Output Constrained Adaptive Controller Design for Nonlinear Saturation Systems

Yongliang Yang; Zhijie Liu; Qing Li; Donald C. Wunsch

doi:10.1109/JAS.2020.1003524

Volume 8 Issue 2

Feb. 2021

IEEE/CAA Journal of Automatica Sinica

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Yongliang Yang, Zhijie Liu, Qing Li and Donald C. Wunsch, "Output Constrained Adaptive Controller Design for Nonlinear Saturation Systems," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 441-454, Feb. 2021. doi: 10.1109/JAS.2020.1003524

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Yongliang Yang, Zhijie Liu, Qing Li and Donald C. Wunsch, "Output Constrained Adaptive Controller Design for Nonlinear Saturation Systems," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 441-454, Feb. 2021. doi: 10.1109/JAS.2020.1003524

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Output Constrained Adaptive Controller Design for Nonlinear Saturation Systems

doi: 10.1109/JAS.2020.1003524

Funds: This work was supported in part by the National Natural Science Foundation of China (61903028, 62073030), in part by the China Post-Doctoral Science Foundation (2019M660463), in part by the Fundamental Research Funds for the China Central Universities of University of Science and Technology Beijing (FRF-TP-18-031A1, FRF-BD-19-002A), and in part by the Postdoctor Research Foundation of Shunde Graduate School of University of Science and Technology Beijing (2020BH002)

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Author Bio:
Yongliang Yang (S’16−M’18) received the B. S. degree in electrical engineering from Hebei University, Baoding, China, in 2011, and the Ph. D. degree in electrical engineering from the University of Science and Technology Beijing (USTB), Beijing, China in 2017. He was a Visiting Scholar with the Missouri University of Science and Technology, Rolla, MO, USA, from 2015 to 2017, sponsored by China Scholarship Council. He is currently an Associate Professor with the School of Automation and Electrical Engineering, USTB. His research interests include adaptive optimal control, distributed optimization and control, and cyber-physical systems.He was a recipient of the Best Ph. D. Dissertation of China Association of Artificial Intelligence, the Best Ph. D. Dissertation of USTB, the Chancellors Scholarship in USTB, and the Excellent Graduates Awards in Beijing. He also serves as the Reviewer of several international journals and conferences, including Automatica, the IEEE Transactions on Automatic Control, IEEE Transactions on Cybernetics, and IEEE Transactions on Neural Networks and Learning Systems

Zhijie Liu (S’17−M’19) received B. Sc. degree from China University of Mining and Technology Beijing, Beijing, China in 2014, and the Ph. D. degree from Beihang University, Beijing, China in 2019.In 2017, he was a Research Assistant with the Department of Electrical Engineering, University of Notre Dame, for twelve months. He is currently an Associate Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China. His research interests include adaptive control, modeling and vibration control for flexible structures, and distributed parameter system

Qing Li received the B. S. degree from the North China University of Science and Technology, Tangshan, China in 1993, and the Ph. D. degree in control theory and its applications from the University of Science and Technology Beijing, Beijing, China in 2000.From February 2006 to February 2007, he was a Visiting Scholar with Ryerson University, Toronto, ON, Canada. He is currently a Professor with the School of Automation and Electrical Engineering, University of Science and Technology Beijing. His research interests include intelligent control and intelligent optimization

Donald C. Wunsch (F'05) received the B. S. degree in applied mathematics from the University of New Mexico, Albuquerque, NM, USA in 1984, the M. S. degree in applied mathematics and the Ph. D. degree in electrical engineering from the University of Washington, Seattle, WA, USA in 1987 and 1991, respectively, and the Executive M.B.A. degree in business administration from Washington University in St. Louis, St. Louis, MO, USA in 2006.He was with Texas Tech University, Lubbock, TX, USA; Boeing, Seattle, WA, USA; Rockwell International, Albuquerque, NM, USA; and International Laser Systems, Albuquerque, NM, USA. He is currently the Mary K. Finley Missouri Distinguished Professor with the Missouri University of Science and Technology (Missouri S&T), Rolla, MO, USA, where he is also the Director of the Applied Computational Intelligence Laboratory, a multidisciplinary research group. His current research interests include clustering/unsupervised learning; biclustering; adaptive resonance and adaptive dynamic programming architectures, hardware, and applications; neurofuzzy regression; autonomous agents; games; and bioinformatics.He was a recipient of the National Science Foundation (NSF) CAREER Award, the 2015 International Neural Networks Society (INNS) Gabor Award, and the 2019 Ada Lovelace Service Award. He has produced 22 Ph. D. recipients in computer engineering, electrical engineering, systems engineering, and computer science. He served as the International Joint Conference on Neural Networks (IJCNN) General Chair, and on several boards, including the St. Patricks School Board, the IEEE Neural Networks Council, the INNS, and the University of Missouri Bioinformatics Consortium, and the Chair of the Missouri S&T Information Technology and Computing Committee as well as the Student Design and Experiential Learning Center Board. He is an INNS Fellow and the former INNS President
Corresponding author: Y. Yang, Z. J. Liu and Q. Li are with the Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China (e-mail: yangyongliang@ieee.org; liuzhijie2012@gmail.com; liqing@ies.ustb.edu.cn)
Received Date: 2020-06-02
Accepted Date: 2020-10-01

Available Online: 2020-10-26

Abstract

Abstract

This paper considers the adaptive neuro-fuzzy control scheme to solve the output tracking problem for a class of strict-feedback nonlinear systems. Both asymmetric output constraints and input saturation are considered. An asymmetric barrier Lyapunov function with time-varying prescribed performance is presented to tackle the output-tracking error constraints. A high-gain observer is employed to relax the requirement of the Lipschitz continuity about the nonlinear dynamics. To avoid the “explosion of complexity”, the dynamic surface control (DSC) technique is employed to filter the virtual control signal of each subsystem. To deal with the actuator saturation, an additional auxiliary dynamical system is designed. It is theoretically investigated that the parameter estimation and output tracking error are semi-global uniformly ultimately bounded. Two simulation examples are conducted to verify the presented adaptive fuzzy controller design.
- Asymmetric barrier Lyapunov function,
- input saturation,
- Nussbaum function,
- time-varying prescribed performance

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References

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A barrier Lyapunov function with an asymmetric time-varying constraint is presented to ensure the prescribed transient performance on the output tracking error.
To estimate the unmeasured states, the high-gain observer with adaptive feedback gain is designed with a relaxed continuity assumption.
The input saturation is solved by introducing an additional auxiliary system, of which the stability analysis is based on the Nussbaum function-based method.

Output Constrained Adaptive Controller Design for Nonlinear Saturation Systems

doi: 10.1109/JAS.2020.1003524

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