The brushless DC motor (BIBCM) is receiving wide attention for industrial applications because of their high torque density, high efficiency, and small size. Conventional controllers suffer from uncertain parameters and the nonlinear of the BLDCM. The fuzzy control has been focussing in the field of the control of the BLDCM. However, a systematic method for designing and tuning the fuzzy logic controller is not developed yet. In this design, an autotuning method for fuzzy logic controller based on the genetic algorithm (GA) is designed and the scheme is applied to the BLDCM control. Two closed loops, the inner loop is current feedback which is to adjust the torque of the motor and outer loop is the fuzzy logic controller whose control rules are optimized offline and parameters are adjusted based on the genetic algorithm. The fuzzy rules are experience rules based on expertise or operators longtime experiences. Below table shows the fuzzy rules. The variables are processed by an inference engine executes 49 rules (7*7). Each rule is expressed in the form as If e is NI3 and e, is PM then U is PM If e is PM and e, is NB then U is PS If e is NS and e, is NM then U is PM ........ THE FUZZY LINGUISTIC RULE TABLE The Genetic Algorithm optimization step is as follow:
 Code the parameter
 The initialization of the population
 Evaluate the fitness of each member
 Selection
 Crossover
 Mutation
 Go to step1 until finding the optimum solution.
The program is written in Matlab and tested using Matlab 2007b version.
Reference Paper: Speed Control of Brushless DC Motor Using Genetic Algorithm Based Fuzzy Controller Author’s Name: Changliang Xia, Peijian Guo, Tingna Shi, and Mingchao Wang Source: IEEE Year: 2004
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