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Deep Reinforcement Learning Based Online Parameter Tuning for Active Disturbance Rejection Control and Application in Ship Course Tracking

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Proceedings of 2021 Chinese Intelligent Systems Conference

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 803))

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Abstract

The linear active disturbance rejection control (LADRC) has been widely used in many control fields. However, the controller with fixed parameters cannot achieve the optimal performance in an environment with compound disturbances. To achieve the online parameter tuning for LADRC, a deep reinforcement learning algorithm called the soft actor-critic (SAC), is used. Then the SAC-LADRC controller is proposed and applied in the ship course control to obtain the optimal parameters of LADRC in different states. In simulations, comparisons with the conventional LADRC controller are presented, and the effectiveness of the proposed method is verified.

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References

  1. Zhang, C., Wan, L., Liu, Y.: Ship heading control based on fuzzy PID control. In: 2019 34rd Youth Academic Annual Conference of Chinese Association of Automation, China, pp. 607–612 (2019)

    Google Scholar 

  2. Hu, J., Guo, J., Ding, B.: Automatic control of paving ship based on LSTM. In: 2019 34rd Youth Academic Annual Conference of Chinese Association of Automation, China, pp. 527-530 (2019)

    Google Scholar 

  3. Hu, J., Guo, J., Ding, B.: Automatic docking for underactuated ships based on multi-objective nonlinear model predictive control. IEEE Access 8, 70044–70057 (2020)

    Article  Google Scholar 

  4. Du, J., Guo, C., Yu, S., Zhao, Y.: Adaptive autopilot design of time-varying uncertain ships with completely unknown control coefficient. IEEE J. Oceanic Eng. 32(2), 346–352 (2007)

    Article  Google Scholar 

  5. Johansen, T., Perez, T., CristofaroLiu, A.: Ship collision avoidance and colregs compliance using simulation-based control behavior selection with predictive hazard assessment. IEEE Trans. Intell. Transp. Syst. 17(12), 3407–3422 (2016)

    Article  Google Scholar 

  6. Liu, Z.: Ship adaptive course keeping control with nonlinear disturbance observer. IEEE Access 5, 17567–17575 (2017)

    Article  Google Scholar 

  7. Gao, Z.Q.: On the centrality of disturbance rejection in automatic control. ISA Trans. 53(4), 850–857 (2014)

    Article  Google Scholar 

  8. Xue, W., Madonski, R., Lakomy, K., Gao, Z., Huang, Y.: Add-on module of active disturbance rejection for set-point tracking of motion control systems. IEEE Trans. Ind. Appl. 53(4), 4028–4040 (2017)

    Article  Google Scholar 

  9. Qin, H.Y., Chen, Z.Q., Sun, M.W., Sun, Q.L.: Application of real-time wavelet de-noising based on sliding window in LADRC. In: Proceedings of 2020 Chinese Intelligent Systems Conference 706 LNEE, pp. 1-10. China (2021)

    Google Scholar 

  10. Han, J.Q.: Auto-disturbance-rejection controller and its applications. Control Decis 13(1), 19–23 (1998). (in Chinese)

    Google Scholar 

  11. Han, J.Q.: From PID to active disturbance rejection control. IEEE Trans. Industr. Electron. 56(3), 900–906 (2009)

    Article  Google Scholar 

  12. Gao, Z.Q.: Scaling and bandwidth-parameterization based controller tuning. In: Proceedings of American Control Conference, pp. 4989-4996. IEEE, Denver, CO, USA (2003)

    Google Scholar 

  13. Haarnoja, T., Zhou, A., et al.: Soft Actor-Critic Algorithms and Applications. arXiv:1812.05905v2 [cs.LG] (2019)

  14. Yasukawa, H., Yoshimura, Y.: Introduction of MMG standard method for ship maneuvering predictions. J. Mar. Sci. Technol. 20(1), 37–52 (2014). https://doi.org/10.1007/s00773-014-0293-y

    Article  Google Scholar 

  15. Jia, X., Yang, Y.: Ship Motion Mathematical Model Mechanism Modeling and Identification Modeling. Dalian Maritime University Press, Dalian (1999)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grants Nos. 61973175, 62073177, 61973172) and Tianjin Research Innovation Project for Postgraduate Students Grant No. 2020YJSZXB02.

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Authors and Affiliations

  1. College of Artificial Intelligence, Nankai University, Tianjin, 300350, China

    Huayang Qin, Zengqiang Chen, Mingwei Sun, Panlong Tan & Qinglin Sun

Authors
  1. Huayang Qin
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  2. Zengqiang Chen
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  3. Mingwei Sun
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  4. Panlong Tan
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  5. Qinglin Sun
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Corresponding author

Correspondence to Zengqiang Chen .

Editor information

Editors and Affiliations

  1. School of Automatic Science and Electrical Engineering, Beihang University, Beijing, China

    Yingmin Jia

  2. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China

    Weicun Zhang

  3. School of Mechanical Engineering and Automation, Beihang University, Beijing, China

    Yongling Fu

  4. Beijing Institute of Precision Mechatronics and Controls, Beijing, China

    Zhiyuan Yu

  5. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian, China

    Song Zheng

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Qin, H., Chen, Z., Sun, M., Tan, P., Sun, Q. (2022). Deep Reinforcement Learning Based Online Parameter Tuning for Active Disturbance Rejection Control and Application in Ship Course Tracking. In: Jia, Y., Zhang, W., Fu, Y., Yu, Z., Zheng, S. (eds) Proceedings of 2021 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 803. Springer, Singapore. https://doi.org/10.1007/978-981-16-6328-4_6

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