Adaptive self-regulation PID control of course-keeping for ships

• Autorzy: Zhang Qiang , Ding Zhongyu , Zhang Meijuan

Identyfikatory

DOI

10.2478/pomr-2020-0004

• Języki publikacji: EN

Abstrakty

EN

To solve the nonlinear control problems of the unknown time-varying environmental disturbances and parametric uncertainties for ship course-keeping control, this paper presents an adaptive self-regulation PID (APID) scheme which can ensure the boundedness of all signals in the ship course-keeping control system by using the Lyapunov direct method. Compared with the traditional PID control scheme, the APID control scheme not only is independent of the model parameters and the unknown input, but also can regulate the gain of PID adaptively and resist time-varying disturbances well. Simulation results illustrate the effectiveness and the robustness of the proposed control scheme.

Słowa kluczowe

EN

ships; course-keeping control; self-regulation PID; adaptive control; Lyapunov

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2020
• Tom: nr 1
• Strony: 39--45
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Zhang Qiang

• Shandong Jiaotong University Hexinglu 1508#, Shuangdaowankejicheng 264209 Weihai, China

autor

Ding Zhongyu

• Shandong Jiaotong University Hexinglu 1508#, Shuangdaowankejicheng 264209 Weihai, China

autor

Zhang Meijuan

• Shandong Jiaotong University Hexinglu 1508#, Shuangdaowankejicheng 264209 Weihai, China

Bibliografia

• 1. Bu R. X., Liu Z. J., Li T. S. (2006): Nonlinear Iterative Sliding Mode Variable Structure PID Control for Ship Heading. Journal of Dalian Maritime University, 2, 9–11.
• 2. Chen W. Q., Chen J., Zhang W. (2016): Adaptive Neural Network Robust Tracking Control for Ship Course. Ship Engineering, 9, 15–20.
• 3. Chen X. J., Zhang X. K. (2015): Nonlinear feedback control based on ANFIS. 12th International Conference on Fuzzy Systems and Knowledge Discovery, Zhangjiajie, China, pp. 559–563.
• 4. Fu Y. Y. (2017): Study of simulated annealing algorithm in parameter optimization of PID controller for ship course. Ship Science and Technology, 2, 25–27.
• 5. Ghommam J., Ferik S., Saad M. (2018): Robust adaptive pathfollowing control of underactuated marine vessel with off-track error constraint. International Journal of Systems Science, 49(07), 1540–1558, DOI: 10.1080/00207721.2018.1460412.
• 6. Hong B. G. (2010): Ship handling, Dalian Maritime University Press, Dalian, China.
• 7. Jia X. L., Yang Y. S. (1997): Mathematical model of ship motion, Dalian Maritime University Press, Dalian, China.
• 8. Jin A. (2017): Adaption of fuzzy self-adapting PID control algorithm on self-propelled model motion controlling. Ship Science and Technology, 39(02), 19–21.
• 9. Khalil H. K. (2000): Universal integral controllers for minimum-phase nonlinear systems. IEEE Transactions on Automatic Control, 3, 490–494.
• 10. Kluska J., Zabinski T. (2019): PID-Like Adaptive Fuzzy Controller Design Based on Absolute Stability Criterion. IEEE Transactions on Fuzzy Systems, PP(99), 1–1.
• 11. Liu Y., Bu R. X., Xu H. J. (2016): Integral compensation PID and parameter adaptive algorithm of ship course control. Journal of Dalian Maritime University, 3, 20–24.
• 12. Liu Z., Ma Y., Yuan S., Zhou Z. (2018) The Path Tracking Control Method Based on LOS Algorithm for Surface Selfpropelled Model. In: Jia Y., Du J., Zhang W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 460. Springer, Singapore.
• 13. Ma P. F., Miao B. L. (2016): Adaptive Single Neural Network Control of Ship Course Tracking System. Ship Engineering, 7, 76–80.
• 14. Mohammad H. K., Saeed B. (2015): Modeling and control of autonomous underwater vehicle (AUV) in heading and depth attitude via self-adaptive fuzzy PID controller. Journal of Marine Science and Technology, 20, 559–578.
• 15. Ouyang Z. L., Yu W. Z., He H. W. (2017): PID control with improved genetic algorithm for ship steering. China Shipping, 40, 13–15.
• 16. Santos L. R. R. D., Durand F. R., Abrăo T. (2019): Adaptive PID Scheme for OCDMA Next Generation PON Based on Heuristic Swarm Optimization. IEEE Systems Journal, 13(1), 500–510.
• 17. Sari N. N., Jahanshahi H., Fakoor M. (2019): Adaptive Fuzzy PID Control Strategy for Spacecraft Attitude Control. International Journal of Fuzzy Systems, 21(3), 769–781.
• 18. Shojaei K. (2019): An Adaptive Output Feedback Proportional- Integral-Derivative Controller for n-Link Type (m, s) Electrically Driven Mobile Manipulators. Journal of Dynamic Systems, Measurement, and Control, 141(9).
• 19. Xia G. Q., Luan T. T. (2015): Study of Ship Heading Control using RBF Neural Network. International Journal of Control and Automation, 10, 227–236.
• 20. Xue H., Zhao Q., Ma F. (2015): A principal and subordinate cooperative firefly algorithm for optimizing fractional-order PID controller in tracking control of ship steering. Computer Measurement and Control, 7, 2389–2391.
• 21. Zhang C. (2016): Ship sailing automatic control system using adaptive fuzzy PID control technology. Ship Science and Technology, 38(10), 88–90.
• 22. Zhang X. J., Liu M. Y., Li Y. (2017): Sliding mode control and Lyapunov based guidance law with impact time constraints. Journal of Systems Engineering and Electronics, 28(06), 1186–1192.
• 23. Zhang X. K., Yang G. P., Zhang Q. (2016): A kind of bipolar sigmoid function decorated nonlinear ship course keeping algorithm. Journal of Dalian Maritime University, 3, 15–19.
• 24. Zhang X. K., Zhang G. Q., Chen X. J. (2015): A kind of linear reduction of backstepping algorithm based on nonlinear feedback. Control and Decision, 9, 1641–1645.
• 25. Zhang X. K. (2012): Ship Motion Concise Robust Control, 1st ed., Science Press, Beijing, China, pp. 1–15.
• 26. Zhao Y., Wang R. Q., Yan K. Y. (2015): Autopilot Designed for Ship Course Based on New Sliding Mode Control. Ship Engineering, 37(9), 58–62.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).