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STUDY ON FUZZY SELF-LEARNING CONTROL SYSTEM FOR SHIP STEERING

机译:船舶转向模糊自学习控制系统的研究

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Fuzzy control has shown success in some application areas and emerged as an alternative to some conventional control schemes. There are also some drawbacks in this approach, for example it is hard to justify the choice of fuzzy controller parameters and control rules, and control precision is low, and so on. Fuzzy control is developing towards self-learning and adaptive. The ship steering motion is a nonlinear, coupling, time-delay complicated system. How to control it effectively is the problem that many scholars are studying. In this paper, based on the repeated control of the robot, the self-learning arithmetic was worked out. The arithmetic was realized in fuzzy logic way and used in cargo steering. It is the first time for the arithmetic to be used in cargo steering. Our simulation results show that the arithmetic is effective and has several potential advantages over conventional fuzzy control. This work lays a foundation in modeling and analyzing the fuzzy learning control system.
机译:模糊控制在某些应用领域已显示出成功,并已成为一些常规控制方案的替代方案。这种方法也有一些缺点,例如很难证明模糊控制器参数和控制规则的选择是合理的,并且控制精度很低等等。模糊控制正在向自学习和自适应发展。船舶转向运动是一个非线性的,耦合的,延时的复杂系统。如何有效地控制它是许多学者研究的问题。本文在机器人的重复控制的基础上,提出了自学习算法。该算法以模糊逻辑的方式实现,用于货物转向。这是该算法首次在货物转向中使用。仿真结果表明,该算法是有效的,与传统的模糊控制相比具有许多潜在的优势。这项工作为模糊学习控制系统的建模和分析奠定了基础。

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