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Output Feedback Asymptotic Tracking Control for Uncertain DC Motors

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  • Intelligent Control and Applications
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Abstract

We are motivated by the real challenges presented in DC motors to develop output feedback controller that are high-accuracy at performance level and with less dynamic information at model level. Existing nonlinear control results that consider full state feedback control are not readily achieving successful design without velocity information, and also requiring relatively detailed model information. This study deals with this issue by establishing a neural network (NN) based robust output feedback control algorithm. The dynamics of the considered DC motors are partially unknown, including unknown friction and unknown external disturbances. The velocity information is estimated by a NN-based velocity observer, where the feed-forward NN aims to alleviate the burden of high feedback gain by approximating the unknown friction. The control gains and the weight parameters in both the observer and the controller are updated on-line, without off-line learning phase required. With the adaptation of control gain, the knowledge of the bound of the unknown external disturbances and NN reconstruction errors are not required in the controller scheme. Eventually, theoretical analysis demonstrates that the developed controller can guarantee asymptotic stability. Extensive comparative simulation results and experiments are implemented to validate the performance of the resulted control strategy.

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Authors and Affiliations

  1. School of Mechanical Engineering, Nan**g University of Science and Technology, Nan**g, 210094, China

    **anglong Liang, Luyue Yin & Jianyong Yao

  2. School of Automation & School of Artificial Intelligence, Nan**g University of Post and Telecommunication, Nan**g, 210023, China

    Zhikai Yao

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  1. **anglong Liang
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Correspondence to Jianyong Yao.

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This work was supported by the National Key R&D Program of China (2021YFB2011300), the National Natural Science Foundation of China (52075262) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX_220474).

**anglong Liang received his B.Tech. degree from Nan**g University of Science and Technology, Nan**g, China in 2019, where he is currently a Ph.D. student. His research interests include nonlinear control, hydraulic robotic manipulators, and reinforcement learning.

Luyue Yin received her B.Tech. degree from Nan**g University of Science and Technology, Nan**g, China in 2017, where she is currently a Ph.D. student. Her research interests include nonlinear control and hydraulic servo control.

Zhikai Yao received his B.Tech. degree from Qingdao University of Technology, Qingdao, China in 2014, and a Ph.D. degree from Nan**g University of Science and Technology, Nan**g, China in 2021. He was an Exchange Student with the School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, USA, from September 2019 to September 2021. In 2021, he joined the School of Automation School of Artificial Intelligence, Nan**g University of Post and Telecommunications, Nan**g, China, as an Assistant Professor. His research interests include intelligent control of mechanical systems, reinforcement learning, and human-robot interaction.

Jianyong Yao received his B.Tech. degree from Tian** University, Tian**, China, in 2006, and a Ph.D. degree in mechatronics from Beihang University, Bei**g, China, in 2012. From October 2010 to October 2011, he was a Visiting Exchange Student with the School of Mechanical Engineering, Purdue University. He joined the School of Mechanical Engineering, Nan**g University of Science and Technology, Nan**g, China, in 2012, where he is currently a Full Professor. His current research interests include servo control of mechatronic systems, adaptive and robust control, fault detection, and accommodation of dynamic systems.

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Liang, X., Yin, L., Yao, Z. et al. Output Feedback Asymptotic Tracking Control for Uncertain DC Motors. Int. J. Control Autom. Syst. 21, 2748–2759 (2023). https://doi.org/10.1007/s12555-022-0147-z

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  • DOI: https://doi.org/10.1007/s12555-022-0147-z

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