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In this paper, the suboptimal control problem is addressed for a class of discrete time-varying systems subjected to the stochastic communication protocol (SCP). The measurements provided by the sensors are transmitted through the communication channel and then fed into the controller. The SCP is utilized to select the sensor getting access to the communication media. In presence of the nonlinearities,...
This paper investigates the remote state estimation problems for a class of linear discrete-time systems. An event-triggered scheme that schedules the transmissions between the sensor and the remote estimator is introduced so as to preserve the network resources. The communication process is assumed to suffer from the missing measurement phenomenon described by a Bernoulli distribution. Additionally,...
In this paper, the state estimation problem is investigated for a class of discrete-time stochastic neural networks with event-triggered transmission (ETT) mechanism. Different from the traditional periodic communication mechanism, the ETT mechanism employed in this paper possesses the advantage of mitigating the network traffic resulting from the unnecessary sending and receiving data between the...
This paper deals with a new filtering problem for linear uncertain discrete-time stochastic systems with randomly varying sensor delay. The system measurements are subject to randomly varying sensor delays, which often occur in information transmissions through networks. The problem addressed is the design of a linear filter such that, for all admissible parameter uncertainties and all probabilistic...
In this paper, the joint fault and state estimation problem is investigated for a class of nonlinear systems with event-triggered transmissions and missing measurements. In the proposed event-triggered transmission scheme, in order to reduce unnecessary network traffic, the current measurement is released only when it changes greatly from the previously transmitted one. A Bernoulli distributed sequence...
In this paper, the distributed filtering problem is addressed for a class of discrete time-varying systems in sensor networks. The stochastic nonlinearities, which are described by first and second-order statistics, enter into both the target plant and the sensor measurements. The goal of the proposed problem is to develop a distributed filter for each sensor node by making use of the topological...
This paper is concerned with the finite-horizon recursive filtering problem for a class of nonlinear time-varying systems with missing measurements. The missing measurements are modeled by a series of mutually independent random variables obeying Bernoulli distributions with possibly different occurrence probabilities. Attention is focused on the design of a recursive filter such that, for the missing...
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