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中国工业与应用数学学会会刊
主管:中华人民共和国教育部
主办:西安交通大学
ISSN 1005-3085  CN 61-1269/O1

工程数学学报 ›› 2023, Vol. 40 ›› Issue (5): 699-714.doi: 10.3969/j.issn.1005-3085.2023.05.002

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四旋翼飞行器事件触发轨迹跟踪控制

叶佩芸,  于  洋,  王  巍   

  1. 辽宁工业大学电气工程学院,锦州 121001
  • 收稿日期:2021-03-24 接受日期:2021-07-02 出版日期:2023-10-15 发布日期:2023-12-15
  • 通讯作者: 于 洋 E-mail: am_yuyang@163.com
  • 基金资助:
    国家自然科学基金(62273170);辽宁省自然科学基金(2023-MS-300).

Event-triggered Control for Trajectory Tracking of Quadrotor Unmanned Aerial Vehicle

YE Peiyun,  YU Yang,  WANG Wei   

  1. School of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001
  • Received:2021-03-24 Accepted:2021-07-02 Online:2023-10-15 Published:2023-12-15
  • Contact: Y. Yu. E-mail address: am_yuyang@163.com
  • Supported by:
    The National Natural Science Foundation of China (62273170); the Natural Science Foundation of Liaoning Province (2023-MS-300).

摘要:

针对具有模型非线性和外界扰动的四旋翼飞行器,设计了基于事件触发的自适应模糊跟踪控制算法。首先将四旋翼飞行器系统分解为位置子系统和姿态子系统,并利用模糊逻辑系统在线辨识模型非线性和外界干扰。然后,基于反步递推技术设计自适应模糊控制律,同时构建自适应事件触发机制,非周期更新控制律和模糊参数自适应律。基于Lyapunov稳定性理论,以脉冲动力系统为工具,证明了闭环系统所有信号最终一致有界,而且跟踪误差能够收敛于零点的邻域内。此外,证明了所提控制算法可以彻底避免Zeno现象出现。最后,仿真结果验证了所提控制方案在保证四旋翼飞行器实现轨迹跟踪控制同时,可以有效减少控制器更新频率,提高系统资源利用率。

关键词: 四旋翼飞行器, 事件触发控制, 轨迹跟踪, 模糊逻辑系统

Abstract:

In this paper, an event-trigger based on adaptive fuzzy control algorithm is proposed for the trajectory tracking of quadrotor unmanned aerial vehicle (QUAV) with model nonlinearities and external disturbances. Dividing the QUAV system into position subsystem and attitude subsystem, fuzzy logic systems are used to identify the nonlinearities. Then, an adaptive fuzzy control law is designed based on backstepping technique, and an adaptive event-triggered mechanism is constructed simultaneously, which determines the event-triggered instants. Thus, the adaptive fuzzy control law and the fuzzy parameter adaptive law are updated in an aperiodic form. Based on Lyapunov stability theory, it is proved that all signals in the closed-loop system are ultimately uniformly bounded via the impulsive dynamical system tool, and the tracking error converges to a small neighborhood of the origin. Besides, it is proved that there is a positive lower bound between the inter-sample time to avoid Zeno behaviour. Finally, simulation results illustrate that the proposed control scheme can guarantee the trajectory tracking control of the QUAV system, while it is able to the update frequency of the controller and improve the resource utilization.

Key words: quadrotor unmanned aerial vehicle, event-triggered control, trajectory tracking, fuzzy logic system

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