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 non-linear 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 auto-tuning 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 off-line and parameters are adjusted based on the genetic algorithm.
The fuzzy rules are experience rules based on expertise or operators long-time 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:
  1. Code the parameter
  2. The initialization of the population
  3. Evaluate the fitness of each member
  4. Selection
  5. Crossover
  6. Mutation
  7. 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

Request source code for academic purpose, fill REQUEST FORM or contact +91 7904568456 by whatsapp or sales@verilogcourseteam.com, fee applicable.

SIMULATION VIDEO DEMO