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A new overhead crane emergency braking method with theoretical analysis and experimental verification

  • He ChenEmail author
  • Bokai Xuan
  • Peng Yang
  • Haiyong Chen
Original paper
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Abstract

Safety requirements are becoming increasingly important for various industrial production processes. Even though many facilities have been designed and used to ensure safety, the occurrence of some unexpected situations is still unavoidable. Therefore, suitable strategies should be designed in industry to deal with emergency situations. In this paper, we propose an emergency braking method for the commonly used overhead crane system to avoid accidents, such as collisions, when an unexpected situation occurs. Different from the typical industrial braking methods that stop the trolley immediately, the proposed method is designed considering payload swing suppression during the braking process, making the braking process safer. Additionally, the important payload safety limit is theoretically ensured. In particular, the detailed controller is designed by using the passivity property and the barrier Lyapunov function technique. Then, the convergence of the closed-loop system is proved using Lyapunov stability theory together with LaSalle’s invariance principle. Furthermore, various experiments are implemented on a self-built overhead crane test bed that validate the effectiveness of this method.

Keywords

Overhead crane Emergency braking Lyapunov stability theory Swing suppression 

Notes

Acknowledgements

The authors would like to thank Prof. Yongchun Fang and Ning Sun from Nankai University for their professional suggestions and kind help.

Funding

This work was supported in part by the National Natural Science Foundation (NNSF) of China (61903120, 61873315), in part by the Natural Science Foundation of Hebei Province (F2018202078) and in part by the Young Talents Project in Hebei Province (210003).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Artificial IntelligenceHebei University of TechnologyTianjinChina

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