Adaptive finite-time event-triggered control for nonlinear systems with quantized input and periodic disturbances

This work emphasizes an adaptive tracking control for nonlinear networked systems that have periodic disturbances and have restricted network channels. An approximator which calculate the estimation of unknown nonlinear function with time-varying disturbance is constructed with the aid of a combined Fourier series expansion and radial basis functions neural networks method. In order to diminish the number of released data when the system tends to stabilize, a dynamic threshold is determined to replace the state-dependent one, and then the event-triggered control was incorporated into the controller constructing process according to backstepping techniques. Lastly, a practical simulation experiment is employed that can confirm the feasibility of the adaptive control approach.