DocumentCode :
3539338
Title :
Linear model predictive control based on approximate optimal control inputs and constraint tightening
Author :
Necoara, Ion ; Nedelcu, Valentin ; Keviczky, Tamas ; Minh Dang Doan ; De Schutter, Bart
Author_Institution :
Autom. Control & Syst. Eng. Dept., Univ. Politeh. Bucharest, Bucharest, Romania
fYear :
2013
fDate :
10-13 Dec. 2013
Firstpage :
7728
Lastpage :
7733
Abstract :
In this paper we propose a model predictive control scheme for discrete-time linear time-invariant systems based on inexact numerical optimization algorithms. We assume that the solution of the associated quadratic program produced by some numerical algorithm is possibly neither optimal nor feasible, but the algorithm is able to provide estimates on primal suboptimality and primal feasibility violation. By tightening the complicating constraints we can ensure the primal feasibility of the approximate solutions generated by the algorithm. Finally, we derive a control strategy that has the following properties: the constraints on the states and inputs are satisfied, asymptotic stability of the closed-loop system is guaranteed, and the number of iterations needed for a desired level of suboptimality can be determined.
Keywords :
asymptotic stability; closed loop systems; discrete time systems; linear systems; numerical analysis; optimal control; predictive control; quadratic programming; approximate optimal control inputs; asymptotic stability; closed-loop system; constraint tightening; discrete-time linear time-invariant systems; inexact numerical optimization algorithms; linear model predictive control scheme; quadratic program; Accuracy; Approximation algorithms; Asymptotic stability; Closed loop systems; Optimization; Prediction algorithms; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on
Conference_Location :
Firenze
ISSN :
0743-1546
Print_ISBN :
978-1-4673-5714-2
Type :
conf
DOI :
10.1109/CDC.2013.6761116
Filename :
6761116
Link To Document :
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