In this study, wireless sensor networks and time base generators are used to solve the fixed-time containment control problem in multi-agent systems with fixed topologies. A new event-triggered control protocol is proposed, which combines a fully distributed method and a time base generator (TBG). The goal is to converge the states of all followers to the convex hull formed by the leader. The controller reduces communication and improves control efficiency by integrating a fully distributed control mechanism using wireless sensor networks. In addition, a time base generator (TBG) is added to ensure that the dwell time continues to be pre-specified and independent of initial conditions. Using matrix theory, the original system is transformed into an error system, and its stability is analyzed by the Lyapunov method. The necessary and sufficient conditions for solving the time consensus containment control problem in multi-agent systems are determined and Zeno behavior is avoided. The effectiveness of the proposed control algorithm is illustrated by numerical examples.

This paper presents an advanced virtual inertia control strategy for wind farms to provide transient power support in the presence of frequency events, where a fuzzy active disturbance rejection controller is developed to enable the operation of an energy storage system (ESS) so as to provide support for frequency regulation of the power grid. To effectively estimate the system frequency under uncertain noises, fuzzy rules are presented to adaptively tune the parameters of the extended state observer and to realize the power-sharing, to improve the anti-interference ability of the wind power system. Finally, simulation analysis in MATLAB/Simulink is provided to validate the effectiveness of the proposed control strategy. Simulation results show that the developed virtual inertia control strategy based on fuzzy active disturbance rejection control has a good inertia support capability. In addition, the proposed method is able to improve the anti-noise capability of the wind power system.