Minimum-Parameter-Learning-Based Adaptive Neural Finite-Time Control for Uncertain Nonlinear Systems With Dynamic Event-Triggered Input

Abstract

This article investigates the finite-time event-triggered controller design with minimum learning parameters (MLP) for nonlinear systems using neural networks in the presence of uncertainty. Specifically, firstly, the neural networks are devised to compensate online for the uncertain nonlinear functions. Then, a finite-time prescribed performance function is employed in the controller design to achieve that the tracking error converges to within a prescribed region at any setting time. At the same time, the transient responses (e.g., maximum overshoot and convergence speed) can be enhanced for the tracking error. After that, unlike ordinary dynamic event-triggered strategy, the developed dynamic event-triggered methodology can further increase the triggering interval, which leads to the network bandwidth can be effectively saved. Moreover, one can prove that all the closed-loop signals remain bounded and the Zeno phenomenon can be excluded. Finally, the advantages of the proposed strategy can be illustrated by two examples.