Optimal Estimation and Control for Lossy Network: Stability, Convergence, and Performance

被引:48
作者
Lin, Hong [1 ,2 ]
Su, Hongye [1 ]
Shi, Peng [3 ,4 ]
Shu, Zhan [5 ]
Lu, Renquan [6 ,7 ]
Wu, Zheng-Guang [1 ]
机构
[1] Zhejiang Univ, Inst Cyber Syst & Control, Hangzhou 310027, Zhejiang, Peoples R China
[2] Univ Hong Kong, Dept Mech Engn, Hong Kong, Hong Kong, Peoples R China
[3] Harbin Engn Univ, Coll Automat, Harbin 150001, Heilongjiang, Peoples R China
[4] Victoria Univ, Coll Engn & Sci, Melbourne, Vic 8001, Australia
[5] Univ Southampton, Electromech Engn Grp, Fac Engn & Environm, Southampton SO17 1BJ, Hants, England
[6] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
[7] Guangdong Key Lab IoT Informat Proc, Guangzhou 510006, Guangdong, Peoples R China
基金
中国国家自然科学基金; 澳大利亚研究理事会;
关键词
Linear quadratic Gaussian (LQG); networked control systems; optimal estimation and control; packet loss; smart sensor; user datagram protocol (UDP)-like system; MARKOVIAN PACKET LOSSES; CONTROL-SYSTEMS; STATE ESTIMATION; LQG CONTROL; INTERMITTENT OBSERVATIONS; TIME-SYSTEMS; LINKS; ACKNOWLEDGMENT; DELAYS; DROP;
D O I
10.1109/TAC.2017.2672729
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we study the problems of optimal estimation and control, i.e., the linear quadratic Gaussian (LQG) control, for systems with packet losses but without acknowledgment. Such acknowledgment is a signal sent by the actuator to inform the estimator of the incidence of control packet losses. For such system, which is usually called as a user datagram protocol (UDP)-like system, the optimal estimation is nonlinear and its calculation is time-consuming, making its corresponding optimal LQG problem complicated. We first propose two conditions: 1) the sensor has some computation abilities; and 2) the control command, exerted to the plant, is known to the sensor. For a UDP-like system satisfying these two conditions, we derive the optimal estimation. By constructing the finite and infinite product probability measure spaces for the estimation error covariances (EEC), we give the stability condition for the expected EEC, and show the existence of a measurable function to which the EEC converges in distribution, and propose some practical methods to evaluate the estimation performance. Finally, the LQG controllers are derived, and the conditions for the mean square stability of the closed-loop system are established.
引用
收藏
页码:4564 / 4579
页数:16
相关论文
共 45 条
[1]   Wireless multimedia sensor networks: A survey [J].
Akyildiz, Ian F. ;
Melodia, Tommaso ;
Chowdury, Kaushik R. .
IEEE WIRELESS COMMUNICATIONS, 2007, 14 (06) :32-39
[2]  
[Anonymous], 1995, Algebraic Riccati Equations
[3]  
[Anonymous], 2014, IFAC PAPERSONLINE
[4]  
[Anonymous], 2 IEEE PES INT C EXH
[5]  
Ash R. B., 2000, PROBABILITY MEASURE
[6]   Control and communication challenges in networked real-time systems [J].
Baillieul, John ;
Antsaklis, Panos J. .
PROCEEDINGS OF THE IEEE, 2007, 95 (01) :9-28
[7]  
BILLINGSLEY P., 2008, Probability and Measure
[8]  
Bressan N, 2010, INT CONF SMART GRID, P49, DOI 10.1109/SMARTGRID.2010.5622015
[9]   Kalman Filtering With Intermittent Observations: Convergence for Semi-Markov Chains and an Intrinsic Performance Measure [J].
Censi, Andrea .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2011, 56 (02) :376-381
[10]   Probabilistic performance of state estimation across a lossy network [J].
Epstein, Michael ;
Shi, Ling ;
Tiwari, Abhishek ;
Murray, Richard M. .
AUTOMATICA, 2008, 44 (12) :3046-3053