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This paper is concerned with the state estimation problem for a class of nonlinear cyber-physical systems (CPSs) where the nonlinear dynamical physical process is approximated by a Takagi-Sugeno fuzzy model. The physical plant is measured by a set of wireless sensors and the sensors communicate with the remote estimator via a communication channel. In the considered CPS, the randomly occurring sensor saturation, signal quantization, packet dropouts as well as the medium access constraint are studied in a unified framework. We develop a sufficient condition such that the filtering error system is asymptotically stable in the mean-square sense and also with a prescribed H ∞ performance level. The filter gain parameters are determined by solving a convex optimization problem. Finally, the simulation study on the networked truck-trailer system is presented to show the effectiveness of the proposed estimator design. Index Terms-Communication constraint, cyber-physical systems (CPSs), H ∞ filtering, incomplete measurement, state estimation, Takagi-Sugeno (T-S) systems.

This technical note is concerned with the distributed Kalman filtering problem for a class of networked multi-sensor fusion systems (NMFSs) with communication bandwidth constraints. To satisfy finite communication bandwidth, only partial components of the local vector estimation signals are transmitted to the fusion center (FC) at each time step, where multiple binary variables are introduced to model this component transmitting process. A novel compensation strategy is proposed to restructure the untransmitted components of each local estimate at the FC end, and a recursive distributed fusion kalman filter (DFKF) is designed in the linear minimum variance sense. Moreover, a simple suboptimal judgement criterion is proposed to determine a group of binary variables such that the mean square error of the designed DFKF is minimal at each time step. An illustrative example is given to show the effectiveness of the proposed methods. Index Terms-Communication bandwidth constraints, distributed Kalman filtering, networked multi-sensor fusion systems (NMFSs).as flexible architectures, simpler installation, easier maintenance and low cost. Note that the communication network can only carry a finite amount of information per unit of time, and communication capabilities are also constrained by the limited energy of sensor nodes [6]. In this sense, information loss is inevitable, and such a fusion estimation with incomplete information may degrade the estimation performance. In this technical note, we focus on the distributed fusion estimation problem for the NMFSs with bandwidth constraints.When the bandwidth constraint is taken into account in NMFSs, most of the existing results resort to the quantization method ([7]-[11]) and the dimensionality reduction method ([12]-[16]). Specifically, the distributed fusion estimation problems have been discussed from the digital communication point of view in [7]- [11], where the bandwidth constraints were modeled by limiting the number of binary bits that each sensor can send to the FC at each time step. To describe the analog transmission scheme more appropriately, the constraint is described by limiting the number of real-valued messages that can be sent from each sensor to the FC at each time step [12], and then the distributed fusion estimation algorithms were given in [13]-[16] by using different dimensionality reduction strategies. Different from the aforementioned methods, the distributed fusion estimation problems have been investigated in [17], [18], where the communication rate between the sensor and the FC was lower than the measurement rate under bandwidth constraints. When the target trajectories change rapidly, however, some important target information may be lost by using the fusion estimation algorithms in [17], [18]. Subsequently, the centralized Kalman fusion estimator was designed in [19] for networked systems with communication constraints, where only partial sensor messages were transmitted to the FC to satisfy the constraints. Note that the deficiency of the ce...