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This paper is concerned with the set-membership filtering problem for a class of time-varying systems with mixed time-delays and communication protocols. Two kinds of well-known protocols (Round-Robin protocol and Weighted Try-Once-Discard protocol) are considered, with which the data transmission between the sensor nodes and the filter is implemented via a shared communication network that allows only one sensor node to send its measurement data at each transmission instant in order to prevent the data from collisions. The transmission order of sensor nodes is orchestrated by the underlying protocol of the network. The aim of the problem addressed is to design a set-membership filter capable of confining the state estimate of the system to certain ellipsoidal region subject to the bounded non-Gaussian noises. Sufficient condition is first derived for the existence of the desired filter at each time step in terms of a recursive algorithm. Then, two optimization problems are solved by optimizing the constraint ellipsoid of the estimation error subject to the underlying protocol. Simulation results demonstrate the effectiveness of the proposed filter design scheme.

This paper is concerned with the probabilistic-constrained filtering problem for a class of time-varying systems with stochastic nonlinearities and state constraints. An improved static event-triggering scheme is used to reduce unnecessary signal transmissions on the communication channel, where a time-varying triggering parameter is designed according to engineering practice. The aim of the problem addressed is to design a time-varying filter such that (1) the prescribed probabilistic constraints on the estimation error are satisfied (ie, the probability for the estimation error to be confined to the given ellipsoidal set is larger than a prescribed value) and (2) the ellipsoid is minimized at each time instant in the sense of the matrix norm. First, the probabilistic constraints are handled by means of the multidimensional Chebyshev bounds. By using recursive matrix inequalities, stochastic analysis is conducted to establish sufficient conditions for the existence of the desired probabilistic-constrained filter. Then, a recursive optimization algorithm is proposed to design the filter gain matrices. Finally, a simulation example is proposed to demonstrate the effectiveness and applicability of the proposed method. KEYWORDS event-triggering scheme, filter design, probabilistic constraints, stochastic nonlinearity, time-varying systems 1484

In this paper, the event-triggered state estimation problem is investigated for a class of complex networks with mixed time delays using sampled data information. A novel state estimator is presented to estimate the network states. A new event-triggered transmission scheme is proposed to reduce unnecessary network traffic between the sensors and the estimator, where the sampled data is transmitted to the estimator only when the so-called "event-triggered condition" is satisfied. The purpose of the problem addressed is to design an estimator for the complex network such that the estimation error is ultimately bounded in mean square. By utilizing Lyapunov theory combined with the stochastic analysis approach, sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square. Then, the desired estimator gain matrices are obtained via solving a convex problem. Finally, a numerical example is given to illustrate the effectiveness of the results.