Real-time model predictive control based on dual gradient projection: Theory and fixed-point FPGA implementation

Summary This paper proposes a method to design robust model predictive control (MPC) laws for discrete‐time linear systems with hard mixed constraints on states and inputs, in case of only an inexact solution of the associated quadratic program is available, because of real‐time requirements. By usi...

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Vydáno v:International journal of robust and nonlinear control Ročník 26; číslo 15; s. 3292 - 3310
Hlavní autoři: Rubagotti, Matteo, Patrinos, Panagiotis, Guiggiani, Alberto, Bemporad, Alberto
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bognor Regis Blackwell Publishing Ltd 01.10.2016
Wiley Subscription Services, Inc
Témata:
Algorithms
Asymptotic properties
Feasibility
Field programmable gate arrays
Fixed point arithmetic
Mathematical models
model predictive control
optimization methods
Predictive control
Real time
uncertain systems
ISSN:1049-8923, 1099-1239
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Shrnutí:Summary This paper proposes a method to design robust model predictive control (MPC) laws for discrete‐time linear systems with hard mixed constraints on states and inputs, in case of only an inexact solution of the associated quadratic program is available, because of real‐time requirements. By using a recently proposed dual gradient‐projection algorithm, it is proved that the discrepancy of the optimal control law as compared with the obtained one is bounded even if the solver is implemented in fixed‐point arithmetic. By defining an alternative MPC problem with tightened constraints, a feasible solution is obtained for the original MPC problem, which guarantees recursive feasibility and asymptotic stability of the closed‐loop system with respect to a set including the origin, also considering the presence of external disturbances. The proposed MPC law is implemented on a field‐programmable gate array in order to show the practical applicability of the method. Copyright © 2016 John Wiley & Sons, Ltd.
Bibliografie:istex:9F048D78B05981C386AD59029107721A19F3E9FD
ArticleID:RNC3507
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.3507