Computation of Feasible and Invariant Sets for Interpolation-based MPC

Ismi Rosyiana Fitri; Jung Su Kim; Shuyou Yu; Young Il Lee

doi:10.1007/s12555-021-0014-3

Computation of Feasible and Invariant Sets for Interpolation-based MPC

Ismi Rosyiana Fitri, Jung Su Kim, Shuyou Yu, Young Il Lee

Department of Electrical Engineering

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

3 Citations (Scopus)

Abstract

The terminal invariant set plays a key role in the stabilizing MPC (Model Predictive Control) formulation. When control gains of the terminal local control laws and corresponding feasible and invariant sets are given, the existing interpolation methods unite them to enlarge the stabilizable region and enhance performance. In this paper, when an invariant set is given, an algorithm is proposed to find another invariant set such that their convex hull is maximized and also invariant. Numerical examples show that the set of the stabilizable initial state of the MPC is enlarged by the terminal constraint set computed by an interpolation-based approach.

Original language	English
Pages (from-to)	3253-3263
Number of pages	11
Journal	International Journal of Control, Automation and Systems
Volume	19
Issue number	10
DOIs	https://doi.org/10.1007/s12555-021-0014-3
Publication status	Published - Oct 2021

Keywords

Interpolation
invariant set
model predictive control
terminal set

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10.1007/s12555-021-0014-3

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abstract = "The terminal invariant set plays a key role in the stabilizing MPC (Model Predictive Control) formulation. When control gains of the terminal local control laws and corresponding feasible and invariant sets are given, the existing interpolation methods unite them to enlarge the stabilizable region and enhance performance. In this paper, when an invariant set is given, an algorithm is proposed to find another invariant set such that their convex hull is maximized and also invariant. Numerical examples show that the set of the stabilizable initial state of the MPC is enlarged by the terminal constraint set computed by an interpolation-based approach.",

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AU - Kim, Jung Su

AU - Yu, Shuyou

AU - Lee, Young Il

N1 - Funding Information: This work was supported by the Advanced Research Project funded by the SeoulTech (Seoul National University of Science and Technology). Publisher Copyright: © 2021, ICROS, KIEE and Springer.

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N2 - The terminal invariant set plays a key role in the stabilizing MPC (Model Predictive Control) formulation. When control gains of the terminal local control laws and corresponding feasible and invariant sets are given, the existing interpolation methods unite them to enlarge the stabilizable region and enhance performance. In this paper, when an invariant set is given, an algorithm is proposed to find another invariant set such that their convex hull is maximized and also invariant. Numerical examples show that the set of the stabilizable initial state of the MPC is enlarged by the terminal constraint set computed by an interpolation-based approach.

AB - The terminal invariant set plays a key role in the stabilizing MPC (Model Predictive Control) formulation. When control gains of the terminal local control laws and corresponding feasible and invariant sets are given, the existing interpolation methods unite them to enlarge the stabilizable region and enhance performance. In this paper, when an invariant set is given, an algorithm is proposed to find another invariant set such that their convex hull is maximized and also invariant. Numerical examples show that the set of the stabilizable initial state of the MPC is enlarged by the terminal constraint set computed by an interpolation-based approach.

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Computation of Feasible and Invariant Sets for Interpolation-based MPC

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Keywords

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