Observer-based self-triggered adaptive neural network control for nonlinear systems with prescribed time

This paper is concerned with the adaptive neural networks (NNs) prescribed-time control problem for a class of strict-feedback nonlinear systems subject to unmeasured states via the self-triggered control (STC). By developing a new state observer with prescribed-time function, an adaptive NNs self-triggered controller is designed to solve the problem of prescribed-time performance (PTP). Due to the initiative of the STC, it has excellent practical significance in terms of contracting computing resources and network communication resources. With the proposed new strategy, the PTP of the closed-loop system can be guaranteed, and all the signals within the closed-loop system are bounded. Finally, the practicability and effectiveness of the above prescribed-time STC algorithm are verified via some physical simulations.