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Adaptive neural network sliding mode control for serially connected hydraulic cylinders of a heavy-duty hydraulic manipulator

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Abstract

A sliding mode control based on adaptive neural network is proposed aiming at the automatic control problem of the heavy-duty hydraulic manipulator, which is widely applied in construction machinery. The simplified state space model is established for the two hydraulic cylinders connected in series for the parallel movement of the boom of a rock drilling jumbo manipulator. By using the square of the norm of the neural network weight vector to replace the elements of the weight vector as the adaptive parameter, the computational burden of the controller is reduced and hence becomes more suitable for practical applications. The control law is designed by combining adaptive neural network with sliding mode control, and Lyapunov stability analysis is performed theoretically for the proposed control algorithm. Simulations are conducted to verify the feasibility of the designed controller. Extensive experimental studies are carried out on the heavy-duty hydraulic manipulator of a rock drilling jumbo. When tracking sinusoidal position, the error of the proposed controller is reduced by 53 % and 71 % compared with the traditional sliding mode controller and PID controller, respectively, thereby proving the effectiveness and practicality of the proposed control algorithm.

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Acknowledgments

This work was supported by the Shanxi Tianju Heavy Industry Machinery Co., Ltd. We thank the company for the experimental conditions they have provided.

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Central South University, Changsha, 410083, China

    Xinping Guo, Hengsheng Wang, Liang Wang & Hua Liu

  2. State Key Laboratory for High Performance Complex Manufacturing, Central South University, Changsha, 410083, China

    Hengsheng Wang

Authors
  1. Xinping Guo
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  2. Hengsheng Wang
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  3. Liang Wang
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  4. Hua Liu
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Corresponding author

Correspondence to Hengsheng Wang.

Additional information

Xinping Guo received his bachelor’s degree in Mechanical Engineering from the Inner Mongolia University of Science and Technology, Baotou, China, in 2017 and his master’s degree in Mechanical Engineering from the Taiyuan University of Technology, Taiyuan, China, in 2020. He is currently working toward his Ph.D. degree in Mechanical Engineering at the College of Mechanical and Electrical Engineering, Central South University, Changsha, China. His research interests include electro-hydraulic servo control and robot control.

Hengsheng Wang received his Ph.D. degree in Mechanical and Electrical Engineering from the Central South University, Changsha, China, in 2006. He is currently a Professor at the College of Mechanical and Electrical Engineering, Central South University. His research interests include dynamics and control of mechanical systems, industrial manipulators, mobile robotics, and applications of artificial intelligence.

Liang Wang received his bachelor’s degree in Mechanical Engineering at the Hohai University, Changzhou, China, in 2008 and his master’s degree in Mechanical Engineering from Nanchang Hangkong University, Nanchang, China, in 2011. He is currently working toward his Ph.D. degree in Mechanical Engineering at the College of Mechanical and Electrical Engineering, Central South University, Changsha, China. His research interests include modeling and control of electromechanical systems.

Hua Liu received his bachelor’s degree in Mechanical Engineering from the Inner Mongolia University of Technology, Hohhot, China, in 2017 and his master’s degree in Mechanical Engineering from the Taiyuan University of Technology, Taiyuan, China, in 2020. He is currently working toward his Ph.D. degree in Mechanical Engineering at the College of Mechanical and Electrical Engineering, Central South University, Changsha, China. His research interests include hydraulic manipulator control and motion planning.

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Guo, X., Wang, H., Wang, L. et al. Adaptive neural network sliding mode control for serially connected hydraulic cylinders of a heavy-duty hydraulic manipulator. J Mech Sci Technol 37, 3763–3775 (2023). https://doi.org/10.1007/s12206-023-0640-1

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  • DOI: https://doi.org/10.1007/s12206-023-0640-1

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