Dynamic Event-Triggered Fixed-Time Neural Adaptive Containment Control for Nonlinear Multiagent Systems With Input Saturation

This study concerns the topic of event-based practical fixed-time containment control for uncertain nonstrict feedback nonlinear multiagent systems with input saturation under the directed graph. The algebraic ring problem in recursive design process can be effectively resolved using Gaussian function's property. An auxiliary signal has been introduced that is practical fixed-time stable and capable of overcoming symmetrical or asymmetrical input saturation. Then, an event-triggered neural adaptive fixed-time cooperative tracking control scheme is developed via dynamic surface control and dynamic event-triggered mechanism. The raised fixed-time control strategy can not only remove the singularity issue but also circumvent the complex explosion problem of backstepping technique. Moreover, all variables in the closed-loop system are proved to be fixed-time bounded by integrating the designed auxiliary signal into the entire Lyapunov function. Finally, simulation results validate the effectiveness of the presented control method.