DocumentCode :
3572776
Title :
A unified framework for optimality analysis of model predictive control
Author :
Xin Cai ; Shaoyuan Li ; Ning Li ; Kang Li
Author_Institution :
Dept. of Autom., Shanghai Jiao Tong Univ., Shanghai, China
fYear :
2014
Firstpage :
1688
Lastpage :
1693
Abstract :
As well-known, model predictive control is closely related to optimal control. This paper studies relationships between them and provides a unified framework for optimality analysis of model predictive controllers (MPC). The optimality is evaluated by comparing total performance of MPC with finite and infinite horizon optimal cost. Based on relaxed value iteration method, upper and lower bounds of optimality evaluation functions are expressed explicitly in terms of optimization horizon. These results reveal detailed characteristics on performance of closed-loop MPC systems due to using “receding horizon optimization” implementation style.
Keywords :
closed loop systems; control system analysis; iterative methods; optimal control; predictive control; closed-loop MPC system; finite horizon optimal cost; infinite horizon optimal cost; model predictive control; optimal control; optimality analysis; optimality evaluation function; receding horizon optimization; relaxed value iteration method; Cost function; Nonlinear systems; Optimal control; Predictive control; Predictive models; Model predictive control; Optimal control; Relaxed dynamic programming; Value iteration;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
Type :
conf
DOI :
10.1109/WCICA.2014.7052974
Filename :
7052974
Link To Document :
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