Periodic Event-Triggered Estimation for Networked Control Systems

Abstract

This paper considers the problem of remote state estimation in a linear discrete invariant system, where a smart sensor is utilized to measure the system state and generate a local estimate. The communication depends on an event scheduler in the smart sensor. When the channel between the remote estimator and the smart sensor is activated, the remote estimator simply adopts the estimate transmitted by the smart sensor. Otherwise, it calculates an estimate based on the available information. The closed-form of the minimum mean-square error (MMSE) estimator is introduced, and we use Gaussian preserving event-based sensor scheduling to obtain an ideal compromise between the communication cost and estimation quality. Furthermore, we calculate a variation range of communication probability, which helps to design the policy of event-triggered estimation. Finally, the simulation results are given to illustrate the effectiveness of the proposed event-triggered estimator.

Congruential Summation-Triggered Identification of FIR Systems under Binary Observations and Uncertain Communications

“…Guo et al [15] introduced an event-triggered communication scheme for binary-valued observations, to save communication resources; they proposed an algorithm for estimating unknown parameters based on weighted least-squares optimization and discussed the trade-off between average communication rate and estimation performance. Cui et al [16] researched the remote state estimation for linear discrete-time invariant systems with event-driven mechanisms introduced by communication, employing Gaussian-conserving, event-based sensor scheduling to optimally balance communication costs and estimation accuracy. Huang and Liu [17] introduced new adaptive tracking methods for systems within two variants of event-driven architectures.…”