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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Bibliographic Details
Published inIEEE/CAA journal of automatica sinica Vol. 11; no. 7; pp. 1656 - 1666
Main Authors Zhu, Bing, Yuan, Xiaozhuoer, Dai, Li, Qiang, Zhiwen
Format Journal Article
LanguageEnglish
Published Piscataway Chinese Association of Automation (CAA) 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Autonomous aerial vehicles
Closed loops
Constraints
Control systems
Convergence
Costs
deadbeat control
Discrete time systems
Feasibility
Feedback control
finite-time stabilization
model predictive control (MPC)
Noise
Nonlinear systems
Predictive control
Stability analysis
Stabilization
Transient analysis
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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.
ISSN:2329-9266
2329-9274
DOI:10.1109/JAS.2024.124212