This note presents a stochastic formulation of the model predictive control for tracking (MPCT), based on the results of the work of Lorenzen et al. The proposed controller ensures constraints satisfaction in probability, and maintains the main features of the MPCT, that are feasibility for any changing setpoints and enlarged domain of attraction, even larger than the one delivered by Lorenzen et al, thanks to the use of artificial references and relaxed terminal constraints. The asymptotic stability (in probability) of the minimal robust positively invariant set centered on the desired setpoint is guaranteed. Simulations on a DC-DC converter show the benefits and the properties of the proposal.

(2020). Stochastic model predictive control for tracking linear systems [journal article - articolo]. In OPTIMAL CONTROL APPLICATIONS & METHODS. Retrieved from http://hdl.handle.net/10446/169406

Stochastic model predictive control for tracking linear systems

Ferramosca, Antonio
2020-01-01

Abstract

This note presents a stochastic formulation of the model predictive control for tracking (MPCT), based on the results of the work of Lorenzen et al. The proposed controller ensures constraints satisfaction in probability, and maintains the main features of the MPCT, that are feasibility for any changing setpoints and enlarged domain of attraction, even larger than the one delivered by Lorenzen et al, thanks to the use of artificial references and relaxed terminal constraints. The asymptotic stability (in probability) of the minimal robust positively invariant set centered on the desired setpoint is guaranteed. Simulations on a DC-DC converter show the benefits and the properties of the proposal.
articolo
2020
D'Jorge, Agustina; Santoro, Bruno; Anderson, Alejandro; González, Alejandro H.; Ferramosca, Antonio
(2020). Stochastic model predictive control for tracking linear systems [journal article - articolo]. In OPTIMAL CONTROL APPLICATIONS & METHODS. Retrieved from http://hdl.handle.net/10446/169406
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/169406
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