| 引用本文: | 刘多能,侯中喜,郭正,等.动态滑翔运动建模、机理分析与航迹优化.[J].国防科技大学学报,2016,38(5):78-85.[点击复制] |
| LIU Duoneng,HOU Zhongxi,GUO Zheng,et al.Motion modeling, mechanism analysis and trajectory optimization for dynamic soaring[J].Journal of National University of Defense Technology,2016,38(5):78-85[点击复制] |
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| 动态滑翔运动建模、机理分析与航迹优化 |
| 刘多能, 侯中喜, 郭正, 杨希祥, 高显忠 |
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(国防科技大学 航天科学与工程学院， 湖南 长沙 410073)
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| 摘要: |
| 信天翁凭借动态滑翔的飞行技巧从梯度风中获取能量，从而在几乎不拍翅膀的情况下进行长时间、长距离飞行，这种技巧应用于小型无人机上可拓展其完成任务的能力。基于飞行器动力学对梯度风场中的无人机运动方程进行推导和简化处理；利用简化的运动方程，分别从非惯性参考系中的动能定理和机械能变化的角度，对动态滑翔获取能量的机理进行分析；利用微分平坦法，以最小平均控制输入变化率为目标函数，对徘徊模式和平移模式的动态滑翔航迹进行优化计算。分析结果表明：逆风爬升、顺风下滑是动态滑翔基本获能方式。优化结果表明：控制输入变得更加平滑，甚至出现阶段性的常值，使得控制更加简化；徘徊模式下，当风梯度作为决策变量时，优化过程可在［0，0.5 s-1］的区间上找到使得目标函数值最小的风梯度；平移模式下，目标函数值在该区间上单调递减。 |
| 关键词: 动态滑翔 运动建模 航迹优化 无人机 |
| DOI：10.11887/j.cn.201605013 |
| 投稿日期：2015-05-29 |
| 基金项目:国家自然科学基金资助项目（11102229,11602298） |
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| Motion modeling, mechanism analysis and trajectory optimization for dynamic soaring |
| LIU Duoneng, HOU Zhongxi, GUO Zheng, YANG Xixiang, GAO Xianzhong |
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(College of Aerospace Sciences and Engineering， National University of Defense Technology， Changsha 410073， China)
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| Abstract: |
| Albatrosses use a flight manoeuvre which is called the dynamic soaring, to gain energy from horizontal wind gradient so as to travel for a very long journey and the period almost goes on without making stopovers or flapping their wings. Dynamic soaring is considered a promising technique which can be widely applied to UAV (unmanned aerial vehicle) for extending mission capabilities. The EOM (equation of motion) of a small UAV in the gradient wind field was derived and simplified in the air path frame of axes based on the flight dynamics. According to the theorem of kinetic energy and mechanical energy variation with respect to the noninertial frame of reference respectively, the energy gain mechanism during dynamic soaring was analyzed by using the simplified EOM. The differential flatness method was employed to solve loiter pattern and travel pattern trajectories for the objective function of minimum average change rate of control inputs. The analysis result indicates that the upwind climb and downwind dive is the basic energy gain ways of dynamic soaring. The optimal results show that the control inputs are smoothed，even the staged constant inputs to make the actual control simpler. In the optimization of loiter pattern，when the wind gradient is treated as a decision variable, the optimization process finds the optimal wind gradient in the range of ［0，0.5 s-1］ for the objective function. While in the optimization of travel pattern, the value of the objective function is monotonically decreasing in the same range. |
| Keywords: dynamic soaring motion modeling trajectory optimization unmanned aerial vehicle |
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