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模糊逻辑系统是一种具有输入输出功能的系统,本文构造的模糊逻辑系统的知识库并不储存If-then形式的模糊规则,而是储存关于输入和输出变量的模糊语言值,这些语言值被从属于这些语言值论域上的单位分解整合在一起,形成了若干关于输入输出变量的模糊蕴涵关系。当一组数据进入该模糊逻辑系统输入端时,它们首先被单点模糊化处理后激活这些模糊蕴涵关系,然后依据模糊推理机及合适的模糊算子得到模糊输出,最后经解模糊化得到最终输出结果。与通常的模糊逻辑系统相比较,这种模糊逻辑系统的主要优势是其输出不仅呈现为某些基函数的线性组合形式,而且组合系数的个数与输入变量的个数相同,因而这种模糊逻辑系统能够有助于解决模糊控制中的“维数灾难”问题。最后,针对一类非线性不确定系统,本文利用这种新的模糊逻辑系统设计了自适应跟踪控制器,并通过仿真算例验证了该方法的有效性。
The fuzzy logic system is a kind of system with input and output functions. The knowledge base of the fuzzy logic system constructed in this paper does not store if-then-type fuzzy rules, but stores fuzzy language values about input and output variables. These language values are The subordination of these linguistic value domain on the unit decomposition integrated together to form a number of input and output variables fuzzy implication relationship. When a set of data enters the input of the fuzzy logic system, they are first unidirectionally fuzzy processed to activate these fuzzy implication relations, and then get the fuzzy output according to the fuzzy inference engine and the suitable fuzzy operator. Finally, the final output is obtained by defuzzification result. Compared with the usual fuzzy logic system, the main advantage of this fuzzy logic system is that its output not only presents as a linear combination of some basis functions, but also has the same number of combination coefficients as the number of input variables, The logic system can help to solve the “dimensionality disaster ” problem in fuzzy control. Finally, for a class of nonlinear uncertain systems, an adaptive tracking controller is designed by using this new fuzzy logic system. The simulation results show the effectiveness of the proposed method.