Event-based adaptive neural network asymptotic tracking control for a class of nonlinear systems

In this work, an event-triggered adaptive neural network asymptotic tracking control scheme is developed for non-lower-triangular nonlinear systems by using the command-filtered backstepping technique. To reduce the communication burden and unnecessary waste of communication resources, an event-triggered control signal based on a relative threshold is designed. In the design process, neural networks are used to approximate the nonlinear function existing in the system, and the upper bounds for the approximation error and the external disturbance together form an adaptive law with one parameter to achieve the asymptotic tracking performance. Additionally, the problem of “explosion of complexity” is avoided by utilizing the command-filtered technique in the backstepping framework. Based on the Lyapunov stability theory and Barbalat's lemma, this developed scheme guarantees that the tracking error asymptotically converges to zero. At the end, two simulation examples are shown to verify the effectiveness of the control method.