Thrust distribution algorithm based on supervision and switching and swarm intelligence
DOI:
https://doi.org/10.54097/hset.v44i.7373Keywords:
ship power positioning control system; group intelligence; supervision and switching; thrust distribution algorithm; optimization analysis.Abstract
The ship power positioning control system is the key to maintain the normal operation of the ship, and it is also the basic premise and foundation for the accurate positioning and stable forward movement of the ship, so many ship management and production enterprises attach great importance to the design and construction of the ship power positioning control system. "Thrust distribution" has always been the key technology for designing and constructing ship power positioning control system. When constructing ship power positioning control system by using thrust distribution technology, it is not only necessary to consider the constraint optimization of thrust limit and thrust rate of change limit of propeller, but also the constraint optimization of azimuth rate of change limit, mechanical wear optimization, restricted area limit optimization, etc. It is also necessary to consider the constraint optimization of azimuth rate of change limit, mechanical wear and tear, restricted area limit, etc. The thrust distribution algorithm based on supervision and switching and group intelligence can comprehensively deal with these constraints, which can improve the functions of the ship power positioning control system and solve the convergence problem in the system operation, and improve the operation efficiency, safety and stability of the ship power positioning control system. In this paper, the author discusses the thrust distribution algorithm based on supervision and switching and group intelligence in depth, combining relevant literature and the actual problem of thrust distribution.
Downloads
References
M. Liu, L. Hua. Research on thrust allocation algorithm based on supervision and switching and swarm intelligence [J]. Ship Mechanics,2022,26(5):667-678.
Shangliubin, Wang Wei, Liu Zhihua. Particle swarm algorithm-based decision variable preference for power positioning thrust distribution[J]. Ship Engineering,2019(10):81-84,97.
Zhang JF, Zhang TT,Ding FG,et al. Optimization of thrust allocation for mission-constrained ships based on genetic bat algorithm[J]. Ship Engineering,2022,44(2):105-111.
Ai Di, Cao Hui. Optimization of naval thrust distribution based on artificial intelligence optimization algorithm[J]. Ship Science and Technology,2021,43(22):73-75.
Liu L, Tang Y, Tao C, et al. Thrust vectoring based on control distribution for short takeoff vertical landing aircraft deceleration transition control[J]. Journal of Harbin Engineering University,2022,43(6):832-841.
Cheng Nan. Research on the optimization algorithm for thrust distribution of semi-submersible drilling rigs[D]. Liaoning:Dalian Maritime University,2012.
Li Changhao. Research on intelligent control algorithm for power positioning system of test vessel[D]. Heilongjiang:Harbin Engineering University,2021.
Zhao W, Liu Huanwei. Research on thrust distribution algorithm based on strict equation constraint[J]. Ship Mechanics,2021,25(4):443-452.
Chen YAHO, Xu HX, Li WJ. Truncated redistribution combined bias thrust distribution algorithm[J]. Journal of Dalian University of Technology,2018,58(6):594-599.
Xu H-X, Wen W, Feng F. Adaptive combined bias thrust distribution algorithm[J]. Journal of Wuhan University of Technology (Transportation Science and Engineering Edition),2016,40(4):569-573.
Chen YAHO,Xu HX,Li WJ,et al. Energy optimal combined bias thrust distribution algorithm[J]. Journal of Dalian Maritime University,2019,45(1):26-32.
Liu M,Hua L,Zhou J,et al. Research on incremental thrust distribution algorithm based on chaotic particle swarm[J]. Ship Engineering,2015,37(10):76-79,93.
Zhou Xing. Research on thrust distribution algorithm and strategy for power positioning system[D]. Hubei:Wuhan University of Technology,2016.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
