Predefined-Time Neural Adaptive Quantized Self- Triggered Control With Appointed Performance for a 2-DOF Helicopter System

This article investigates the predefined-time neural adaptive self-triggered quantized control design for a two-degree of freedom (2-DOF) helicopter system with appointed tracking performance. To begin with, a fixed-time performance function is integrated into the predefined-time dynamic surface control (PTDSC) framework, and the transient fluctuations of the tracking error can be effectively constrained by the performance function. Then, a neural adaptive self-triggered quantized resilient controller is designed, where the self-triggered mechanism and input quantization are incorporated into to recursive control design to reduce the bandwidth consumption. Furthermore, the obtained stability results have proved that the designed controller ensures all the signals in the closed-loop system are practical predefined-time bounded (PPTB), and the tracking errors can be strictly confined to a region with preset transient and steady-state performance, even under limited communication resources. Finally, the simulation results demonstrate the effectiveness of the proposed control scheme.