Adaptive fault-tolerant attitude control for hypersonic reentry vehicle subject to complex uncertainties |
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| Institution: | 1. College of Automation Engineering, Nanjing Univ. of Aeronautics and Astronautics, Nanjing 210016, China;2. Key Laboratory of Navigation, Control and Health-Management Technologies of Advanced Aerocraft (Nanjing Univ. of Aeronautics and Astronautics), Ministry of Industry and Information Technology, Nanjing, China;1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China;2. University Paris-Saclay, Univ Evry, IBISC, Evry 91020, France;3. Qingdao Innovation and Development Center of Harbin Engineering University, Qingdao, Shandong 266400, China;1. College of Information Engineering, Henan University of Science and Technology, Luoyang, China;2. Henan Key Laboratory of Robot and Intelligent Systems, Henan University of Science and Technology, Luoyang, China;1. Department of Mathematics, Harbin University of Science and Technology, Harbin 150080, China;2. Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems, Harbin University of Science and Technology, Harbin 150080, China;3. School of Automation, Harbin University of Science and Technology, Harbin 150080, China;4. School of Mathematics-Physics and Finance, Anhui Polytechnic University, Wuhu 241000, China;1. Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 37673, Korea;2. Department of Electrical Engineering and Convergence IT Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 37673, Korea;1. School of Transportation Science and Engineering, Beihang University, Beijing 100083, PR China;2. Department of Mathematics, Beijing Jiaotong University, Beijing 100044, PR China;3. Texas A & M University at Qatar, Doha 23874, Qatar |
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| Abstract: | In this paper, a novel fast attitude adaptive fault-tolerant control (FTC) scheme based on adaptive neural network and command filter is presented for the hypersonic reentry vehicles (HRV) with complex uncertainties which contain parameter uncertainties, un-modeled dynamics, actuator faults, and external disturbances. To improve the performance of closed-loop FTC, command filter and neural network are introduced to reconstruct system nonlinearities that are related to complex uncertainties. Compared with the FTC scheme with only neural network, the FTC scheme with command filter and neural network has fewer controller design parameters so that the computational complexity is decreased and the control efficiency is improved, which is of great significance for HRV. Then, the adaptive backstepping fault-tolerant controller based on command filter and neural network is designed, which can solve the complexity explosion problem in the standard backstepping control and the small uncertainty problem in the backstepping control only containing command filter. Moreover, to improve the approximation accuracy of the neural network-based universal approximator, an adaptive update law of neural network weights is designed by using the convex optimization technique. It is proved that the presented FTC scheme can ensure that the closed-loop control system is stable and the tracking errors are convergent. Finally, simulation results are carried out to verify the superiority and effectiveness of the presented FTC scheme. |
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