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Robust Adaptive Fault-tolerant Asymptotic Tracking Control for Magnetic Levitation System Based on Nussbaum Gain and Neural Network

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  • Control Theory and Applications
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Abstract

This paper studies a new fault-tolerant tracking control for magnetic levitation (MagLev) systems. Remarkably, the controlled system not merely admits unknown functions, but also allows unknown control directions. Under the backstepping framework, the neural network (NN) is constructively framed to estimate the unknown function in the MagLev system. Besides, to avoid high derivatives in the backstepping method, adaptive nonlinear filtering is applied to calculate the virtual control signal. Then, the Nussbaum gain technique is adopted to overcome the unknown control direction problem. Meanwhile, adaptive law in the proposed method is exploited to compensate for the influence of actuator failures. It turns out that the proposed adaptive fault-tolerant controller has the capability of guaranteeing the boundedness of all signals in the closed-loop system while steering the tracking error to converge to zero. Simulation analysis demonstrates the effectiveness of the proposed method.

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Authors and Affiliations

  1. School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China

    Shengya Meng, Fanwei Meng & Qi Li

  2. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Wang Yang

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Correspondence to Fanwei Meng.

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This work was supported by the National Natural Science Foundation of China Regional Fund (12162007).

Shengya Meng received her B.S. degree in automation specialty from Guizhou University, Guiyang, China, in 2020. She is currently pursuing an M.S. degree in Northeastern University at Qinhuangdao, Qinhuangdao, China. She works with Fanwei Meng as a Student Researcher. Her current research interests include adaptive control and fault-tolerant control.

Fanwei Meng received his B.S. degree in automation specialty from Harbin Engineering University, Harbin, China, in 2005. He received his M.S. and Ph.D. degrees in control science and engineering from Harbin Institute of Technology, Harbin, China, in 2007 and 2013, respectively. From 2005 to 2013, He was a naval technical officer. Since 2014, he has been a lecturer with the Control Science and Engineering Department, Northeastern University at Qinhuangdao, China. He is the author of one book and more than 20 articles. His research interests include robust control and control system design.

Wang Yang received his B.S. degree in automation from Heilongjiang University of Science and Technology, Harbin, China, in 2018, and an M.S. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2021, where he is currently pursuing a Ph.D. degree in control science and engineering. His current research interests include distributed control, fault-tolerant control, adaptive control, and event-triggered control.

Qi Li is currently pursuing an M.S. degree in Northeastern University at Qinhuangdao, Qinhuangdao, China. He works with Fanwei Meng as a Student Researcher. His current research interests include adaptive control.

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Meng, S., Meng, F., Yang, W. et al. Robust Adaptive Fault-tolerant Asymptotic Tracking Control for Magnetic Levitation System Based on Nussbaum Gain and Neural Network. Int. J. Control Autom. Syst. 22, 163–173 (2024). https://doi.org/10.1007/s12555-022-0414-z

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