Optimal Sensor Scheduling Under Intermittent Observations Subject to Network Dynamics

• Published in: IEEE transactions on automatic control Vol. 68; no. 3; pp. 1399 - 1414
• Main Authors: Hmedi, Hassan, Carroll, Johnson, Arapostathis, Ari
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerospace electronics; Costs; Dynamic scheduling; Horizon; Intermittent observations; Iterative algorithms; linear quadratic Gaussian (LQG) control; Loss measurement; Markov decision processes (MDPs); networked control systems; Noise measurement; Optimal scheduling; Qualitative analysis; Random noise; Schedules; Scheduling; sensor scheduling
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2022.3151578

Motivated by various distributed control applications, we consider a linear system with Gaussian noise observed by multiple sensors which transmit measurements over a dynamic lossy network. We characterize the stationary optimal sensor scheduling policy for the finite horizon, discounted, and long-term average cost problems and show that the value iteration algorithm converges to a solution of the average cost problem. We further show that the suboptimal policies provided by the rolling horizon truncation of the value iteration also guarantee stability and provide near-optimal average cost. Lastly, we provide qualitative characterizations of the multidimensional set of measurement loss rates for which the system is stabilizable for a static network, thus extending earlier results on intermittent observations.