Data-Driven Meets Geometric Control: Zero Dynamics, Subspace Stabilization, and Malicious Attacks

Celi, Federico; Pasqualetti, Fabio

doi:10.1109/lcsys.2022.3170866

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…A. Data-driven trajectories of (1) We start by recalling a structural Lemma, which appeared for input-output trajectories in [39], and is here adapted for input-state trajectories.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Data-Driven Control of Network Systems

Celi

Baggio

Pasqualetti

2023

IEEE Open J. Control. Syst.

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Imperfect models lead to imperfect controllers and deriving accurate models from first principles or system identification is especially challenging in networked systems. Instead, data can be used to directly compute controllers, without requiring any system identification or modeling. In this paper we propose a strategy to directly learn control actions when data from past system trajectories is distributed among multiple agents in a network. The approach we develop provably converges to a suboptimal solution in a finite number of steps, bounded by the diameter of the network, and with a sub-optimality gap that can be characterized as a function of data, and that can be made arbitrarily small. We further characterize the robustness properties of our approach and give provable guarantees on its performance when data are affected by noise or by a class of attacks.

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“…A. Data-driven trajectories of (1) We start by recalling a structural Lemma, which appeared for input-output trajectories in [39], and is here adapted for input-state trajectories.…”

Section: Discussionmentioning

confidence: 99%

“…Assumption II.1 is standard in data driven studies [20], [21] and places a lower bound on the number of experiments needed to build exact data-driven expressions, namely, N ≥ n + mT , where T is the length of each experiment [39].…”

Section: B Problem Definitionmentioning

confidence: 99%

Distributed Data-Driven Control of Network Systems

Celi

Baggio

Pasqualetti

2023

IEEE Open J. Control. Syst.

Self Cite

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“…The design of context-aware switches between multiple datasets for autonomous agents has been recently considered in [3], [4], where the design problem, formalized as a data-driven control (DDC) problem, did not take into account safety requirements. Results in DDC include [6]- [8], which take a behavioral systems [9] perspective, [10], which finds data-driven formulas towards a model-free theory of geometric control. We also recall e.g., [11]- [13] for results inspired from MPC, [5] that considers data-driven control policies transfer and [14] that tackles he problem of computing data-driven minimum-energy control for linear systems.…”

Section: Introductionmentioning

confidence: 99%

Optimal Decision-Making for Autonomous Agents via Data Composition

Emiland¹,

Lamberti²,

Russo³

2023

Preprint

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We consider the problem of designing agents able to compute optimal decisions by composing data from multiple sources to tackle tasks involving: (i) tracking a desired behavior while minimizing an agent-specific cost; (ii) satisfying safety constraints. After formulating the control problem, we show that this is convex under a suitable assumption and find the optimal solution. The effectiveness of the results, which are turned in an algorithm, is illustrated on a connected cars application via in-silico and in-vivo experiments with real vehicles and drivers. All the experiments confirm our theoretical predictions and the deployment of the algorithm on a real vehicle shows its suitability for in-car operation.

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