Abstract
This paper introduces a finite-time fault-tolerant tracking control (FTTC) scheme by intelligent learning technology for a fixed-wing unmanned aerial vehicle (UAV) against actuator-sensor faults and input saturation. To enhance the security of UAV, radial basis function neural networks are designed to approximate the unknown terms caused by the unexpected actuator-sensor faults and input saturation. Moreover, a finite-time command filter and an auxiliary system are utilized to tackle the computational explosion in the back-stepping architecture and input saturation, respectively. Additionally, a Nussbaum function is integrated in the FTTC design to reduce the computation in auxiliary system. Eventually, the effectiveness is demonstrated by simulation results.
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Acknowledgments
This work was supported in part by National Natural Science Foundation of China (No. 62003162, 61833013), Natural Science Foundation of Jiangsu Province of China (No. BK20200416), China Postdoctoral Science Foundation (No. 2020 TQ0151 and 2020M681590), Fundamental Research Funds for the Central Universities (No. NJ2020019 and NS2021025), State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, China (No. MCMS-I-0121G03), Aeronautical Science Foundation of China (No.20200007018001), Natural Sciences and Engineering Research Council of Canada, and Postgraduate Research & Practice Innovation Program of NUAA under Grant xcxjh20210315.
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Xu, Y., Yu, Z., Chen, F., Zhang, Y. (2023). Nussbaum-Based Finite-Time Adaptive Fault-Tolerant Tracking Control Against Actuator-Sensor Faults and Input Saturation. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_249
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DOI: https://doi.org/10.1007/978-981-19-6613-2_249
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