Optimal sensor scheduling for remote state estimation with limited bandwidth: a deep reinforcement learning approach

DOI10.1016/j.ins.2021.12.043OpenAlexW4200376042WikidataQ110541582 ScholiaQ110541582MaRDI QIDQ6154482

Ming Lin, Yong Xu, Li-xin Yang, Hong-Xia Rao, Peng Shi

Publication date: 15 February 2024

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2021.12.043

zbMATH Keywords

state estimationMarkov decision processsensor schedulingdeep reinforcement learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Estimation and detection in stochastic control theory (93E10) Markov and semi-Markov decision processes (90C40) Networked control (93B70)

Cites Work

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• Sensor scheduling over a packet-delaying network
• Quantized control for networked control systems with packet dropout and unknown disturbances
• Deep reinforcement learning for wireless sensor scheduling in cyber-physical systems
• Decentralized event-triggered \(H_\infty\) control for neural networks subject to cyber-attacks
• Two updating schemes of iterative learning control for networked control systems with random data dropouts
• Optimal sensor scheduling for multiple linear dynamical systems
• Output feedback control of networked control systems with packet dropouts in both channels
• Linear estimation for networked control systems with random transmission delays and packet dropouts
• Learning Algorithms for Markov Decision Processes with Average Cost
• Dynamic Quantizer Design for Hidden Markov State Estimation Via Multiple Sensors With Fusion Center Feedback
• Average Optimality in Dynamic Programming with General State Space
• Reset Moving Horizon Estimation for Quantized Discrete Time Systems
• Learning Optimal Scheduling Policy for Remote State Estimation Under Uncertain Channel Condition
• Optimal Scheduling of Multiple Sensors Over Lossy and Bandwidth Limited Channels
• Multiple Lyapunov Functions Analysis Approach for Discrete-Time-Switched Piecewise-Affine Systems Under Dwell-Time Constraints
• Observer-Based Control for Cyber-Physical Systems With Periodic DoS Attacks via a Cyclic Switching Strategy
• Kalman Filtering With Intermittent Observations
• Optimal Periodic Sensor Scheduling With Limited Resources
• Hybrid-triggered interval type-2 fuzzy control for networked systems under attacks

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