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Adaptive Equivalent-input-disturbance Approach to Improving Disturbance-rejection Performance

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Abstract

This paper presents an adaptive equivalent-input-disturbance (AEID) approach that contains a new adjustable gain to improve disturbance-rejection performance. A linear matrix inequality is derived to design the parameters of a control system. An adaptive law for the adjustable gain is presented based on the combination of the root locus method and Lyapunov stability theory to guarantee the stability of the AEID-based system. The adjustable gain is limited in an allowable range and the information for adjusting is obtained from the state of the system. Simulation results show that the method is effective and robust. A comparison with the conventional EID approach demonstrates the validity and superiority of the method.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61873348), National Key R&D Program of China (No. 2017YFB1300900), Hubei Provincial Natural Science Foundation of China (No. 2015CFA010), and the 111 Project, China (No. B17040).

Author information

Authors and Affiliations

  1. School of Automation, China University of Geosciences, Wuhan, 430074, China

    Ze-Wen Wang, Jin-Hua She & Guang-Jun Wang

  2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China

    Ze-Wen Wang, Jin-Hua She & Guang-Jun Wang

  3. School of Engineering, Tokyo University of Technology, Hachioji, 192-0982, Japan

    Jin-Hua She

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Correspondence to Jin-Hua She.

Additional information

Recommended by Associate Editor Min Wu

Ze-Wen Wang received the B.Sc. degree in engineering from Central China Normal University, China in 2017. He is currently a master student in control engineering from China University of Geosciences, China.

His research interests include the application of control theory, and robust control.

Jin-Hua She received the B.Sc. degree in engineering from Central South University, China in 1983, and the M.Sc. and Ph.D. degrees in engineering from Tokyo Institute of Technology, Japan in 1990 and 1993, respectively. In 1993, he joined School of Engineering, Tokyo University of Technology, where he is currently a professor. He is a member of the Society of Instrument and Control Engineers, the Institute of Electrical Engineers of Japan, the Japan Society of Mechanical Engineers, and the Asian Control Association. He received the International Federation of Automatic Control Control Engineering Practice Prize Paper Award in 1999 (jointly with M. Wu and M. Nakano).

His research interests include the application of control theory, repetitive control, process control, Internet-based engineering education, and assistive robotics.

Guang-Jun Wang received the B.Sc. and M.Sc. degrees from Central China Normal University, China in 1992, and the Ph.D. degree from the Huazhong University of Science and Technology, China in 2001. In 2002, he was with The Chinese University of Hong Kong as a visiting researcher. He was with the University of New Brunswick, Canada, as a Visiting Researcher from 2009 to 2010. He was a lecturer with School of Mechanical Engineering and Electronic Information, China University of Geosciences, China from 1994 to 1999, and an associate professor from 1999 to 2005, where he is currently a professor.

His research interests include electronic information technology, pattern recognition, and intelligent systems.

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Wang, ZW., She, JH. & Wang, GJ. Adaptive Equivalent-input-disturbance Approach to Improving Disturbance-rejection Performance. Int. J. Autom. Comput. 17, 701–712 (2020). https://doi.org/10.1007/s11633-020-1230-7

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