Data-driven design of explicit predictive controllers

Sassella, Andrea; Breschi, Valentina; Formentin, Simone

doi:10.1109/cdc51059.2022.9993004

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…where x i (t) and v i (t) denote the ith components of the state and the measurement noise, respectively. The parameters characterizing the DDPC problem to be solved are selected as in [2], namely L = 2, Q = I, R = 0.01 and Q L is chosen from data as explained in [17]. By setting γ = 10, the partition associated with the explicit data-driven predictive controller is the one shown in Fig.…”

Section: Numerical Examplementioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Design of Explicit Predictive Controllers With Structural Priors

Breschi

Sassella

Formentin

2023

IEEE Control Syst. Lett.

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Section: Numerical Examplementioning

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.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Design of Explicit Predictive Controllers With Structural Priors

Breschi

Sassella

Formentin

2023

IEEE Control Syst. Lett.

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“…How to carry out the design directly through noisy input/output data while guaranteeing robust stabilization is less understood and has comparatively been less investigated; at the same time, it is more relevant and realistic in real-world applications. Among the works that, within the aforementioned thread, have treated input/ output data, we mention [3], [12], [13], [14], [15]. Reference [3,Sec.…”

Section: Introductionmentioning

confidence: 99%

“…VI] shows how an output feedback controller can be designed from noise-free input/output data of a single-input single-output (SISO) system. In [15] a data pre-processing procedure is proposed to mitigate the effect of the measurement noise in a given batch of input/output data. Reference [12, Sec.…”

Section: Introductionmentioning

confidence: 99%

Data-Based Control Design for Output-Error Linear Discrete-Time Systems With Probabilistic Stability Guarantees

D'Amico

Bisoffi

Farina

2023

IEEE Control Syst. Lett.

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In this letter we propose a novel method for direct data-based design of an output feedback controller for output-error processes in the single-input-single-output case. We consider a finite number of input/output data points collected from the system. Based on them, we define a set of state-space perturbed models consistent with data, where a bound on the disturbance is obtained by scenario optimization, and the structural properties of the models in this set are theoretically analyzed. This leads to a linear matrix inequality for the design of the feedback control law with probabilistic asymptotic stability guarantees. A simulated non-minimum phase system illustrates the approach.

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