LAUSR

This brief presents an event-based adaptive containment control scheme for nonlinear multi-agent systems with periodic disturbances and unknown coefficients. To decrease some unnecessary data transmissions, an improved event-triggered mechanism is introduced, i.e., a dynamically adjusted threshold is designed in the event-triggered condition. Then, a first-order sliding-mode differentiator is employed to address the “explosion of complexity” problem, and neural networks and fourier series expansion are combined to model the uncertain nonlinear dynamics subject to periodically time-varying disturbances. Stability analysis verifies that the containment errors are bounded, and a practical simulation is implemented to evaluate the validity of the developed scheme.