Event-triggered fixed-time adaptive neural control for time-delay nonlinear systems

This paper investigates the issue of event-triggered fixed-time adaptive neural control for time-delay nonlinear systems. First, the radial basis function neural networks (RBFNNs) are employed to approximate uncertain nonlinearities. Then, the effect of input delay is solved via the Pade approximation method. Moreover, an event-triggered mechanism is incorporated into controller to avoid the over-consumption of network resources. Based on Lyapunov stability theory and the fixed-time command filtering technology, the designed controller can ensure the boundedness of all closed-loop signals, and handle the issue of “explosion of complexity”. Finally, a practical instance is simulated to demonstrate the usefulness of the designed controller.