Practical prescribed time tracking control for a class of nonlinear systems with event triggering and output constraints

This paper investigates the practical prescribed time tracking for a class of uncertain nonlinear systems based on neural networks and event-triggered control. Introducing a time-varying constraint function transforms the original practical prescribed time-tracking control issue into a tracking error constraint problem. An event-triggered adaptive control has been proposed, which can effectively reduce the communication burden between the controller and the actuator. Using neural networks to approximate unknown nonlinear functions avoids the differentiation of virtual controllers, thereby reducing the computational burden. In addition, users can independently choose preset time and tracking accuracy without changing the control structure, which remains independent of the initial conditions and any design parameters. Finally, the effectiveness of this method is verified through simulation examples.