Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/83455
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Type: Journal article
Title: Robust constrained model predictive control based on parameter-dependent Lyapunov functions
Author: Xia, Y.
Liu, G.
Shi, P.
Chen, J.
Rees, D.
Citation: Circuits, Systems and Signal Processing, 2008; 27(4):429-446
Publisher: Birkhauser Boston Inc
Issue Date: 2008
ISSN: 0278-081X
1531-5878
Statement of
Responsibility: 
Yuanqing Xia, G.P. Liu, P. Shi, J. Chen, D. Rees
Abstract: The problem of robust constrained model predictive control (MPC) of systems with polytopic uncertainties is considered in this paper. New sufficient conditions for the existence of parameter-dependent Lyapunov functions are proposed in terms of linear matrix inequalities (LMIs), which will reduce the conservativeness resulting from using a single Lyapunov function. At each sampling instant, the corresponding parameter-dependent Lyapunov function is an upper bound for a worst-case objective function, which can be minimized using the LMI convex optimization approach. Based on the solution of optimization at each sampling instant, the corresponding state feedback controller is designed, which can guarantee that the resulting closed-loop system is robustly asymptotically stable. In addition, the feedback controller will meet the specifications for systems with input or output constraints, for all admissible time-varying parameter uncertainties. Numerical examples are presented to demonstrate the effectiveness of the proposed techniques. © Birkhäuser Boston 2008.
Keywords: MPC
Robust control
Polytopic uncertainty
Stability
Optimization
LMI
Rights: © Birkhäuser Boston 2008
DOI: 10.1007/s00034-008-9036-9
Appears in Collections:Aurora harvest 4
Electrical and Electronic Engineering publications

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