基于离散傅立叶变换的扰动估计反转向飞行控制

IF 2.7 4区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS Asian Journal of Control Pub Date : 2024-09-06 DOI:10.1002/asjc.3491
Ruichen Ming, Xiaoxiong Liu, Yu Li, Weiguo Zhang
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引用次数: 0

摘要

本文介绍了针对飞行控制中的时变干扰设计自适应反步态(BS)非线性控制器的方法。本文利用离散傅立叶变换(DFT)设计了一种抑制时变干扰影响的自适应律。传统的自适应 BS 方法在推导过程中忽略了干扰的导数,不能很好地解决某些快速时变干扰。因此,我们提出了一种基于 DFT 的改进型自适应 BS 方法。该方法不直接估计扰动,而是通过估计扰动频谱和执行反 DFT 间接获得扰动的时域表达式。所提出的方法有效克服了传统自适应 BS 方法无法准确估计快速时变干扰的问题。最后,将提出的方法与传统的自适应 BS 和径向基函数 (RBF) 神经网络控制方法进行了比较。仿真结果证实,在高斯干扰和湍流条件下,所提出的方法优于其他方法。
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Disturbance estimation backstepping flight control based on the discrete Fourier transform
This paper presents the design of an adaptive backstepping (BS) nonlinear controller for time‐varying disturbances in flight control. The discrete Fourier transform (DFT) is used to design an adaptive law to suppress the influence of time‐varying disturbances. The traditional adaptive BS method ignores the derivatives of disturbances in the deduction process and cannot address certain fast, time‐varying disturbances well. Therefore, an improved adaptive BS method based on the DFT is proposed. Instead of estimating the disturbance directly, the proposed method obtains the time‐domain expression of the disturbance indirectly by estimating the disturbance spectrum and performing inverse DFT. The proposed method effectively overcomes the inability of the traditional adaptive BS method to estimate fast, time‐varying disturbances accurately. Finally, the proposed method is compared with traditional adaptive BS and radial basis function (RBF) neural network control methods. Simulation results confirm that the proposed method outperforms other methods under Gaussian disturbance and turbulence.
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来源期刊
Asian Journal of Control
Asian Journal of Control 工程技术-自动化与控制系统
CiteScore
4.80
自引率
25.00%
发文量
253
审稿时长
7.2 months
期刊介绍: The Asian Journal of Control, an Asian Control Association (ACA) and Chinese Automatic Control Society (CACS) affiliated journal, is the first international journal originating from the Asia Pacific region. The Asian Journal of Control publishes papers on original theoretical and practical research and developments in the areas of control, involving all facets of control theory and its application. Published six times a year, the Journal aims to be a key platform for control communities throughout the world. The Journal provides a forum where control researchers and practitioners can exchange knowledge and experiences on the latest advances in the control areas, and plays an educational role for students and experienced researchers in other disciplines interested in this continually growing field. The scope of the journal is extensive. Topics include: The theory and design of control systems and components, encompassing: Robust and distributed control using geometric, optimal, stochastic and nonlinear methods Game theory and state estimation Adaptive control, including neural networks, learning, parameter estimation and system fault detection Artificial intelligence, fuzzy and expert systems Hierarchical and man-machine systems All parts of systems engineering which consider the reliability of components and systems Emerging application areas, such as: Robotics Mechatronics Computers for computer-aided design, manufacturing, and control of various industrial processes Space vehicles and aircraft, ships, and traffic Biomedical systems National economies Power systems Agriculture Natural resources.
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