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This paper investigates the distributed consensus problem for heterogeneous multi-agent systems via event-triggered scheme. To alleviate the communication burden, a distributed event-triggered consensus algorithm using an open-loop estimate and neighboring information is proposed. In the proposed event-triggered scheme, the control input of each agent is dependent on the estimate and information at discrete instants. Firstly, the novel event-triggered consensus algorithm is proposed for fixed topology, which involves the discrete communication between neighboring agents. Then, for the switching topology, the triggering condition is proposed by virtue of a topology-dependent dwell time approach. Further, the corresponding consensus procedures are given. Finally, the effectiveness of the proposed scheme is illustrated by numerical results.

Without the known state equation, a new state estimation strategy is designed to be against malicious attacks for cyber physical systems. Inspired by the idea of data reconstruction, the compressive sensing (CS) is applied to reconstruction of residual measurements after the detection and identification scheme based on the Markov graph of the system state, which increases the resilience of state estimation strategy against deception attacks. First, the observability analysis is introduced to decide the triggering time of the measurement reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over-completed dictionary by K-singular value decomposition (K-SVD), which is produced adaptively according to the characteristics of the measurement data. In addition, due to the irregularity of residual measurements, a sampling matrix is designed as the measurement matrix. Finally, the simulation experiments are performed on 6-bus power system. Results show that the reconstruction of measurements is completed well by the proposed reconstruction method, and the corresponding effects are better than reconstruction scheme based on the joint dictionary and the traditional Gauss or Bernoulli random matrix respectively. Especially, when only 29% available clean measurements are left, performance of the proposed strategy is still extraordinary, which reflects generality for five kinds of recovery algorithms.

Wireless sensor and actuator networks (WSANs) have a wide range of applications. To perform effective sensing and acting tasks, multiple coordination mechanisms among the nodes are required. As attempt in this direction, this paper describes collaborative estimation and control algorithms design for WSANs. First, a sensor-actuator coordination model is proposed based on distributed Kalman filter in federated configuration. This method provides the capability of fault tolerance and multi-source information fusion. On this basis, an actuator-actuator coordination model based on even-driven task allocation is introduced. Actuators utilize fused sensory information to adjust their action that incur the minimum energy cost to the system subject to the constraints that user’s preferences regarding the states of the system are approximately satisfied. Finally, according to system requirements, a distributed algorithm is proposed to solve the task allocation problem. Simulations demonstrate the effectiveness of our proposed methods.

This paper focuses on sensor scheduling and information quantization issues for target tracking in wireless sensor networks (WSNs). To reduce the energy consumption of WSNs, it is essential and effective to select the next tasking sensor and quantize the WSNs data. In existing works, sensor scheduling’ goals include maximizing tracking accuracy and minimizing energy cost. In this paper, the integration of sensor scheduling and quantization technology is used to balance the tradeoff between tracking accuracy and energy consumption. The main characteristic of the proposed schemes includes a novel filtering process of scheduling scheme, and a compressed quantized algorithm for extended Kalman filter (EKF). To make the algorithms more efficient, the proposed platform employs a method of decreasing the threshold of sampling intervals to reduce the execution time of all operations. A real tracking system platform for testing the novel sensor scheduling and the quantization scheme is developed. Energy consumption and tracking accuracy of the platform under different schemes are compared finally.

An energy-balanced multiple-sensor collaborative scheduling is proposed for maneuvering target tracking in wireless sensor networks (WSNs). According to the position of the maneuvering target, some sensor nodes in WSNs are awakened to form a sensor cluster for target tracking collaboratively. In the cluster, the cluster head node is selected to implement tracking task with changed sampling interval. The distributed interactive multiple model (IMM) filter is employed to estimate the target state. The estimation accuracy is improved by collaboration and measurement information fusion of the tasking nodes. The balanced distribution model of energy in WSNs is constructed to prolong the lifetime of the whole network. In addition, the communication energy and computation resource are saved by adaptively changed sampling intervals, and the real-time performance is satisfactory. The simulation results show that the estimation accuracy of the proposed scheme is improved compared with the nearest sensor scheduling scheme (NSSS) and adaptive sensor scheduling scheme (ASSS). Under satisfactory estimation accuracy, it has better performance in saving energy and energy balance than the dynamic collaborative scheduling scheme (DCSS).

A remote control system that can control a mobile robot in real time via the internet is proposed. To compensate for the network delay and counteract its impact on the teleoperation system, a predictive control scheme based on the modified Smith predictor proposed is selected. To ensure the stability and transparency of the system, a dynamic model manager is designed based on the information exchange between the sensors at the master and slave sides. To precisely predict the time delay, a new timer synchronization algorithm is proposed. To decrease delay-jitter, a new data buffer scheme is performed. Force feedback and a virtual predictive display are introduced to enhance the real-time efficiency of teleoperation. The usefulness and effectiveness of the proposed method and system are proven by teleoperation experiments via the internet over a long distance.

New delay-independent and delay-dependent stability criteria for linear systems with multiple time-varying delays are established by using the time-domain method. The results are derived based on a new-type stability theorem for general retarded dynamical systems and new analysis techniques developed in the author's previous work. Unlike some results in the literature, all of the established results do not depend on the derivative of time-varying delays. Therefore, they are suitable for the case with very fast time-varying delays. In addition, some remarks are also given to explain the obtained results and to point out the limitations of the previous results in the literature.