基于仿生技术的尾座式VTOL无人机起降稳定控制及轨迹优化  

Stability Control and Trajectory Optimization of Tail⁃Sitter VTOL UAV Takeoff/Landing Incorporating Bionic Technology

作　　者：齐浩 李青洋 吴嘉越 彭一明[1,2] 聂宏[1,2] 魏小辉[1,2] QI Hao;LI Qingyang;WU Jiayue;PENG Yiming;NIE Hong;WEI Xiaohui(State Key Laboratory of Mechanics and Control for Aerospace Structures,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,P.R.China;Key Laboratory of Fundamental Science for National Defense Advanced Design Technology of Flight Vehicle,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,P.R.China;China Aviation Power Plant Research Institute,Zhuzhou 412002,P.R.China;School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,P.R.China)

机构地区：[1]南京航空航天大学航空航天结构力学及控制全国重点实验室,南京210016 [2]南京航空航天大学飞行器先进设计技术国防重点学科实验室,南京210016 [3]中国航空动力机械研究所,株洲412002 [4]北京航空航天大学自动化科学与电气工程学院,北京100191

出　　处：《Transactions of Nanjing University of Aeronautics and Astronautics》2024年第3期263-277,共15页南京航空航天大学学报（英文版）

基　　金：This work was supported by the National Defense Outstanding Youth Science Foundation(No.2018-JCJQ-ZQ-053);the National Natural Science Foundation of China(No.52275114);the China Postdoctoral Science Foundation Funded Project(No.2019M651827);the Priority Academic Program Development of Jiangsu Higher Education Institutions.

摘　　要：尾座式垂直起降(Vertical takeoff/landing,VTOL)无人机(Unmanned aerial vehicle,UAV)稳定性差,与传统起落架的适配性低。针对上述问题,提出了一种采用自由尾翼技术的新型起落架。该起落架采用串联多级结构,在降低机身高度的同时,保证了巡航状态下尾翼力臂的长度,并通过虚拟中心点力分布技术对动态着陆过程进行调控,简化了控制过程,有利于模式转换过程中的无缝轨迹优化。随后基于猫中心点单点数据集,利用神经网络对起落架控制进行训练,有效提高了起落架自适应起落速度和精度。最后,结合无人机不同模式的参数要求,进行多目标优化和相似性转换,有效提高了尾座式垂直起降无人机的着陆适应性和稳定性。The tail-sitter vertical takeoff/landing(VTOL)unmanned aerial vehicle(UAV)exhibits poor stability and limited compatibility with traditional landing gears.To address the aforementioned issues,a novel landing gear incorporating free-tail technology is proposed.The landing gear adopts a tandem multi-stage structure,which can ensure the length of the tail force arm in cruise condition while lowering the fuselage altitude.Furthermore,the dynamic landing process is regulated through the employment of virtual centroid force distribution techniques,streamlining the control process and facilitating seamless trajectory optimization during mode transition.Based on the single-point dataset of the cat center point,a neural network is used to train the landing gear control,which makes the landing gear adaptive takeoff and landing speed and accuracy effectively improved.Subsequently,multi-objective optimization and similarity conversion are executed in conjunction with parameter requirements of different modes of the UAV,effectively enhancing landing adaptability and stability of the tail-sitter VTOL UAV.

关 键 词：无人机 垂直起降 起飞/着陆技术 稳定性控制 仿生技术 

分 类 号：V279[航空宇航科学与技术—飞行器设计] V249.1