Adaptive Fuzzy Logic Controllers Using Hybrid Genetic Algorithms
Adaptive Fuzzy Logic Controllers Using Hybrid Genetic Algorithms
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DOI：
10.1142/s021848851950003x
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发表时间：
2019-02
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影响因子：
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通讯作者：
P. C. Shill;A. Paul;K. Murase
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文献类型：
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作者：
P. C. Shill;A. Paul;K. Murase
在本文中，模糊逻辑控制器（FLC）与混合遗传算法（HGAs）的集成开发，以使设计过程完全自动化，而不需要任何人类专家和数值数据。我们的方法包括两个阶段：第一阶段涉及的模糊控制规则的选择和定义，以及调整隶属函数参数，而第二阶段进行相应的模糊控制规则的隶属函数类型的最佳选择。同时学习这两个部分代表了一种提高FLC准确性以最小化误差的方法。有人认为，FLC的性能很大程度上取决于参数以及类型的隶属函数。因此，上述HGA是用于设计高效自适应FLC系统的可行解决方案。为了证明所提出的方法的FLC的优化设计的有效性，所提出的方法被施加到一个著名的基准控制器设计任务，汽车和卡车和拖车一样的机器人系统。仿真结果表明，该优化方法可以找到最优的模糊规则及其相应的隶属函数类型，具有较高的准确率。新的HGAs优化的自适应FLC不仅优于被动控制策略，而且优于人工设计的FLC，神经编码控制器与聚类和神经模糊控制算法。
In this paper, an integration of fuzzy logic controllers (FLCs) with hybrid genetic algorithms (HGAs) is developed with a view to make the design process fully automatic, without requiring any human expert and numerical data. Our approach consists of two phases: first phase involves selection and definition of fuzzy control rules as well as adjustment of membership functions parameters, while the second phase performs an optimal selection of membership function types corresponding to fuzzy control rules. Learning both parts concurrently represents a way to improve the accuracy of the FLCs to minimize the errors. It has been argued that the performance of FLCs greatly depends on the parameters as well as types of membership functions. Thus, the aforementioned HGAs are a viable solution for designing an efficient adaptive FLCs system. To demonstrate the effectiveness of the proposed method for optimal design of the FLCs, the proposed approach is applied to a well-known benchmark controller design tasks, car and truck-and-trailer like robot system. Simulation results illustrates that proposed optimization approach can find optimal fuzzy rules and their corresponding membership functions types with a high rate of accuracy. The new HGAs optimized adaptive FLCs outperforms not only a passive control strategy but also human-designed FLCs, a neural coded controller with clustering and a neural-fuzzy control algorithm.