电光与控制, 2019, 26 (9): 98, 网络出版: 2021-01-31
基于小脑模型四旋翼无人机高度跟踪控制研究
Height Tracking Control of Quadrotor UAVs Based on CMAC
摘要
针对四旋翼无人飞行器的强耦合、欠驱动、非线性强以及参数不确定等因素, 将小脑模型(CMAC)神经网络算法引入系统, 并与传统PD控制算法结合, 以改善系统的动静态性能。以传统PD控制实现对高度的反馈控制, 以CMAC神经网络进行前馈控制, 实现对高度模型的逆模型控制。仿真结果表明,该方法较传统PID控制的动态过程超调量小、响应快速, 且稳定性好, 系统抗干扰能力强。
Abstract
Considering the problems of strong coupling, underactuation, nonlinearity and parameter perturbations in the system of quadrotor Unmanned Aerial Vehicles (UAVs), the Cerebellar Model Articulation Controller (CMAC) is introduced into the system and combined with the traditional PD controller to improve the static and dynamic performance of quadrotor UAV. The traditional PD controller is used to achieve feedback control of the height, and CMAC is for the feedforward control to realize inverse control of height mathematic model. The simulation results prove that: Compared with the traditional PID controller, our control method has less overshoot, faster response, better stability and stronger anti-jamming ability.
王辉, 刘红霞, 费致根, 王建辉, 张九江. 基于小脑模型四旋翼无人机高度跟踪控制研究[J]. 电光与控制, 2019, 26(9): 98. WANG Hui, LIU Hongxia, FEI Zhigen, WANG Jianhui, ZAHNG Jiujiang. Height Tracking Control of Quadrotor UAVs Based on CMAC[J]. Electronics Optics & Control, 2019, 26(9): 98.