航空学报 > 2023, Vol. 44 Issue (S2): 729950-729950   doi: 10.7527/S1000-6893.2023.29950

基于MPCC的鸭翼尾座式垂直起降无人机轨迹跟踪控制算法

曹煜琪1,2, 付皓然1, 高飞2, 吕熙敏1()   

  1. 1.中山大学·深圳 智能工程学院,深圳 518107
    2.浙江大学 控制科学与工程学院,杭州 310007
  • 收稿日期:2023-12-07 修回日期:2023-12-08 接受日期:2023-12-24 出版日期:2023-12-25 发布日期:2024-01-04
  • 通讯作者: 吕熙敏 E-mail:lvxm6@mail.sysu.edu.cn
  • 基金资助:
    深圳市优秀科技创新人才培养项目(RCBS20221008093104017)

Trajectory tracking control algorithm for canard⁃equipped tail⁃sitting vertical takeoff and landing UAV based on MPCC

Yuqi CAO1,2, Haoran FU1, Fei GAO2, Ximin LYU1()   

  1. 1.School of Intelligent Systems Engineering,Shenzhen Campus of Sun Yat?sen University,Shenzhen 518107,China
    2.College of Control Science and Engineering,Zhejiang University,Hangzhou 310007,China
  • Received:2023-12-07 Revised:2023-12-08 Accepted:2023-12-24 Online:2023-12-25 Published:2024-01-04
  • Contact: Ximin LYU E-mail:lvxm6@mail.sysu.edu.cn
  • Supported by:
    Outstanding Science and Technology Innovation Talent Cultivation Program of Shenzhen(RCBS20221008093104017)

摘要:

目前针对鸭翼尾座式垂直起降无人机(UAV)的高速轨迹跟踪控制还没有成熟的解决方案。本文设计了一种轮廓模型预测控制(MPCC)以实现无人机的轨迹跟踪控制。给定一段轨迹,此控制器能够预测并选择最优的状态和输出,使得无人机能够最大化自己的飞行速度和最小化自己离轨迹的距离。通过调整飞行速度和距离误差的权重参数,无人机能够平衡两者的侧重点,以适应不同的飞行环境。另外,本文将此优化问题进行线性化,使其转化为一个凸二次规划问题,以减小求解时间。最终通过仿真实验跟踪不同的轨迹,验证了算法的有效性。

关键词: 尾座式无人机, 垂直起降, 鸭翼布局, 轮廓模型预测控制, 二次规划

Abstract:

Currently, there is no mature solution for high-speed trajectory tracking control of canard-equipped tail-sitter vertical takeoff and landing Unmanned Aerial Vehicle (UAV). This paper proposes a Model Predictive Contouring Control (MPCC) for achieving trajectory tracking control of the UAV. Given a trajectory segment, this controller predicts and selects optimal states and outputs, allowing the UAV to maximize its flight velocity and minimize its deviation from the trajectory. By adjusting the weight parameters of flight velocity and distance error, the UAV can balance the emphasis between the two aspects to adapt to various flight environments. Additionally, this paper linearizes the optimization problem by transforming it into a convex quadratic programming problem to reduce computation time. Finally, through simulation experiments involving various trajectories, the effectiveness of the algorithm is verified.

Key words: tail-sitter UAV, vertical takeoff and landing, canard layout, MPCC, quadratic programming

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