引用本文:王娟,赵成璟,杨智杰.基于MPC迭代学习的四旋翼无人机轨迹跟踪控制[J].控制理论与应用,2025,42(12):2528~2534.[点击复制]
WANG juan,ZHAO Cheng-jing,YANG Zhi-jie.Trajectory tracking control of quadcopter UAV based on MPC iterative learning[J].Control Theory & Applications,2025,42(12):2528~2534.[点击复制]
基于MPC迭代学习的四旋翼无人机轨迹跟踪控制
Trajectory tracking control of quadcopter UAV based on MPC iterative learning
摘要点击 142  全文点击 19  投稿时间:2024-06-04  修订日期:2025-11-14
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DOI编号  10.7641/CTA.2025.40310
  2025,42(12):2528-2534
中文关键词  模型预测控制  迭代学习  四旋翼无人机  轨迹跟踪
英文关键词  model predictive control  iterative learning  quadcopter UAV  trajectory tracking
基金项目  机器人学国家重点实验室开放基金项目(2024–O22),辽宁省自然科学基金项目(2023–MS–224)资助.
作者单位E-mail
王娟* 沈阳建筑大学电气与控制工程学院 wangjuanneu@163.com 
赵成璟 大连华锐重工股份有限公司  
杨智杰 沈阳建筑大学电气与控制工程学院  
中文摘要
      本文研究四旋翼无人机在外部扰动影响下的轨迹跟踪问题,提出一种基于MPC的迭代学习方法.首先,设 计MPC控制器作为四旋翼无人机系统的反馈控制器,且在MPC反馈环节引入预测误差进行反馈矫正.在此基础上, 设计迭代学习控制器作为无人机系统的前馈控制器;采用双曲正切函数对迭代学习控制器的学习增益进行优化, 减少迭代次数的同时能够提高轨迹跟踪精度.MPC反馈和迭代前馈的复合控制能够很好的克服外部扰动带来的影 响, 实现无人机高精度的轨迹跟踪.最后,通过仿真验证本文所提方法的可行性和有效性,通过实验对比,本文设计 的方法跟踪精度更高,具有强鲁棒性.
英文摘要
      This paper investigates the trajectory tracking problem of quadcopter UAV under external disturbances and proposes an iterative learning control method based on MPC. Firstly, design the MPC controller as the feedback controller of the quadcopter UAV system, and introduce prediction error into the MPC feedback loop for feedback correction. On this basis, the iterative learning controller is designed as the feedforward controller of the quadcopter UAV system. Employing hyperbolic tangent function optimizes the learning gain of the iterative learning controller, which can reduce the number of iterations and improve trajectory tracking accuracy. The composite control of MPC feedback and iterative feedforward can effectively overcome the impact of external disturbances and achieve high-precision trajectory tracking of UAV. Finally, the feasibility and effectiveness of the proposed method are verified through the simulation. By the experimental comparison, the method designed in this paper has higher tracking accuracy and strong robustness.