Wen-An Zhang

Distributed Fusion Estimation With Missing Measurements, Random Transmission Delays and Packet Dropouts

This technical note is concerned with the distributed Kalman filtering problem for a class of networked multi-sensor fusion systems (NMFSs) with missing sensor measurements, random transmission delays and packet dropouts. A novel stochastic model is proposed to describe the transmission delays and packet dropouts, and an optimal distributed fusion Kalman filter (DFKF) is designed based on the optimal fusion criterion weighted by matrices. Some sufficient conditions are derived such that the MSE of the designed DFKF is bounded or convergent. Moreover, steady-state DFKF is also presented for the NMFSs. An illustrative example is given to demonstrate the effectiveness of the proposed results.

A switched system approach to H∞ control of networked control systems with time-varying delays

Abstract This paper is concerned with the H ∞ control problem for a class of networked control systems (NCSs) with time-varying delay that is less than one sampling period. By applying a new working mode of the actuator and considering state feedback controllers, a new discrete-time switched system model is proposed to describe the NCS. Based on the obtained switched system model, a sufficient condition is derived for the closed-loop NCS to be exponentially stable and ensure a prescribed H ∞ performance level. The obtained condition establishes relations among the delay length, the delay variation frequency, and the system performances of the closed-loop NCS. Moreover, a convex optimization problem is formulated to design the H ∞ controllers which minimize the H ∞ performance level. An illustrative example is given to show the effectiveness of the proposed results.

Robust Information Fusion Estimator for Multiple Delay-Tolerant Sensors With Different Failure Rates

In this paper, the robust information fusion Kalman filtering problem is considered for multi-sensor systems with parameter uncertainties, randomly delayed measurements and sensor failures. The stochastic parameter perturbations are included in the state space models such that the proposed fusion estimator has robustness for the varying system parameters. For each observation subsystem, multiple binary random variables with known probabilities are introduced to model sensor failures and random delays in the measurements. Without resorting to the augmentation of system states and measurements, a robust optimal recursive filter for each subsystem is derived in the linear minimum variance sense by using the innovation analysis method, and the estimation error cross-covariance matrix between any two subsystems is given recursively. Based on the optimal fusion algorithm weighted by matrices, a robust distributed state fusion Kalman filter is derived for the considered system, and the dimension of the designed filter is the same as the original system, which can reduce computation costs as compared with the augmentation method. Moreover, the performance of the designed fusion filter is dependent on the sensor failure rates. Finally, two illustrative examples are given to show the effectiveness of the proposed method.

Group consensus in multi-agent systems with hybrid protocol

Abstract This paper investigates a group consensus problem with discontinuous information transmissions among different groups of dynamic agents. In the group consensus problem, the agents reach more than one consistent state asymptotically. We consider that the communication topology of these agents, represented by a network, is undirected. Then a novel group consensus protocol, called hybrid protocol, is proposed to solve the couple-group average-consensus problem. The convergence analysis is presented and the algebraic criterions are established. Furthermore, the multi-group consensus is discussed as an extension of the couple-group consensus. By similar techniques, some analysis results are presented. The analysis tools developed in this paper are based on algebraic graph theory, matrix theory, and control theory. Finally, the simulations are provided to demonstrate the effectiveness of the proposed theoretical results.

Moving Horizon Estimation for Networked Systems With Quantized Measurements and Packet Dropouts

This paper is concerned with the moving horizon estimation (MHE) problem for linear discrete-time systems with limited communication, including quantized measurements and packet dropouts. The measured output is quantized by a logarithmic quantizer and the packet dropout phenomena is modeled by a binary switching random sequence. The main purpose of this paper is to design an estimator such that, for all possible quantized errors and packet dropouts, the state estimation error sequence is convergent. By choosing a stochastic cost function, the optimal estimator is obtained by solving a regularized least-squares problem with uncertain parameters. The proposed method can be used to deal with the estimation and prediction problems for systems with quantized errors and packet dropouts in a unified framework. The stability properties of the optimal estimator are also studied. The obtained stability condition implicitly establishes a relation between the upper bound of the estimation error and two parameters, namely, the quantization density and the packet dropout probability. Moreover, the maximum quantization density and the maximum packet dropout probability are given to ensure the convergence of the upper bound of the estimation error sequence. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.

