Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

Yuncheng Ouyang; Lu Dong; Lei Xue; Changyin Sun

doi:10.1109/JAS.2019.1911495

Volume 6 Issue 3

May 2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(3): 807-815

Yuncheng Ouyang, Lu Dong, Lei Xue and Changyin Sun, "Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints," IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 807-815, May 2019. doi: 10.1109/JAS.2019.1911495

Citation:

Yuncheng Ouyang, Lu Dong, Lei Xue and Changyin Sun, "Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints," IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 807-815, May 2019. doi: 10.1109/JAS.2019.1911495

Citation:

Yuncheng Ouyang, Lu Dong, Lei Xue and Changyin Sun, "Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints," IEEE/CAA J. Autom. Sinica, vol. 6, no. 3, pp. 807-815, May 2019. doi: 10.1109/JAS.2019.1911495

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Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

doi: 10.1109/JAS.2019.1911495

Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, China

Funds:

the National Natural Science Foundation of China 61803085

the National Natural Science Foundation of China 61806052

the National Natural Science Foundation of China U1713209

the Natural Science Foundation of Jiangsu Province of China BK20180361

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Author Bio:
Yuncheng Ouyang (S'16) received the B.S. degree at the School of Automation, Anhui University of Technology (AHUT), China in 2013, and the M.S. degree at the School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China in 2018. He is currently working toward the Ph.D. degree in control science and engineering at the School of Automation, Southeast University, Nanjing, China. His current research interests include robotics, intelligent control, neural network control, and adaptive control. (e-mail: oyycjeson@163.com)

Lu Dong (S'16-M'18) received the B.S. degree in physics and Ph.D. degree in electrical engineering from Southeast University, Nanjing, China, in 2012 and 2017, respectively. She was a joint Ph.D. student with the Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA, from 2014 to 2016. She is currently an Assistant Professor with the School of Automation, Southeast University. Her current research interests include adaptive dynamic programming, eventtriggered control, nonlinear system control, and optimization. (e-mail: ldong90@seu.edu.cn)

Lei Xue (M'17) received the Ph.D. degree in control science and engineering from Southeast University, Nanjing, China, in 2017. From September 2013 to September 2014, he was a Visiting Ph.D. student with Applied Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA. Currently he is a Postdoctoral student with the School of Automation, Southeast University, Nanjing, China. His research interests include game theory, multi-agent system, and optimization control. (e-mail: rayxue@seu.edu.cn)

Changyin Sun received the B.S. degree in applied mathematics from the College of Mathematics, Sichuan University, Chengdu, China, in 1996, and the M.S. and Ph.D. degrees in electrical engineering from Southeast University, Nanjing, China, in 2001 and 2003, respectively. He is currently a Professor with the School of Automation, Southeast University. His research interests include intelligent control, flight control, pattern recognition, optimal theory, etc. Dr. Sun is an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, Neural Processing Letters, and the IEEE/CAA Journal of Automatica Sinica. (e-mail: cysun@seu.edu.cn)
Corresponding author: Changyin Sun, e-mail: cysun@seu.edu.cn
Received Date: 2018-11-02
Revised Date: 2018-12-13
Accepted Date: 2019-01-02

Abstract

Abstract

In this paper, a study of control for an uncertain 2-degree of freedom (DOF) helicopter system is given. The 2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function (IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.
- 2-degree of freedom (DOF) helicopter,
- adaptive control,
- input deadzone,
- integral barrier Lyapunov function,
- neural networks,
- output constraints

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References(41)

References

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Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

doi: 10.1109/JAS.2019.1911495

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