Wavelet Neural Network based adaptive event triggered control scheme for a class of nonlinear systems with uncertain dynamics

In this paper, an adaptive event-triggered control approach for a class of unknown dynamics networked stringent feedback nonlinear systems is developed. The approximation of system uncertainties by a wavelet neural network (WNN) frequently presents a significant obstacle in the development of a precise control strategy. In order to guarantee the specified system performance and Zeno-free behaviour of networked control systems, we build an adaptive event triggering mechanism that is enhanced with WNN and outfitted with predetermined event triggering circumstances. In order to ensure the uniform ultimate boundedness (UUB) of all closed loop signals, the controller works to reduce the amount of information exchanged between the sensor and the controller. We offer numerical simulations to demonstrate the efficiency of the suggested plan.