Robust model predictive control for constrained linear system based on a sliding mode disturbance observer

For perturbed continuous-time systems, this paper proposes a robust model predictive control (RMPC) strategy for the regulation problem, exploiting a sliding mode disturbance observer. The main advantage is that it effectively enables the RMPC to be designed based on a model with reduced uncertainti...

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Bibliographic Details
Published in:	Automatica (Oxford) Vol. 154; p. 111101
Main Authors:	Zhang, Yao, Edwards, Christopher, Belmont, Michael, Li, Guang
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.08.2023
Subjects:	Disturbance observer Linear systems Robust model predictive control (RMPC) Sliding mode observer (SMO) Linear systems Sliding mode observer (SMO) Disturbance observer Robust model predictive control (RMPC)
ISSN:	0005-1098, 1873-2836
Online Access:	Get full text
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Summary:	For perturbed continuous-time systems, this paper proposes a robust model predictive control (RMPC) strategy for the regulation problem, exploiting a sliding mode disturbance observer. The main advantage is that it effectively enables the RMPC to be designed based on a model with reduced uncertainties. The proposed sliding mode observer (SMO) is finite-time convergent allowing the estimation error of the additive disturbance to be explicitly bounded by a predictable and decreasing limit. Due to the compensation of the estimated disturbance, the uncertainty that the RMPC has to handle is reduced from the original disturbance to the estimation error of the disturbance. This ensures all the admissible state trajectories are limited within a shrinking neighborhood of the origin and the steady-state error is therefore reduced. Simulation results show the effectiveness of the proposed method.
ISSN:	0005-1098 1873-2836
DOI:	10.1016/j.automatica.2023.111101