Analysis and design of inference mechanisms for fuzzy feedback control

S. Zhang, Y. Wong, A. Poo
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Abstract

The influence of the different combinations of implications and the defuzzifications on the performance of fuzzy feedback control are examined. The characteristics of two popular implications (the min and the prod implications) and two widely used defuzzification algorithms (COA and MAX algorithms) are analyzed in terms of recoverability, change in fuzziness and reasoning strategy. With the aid of simulation of examples of fuzzy force control in milling. It is shown that not every combination of implication should be complemented only by a defuzzification such that the resultant interference mechanism does not increase the fuzziness substantially. A detailed comparitive analysis of the fuzzy milling force control approaches based on the min and prod implications and the COA and MAX defuzzifications are given.<>
模糊反馈控制推理机制的分析与设计
研究了不同含义组合和去模糊化对模糊反馈控制性能的影响。从可恢复性、模糊度变化和推理策略等方面分析了两种常用的解模糊化算法(最小解模糊和最大解模糊)和两种常用的解模糊算法(COA和MAX算法)的特点。结合铣削过程中的模糊力控制实例进行仿真。结果表明,并不是每一个隐含的组合都应该只通过去模糊化来补充,这样所产生的干扰机制不会大大增加模糊性。对基于最小值和最大值含义的模糊铣削力控制方法以及COA和MAX解模糊方法进行了详细的比较分析。
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