Design of the predictive management and control system for combined propulsion complex

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.313627

Keywords:

propulsion system, predictive control, distributed system, high-level controller

Abstract

The object of this researching is the process of maneuvering a sea-based vehicle under compressed conditions, which requires one hundred percent reserve of thrusters (THRs) of various modifications and locations. The main problem is the provision of energy-efficient control over the ship's motion at low speed in the horizontal plane using a high-level predictive controller. The hierarchy of the motion control system (MCS) is usually divided into several levels with the help of a high-level motion controller and the THR motor control distribution algorithm. This allows for a modular software structure where a high-level controller (HLC) can be designed without using comprehensive information about the THR motors. However, for a certain reference of THR configurations, such a decoupling can lead to reduced control performance due to the limitations of HLC regarding the physical constraints of the vessel and the behavior of MCS.

The main results of the researching are methods to improve control performance using a nonlinear model predictive control (MPC) as a basis for the designed motion controllers due to its optimized solution and ability to consider constraints. A decoupled system was implemented for two simple motor tasks showing dissociation problems. The shortcomings were eliminated through the development of a nonlinear MPC controller, which combines the motion controller and the distribution of control over THR motors. To preserve the discrete modularity of the control system and achieve adequate performance, a nonlinear MPC controller with time-varying constraints was designed. This has made it possible to take into account the current limitations of the THR control system, increase the accuracy of control, and reduce the response time of the system by 10 %.

Author Biographies

Vitalii Budashko, National University "Odessa Maritime Academy"

Doctor of Technical Sciences, Professor

Department of Electrical Engineering and Electronics

Educational and Scientific Institute of Automation and Electromechanics

Albert Sandler, National University "Odessa Maritime Academy"

PhD, Associate Professor

Department of the theory of automatic control and computer technology

Educational and Scientific Institute of Automation and Electromechanics

Sergii Khniunin, National University "Odessa Maritime Academy"

PhD, Associate Professor

Educational and Scientific Institute of Automation and Electromechanics

Valentyn Bogach, National University "Odessa Maritime Academy"

