四旋翼飞行器的RBF神经网络鲁棒自适应控制

马振伟; 白浩; 陈洪波; 王劲博

doi:10.13700/j.bh.1001-5965.2022.0595

四旋翼飞行器的RBF神经网络鲁棒自适应控制

doi: 10.13700/j.bh.1001-5965.2022.0595

中山大学系统科学与工程学院，广州 510006

详细信息

通讯作者:
E-mail：chenhongbo@mail.sysu.edu.cn

中图分类号: V275.1；TP183
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出版历程
- 收稿日期: 2022-07-07
- 录用日期: 2022-08-14
- 网络出版日期: 2022-10-09
- 整期出版日期: 2024-05-29

RBF neural network robust adaptive control of quadrotor aircraft

School of Systems Science and Engineering，Sun Yat-Sen University，Guangzhou 510006，China

More Information

Corresponding author: E-mail：chenhongbo@mail.sysu.edu.cn

摘要

摘要:
针对具有模型不确定性和有界外部扰动的四旋翼飞行器，提出了一种基于径向基函数神经网络的鲁棒自适应全局控制方法（RRAC）。所提方法结合了神经网络控制对未知非线性的强拟合能力和鲁棒控制的全局稳定性，解决了神经网络控制仅能实现半全局一致最终有界的问题，实现了控制精度和鲁棒性的双重提升。所设计的控制器由在近似域内工作的神经网络控制器和在近似域外工作的鲁棒控制器组成。引入一种新型切换函数来实现两者之间的平滑切换，以保证闭环系统的所有信号是全局一致最终有界的。利用Lyapunov函数和Barbalat引理严格证明了非线性四旋翼飞行器系统的稳定性。仿真表明，所设计的控制器在模型不确定性和有界外部扰动下对参考轨迹依旧保持良好的跟踪性能，且跟踪误差趋近于零。
- 四旋翼飞行器 /
- RBF神经网络 /
- 鲁棒自适应控制 /
- 平滑切换函数 /
- 全局一致最终有界
Abstract:
The paper presents a robust adaptive global control method based on radial basis function (RBF) neural network for quadrotors with model uncertainties and bounded external disturbances. The method combines the strong fitting ability of neural network control to unknown nonlinearities and the global stability of robust control, which solves the problem that neural network control is only semi-globally uniformity ultimately bounded, and achieves the double improvement of control accuracy and robustness. A robust controller that operates outside of the approximation domain and a neural network controller that operates within it make up the planned controller. A smooth switching function is introduced to achieve smooth switching between the two to ensure that all signals of the closed-loop system are globally uniform and ultimately bounded. Using the Lyapunov function and Barbalat's lemma, the stability of the nonlinear quadrotor aircraft system is strictly proved. Under model uncertainty and constrained external disturbance, simulations demonstrate that the suggested controller still maintains a good tracking performance for the reference trajectory, and the tracking error approaches zero.
- quadrotor aircraft /
- RBF neural network /
- robust adaptive control /
- smooth switching function /
- globally uniformity ultimately bounded

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图 1 开关函数M(x)

Figure 1. Switching function M(x)

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图 2 闭环控制系统结构

Figure 2. Closed-loop system structure

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图 3 无扰动下的位置跟踪曲线

Figure 3. Position tracking curve without disturbance

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图 4 无扰动下的位置跟踪误差曲线

Figure 4. Position tracking error curve without disturbance

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图 5 无扰动下的速度曲线

Figure 5. The velocity curve without disturbance

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图 6 无扰动下的姿态角跟踪曲线

Figure 6. Attitude angle tracking curve without disturbance

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图 7 无扰动下的姿态角跟踪误差曲线

Figure 7. Attitude angle tracking error curve without disturbance

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图 8 无扰动下的飞行器姿态角速率曲线

Figure 8. The attitude angular rate curve without disturbance

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图 9 无扰动下的控制曲线

Figure 9. Control curve without disturbance

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图 10 含扰动下的位置跟踪曲线

Figure 10. Position tracking curve with disturbance

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图 11 含扰动下的位置跟踪误差曲线

Figure 11. Position tracking error curve with disturbance

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图 12 含扰动下的速度曲线

Figure 12. The velocity curve with disturbance

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图 13 含扰动下的姿态角跟踪曲线

Figure 13. Attitude angle tracking curve with disturbance

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图 14 含扰动下的姿态角跟踪误差曲线

Figure 14. Attitude angle tracking error curve with disturbance

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图 15 含扰动下的飞行器姿态角速率曲线图

Figure 15. The attitude angular rate curve with disturbance

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图 16 含扰动下的控制曲线

Figure 16. Control curve with disturbance

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