Tobit Kalman filtering for fractional-order systems with stochastic nonlinearities under Round-Robin protocol

In this article, the Tobit Kalman filtering problem is investigated for a class of discrete time-varying fractional-order systems in the presence of measurement censoring and stochastic nonlinearities under the Round-Robin protocol (RRP). The fractional-order dynamic model is described by the Grunwald-Letnikov difference equation, and the statistical means are utilized to characterize the stochastic nonlinearities that include state-dependent stochastic disturbances as a special case. The RRP is employed to decide the transmission sequence of sensors so as to alleviate undesirable data collisions. Under the RRP scheduling, only one sensor is permitted to transmit its measurement over the network at each time instant. In light of the renowned orthogonality projection principle, a protocol-based fractional Tobit Kalman filter is devised with the fractional dynamics and stochastic nonlinearities elaborately addressed. In the pursuit of the filter design, a couple of new terms appear which are in relation to the RRP, fractional dynamics and stochastic nonlinearities arise, and these terms are adequately handled recursively or off-line. Simulation results are provided to demonstrate the usefulness of the proposed method.

Automated summary

In this article, the Tobit Kalman filtering problem for a class of discrete time-varying fractional-order systems with measurement censoring and stochastic nonlinearities under the Round-Robin protocol (RRP) is investigated. A protocol-based fractional Tobit Kalman filter is designed, with statistical means used to characterize the stochastic nonlinearities. The paper addresses the issues related to RRP, fractional dynamics, and stochastic nonlinearities in the filter design process.