Finite Horizon Stochastic Optimal Control of Uncertain Linear Networked Control System

被引：0
|
作者：
Xu, Hao [1 ]
Jagannathan, S. [1 ]
机构：
[1] Missouri Univ Sci & Technol, Dept Elect & Comp Engn, Rolla, MO 65409 USA
来源：
PROCEEDINGS OF THE 2013 IEEE SYMPOSIUM ON ADAPTIVE DYNAMIC PROGRAMMING AND REINFORCEMENT LEARNING (ADPRL) | 2013年
关键词：
Networked Control System; Adaptive Dynamics Programming and Reinforcement learning; Finite horizon; Stochastic Optimal Control; Adaptive Estimator; STABILITY;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：24 / 30
页数：7
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