Optimal Infinite Horizon Decentralized Networked Controllers With Unreliable Communication

被引:13
|
作者
Ouyang, Yi [1 ]
Asghari, Seyed Mohammad [2 ]
Nayyar, Ashutosh [2 ]
机构
[1] Preferred Networks Amer Inc, Albany, CA 94706 USA
[2] Univ Southern Calif, Dept Elect Engn, Los Angeles, CA 90007 USA
关键词
Markov processes; Decentralized control; Linear systems; Sensor systems; Riccati equations; Networked control systems; networked control system; optimal control; stochastic systems;
D O I
10.1109/TAC.2020.2996411
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We consider a decentralized networked control system (DNCS) consisting of a remote controller and a collection of linear plants, each associated with a local controller. Each local controller directly observes the state of its colocated plant and can inform the remote controller of the plant's state through an unreliable uplink channel. The downlink channels from the remote controller to local controllers were assumed to be perfect. The objective of the local controllers and the remote controller is to cooperatively minimize the infinite horizon time average of expected quadratic cost. The finite horizon version of this problem was solved in our prior work ("Optimal local and remote controllers with unreliable uplink channels," IEEE, May 2019). The optimal strategies in the finite horizon case were shown to be characterized by coupled Riccati recursions. In this article, we show that if the link failure probabilities are below certain critical thresholds, then the coupled Riccati recursions of the finite horizon solution reach a steady state and the corresponding decentralized strategies are optimal. Above these thresholds, we show that no strategy can achieve finite cost. We exploit a connection between our DNCS Riccati recursions and the coupled Riccati recursions of an auxiliary Markov jump linear system to obtain our results. Our main result in Theorem 1 explicitly identifies the critical thresholds for the link failure probabilities and the optimal decentralized control strategies when all link failure probabilities are below their thresholds.
引用
收藏
页码:1778 / 1785
页数:8
相关论文
共 50 条
  • [1] Optimal local and remote controllers with unreliable communication: the infinite horizon case
    Ouyang, Yi
    Asghari, Seyed Mohammad
    Nayyar, Ashutosh
    2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC), 2018, : 6634 - 6639
  • [2] Optimal Local and Remote Controllers with Unreliable Communication
    Yi Ouyang
    Asghari, Seyed Mohammad
    Nayyar, Ashutosh
    2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC), 2016, : 6024 - 6029
  • [3] Finite and Infinite Horizon Optimal Triggering of Networked Control Systems
    Heydari, Ali
    2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016, : 4255 - 4262
  • [4] Control for networked control systems with remote and local controllers over unreliable communication channel
    Liang, Xiao
    Xu, Juanjuan
    AUTOMATICA, 2018, 98 : 86 - 94
  • [5] On the Infinite Horizon Performance of Receding Horizon Controllers
    Gruene, Lars
    Rantzer, Anders
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2008, 53 (09) : 2100 - 2111
  • [6] On the Structure of Decentralized Controllers in Networked MDPs
    Horowitz, Matanya
    2013 IEEE 52ND ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2013, : 5870 - 5877
  • [7] A decentralized receding horizon optimal approach to formation control of networked mobile robots
    Yamchi, Mohammad Hosscinzadeh
    Esfanjani, Reza Mahboobi
    OPTIMAL CONTROL APPLICATIONS & METHODS, 2018, 39 (01): : 51 - 64
  • [8] Optimal realizable networked controllers for networked systems
    Vamsi, Andalam Satya Mohan
    Elia, Nicola
    2011 AMERICAN CONTROL CONFERENCE, 2011, : 336 - 341
  • [9] Synthesis of Networked Switching Linear Decentralized Controllers
    Barcelli, D.
    Bernardini, D.
    Bemporad, A.
    49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2010, : 2480 - 2485
  • [10] Infinite-horizon optimal control for networked control systems with Markovian packet losses
    Wang, Hongxia
    Li, Zixing
    Liu, Tao
    Liang, Xiao
    OPTIMAL CONTROL APPLICATIONS & METHODS, 2024, 45 (01): : 29 - 44