Risk-averse model predictive control

Pantelis Sopasakis; Domagoj Herceg; Alberto Bemporad; Panagiotis Patrinos

doi:10.1016/j.automatica.2018.11.022

Risk-averse model predictive control

Pantelis Sopasakis, Domagoj Herceg, Alberto Bemporad, Panagiotis Patrinos

School of Electronics, Electrical Engineering and Computer Science

Research output: Contribution to journal › Article › peer-review

16 Citations (Scopus)

198 Downloads (Pure)

Abstract

Risk-averse model predictive control (MPC) offers a control framework that allows one to account for ambiguity in the knowledge of the underlying probability distribution and unifies stochastic and worst-case MPC. In this paper we study risk-averse MPC problems for constrained nonlinear Markovian switching systems using generic cost functions, and derive Lyapunov-type risk-averse stability conditions by leveraging the properties of risk-averse dynamic programming operators. We propose a controller design procedure to design risk-averse stabilizing terminal conditions for constrained nonlinear Markovian switching systems. Lastly, we cast the resulting risk-averse optimal control problem in a favorable form which can be solved efficiently and thus deems risk-averse MPC suitable for applications.

Original language	English
Pages (from-to)	281
Number of pages	8
Journal	Automatica
Volume	100
Early online date	04 Dec 2018
DOIs	https://doi.org/10.1016/j.automatica.2018.11.022
Publication status	Published - Feb 2019

Keywords

Risk measures
Nonlinear Markovian switching systems
Model predictive control

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Risk-averse model predictive control
© 2018 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/,which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited
Accepted author manuscript, 707 KBLicence: CC BY-NC-ND

Pantelis Sopasakis
- P.Sopasakisqub.acuk
- School of Electronics, Electrical Engineering and Computer Science - Lecturer
- Energy Power and Intelligent Control
Person: Academic

Cite this

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title = "Risk-averse model predictive control",

abstract = "Risk-averse model predictive control (MPC) offers a control framework that allows one to account for ambiguity in the knowledge of the underlying probability distribution and unifies stochastic and worst-case MPC. In this paper we study risk-averse MPC problems for constrained nonlinear Markovian switching systems using generic cost functions, and derive Lyapunov-type risk-averse stability conditions by leveraging the properties of risk-averse dynamic programming operators. We propose a controller design procedure to design risk-averse stabilizing terminal conditions for constrained nonlinear Markovian switching systems. Lastly, we cast the resulting risk-averse optimal control problem in a favorable form which can be solved efficiently and thus deems risk-averse MPC suitable for applications.",

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author = "Pantelis Sopasakis and Domagoj Herceg and Alberto Bemporad and Panagiotis Patrinos",

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AU - Sopasakis, Pantelis

AU - Herceg, Domagoj

AU - Bemporad, Alberto

AU - Patrinos, Panagiotis

PY - 2019/2

Y1 - 2019/2

N2 - Risk-averse model predictive control (MPC) offers a control framework that allows one to account for ambiguity in the knowledge of the underlying probability distribution and unifies stochastic and worst-case MPC. In this paper we study risk-averse MPC problems for constrained nonlinear Markovian switching systems using generic cost functions, and derive Lyapunov-type risk-averse stability conditions by leveraging the properties of risk-averse dynamic programming operators. We propose a controller design procedure to design risk-averse stabilizing terminal conditions for constrained nonlinear Markovian switching systems. Lastly, we cast the resulting risk-averse optimal control problem in a favorable form which can be solved efficiently and thus deems risk-averse MPC suitable for applications.

AB - Risk-averse model predictive control (MPC) offers a control framework that allows one to account for ambiguity in the knowledge of the underlying probability distribution and unifies stochastic and worst-case MPC. In this paper we study risk-averse MPC problems for constrained nonlinear Markovian switching systems using generic cost functions, and derive Lyapunov-type risk-averse stability conditions by leveraging the properties of risk-averse dynamic programming operators. We propose a controller design procedure to design risk-averse stabilizing terminal conditions for constrained nonlinear Markovian switching systems. Lastly, we cast the resulting risk-averse optimal control problem in a favorable form which can be solved efficiently and thus deems risk-averse MPC suitable for applications.

KW - Risk measures

KW - Nonlinear Markovian switching systems

KW - Model predictive control

U2 - 10.1016/j.automatica.2018.11.022

DO - 10.1016/j.automatica.2018.11.022

M3 - Article

VL - 100

SP - 281

JO - Automatica

JF - Automatica

SN - 0005-1098

ER -

Risk-averse model predictive control

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Pantelis Sopasakis

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