Neural Network Based Adaptive Event-Triggered Control for Quadrotor Unmanned Aircraft Robotics

With the aim of addressing problem trajectory tracking control quadrotor unmanned aircraft robots (UARs), in this study, we developed a neural network and event-triggering mechanism-based adaptive scheme for UAR system. The main technologies included are as follows. (1) Under condition that only quadrotor’s position information can be obtained, modified high-gain state observer-based dynamic surface (DSC) method was applied UARs acquired. (2) An event-triggered mechanism designed, which energy consumption greatly reduced communication efficiency between system terminal improved. (3) By selecting appropriate parameters, initial conditions laws, establishing observer, performance L? could achieved. Finally, simulation results hardware-in-loop strategy presented. propose here outperformed traditional backstepping sliding mode (BSMC) scheme.