Value of Information in Feedback Control: Quantification - Soleymani, Touraj; Baras, John S.; Hirche, Sandra (IEEE transactions on automatic control, 2022)
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 trade-off 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 trade-off. In particular, we characterize an equilibrium in the rate-regulation trade-off, and quantify the value of information {\rm{VoI}}_k 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, {\rm{VoI}}_k 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 {\rm{VoI}}_k is nonnegative. Moreover, we discuss that {\rm{VoI}}_k can be computed with arbitrary accuracy, and that it can be approximated by a closed-form quadratic function with a performance guarantee.