基于极值搜索的四旋翼无人机姿态跟踪控制

郭大力; 赵中原; 罗子娟

doi:10.11959/j.issn.2096-6652.202337

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基于极值搜索的四旋翼无人机姿态跟踪控制

学术论文 | 更新时间：2024-04-07

- 基于极值搜索的四旋翼无人机姿态跟踪控制
- Attitude tracking control for quadrotor UAVs based on extremum seeking
- 智能科学与技术学报 2023年5卷第4期页码：486-493
- 作者机构：
  
  1.南京信息工程大学自动化学院，江苏南京 210044
  2.中国电子科技集团公司第二十八研究所，江苏南京 210007
- 作者简介：
  
  [ "郭大力（1998- ），男，南京信息工程大学自动化学院硕士生，主要研究方向为四旋翼无人机控制、极值搜索算法。" ]
  [ "赵中原（1987- ），男，博士，南京信息工程大学自动化学院讲师、硕士生导师，主要研究方向为多智能体协同控制理论、微电网优化、无人系统集群控制等。" ]
  [ "罗子娟（1985- ），女，中国电子科技集团第二十八所信息系统工程重点实验室高级工程师，主要研究方向为智能图像处理。" ]
- 基金信息：
  
  江苏省自然科学基金项目(BK20200824);南京信息工程大学人才启动经费项目(2019r082)
- DOI：10.11959/j.issn.2096-6652.202337
  中图分类号： TP273
- 收稿：2023-04-04，
  
  修回：2023-07-05，
  
  纸质出版：2023-12-15
- 稿件说明：
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郭大力, 赵中原, 罗子娟. 基于极值搜索的四旋翼无人机姿态跟踪控制[J]. 智能科学与技术学报, 2023,5(4):486-493.

GUO Dali, ZHAO Zhongyuan, LUO Zijuan. Attitude tracking control for quadrotor UAVs based on extremum seeking[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 486-493.
郭大力, 赵中原, 罗子娟. 基于极值搜索的四旋翼无人机姿态跟踪控制[J]. 智能科学与技术学报, 2023,5(4):486-493. DOI： 10.11959/j.issn.2096-6652.202337.

GUO Dali, ZHAO Zhongyuan, LUO Zijuan. Attitude tracking control for quadrotor UAVs based on extremum seeking[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 486-493. DOI： 10.11959/j.issn.2096-6652.202337.

摘要

针对具有不确定性四旋翼无人机姿态跟踪问题，提出了基于极值搜索的鲁棒控制方法。首先，建立四旋翼无人机非线性姿态模型，并考虑模型参数的不确定性，设计鲁棒控制器来确保跟踪误差动态的有界性。然后，将鲁棒控制器与无模型学习算法结合，设计基于学习的控制器，从而自动迭代地调整鲁棒控制器的反馈增益，并在线优化期望性能成本函数。最后，通过MATLAB进行数值仿真，所述控制方法的系统稳态跟踪误差相较于经典鲁棒控制方法降低了0.246，证明了所述方法鲁棒性和优越性。

Abstract

A robust control method based on extremum seeking is proposed for the attitude tracking problem of quadrotor UAV with uncertainty. Firstly

a nonlinear attitude model of a four rotor UAV was established

and considering the uncertainty of model parameters

a robust controller was designed to ensure the dynamic boundedness of tracking errors. Secondly

a learning-based controller was designed by combining a robust controller with a model-free learning algorithm

which automatically and iteratively adjusted the feedback gain of the robust controller and optimized the expected performance cost function online. Finally

numerical simulations were performed using MATLAB

which demonstrated a reduction of 0.246 in the steady-state tracking error of the proposed control method compared to the classical robust control method. This result confirms the robustness and superiority of the proposed method.

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references

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