基于改进人工势场法和自适应神经网络的共轴双旋翼无人机避障飞行控制  

Obstacle Avoidance and Flight Control of Coaxial Rotor UAV Based on Improved Artificial Potential Field Method and Adaptive Neural Network

作　　者：危怡然 吴碧 邓宏彬[1] 潘振华 WEI Yiran;WU Bi;DENG Hongbin;PAN Zhenhua(School of Mechatronical Engineering,Beijing Institute of Technology,Beijing 100081,China;Beijing Blue Sky Innovation Center for Frontier Science,Beijing 100160,China;School of Automation,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]北京理工大学机电学院,北京100081 [2]北京蓝天前沿科技创新中心,北京100160 [3]北京理工大学自动化学院,北京100081

出　　处：《信息与控制》2023年第2期154-165,共12页Information and Control

摘　　要：针对共轴双旋翼无人机(coaxial rotor unmanned aerial vehicle, CR-UAV)在未知和危险环境中飞行避障的控制问题,基于飞行态势图和改进人工势场算法,提出了一种新的避障方法。首先,该方法通过飞行态势图对障碍物信息进行建模,考虑了CR-UAV飞行控制的约束条件,可使无人机有效利用障碍物信息进行避障,避免了局部极小点的问题,无人机的控制和避障能力显著提高;其次,采用基于径向基函数的神经网络(RBFNN)对CR-UAV飞行中的干扰进行估计并实时补偿,采用非线性状态误差反馈实现CR-UAV的姿态跟踪控制,从而保持无人机避障飞行过程中的姿态稳定;最后,进行了仿真实验。从仿真结果可以看出,无人机在避障飞行具有良好的姿态稳定能力。We propose a method based on a flight situation diagram and an improved artificial potential field algorithm for obstacle avoidance of coaxial rotor unmanned aerial vehicle(CR-UAV)flying in unknown and dangerous environments.First,a flight situation diagram is used to model obstacle information that considers the constraint conditions of flight control of CR-UAVs.By using this obstacle information,the CR-UAV can effectively avoid obstacles,avoid falling into a local minimum,and enhance its control and obstacle avoidance ability.Second,the CR-UAV adopts the unknown parameter adaptive control,which is based on radial basis function neural network(RBFNN)approximation,to approximate estimation and real-time compensation of disturbances for obstacle avoidance.Attitude tracking is realized using the method of state error feedback,which can facilitate attitude stability in obstacle avoidance flight.Finally,we carry out some simulation experiments.The results of our simulation experiments show that the CR-UAV has a good ability for attitude stability in obstacle avoidance flight.

关 键 词：共轴双旋翼无人机 飞行态势图 人工势场 避障 基于径向基函数的神经网络 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]