Dynamic Event-Triggered Adaptive Neural Practical Predefined-Time Control for Uncertain Nonlinear Systems With Unknown Control Directions

It is nontrivial to achieve practical predefined-time (PPT) control for uncertain nonlinear systems (UNSs) in strict-feedback form. The main technical difficulty is that there is no relevant lemma to prove the predefined-time stability of the UNS with unknown control directions. This article proposes an adaptive neural predefined-time control scheme, capable of achieving PPT control for UNS with unknown control directions. The presented scheme is derived from a PPT function, associating with backstepping technique, generating a predefined-time control solution. Meanwhile, a dynamic event-triggered schedule is developed to update the control law, therefore reducing data transmission. Finally, an illustrative example is given to illustrate the effectiveness of the presented scheme.