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

Abstract

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