On the Design of Regularized Explicit Predictive Controllers From Input–Output Data

Breschi, Valentina; Sassella, Andrea; Formentin, Simone

doi:10.1109/tac.2022.3219346

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…1, which approximately correspond to that reported in [2]. We stress that the negligible differences with respect to the model-based partition are due to noise, while both the value of γ and the attained behavior in closedloop match the ones obtained with the method in [4]. Prior to the controller deployment, we have performed the data-based closed-loop stability check in (33), resulting in 4 P = 24.8695 10.5595 10.5595 43.2657 0.…”

Section: Numerical Examplesupporting

confidence: 82%

“…It is worth pointing out that similar manipulations are required in [4] to retrieve the explicit law from input/output data.…”

Section: Explicit Ddpc With Structural Priorsmentioning

confidence: 99%

“…We should mention that an early attempt to obtain a datadriven counterpart of explicit MPC (E-MPC) was already done in [17], which however required an implicit open-loop identification step. Alternative methods were also presented in [13] for a deterministic setting, and in [4], where a strategy to cope with noisy data is discussed. Both these approaches rely on the predictor used in [3] and, thus, they do not require state measurements nor involve open-loop identification phases.…”

Section: Introductionmentioning

confidence: 99%

“…However, they do not allow to use existing tools straightforwardly to perform a stability check of the closed-loop system before deployment, which can instead be done with our approach. Such test can be useful to preliminary assess the quality of the controller and help in tuning hyper-parameters (e.g., the ones exploited in [4] to cope with noise).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Data-Driven Design of Explicit Predictive Controllers With Structural Priors

Breschi

Sassella

Formentin

2023

IEEE Control Syst. Lett.

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Section: Numerical Examplesupporting

confidence: 82%

“…It is worth pointing out that similar manipulations are required in [4] to retrieve the explicit law from input/output data.…”

Section: Explicit Ddpc With Structural Priorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data-Driven Design of Explicit Predictive Controllers With Structural Priors

Breschi

Sassella

Formentin

2023

IEEE Control Syst. Lett.

Self Cite

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“…Further data-driven MPC formulations in the literature include explicit MPC (100), which relies on an explicit solution of the underlying quadratic program via the Karush-Kuhn-Tucker conditions, and economic MPC (101), which addresses stage cost functions that are not positive (semi-)definite and can also handle unknown linear cost functions. In the context of networked and cyber-physical control systems, data-driven MPC schemes have been proposed that address resilience against denial-of-service attacks (102) as well as self-triggered ( 103) and event-triggered (104) MPC formulations.…”

Section: Data-driven Mpc For More Advanced Control Objectivesmentioning

confidence: 99%

Data-Driven Model Predictive Control With Stability and Robustness Guarantees

Berberich

Köhler

Müller

et al. 2021

IEEE Trans. Automat. Contr.

461

416

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The development of control methods based on data has seen a surge of interest in recent years. When applying data-driven controllers in real-world applications, providing theoretical guarantees for the closedloop system is of crucial importance to ensure reliable operation. In this review, we provide an overview of data-driven model predictive control (MPC) methods for controlling unknown systems with guarantees on systems-theoretic properties such as stability, robustness, and constraint satisfaction. The considered approaches rely on the Fundamental Lemma from behavioral theory in order to predict input-output trajectories directly from data. We cover various setups, ranging from linear systems and noise-free data to more realistic formulations with noise and nonlinearities, and we provide an overview of different techniques to ensure guarantees for the closed-loop system. Moreover, we discuss avenues for future research that may further improve the theoretical understanding and practical applicability of data-driven MPC.

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Data-Enabled Neighboring Extremal Optimal Control: A Computationally Efficient DeePC

Vahidi-Moghaddam,

Zhang,

et al. 2023

2023 62nd IEEE Conference on Decision and Control (CDC)

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On the Design of Regularized Explicit Predictive Controllers From Input–Output Data

Cited by 5 publications

References 31 publications

Data-Driven Design of Explicit Predictive Controllers With Structural Priors

Data-Driven Design of Explicit Predictive Controllers With Structural Priors

Data-Driven Model Predictive Control With Stability and Robustness Guarantees

Data-Enabled Neighboring Extremal Optimal Control: A Computationally Efficient DeePC

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