Distributionally Robust Model Predictive Control with Output Feedback

Bin Li; Tao Guan; Li Dai; Guang Ren Duan

doi:10.1109/TAC.2023.3321375

Distributionally Robust Model Predictive Control with Output Feedback

Bin Li, Tao Guan, Li Dai, Guang Ren Duan

School of Automation

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

22 Citations (Scopus)

Abstract

An output feedback stochastic model predictive control (SMPC) is proposed in this paper for a class of stochastic linear discrete-time systems, in which the uncertainties from external disturbance, measurement noise and initial state estimation error are all considered. Particularly, the support sets of the uncertainties are unbounded and the distributions are not exactly known. Based on distributionally robust optimization (DRO), a deterministic convex reformulation is derived for handling chance constraints. Recursive feasibility and convergence of the algorithm are proven. A numerical example is provided to demonstrate the effectiveness of the proposed method.

Original language	English
Pages (from-to)	1-8
Number of pages	8
Journal	IEEE Transactions on Automatic Control
DOIs	https://doi.org/10.1109/TAC.2023.3321375
Publication status	Accepted/In press - 2023

Keywords

Observers
Optimization
Output feedback
Predictive control
Predictive models
Stochastic model predictive control
Stochastic processes
Uncertainty
chance constraints
distributionally robust optimization
output feedback control
unbounded disturbance

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10.1109/TAC.2023.3321375

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title = "Distributionally Robust Model Predictive Control with Output Feedback",

abstract = "An output feedback stochastic model predictive control (SMPC) is proposed in this paper for a class of stochastic linear discrete-time systems, in which the uncertainties from external disturbance, measurement noise and initial state estimation error are all considered. Particularly, the support sets of the uncertainties are unbounded and the distributions are not exactly known. Based on distributionally robust optimization (DRO), a deterministic convex reformulation is derived for handling chance constraints. Recursive feasibility and convergence of the algorithm are proven. A numerical example is provided to demonstrate the effectiveness of the proposed method.",

keywords = "Observers, Optimization, Output feedback, Predictive control, Predictive models, Stochastic model predictive control, Stochastic processes, Uncertainty, chance constraints, distributionally robust optimization, output feedback control, unbounded disturbance",

author = "Bin Li and Tao Guan and Li Dai and Duan, {Guang Ren}",

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year = "2023",

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AU - Guan, Tao

AU - Dai, Li

AU - Duan, Guang Ren

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PY - 2023

Y1 - 2023

N2 - An output feedback stochastic model predictive control (SMPC) is proposed in this paper for a class of stochastic linear discrete-time systems, in which the uncertainties from external disturbance, measurement noise and initial state estimation error are all considered. Particularly, the support sets of the uncertainties are unbounded and the distributions are not exactly known. Based on distributionally robust optimization (DRO), a deterministic convex reformulation is derived for handling chance constraints. Recursive feasibility and convergence of the algorithm are proven. A numerical example is provided to demonstrate the effectiveness of the proposed method.

AB - An output feedback stochastic model predictive control (SMPC) is proposed in this paper for a class of stochastic linear discrete-time systems, in which the uncertainties from external disturbance, measurement noise and initial state estimation error are all considered. Particularly, the support sets of the uncertainties are unbounded and the distributions are not exactly known. Based on distributionally robust optimization (DRO), a deterministic convex reformulation is derived for handling chance constraints. Recursive feasibility and convergence of the algorithm are proven. A numerical example is provided to demonstrate the effectiveness of the proposed method.

KW - Observers

KW - Optimization

KW - Output feedback

KW - Predictive control

KW - Predictive models

KW - Stochastic model predictive control

KW - Stochastic processes

KW - Uncertainty

KW - chance constraints

KW - distributionally robust optimization

KW - output feedback control

KW - unbounded disturbance

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DO - 10.1109/TAC.2023.3321375

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SN - 0018-9286

SP - 1

EP - 8

JO - IEEE Transactions on Automatic Control

JF - IEEE Transactions on Automatic Control

ER -

Distributionally Robust Model Predictive Control with Output Feedback

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Keywords

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