{"title":"基于离散傅立叶变换的扰动估计反转向飞行控制","authors":"Ruichen Ming, Xiaoxiong Liu, Yu Li, Weiguo Zhang","doi":"10.1002/asjc.3491","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":55453,"journal":{"name":"Asian Journal of Control","volume":"58 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Disturbance estimation backstepping flight control based on the discrete Fourier transform\",\"authors\":\"Ruichen Ming, Xiaoxiong Liu, Yu Li, Weiguo Zhang\",\"doi\":\"10.1002/asjc.3491\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":55453,\"journal\":{\"name\":\"Asian Journal of Control\",\"volume\":\"58 1\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Asian Journal of Control\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1002/asjc.3491\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Journal of Control","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1002/asjc.3491","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
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.
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.