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This study is concerned with dynamic output feedback (DOF) H∞ control for continuous-time networked control systems (NCSs). The packet dropouts and network-induced delays in the sensor-to-controller channel, and network-induced delays in the controller-to-actuator channel are taken into account. A linear estimation-based method is proposed to estimate the measurement output, and the non-uniform distribution characteristic of the measurement output arrival instant is taken into full consideration to establish a new model for the considered NCSs. Based on the newly established model and a new Lyapunov functional, the problem of DOF controller design is studied. A numerical example is given to illustrate the effectiveness of the proposed DOF controller design. [ABSTRACT FROM AUTHOR]

This study is concerned with the problem of dual communication channels sharing-based network-induced delay and packet dropout compensation for continuous-time networked control systems (NCSs). By introducing the dual communication channels sharing-based compensation method, a new closed-loop model for continuous-time NCSs, which can receive more than one control input during a control input updating time interval, is established. Based on the newly established model and a new Lyapunov functional, an effective controller design criterion is derived to optimise the H1 performance of the considered systems. Even for NCSs without considering network-induced delay and packet dropout compensation, the newly derived controller design method is also applicable. Two numerical examples are given to illustrate the merits and effectiveness of the proposed compensation method. [ABSTRACT FROM AUTHOR]

Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy-efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption. [ABSTRACT FROM AUTHOR]