Robust decoupling MPC for linear systems with bounded disturbances
This paper presents a robust decoupling Model Predictive Controller (MPC) for constrained linear systems with bounded disturbances. The usual explicit decouplers are combined with a suitable augmented state–space representation in order to ensure recursive feasibility, robust constraint satisfaction...
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Veröffentlicht in: | ISA transactions 2023-08, Vol.139, p.1-12 |
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Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | This paper presents a robust decoupling Model Predictive Controller (MPC) for constrained linear systems with bounded disturbances. The usual explicit decouplers are combined with a suitable augmented state–space representation in order to ensure recursive feasibility, robust constraint satisfaction, and cross-coupling reduction during set-point changes. The proposed approach is flexible with respect to the decoupler choice and the robust MPC algorithm. A robust MPC for tracking piecewise constant references with an artificial target based on nominal prediction is proposed in order to achieve offset-free piecewise constant reference tracking. Two case studies are presented to illustrate the effectiveness of the proposed decoupling strategy.
•A robust decoupling MPC for tracking piecewise constant references has been proposed.•A new augmented model is used to provide a decoupled set-point tracking response.•Recursive feasibility and constraint satisfaction are ensured despite disturbances. |
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ISSN: | 0019-0578 1879-2022 |
DOI: | 10.1016/j.isatra.2023.05.004 |