Abstract
This paper investigates an adaptive neural network control strategy for a two-degree-of-freedom helicopter system with input saturation and unknown external disturbances. Firstly, the radial basis function neural network is used to compensate the uncertainty and input saturation error of the system. Furthermore, a disturbance observer is designed to deal with complex disturbances composed of unknown disturbances and neural network errors. By constructing and analyzing the Lyapunov function, the stability of the helicopter system is strictly guaranteed. Finally, the numerical simulations and experiments conducted on the Quanser laboratory platform reveal that the proposed control strategy is suitable and effective.
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Jian Zhang received his B.Eng. degree from West Anhui University, Luan, China, in 2020. He is currently pursuing his Master’s degree in the School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China. His research interests include adaptive control, intelligent control, and robotics.
Yubao Yang received his M.Eng. degree in South China Normal University, Guangzhou, China, in 2006. He is currently a senior experimentalist and a doctoral candidate in education at Guangzhou University, with research interests in information-based instructional design, artificial intelligence, and higher education management.
Zhijia Zhao received his B. Eng. degree in automatic control from North China University of Water Resources and Electric Power, Zhengzhou, China, in 2010, and his M.Eng. and Ph.D. degrees in automatic control from South China University of Technology, Guangzhou, China, in 2013 and 2017, respectively. He is currently an Associate Professor in the School of Mechanical and Electrical Engineering, Guangzhou University. His research interests include adaptive and learning control, flexible mechanical systems, and robotics.
Keum-Shik Hong Please see vol. 17, no. 12, p. 3008, December, 2019 of this journal.
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This work was supported in part by the Science and Technology Planning Project of Guangdong Province under Grant 2020B0101050001, in part by the National Natural Science Foundation of China under Grant 61803109, in part by the Science and Technology Planning Project of Guangzhou City under Grant 202102010398 and Grant 202102010411, in part by the Scientific Research Projects of Guangzhou Education Bureau under Grant 202032793, in part by the Guangzhou University Graduate Student Innovative Research Grant Program under Grant 2021GDJC-M30 and in part by the Korea Institute of Energy Technology Evaluation and Planning under the auspices of the Ministry of Trade, Industry and Energy, Korea (grant no. 20213030020160).
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Zhang, J., Yang, Y., Zhao, Z. et al. Adaptive Neural Network Control of a 2-DOF Helicopter System with Input Saturation. Int. J. Control Autom. Syst. 21, 318–327 (2023). https://doi.org/10.1007/s12555-021-1011-2
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DOI: https://doi.org/10.1007/s12555-021-1011-2