Wangyan Li

Weighted Average Consensus-Based Unscented Kalman Filtering

In this paper, we are devoted to investigate the consensus-based distributed state estimation problems for a class of sensor networks within the unscented Kalman filter (UKF) framework. The communication status among sensors is represented by a connected undirected graph. Moreover, a weighted average consensus-based UKF algorithm is developed for the purpose of estimating the true state of interest, and its estimation error is bounded in mean square which has been proven in the following section. Finally, the effectiveness of the proposed consensus-based UKF algorithm is validated through a simulation example.

A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002 and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL1401

Probability-Dependent Static Output Feedback Control for Discrete-Time Nonlinear Stochastic Systems with Missing Measurements

This paper is devoted to the problems of gain-scheduled control for a class of discrete-time stochastic systems with infinite-distributed delays and missing measurements by utilizing probability-dependent Lyapunov functional. The missing-measurement phenomenon is assumed to occur in a random way, and the missing probability is time varying with securable upper and lower bounds that can be measured in real time. The purpose is to design a static output feedback controller with scheduled gains such that, for the admissible random missing measurements, time delays, and noises, the closed-loop system is exponentially mean-square stable. At last, a simulation example is exploited to illustrate the effectiveness of the proposed design procedures.

Distributed H∞-consensus filtering for piecewise discrete-time linear systems

Abstract This paper is concerned with the distributed H ∞ - consensus filtering problem for a class of piecewise discrete-time linear systems. Firstly, the modes and their transitions of augmented piecewise linear systems as well as distributed filters are formulated. Also, the structure of augmented distributed filter gains is presented in virtue of the adjacent matrix of sensor networks. Then, a set of sufficient conditions are provided for the distributed filter to ensure that its dynamics is global asymptotically stable with the H ∞ - consensus performance constraint. In addition, the distributed filter gains are obtained with the aid of the convex optimal method. At last, an illustrative simulation is presented to demonstrate the effectiveness and applicability of the proposed distributed filtering algorithm.

Local condition-based finite-horizon distributed H∞-consensus filtering for random parameter system with event-triggering protocols

This paper investigates the distributed H ∞ - consensus filtering problem for a class of discrete time-varying systems with random parameters and event-triggering protocols. An event-triggering protocol for each node is employed to reduce the burden of the network communication. A novel matrix named by information matrix is proposed to describe the complicated correlations among the elements of random matrix. By virtue of the presented information matrix, a weighted covariance matrix can be easily obtained to analyze the system with random parameters. With the aid of the newly constructed dissipation matrix and vector supplied rate functions, a set of local coupled conditions for each node is obtained such that the stochastic vector dissipativity-like over the finite-horizon of the filtering error dynamics can be guaranteed. As well, these sufficient conditions together could effectively solve the distributed H ∞ - consensus filtering problem. Notably, the designed filtering algorithm can be implemented on each node to obtain the desirable distributed filter gains. Finally, the effectiveness and applicability of the proposed algorithm is illustrated by a numerically simulative example.

A Survey on Gain-Scheduled Control and Filtering for Parameter-Varying Systems

This paper presents an overview of the recent developments in the gain-scheduled control and filtering problems for the parameter-varying systems. First of all, we recall several important algorithms suitable for gain-scheduling method including gain-scheduled proportional-integral derivative (PID) control, , and mixed gain-scheduling methods as well as fuzzy gain-scheduling techniques. Secondly, various important parameter-varying system models are reviewed, for which gain-scheduled control and filtering issues are usually dealt with. In particular, in view of the randomly occurring phenomena with time-varying probability distributions, some results of our recent work based on the probability-dependent gain-scheduling methods are reviewed. Furthermore, some latest progress in this area is discussed. Finally, conclusions are drawn and several potential future research directions are outlined.

Nonfragile Gain-Scheduled Control for Discrete-Time Stochastic Systems with Randomly Occurring Sensor Saturations

This paper is devoted to tackling the control problem for a class of discrete-time stochastic systems with randomly occurring sensor saturations. The considered sensor saturation phenomenon is assumed to occur in a random way based on the time-varying Bernoulli distribution with measurable probability in real time. The aim of the paper is to design a nonfragile gain-scheduled controller with probability-dependent gains which can be achieved by solving a convex optimization problem via semidefinite programming method. Subsequently, a new kind of probability-dependent Lyapunov functional is proposed in order to derive the controller with less conservatism. Finally, an illustrative example will demonstrate the effectiveness of our designed procedures.

Consensus-based unscented Kalman filter for sensor networks with sensor saturations

In this paper, we devote to investigating the consensus-based unscented Kalman filtering problem for a certain kind of sensor networks with sensor saturations. The communication status among sensors is represented by a connected undirect graph. Saturation phenomenon exists both in the state of the system and individual sensors. Moreover, a distributed unscented Kalman filtering algorithm based on CI (consensus on information) consensus approach is developed to estimate the ture state of interest. Finally, the effectiveness of the proposed consensus-based unscented Kalman filtering scheme is validated through a simulation example.

A New Look at Boundedness of Error Covariance of Kalman Filtering

In this correspondence paper, we provide a new look at the boundedness problems of error covariance of Kalman filtering. First, by utilizing the mathematical induction technique, a new bound function which is dependent on system parameters is proposed. In this manner, the boundedness problems of the error covariance can be converted to the study of the corresponding uniform bounds of the bound function. Second, based on such a bound function, the dynamic behaviors, monotonicities, and boundedness problems of error covariance are deeply explored. Consequently, a few quantitative results under minimal conditions about the uniform bounds on error covariance are obtained. Finally, examples are given to verify the correctness and effectiveness of our theoretical analyses.

Probability-dependent gain-scheduled control for discrete-time stochastic systems with randomly occurring sensor saturations

This paper is devoted to tackling the control problem for a class of discrete-time stochastic systems with randomly occurring sensor saturations by utilizing gain-scheduled method, the sensor saturation phenomenon is assumed to occur in a randomly way based on time-varying Bernoulli distribution with measurable probability in real time. The aim of the paper is to design a gain-scheduled controller with probability-dependent gain which can be achieved by solving a convex optimization problem via semi-definite programme method. Subsequently, a new kind functional, probability-dependent Lyapunov functional is proposed to make the theory sound. Finally, an illustration example will demonstrate the effectiveness of the procedures we design.