H∞ control for network-based systems with time-varying delay and packet disordering

Abstract The H∞ control problem is investigated in this paper for a class of networked control systems (NCS) with time-varying delay and packet disordering. A new model is proposed to describe the packet disordering phenomenon and then converted into a parameter-uncertain system with multi-step delay. Based on the obtained system model, a sufficient condition for robust stability of the NCS is derived. Furthermore, an optimization problem with linear matrix inequalities (LMIs) constraints is formulated to design the state feedback H∞ controller such that the closed-loop NCS is robust stable and has an optimal H∞ disturbance attenuation level. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed method.

Distributed Finite-Horizon Fusion Kalman Filtering for Bandwidth and Energy Constrained Wireless Sensor Networks

This paper is concerned with the distributed finite-horizon fusion Kalman filtering problem for a class of networked multi-sensor fusion systems (NMFSs) in a bandwidth and energy constrained wireless sensor network. To satisfy the finite communication bandwidth, only partial components of each local vector estimate are allowed to be transmitted to the fusion center (FC) at a particular time, while each sensor intermittently sends information to the FC for reducing energy consumptions. At the FC end, a novel compensation strategy is proposed to compensate the untransmitted components of each local estimates, then a recursively distributed fusion Kalman filter (DFKF) is derived in the linear minimum variance sense. Notice that the designed DFKF update does not need to know the transmitting situation of each component at a particular time, which means that the proposed fusion estimation algorithm is easily implemented for the addressed NMFSs. Since the performance of the designed DFKF is dependent on the selecting probability of each component, some criteria for the choice of probabilities are derived such that the mean squared errors (MSEs) of the designed DFKF are bounded or convergent. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.

Energy Efficient Distributed Filtering for a Class of Nonlinear Systems in Sensor Networks

In this paper, the distributed filtering problem is investigated for a class of nonlinear systems in the wireless sensor networks. The main goal is to design a set of distributed filters and simultaneously to prolong the lifetime of networks. In order to save limited power in sensors, signal quantization is employed to reduce packet size, where the robust control approach is used to handle the quantization error. Then, the communication rate change is allowed and treated under a switched system model. Sufficient conditions are derived such that the filtering error system is exponentially stable in the mean-square sense and achieves a prescribed H∞ performance level. The filter parameters are determined by solving an optimization problem. Finally, the effectiveness of the proposed results is demonstrated by two numerical examples.

One-step receding horizon H∞ control for networked control systems with random delay and packet disordering

The receding horizon H(∞) control (RHHC) problem is investigated in this paper for a class of networked control systems (NCSs) with random delay and packet disordering. A new model is proposed to describe the NCS with random delay which may be larger than one sampling period. The random delay is modeled as a Markov chain while the closed-loop system is described as a Markovian jump system. Sufficient conditions for the closed-loop NCS to be stochastically stable and the performance index to be upper bounded are derived by using the receding optimization principle. Furthermore, by solving a semi-definite programming (SDP) with linear matrix inequalities (LMIs) constraint, a piecewise-constant receding horizon H(∞) controller is obtained, and the designed piecewise-constant controller ensures that the closed-loop NCS achieves a prescribed H(∞) disturbance attenuation level. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.

Hierarchical Fusion Estimation for Clustered Asynchronous Sensor Networks

In this note, a hierarchical fusion estimation method is presented for clustered sensor networks with a very general setup where sensors (sensor nodes) and estimators (cluster heads) are allowed to work asynchronously with aperiodic sampling and estimation rates. A sequential measurement fusion (SMF) method is presented to design local estimators, and it is shown that the SMF estimator is equivalent to the measurement augmentation (MA) estimator in precision but with much lower computational complexity. Two types of sequential covariance intersection (CI) fusion estimators are presented for the fusion estimation. The proposed SCI fusion estimators provide a satisfactory estimation precision that is close to the centralized batch CI (BCI) estimator while requiring smaller computational burden as compared with the BCI estimator. Therefore, the proposed hierarchical fusion estimation method is suitable for real-time applications in asynchronous sensor networks with energy constraints. Moreover, the method is applicable to the case with packet delays and losses.