Mixed $H_2/H_\infty$  State Estimation for Discrete-Time Switched Complex Networks With Random Coupling Strengths Through Redundant Channels

et al. 2021

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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.

Section: Introductionmentioning

confidence: 99%

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

Geng

et al. 2021

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“…The past decades have witnessed a surge of an increasing research interest on the state estimation problem due primarily to its extensive applications in various areas including target tracking, observer-based control, and fault diagnosis. [1][2][3][4][5][6][7][8][9][10][11][12] The purpose of the state estimation problem is to obtain the reliable estimate about the internal state for a dynamical system through the available measurement outputs. [13][14][15] By now, there have been a variety of state estimation methods available in the literature that have been developed under different performance requirements on different problem settings with different applications.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Moving horizon estimation with multirate measurements and correlated noises

Zou

Dong

et al. 2020

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This article is concerned with the moving horizon estimation (MHE) problem for a class of linear discrete-time systems subject to multirate measurements and correlated noises. The sensors of the plant are grouped into two nodes with different sampling periods, which give rise to the multirate measurements. Under the effects of multirate measurements, the underlying system is then described by a periodic system with correlated noises. A MHE strategy is formulated and the corresponding analytical solution of the state estimation is derived by using the "completing the square" technique. Based on the lifting approach, the dynamics of the estimation error covariance is analyzed and the desired estimator parameters are obtained by solving a set of linear matrix inequalities. An illustrative example is given to show the effectiveness of our proposed MHE method.

“…For networked control systems with limited communication capacities, in order to avoid the data collisions and reduce the occurrence of the resulted network‐induced phenomena, 22‐24 a practical yet efficient way is to introduce certain data scheduling protocols to allocate the network resource for multiple data packet transmissions. In industry applications, the broadly employed communication protocols include the Round‐Robin (RR) protocol, the try‐once‐discard protocol and the stochastic communication protocol, based on which the corresponding control/filtering problems have stirred some initial attention in the past few years, see, for example, References 25‐34.…”

Section: Introductionmentioning

confidence: 99%

Protocol‐based extended Kalman filtering with quantization effects: The Round‐Robin case

Liu

et al. 2020

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This article investigates the extended Kalman filtering problem for a class of stochastic nonlinear systems with quantization effects and Round-Robin (RR) communication protocols. The uniform quantization is considered and the resulting quantization error is characterized as an additive white noise sequence obeying the uniform distribution over certain intervals. For the sake of reducing communication traffic of the network as well as alleviating data collisions, the RR mechanism is introduced to schedule the data transmission from the sensors to the filter. By combining the periodic property of the RR protocol and the zero-order holder strategy, the input signal of the filter is modeled by a sequence of delayed quantized measurements. The main purpose of this article is to design an extended Kalman filter for the stochastic nonlinear systems, in the simultaneous presence of quantization errors, stochastic nonlinearities, and stochastic noises, such that an optimized upper bound for the filtering error covariance is derived. By solving two coupled Riccati-like difference equations, the filter gain matrix is explicitly formulated. An RR protocol-based recursive filtering algorithm is developed for the online implementation. Furthermore, a sufficient condition is established to ensure the uniform boundedness of the filtering error in the mean-square sense. Finally, a simulation example is given to demonstrate the practical validity of the designed filter algorithm.