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Design of Autopilot Event-Triggered Control Systems

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DOI: 10.23977/autml.2024.050206 | Downloads: 25 | Views: 1099

Author(s)

Ang Zheng 1, Yizhong Ding 1, Xiaoming Xia 2

Affiliation(s)

1 Makarov College of Marine Engineering, Jiangsu Ocean University, Lianyungang, Jiangsu, China
2 College of Marine Engineering, Jiangsu Ocean University, Lianyungang, Jiangsu, China

Corresponding Author

Ang Zheng

ABSTRACT

Addressing the challenges of designing unmanned ship autopilot control systems in a variable marine environment, this study proposes an anti-disturbance control method based on disturbance estimation and compensation under an event-triggered mechanism. The disturbances faced by unmanned ships are modeled as a first-order Nomoto process, capturing the marine disturbances caused by wind, waves, and currents as well as unmodeled dynamics. A extended state observer is constructed to estimate environmental disturbances and uncertainties within the ship model. In the control design, the estimated disturbances are used for compensation to mitigate the impacts of disturbances and uncertainties on the navigation control of unmanned ships. An auxiliary dynamic system is designed to reduce the effects of autopilot input saturation, and an event-triggered mechanism is introduced to decrease the frequency of autopilot actions to avoid excessive wear. Stability analysis using Lyapunov functions indicates that all error signals in the closed-loop system are bounded. Simulation results demonstrate the effectiveness and feasibility of the autopilot event-triggered control system.

KEYWORDS

Autopilot, Extended state observer, Auxiliary dynamic system, Event-triggered control

CITE THIS PAPER

Ang Zheng, Yizhong Ding, Xiaoming Xia, Design of Autopilot Event-Triggered Control Systems. Automation and Machine Learning (2024) Vol. 5: 46-58. DOI: http://dx.doi.org/10.23977/autml.2024.050206.

REFERENCES

[1] Yu H. An Autonomous Obstacle Avoidance System for Unmanned Ship Navigation with Improved Artificial Potential Field Method [J]. Ship Science and Technology, 2023, 45 (20): 97-100.
[2] Huang Z. Design and Implementation of Tracking Control System for Measuring Ships [D]. Tianjin University, 2022. 
[3] Jiang Yudong. Design and Optimization of Autonomous Rudder Control System for Unmanned Ships [D]. Dalian University of Technology, 2020.
[4] Fossen T I. Handbook of marine craft hydrodynamics and motion control [M]. West Sussex: Wiley, 2011.
[5] O. H F. Discussion: "Note on Angular Motions of Ships" (Minorsky, Nicholas, 1941, ASME J. Appl. Mech., 8, pp. A111–A120) [J]. J. Appl. Mech, 1942, 9(2): A100-A112.
[6] Banazadeh A, Ghorbani M T. Frequency domain identification of Nomoto model to facilitate Kalman filter estimation and PID heading control of a patrol vessel [J]. Ocean Engineering, 2013, 72: 344-355.
[7] Perera L P, Soares C G. Lyapunov and Hurwitz based controls for input-output linearization applied to non linear vessel steering [J]. Ocean Engineering, 2013, 66: 58-68.
[8] Wang N, Lü S L, Liu Z Z. Global finite-time heading control of surface vehicles[J]. Neurocomputing, 2016, 175: 662-666.
[9] DO K D. Synchronization motion tracking control of multiple underactuated ships with collision avoidance [J]. IEEE Transactions on Industrial Electronics, 2016, 63(5): 2976-2989. 
[10] HU X, WEI X J, KAO Y G, et al. Robust synchronization for under-actuated vessels based on disturbance observer [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(6): 5470-5479. 
[11] NING J, CHEN H M, LI W, et al. Finite-time ship formation control based on extended state observer [J]. Chinese Journal of ShipResearch, 2023, 18 (1): 60-66.
[12] LIU Y, JIA H C, LIU L, et al. Anti-disturbance optimal coverage control of ASVs[J]. Chinese Journal of Ship Research, 2023, 18 (1): 67-77.
[13] ZHANG R, XU B, SHI P. Output feedback control of micromechanical gyroscopes using neural networks and disturbance observer [J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(3): 962-972. 
[14] LI L Y, PEI G J, LIU J X, et al. 2-DOF robust H∞ control for permanent magnet synchronous motor with disturbance observer[J]. IEEE Transactions on Power Electronics, 2021, 36(3): 3462-3472G.
[15] YIM J, YOU S, LEE Y, et al. Chattering attenuation disturbance observer for sliding mode control: application to permanent magnet synchronous motors [J]. IEEE Transactions on Industrial Electronics, 2023, 70(5): 5161-5170. 
[16] ZHU G B, MA Y, LI Z X, et al. Event-triggered adaptive neural fault-tolerant control of underactuated MSVs with input saturation [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7): 7045-7057.
[17] Fossen T I, Marine Control Systems, Marine Cybernetics, Trondheim, Norway, 2002.
[18] Zhang G, Zhang X, Mathematical Model of Ship Motion and MATLAB Simulation [M] Xuzhou: China University of Mining and Technology Press, June 47-50, 2020.
[19] G. Xia, C. Sun, B. Zhao, X. Xia, and X. Sun, Neuroadaptive distributed output feedback tracking control for multiple marine surface vessels with input and output constraints, IEEE Access, 2019, 7: 123076–123085.
[20] G. Xia, C. Sun, B. Zhao, and J. Xue, Cooperative control of multiple dynamic positioning vessels with input saturation based on finite-time disturbance observer, International Journal of Control, Automation and Systems, 2019, 17(2): 370–379.

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