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Neural Network-based Robust Anti-sway Control of an Industrial Crane Subjected to Hoisting Dynamics and Uncertain Hydrodynamic Forces

  • Intelligent Control and Applications
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Abstract

In this paper, a neural network-based robust anti-sway control is proposed for a crane system transporting an underwater object. A dynamic model of the crane system is developed by incorporating hoisting dynamics, hydrodynamic forces, and external disturbances. Considering the various uncertain factors that interfere with accurate payload positioning in water, neural networks are designed to compensate for unknown parameters and unmodeled dynamics in the formulated problem. The neural network-based estimators are embedded in the anti-sway control algorithm, which improves the control performance against uncertainties. A sliding mode control with an exponential reaching law is developed to suppress the sway motions during underwater transportation. The asymptotic stability of the sliding manifold is proved via Lyapunov analysis. The embedded estimator prevents the conservative gain selection of the sliding mode control, thus reducing the chattering phenomena. Simulation results are provided to verify the effectiveness and robustness of the proposed control method.

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Pusan National University, Busan, 46241, Korea

    Gyoung-Hahn Kim & Phuong-Tung Pham

  2. Department of Mechanical Engineering, Can Tho University, Can Tho, Vietnam

    Quang Hieu Ngo

  3. Mechatronics Department, Ho Chi Minh City University of Technology (HCMUT)-Vietnam National University Ho Chi Minh City, 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam

    Quoc Chi Nguyen

  4. Faculty of Electrical Engineering-Electronics-Mechanics-Construction, Dong Nai Technology University, Bien Hoa, Vietnam

    Quoc Chi Nguyen

Authors
  1. Gyoung-Hahn Kim
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  2. Phuong-Tung Pham
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  3. Quang Hieu Ngo
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  4. Quoc Chi Nguyen
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Corresponding author

Correspondence to Quoc Chi Nguyen.

Additional information

Recommended by Associate Editor Chang-Sei Kim under the direction of Editor-in-Chief Keum-Shik Hong.

This work was supported by the National Research Foundation (NRF) of Korea under the auspices of the Ministry of Science and ICT, Korea (grant no. NRF-2020R1A2B5B03096000). We would like to thank Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, and the Dong Nai Technology University for the support of time and facilities for this research.

Gyoung-Hahn Kim received his B.S. degree in mechanical engineering from Yeungnam University, Gyeongsan, Korea in 2013, and an M.S. degree in mechanical engineering at Pusan National University, Busan, Korea in 2019. He is currently a Researcher, Pusan National University, pursuing his Ph.D. degree. His research interests include sliding mode control, adaptive neural network control, reinforcement deep learning, control theory, and control applications to industrial robotics.

Phuong-Tung Pham received his B.S. and M.S. degrees in mechanical engineering from Ho Chi Minh City University of Technology, in 2016 and 2018, respectively. He is currently a Ph.D. candidate in the School of Mechanical Engineering, Pusan National University, Korea. His research interests include nonlinear control, adaptive control, vibration control, and control of distributed parameter systems.

Quang Hieu Ngo received his B.S. degree in mechanical engineering from Ho Chi Minh City University of Technology, Vietnam, in 2002, an M.S. degree in mechatronics from Asian Institute of Technology, Thailand, in 2007, and a Ph.D. degree in mechanical engineering from Pusan National University, Korea, in 2012. He is currently an Associate Professor in the Department of Mechanical Engineering, Can Tho University. His research interests include port automation, control of axially moving systems, sliding mode control, adaptive control, and input shaping control.

Quoc Chi Nguyen received his B.S. degree in mechanical engineering from Ho Chi Minh City University of Technology (HCMUT), Vietnam, in 2002, an M.S. degree in cybernetics from HCMUT, Vietnam, in 2006, and a Ph.D. degree in mechanical engineering from the Pusan National University, Korea, in 2012. Dr. Nguyen was a Marie Curie FP7 Postdoctoral Fellow at the School of Mechanical Engineering, Tel Aviv University, from 2013 to 2014. He is currently an Associate Professor with the Department of Mechatronics, HCMUT. Dr. Nguyen’s current research interests include nonlinear systems theory, adaptive control, robotics, and distributed parameter systems.

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Kim, GH., Pham, PT., Ngo, Q.H. et al. Neural Network-based Robust Anti-sway Control of an Industrial Crane Subjected to Hoisting Dynamics and Uncertain Hydrodynamic Forces. Int. J. Control Autom. Syst. 19, 1953–1961 (2021). https://doi.org/10.1007/s12555-020-0333-9

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  • DOI: https://doi.org/10.1007/s12555-020-0333-9

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