Linearized modeling and gain scheduling control for UAV taxiing

DUAN Zhen; GAO Jiu-zhou; JIA Hong-guang; CHEN Juan

doi:10.3788/OPE.20142206.1507

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Linearized modeling and gain scheduling control for UAV taxiing

更新时间：2020-08-13

- Linearized modeling and gain scheduling control for UAV taxiing
- Optics and Precision Engineering Vol. 22, Issue 6, Pages: 1507-1516(2014)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院大学北京,中国,100049
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20142206.1507
  CLC： TP391;V249.1
- Received：11 December 2013，
  
  Revised：27 January 2014，
  
  Published：25 June 2014
- 稿件说明：
移动端阅览
段镇, 高九州, 贾宏光等. 无人机滑跑线性化建模与增益调节纠偏控制[J]. 光学精密工程, 2014,22(6): 1507-1516

DUAN Zhen, GAO Jiu-zhou, JIA Hong-guang etc. Linearized modeling and gain scheduling control for UAV taxiing[J]. Editorial Office of Optics and Precision Engineering, 2014,22(6): 1507-1516
段镇, 高九州, 贾宏光等. 无人机滑跑线性化建模与增益调节纠偏控制[J]. 光学精密工程, 2014,22(6): 1507-1516 DOI： 10.3788/OPE.20142206.1507.

DUAN Zhen, GAO Jiu-zhou, JIA Hong-guang etc. Linearized modeling and gain scheduling control for UAV taxiing[J]. Editorial Office of Optics and Precision Engineering, 2014,22(6): 1507-1516 DOI： 10.3788/OPE.20142206.1507.

摘要

由于无人机的三轮滑跑阶段是整个飞行过程最容易出问题的环节，本文对前三点式无人机地面滑跑进行建模并研究了前轮转向纠偏控制方法。首先，分析了无人机三轮滑跑阶段的受力情况；考虑发动机扭矩、推力偏心及停机角对模型的影响，建立了无人机地面三轮滑跑非线性数学模型；然后，利用小扰动原理在合理简化的前提下将非线性模型线性化，分别推导了前轮转角为输入，偏航角速度、偏航角、侧偏距为输出的传递函数，设计了三回路增益调节前轮转向纠偏控制律；最后，通过滑跑试验进行了验证。试验结果表明：在初始航向偏差为3°，初始侧向位置偏差为0.2 m的情况下，无人机由静止滑行至速度为32 m/s过程中的最大侧偏距为0.3 m，最大偏航角为4.5°，并且对不大于4.6 m/s的侧风干扰有较好的抑制作用，对跑道路况、轮胎侧偏刚度及轮胎弹性等不确定因素的影响有较好的鲁棒性。该设计已成功应用于某无人机。

Abstract

As the three wheel taxiing stage of an Unmanned Aerial Vehicle (UAV) is the most vulnerable in whole flight process

this paper explores linear modeling and a gain scheduling control method of nose wheel steering turning for a tricycle-undercarriage UAV in taxiing on the ground. The force of three wheel taxiing of the UAV was analyzed

and its nonlinear mathematical model was established considering the effect of engine torque

thrust misalignment and ground angles. Then

the nonlinear model was linearized using small perturbation theory under reasonable assumptions

and the transfer function was deduced by using the nose wheel steering angle as a input and the yaw rate

yaw angle

and lateral deviation as outputs. The three gain scheduling control law through nose wheel steering turning was designed. Finally

the method was verified through a field taxiing test. The result shows that the most lateral position deviation is 0.3 m and the most yaw deviation is 4.5° under an initial yaw deviation of 3° and a lateral position deviation of 0.2 m during the process of taxiing from rest to the speed of 32 m/s. Moreover

the crosswind disturbance of not more than 4.6 m/s was inhibited. The control law designed by using the method above is robustness to the uncertain factors of the runway

tire cornering stiffness and tire deflection and has been applied to a practical UAV successfully.

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references

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