Beyond Persistent Excitation: Online Experiment Design for Data-Driven Modeling and Control

Waarde, Henk J. van

doi:10.1109/lcsys.2021.3073860

Cited by 34 publications

(23 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A common feature of learning and control approaches that rely solely on the available data is that these need to be sufficiently informative. Ideas from data-informativity [106,105,164] and optimal experiment design [165,166] might be leveraged to define a metric quantifying the value of information (similar in spirit to e.g. [167], see also [168]) gained at the expenses of new experiments.…”

Section: Concluding Discussionmentioning

confidence: 99%

Probabilistic design of optimal sequential decision-making algorithms in learning and control

Emiland¹,

Russo²

2022

Preprint

View full text Add to dashboard Cite

This survey is focused on certain sequential decision-making problems that involve optimizing over probability functions. We discuss the relevance of these problems for learning and control. The survey is organized around a framework that combines a problem formulation and a set of resolution methods. The formulation consists of an infinite-dimensional optimization problem. The methods come from approaches to search optimal solutions in the space of probability functions. Through the lenses of this overarching framework we revisit popular learning and control algorithms, showing that these naturally arise from suitable variations on the formulation mixed with different resolution methods. A running example, for which we make the code available, complements the survey. Finally, a number of challenges arising from the survey are also outlined.

show abstract

Section: Concluding Discussionmentioning

confidence: 99%

Probabilistic design of optimal sequential decision-making algorithms in learning and control

Emiland¹,

Russo²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…From a computational point of view, Theorem 2 may thus be preferred in cases where the inputs of the experiment are chosen to be persistently exciting [25], [26] since this puts a lower bound T ≥ n + m + nm on the required number of samples. On the other hand, it has recently been shown [27] that for controllable pairs (A s , B s ), the data (U − , X) can be made informative for stabilization with at most T = n + m samples, using an online input design method. In this case, the LMI (5) may be preferred since (5) has dimension 2n × 2n which is smaller than the dimension (3n + m) × (3n + m) of ( 16).…”

Section: Bridging the Exact And Noisy Casesmentioning

confidence: 99%

A Matrix Finsler's Lemma with Applications to Data-Driven Control

Waarde¹,

Camlibel²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

In a recent paper it was shown how a matrix S-lemma can be applied to construct controllers from noisy data. The current paper complements these results by proving a matrix version of the classical Finsler's lemma. This matrix Finsler's lemma provides a tractable condition under which all matrix solutions to a quadratic equality also satisfy a quadratic inequality. We will apply this result to bridge known datadriven control design techniques for both exact and noisy data, thereby revealing a more general theory. The result is also applied to data-driven control of Lur'e systems.

show abstract

“…. , u(−1)} is persistently exciting 2 For related work on selecting a suitable input sequence so as to preserve persistence of excitation, see [40].…”

Section: Online Data-driven Controlmentioning

confidence: 99%

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

Rotulo¹,

Persis²,

Tesi³

2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Beyond Persistent Excitation: Online Experiment Design for Data-Driven Modeling and Control

Cited by 34 publications

References 33 publications

Probabilistic design of optimal sequential decision-making algorithms in learning and control

Probabilistic design of optimal sequential decision-making algorithms in learning and control

A Matrix Finsler's Lemma with Applications to Data-Driven Control

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

Contact Info

Product

Resources

About