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Self-organizing fuzzy learning CLOS guidance law design

机译：自组织模糊学习CLOS制导律设计

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摘要

A new self-organizing fuzzy logic control (SOFLC) design method is proposed. The proposed method is applied to the command line-of-sight (CLOS) guidance law design. The SOFLC contains two sets of fuzzy inference logic. One is the fuzzy logic controller and the other is the rule modifier. The new learning method of the rule modifier is developed based on a fuzzy learning algorithm. The modification value of each rule is based on the fuzzy firing weight, so that learning of the rule bases is reasonable. Finally, two engagement scenarios are examined, and a comparison between a fuzzy logic control (FLC), an optimal learning FLC, and the proposed SOFLC CLOS guidance laws is made. Simulation results show that the proposed SOFLC guidance law can achieve better guidance performance than the other guidance laws.
机译：提出了一种新的自组织模糊逻辑控制（SOFLC）设计方法。所提出的方法被应用于命令行视线（CLOS）指导法设计。 SOFLC包含两组模糊推理逻辑。一个是模糊逻辑控制器，另一个是规则修饰符。基于模糊学习算法，开发了规则修改器的新学习方法。每个规则的修改值基于模糊触发权重，因此学习规则库是合理的。最后，研究了两种参与方案，并对模糊逻辑控制（FLC），最佳学习FLC和拟议的SOFLC CLOS指导律进行了比较。仿真结果表明，提出的SOFLC制导律比其他制导律具有更好的制导性能。

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