Finite-Time Stabilization for Constrained Discrete-time Systems by Using Model Predictive Control
In this paper, a model predictive control (MPC) framework is proposed for finite-time stabilization of linear and nonlinear discrete-time systems subject to state and control constraints. The proposed MPC framework guarantees the finite-time convergence property by assigning the control horizon equa...
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Published in | IEEE/CAA journal of automatica sinica Vol. 11; no. 7; pp. 1656 - 1666 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Piscataway
Chinese Association of Automation (CAA)
01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | In this paper, a model predictive control (MPC) framework is proposed for finite-time stabilization of linear and nonlinear discrete-time systems subject to state and control constraints. The proposed MPC framework guarantees the finite-time convergence property by assigning the control horizon equal to the dimension of the overall system, and only penalizing the terminal cost in the optimization, where the stage costs are not penalized explicitly. A terminal inequality constraint is added to guarantee the feasibility and stability of the closed-loop system. Initial feasibility can be improved via augmentation. The finite-time convergence of the proposed MPC is proved theoretically, and is supported by simulation examples. |
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ISSN: | 2329-9266 2329-9274 |
DOI: | 10.1109/JAS.2024.124212 |