PhD, Associate Professor

Department of Materials Technology and Ship Repair

Educational and Scientific Institute of of Engineering

References

  1. Budashko, V. (2017). Formalization of design for physical model of the azimuth thruster with two degrees of freedom by computational fluid dynamics methods. Eastern-European Journal of Enterprise Technologies, 3 (7 (87)), 40–49. https://doi.org/10.15587/1729-4061.2017.101298
  2. Fossen, T. I. (2021). Handbook of Marine Craft Hydrodynamics and Motion Control. Wiley. https://doi.org/10.1002/9781119575016
  3. van Goor, P., Hamel, T., Mahony, R. (2023). Constructive Equivariant Observer Design for Inertial Navigation. IFAC-PapersOnLine, 56 (2), 2494–2499. https://doi.org/10.1016/j.ifacol.2023.10.1229
  4. Maidana, R. G., Kristensen, S. D., Utne, I. B., Sørensen, A. J. (2023). Risk-based path planning for preventing collisions and groundings of maritime autonomous surface ships. Ocean Engineering, 290, 116417. https://doi.org/10.1016/j.oceaneng.2023.116417
  5. Bekker, J. R., Dou, S. X. (2002). A Packaged System Approach to DP Vessel Conversion. Dynamic Positioning Conference. Available at: http://dynamic-positioning.com/proceedings/dp2002/workboats_packaged_system.pdf
  6. Cozijn, H., Hallmann, R., Koop, A. (2010). Analysis of the velocities in the wake of an azimuthing thruster, using PIV measurements and CFD calculations. Dynamic positioning conference: thrusters session. Available at: https://dynamic-positioning.com/proceedings/dp2010/thrusters_cozijn.pdf
  7. Furmanik, M., Konvičný, D., Rafajdus, P. (2023). Low-Speed Sensorless Control for Six-Phase PMSM Based on Magnetic Anisotropy. Transportation Research Procedia, 74, 892–899. https://doi.org/10.1016/j.trpro.2023.11.222
  8. Hemalatha, N., Venkatesan, S., Kannan, R., Kannan, S., Bhuvanesh, A., Kamaraja, A. S. (2024). Sensorless speed and position control of permanent magnet BLDC motor using particle swarm optimization and ANFIS. Measurement: Sensors, 31, 100960. https://doi.org/10.1016/j.measen.2023.100960
  9. Sun, L. (2022). Low speed sensorless control method of brushless DC motor based on pulse high frequency voltage injection. Alexandria Engineering Journal, 61(8), 6457–6463. https://doi.org/10.1016/j.aej.2021.12.005
  10. Budashko, V., Sandler, A., Khniunin, S. (2023). Improving the method of linear-quadratic control over a physical model of vessel with azimuthal thrusters. Eastern-European Journal of Enterprise Technologies, 1 (2 (121)), 49–71. https://doi.org/10.15587/1729-4061.2023.273934
  11. de A. Fernandes, D., Sorensen, A. J., Donha, D. C. (2013). Trajectory Tracking Motion Control System for Observation Class ROVs. IFAC Proceedings Volumes, 46 (33), 251–256. https://doi.org/10.3182/20130918-4-jp-3022.00025
  12. Houska, B., Ferreau, H. J., Diehl, M. (2011). ACADO toolkit – An open‐source framework for automatic control and dynamic optimization. Optimal Control Applications and Methods, 32 (3), 298–312. https://doi.org/10.1002/oca.939
  13. Johansen, T. A., Fossen, T. I. (2013). Control allocation – A survey. Automatica, 49 (5), 1087–1103. https://doi.org/10.1016/j.automatica.2013.01.035
  14. Yari, E., Ghassemi, H. (2016). Hydrodynamic analysis of the surface-piercing propeller in unsteady open water condition using boundary element method. International Journal of Naval Architecture and Ocean Engineering, 8 (1), 22–37. https://doi.org/10.1016/j.ijnaoe.2015.09.002
  15. Budashko, V., Sandler, A., Shevchenko, V. (2022). Diagnosis of the Technical Condition of High-tech Complexes by Probabilistic Methods. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 16 (1), 105–111. https://doi.org/10.12716/1001.16.01.11
  16. Glad, T., Ljung, L. (2018). Control Theory. CRC Press. https://doi.org/10.1201/9781315274737
  17. Budashko, V. (2020). Thrusters Physical Model Formalization with regard to Situational and Identification Factors of Motion Modes. 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 10, 1–6. https://doi.org/10.1109/icecce49384.2020.9179301
  18. Brezina, A., Thomas, S. (2013). Measurement of Static and Dynamic Performance Characteristics of Electric Propulsion Systems. 51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition. https://doi.org/10.2514/6.2013-500
  19. Bucknall, R. W. G., Ciaramella, K. M. (2010). On the Conceptual Design and Performance of a Matrix Converter for Marine Electric Propulsion. IEEE Transactions on Power Electronics, 25 (6), 1497–1508. https://doi.org/10.1109/tpel.2009.2037961
  20. Zhong, Y., Yu, C., Bai, Y., Zeng, Z., Lian, L. (2024). Diving dynamics identification and motion prediction for marine crafts using field data. Journal of Ocean Engineering and Science, 9 (4), 391–400. https://doi.org/10.1016/j.joes.2023.12.001
  21. Abdessameud, A., Polushin, I. G., Tayebi, A. (2015). Motion coordination of thrust-propelled underactuated vehicles with intermittent and delayed communications. Systems & Control Letters, 79, 15–22. https://doi.org/10.1016/j.sysconle.2015.02.006
  22. Babadi, M. K., Ghassemi, H. (2013). Effect of hull form coefficients on the vessel sea-keeping performance. Journal of Marine Science and Technology. – 2013. – 11 p. https://doi.org/10.6119/JMST-013-0117-2
  23. Budashko, V., Sandler, A., Shevchenko, V. (2022). Optimization of the control system for an electric power system operating on a constant-power hyperbole. Eastern-European Journal of Enterprise Technologies, 1 (8 (115)), 6–17. https://doi.org/10.15587/1729-4061.2022.252172
  24. Carrera, A., Palomeras, N., Hurtós, N., Kormushev, P., Carreras, M. (2015). Cognitive system for autonomous underwater intervention. Pattern Recognition Letters, 67, 91–99. https://doi.org/10.1016/j.patrec.2015.06.010
  25. Budashko, V., Golikov, V. (2017). Theoretical-applied aspects of the composition of regression models for combined propulsion complexes based on data of experimental research. Eastern-European Journal of Enterprise Technologies, 4 (3 (88)), 11–20. https://doi.org/10.15587/1729-4061.2017.107244
  26. Myrhorod, V., Hvozdeva, I., Budashko, V. (2020). Multi-parameter Diagnostic Model of the Technical Conditions Changes of Ship Diesel Generator Sets. 2020 IEEE Problems of Automated Electrodrive. Theory and Practice (PAEP), 1895, 1–4. https://doi.org/10.1109/paep49887.2020.9240905
  27. Budashko, V., Shevchenko, V. (2021). The synthesis of control system to synchronize ship generator assemblies. Eastern-European Journal of Enterprise Technologies, 1 (2 (109)), 45–63. https://doi.org/10.15587/1729-4061.2021.225517
  28. Budashko, V., Shevchenko, V. (2021). Solving a task of coordinated control over a ship automated electric power system under a changing load. Eastern-European Journal of Enterprise Technologies, 2 (2 (110)), 54–70. https://doi.org/10.15587/1729-4061.2021.229033
  29. Sandler, A., Budashko, V. (2022). Improving tools for diagnosing technical condition of ship electric power installations. Eastern-European Journal of Enterprise Technologies, 5 (5 (119)), 25–33. https://doi.org/10.15587/1729-4061.2022.266267
  30. Sandler, A., Budashko, V., Khniunin, S., Bogach, V. (2023). Improving the mathematical model of a fiber-optic inclinometer for vibration diagnostics of elements in the propulsion system with sliding bearings. Eastern-European Journal of Enterprise Technologies, 5 (5 (125)), 24–31. https://doi.org/10.15587/1729-4061.2023.289773
Design of the predictive management and control system for combined propulsion complex

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Published

2024-10-30

How to Cite

Budashko, V., Sandler, A., Khniunin, S., & Bogach, V. (2024). Design of the predictive management and control system for combined propulsion complex. Eastern-European Journal of Enterprise Technologies, 5(2 (131), 90–102. https://doi.org/10.15587/1729-4061.2024.313627