引用本文:蔡佳明,鲜 斌.基于事件驱动的无人机吊挂系统在线自适应轨迹规划[J].控制理论与应用,2024,41(5):817~828.[点击复制]
CAI Jia-ming,XIAN Bin.Event-driven based online adaptive trajectory planning for the UAV slung-payload transportation system[J].Control Theory and Technology,2024,41(5):817~828.[点击复制]
基于事件驱动的无人机吊挂系统在线自适应轨迹规划
Event-driven based online adaptive trajectory planning for the UAV slung-payload transportation system
摘要点击 3107  全文点击 106  投稿时间:2022-05-22  修订日期:2024-04-08
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DOI编号  10.7641/CTA.2023.20423
  2024,41(5):817-828
中文关键词  轨迹规划  事件驱动  无人机吊挂系统  摆动抑制
英文关键词  trajectory planning  event-driven  UAV slung payload system  swing suppression
基金项目  国家重点研发计划项目(2018YFB1403900), 国家自然科学基金项目(91748121, 90916004)
作者单位E-mail
蔡佳明 天津大学  
鲜 斌* 天津大学 电气自动化与信息工程学院 xbin@tju.edu.cn 
中文摘要
      本文针对四旋翼无人机吊挂系统空运过程中负载摆角的非线性最优调节问题, 提出了一种基于事件驱动的在线自适应轨迹规划策略. 在事件驱动的自适应评价网框架下, 利用神经网络的逼近学习能力得到吊挂负载摆动抑制的折现最优控制律. 同时结合该控制律进一步规划四旋翼无人机的飞行轨迹, 实现了对无人机位置的精确调节和吊挂负载摆动的快速抑制, 并且显著降低了无人机机载处理器的计算负担. 然后采用基于Lyapunov稳定性的分析方法, 证明了神经网络输出权值估计误差一致最终有界, 并证明了无人机位置跟踪误差和吊挂负载摆动运动的收敛. 最后, 通过飞行对比实验验证了所提出的轨迹规划策略的有效性.
英文摘要
      This paper proposes a new event-driven based online trajectory planning strategy for the nonlinear optimal adjustment control of the payload’s swing motion for the quadrotor slung-payload transportation system. By using the approximate structure of the neural network, the optimal control law of the payload’s swing angle is obtained by training the neural network under the framework of the event-driven adaptive critic network. At the same time, the flight trajectory of the quadrotor unmanned aerial vehicle (UAV) is further planned based on the optimal control law, which achieves accurate position regulation of the UAV and fast suppression of the payload’s swing motion during the flight while the computation cost of the UAV’s airborne processor is reduced significantly. Then, it is proved that the output weight estimation error of the improved neural network is uniformly ultimately bounded, and the convergence of the quadrotor’s positioning and payload’s swing suppression is proved via the Lyapunov based stability analysis. Finally, flight experimental results are presented to validate the effectiveness of the proposed trajectory planning strategy comparing with other methods.