DocumentCode :
3269329
Title :
Finite horizon stochastic optimal control of uncertain linear networked control system
Author :
Hao Xu ; Jagannathan, Sarangapani
Author_Institution :
Dept. of Electr. & Comput. Eng., Missouri Univ. of Sci. & Technol., Rolla, MO, USA
fYear :
2013
fDate :
16-19 April 2013
Firstpage :
24
Lastpage :
30
Abstract :
In this paper, finite horizon stochastic optimal control issue has been studied for linear networked control system (LNCS) in the presence of network imperfections such as network-induced delays and packet losses by using adaptive dynamic programming (ADP) approach. Due to an uncertainty in system dynamics resulting from network imperfections, the stochastic optimal control design uses a novel adaptive estimator (AE) to solve the optimal regulation of uncertain LNCS in a forward-in-time manner in contrast with backward-in-time Riccati equation-based optimal control with known system dynamics. Tuning law for unknown parameters of AE has been derived. Lyapunov theory is used to show that all the signals are uniformly ultimately bounded (UUB) with ultimate bounds being a function of initial values and final time. In addition, the estimated control input converges to optimal control input within finite horizon. Simulation results are included to show the effectiveness of the proposed scheme.
Keywords :
Lyapunov methods; Riccati equations; adaptive control; control system synthesis; dynamic programming; dynamics; linear systems; networked control systems; optimal control; stochastic systems; tuning; uncertain systems; ADP approach; AE; Lyapunov theory; UUB signals; adaptive dynamic programming approach; adaptive estimator; backward-in-time Riccati equation-based optimal control; finite horizon stochastic optimal control design; network imperfections; network-induced delays; packet losses; system dynamics; tuning law; uncertain LNCS; uncertain linear networked control system; uniformly ultimately bounded signals; Adaptive systems; Delays; Equations; Estimation error; Optimal control; Packet loss; Adaptive Dynamics Programming and Reinforcement learning; Adaptive Estimator; Finite horizon; Networked Control System; Stochastic Optimal Control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Adaptive Dynamic Programming And Reinforcement Learning (ADPRL), 2013 IEEE Symposium on
Conference_Location :
Singapore
ISSN :
2325-1824
Type :
conf
DOI :
10.1109/ADPRL.2013.6614985
Filename :
6614985
Link To Document :
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