. In fact, in these cases data-driven analysis/control is possible while the combination of (unique) system identification and model-based control is not. For certain other control problems, our results justify the use of persistently exciting data as data-driven control is possible only with data that are informative for system identification.

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Section: Introductionmentioning

confidence: 99%

Data Informativity: A New Perspective on Data-Driven Analysis and Control

Waarde

Eising

Trentelman

et al. 2020

IEEE Trans. Automat. Contr.

312

354

View full text Add to dashboard Cite

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“…This result has been utilized in developing identification algorithms [4,Chapter 8] and in data-driven simulation and control [5]. Recently, the fundamental lemma has received considerable attention due to growing interest in data-driven control problems [6], [7], [8], [9], [10], [11], [12], [13]. A weakness of the fundamental lemma, however, is that it requires an a priori assumption that the system under consideration is controllable.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Tests for Controllability

Mishra

Markovsky

Großmann

2021

IEEE Control Syst. Lett.

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The fundamental lemma due to Willems et al. "A note on persistency of excitation," Syst. Control Lett., vol. 54, no. 4, pp. 325-329, 2005 plays an important role in system identification and data-driven control. One of the assumptions for the fundamental lemma is that the underlying linear timeinvariant system is controllable. In this paper, the fundamental lemma is extended to address system identification for uncontrollable systems. Then, a data-driven algebraic test is derived to check whether the underlying system is controllable or not. An algorithm based on the singular value decomposition of a Hankel matrix constructed from the data is provided to implement the developed test. The algorithm has cubic computational cost. Examples are given to illustrate the theoretical results.

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“…In Algorithm 1, the first part of the optimization is carried out sequentially from subsystem 1 to subsystem N. After each optimization (34), the current subsystem sends the updated variables to the connected subsystems. The update of the dual variables, on the other hand, can be carried out in parallel.…”

Section: Distributed Optimization Via Admmmentioning

confidence: 99%

“…Differential dissipativity is an extension of the conventional dissipativity, which is an inputoutput property that can be used to study the stability of a system as well as interactions among subsystems in a plantwide process. For a given nonlinear process, its dissipativity property can be obtained through various model-based 31,32 or data-driven 33,34 techniques.…”

Section: Introductionmentioning

confidence: 99%

Robust distributed economic model predictive control based on differential dissipativity

et al. 2021

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In this article, a robust distributed economic model predictive control (DEMPC) approach is developed for plant-wide chemical processes. The proposed approach achieves arbitrary feasible setpoints that may vary frequently, attenuates the plantwide effects of unknown disturbances and minimizes a plant-wide economic cost. In this approach, a plant-wide process is represented as a network of process units and each process unit is controlled by an individual controller which shares a plant-wide optimization economic objective and stability conditions through the network. To

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One-Shot Verification of Dissipativity Properties From Input–Output Data

Cited by 104 publications

References 12 publications

Data Informativity: A New Perspective on Data-Driven Analysis and Control

Data Informativity: A New Perspective on Data-Driven Analysis and Control

Data-Driven Tests for Controllability

Robust distributed economic model predictive control based on differential dissipativity

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