Value of Information in Feedback Control: Quantification
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Abstract (arXiv)
Although transmission of a data packet containing sensory information in a networked control system improves the quality of regulation, it has indeed a price from the communication perspective. It is, therefore, rational that such a data packet be transmitted only if it is valuable in the sense of a cost-benefit analysis. Yet, the fact is that little is known so far about this valuation of information and its connection with traditional event-triggered communication. In the present article, we study this intrinsic property of networked control systems by formulating a rate-regulation tradeoff between the packet rate and the regulation cost with an event trigger and a controller as two distributed decision makers, and show that the valuation of information is conceivable and quantifiable grounded on this tradeoff. In particular, we characterize an equilibrium in the rate-regulation tradeoff, and quantify the value of information VoIk there as the variation in a so-called value function with respect to a piece of sensory information that can be communicated to the controller at each time k. We prove that, for a multi-dimensional Gauss-Markov process, VoIk is a symmetric function of the discrepancy between the state estimates at the event trigger and the controller, and that a data packet containing sensory information at time k should be transmitted to the controller only if VoIk is nonnegative. Moreover, we discuss that VoIk can be computed with arbitrary accuracy, and that it can be approximated by a closed-form quadratic function with a performance guarantee.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Dec 2018.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 3; 2026: 2.
6 papers.
- 2023Task-Oriented Communication Design at Scalecited by 3
- 2025InterQ: A DQN Framework for Optimal Intermittent Controlcited by 2
- 2026World Model-Enabled Causal Digital Twins for Semantic Communications in Physical AI Systemscited by 2
- 2025VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Networkcited by 1
- 2025The Silence that Speaks: Neural Estimation via Communication Gapscited by 0
- 2026When Does Test-Time Physical Diagnosis Pay? A Frozen Policy Buys Evidence It Never Readscited by 0
- 2026When Does Test-Time Physical Diagnosis Pay? A Frozen Policy Buys Evidence It Never Reads14 Sep 2026
- 2026World Model-Enabled Causal Digital Twins for Semantic Communications in Physical AI Systems15 May 2026
- 2025The Silence that Speaks: Neural Estimation via Communication Gaps30 Nov 2025
- 2025VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Network12 May 2025
- 2025InterQ: A DQN Framework for Optimal Intermittent Control12 Apr 2025
- 2023Task-Oriented Communication Design at Scale15 May 2023