Implementation of Fuzzy Tuned PID Controller Using PIC18F2520 for Speed Control of BLDC Motor

Angeti Jival ,Iliya Tizhe Thuku ,Allo Iliya Alhassan

doi:10.7176/cti/8-08

Abstract

Brushless direct current (BLDC) Motor finds widespread application in Engineering such as robotics, aerospace, biomedical systems, conveyor systems, electric vehicles etc. This is due to their superior electrical and mechanical characteristics. This research work focus on implementation of a Fuzzy-Tuned PID controller around PIC18F2520 microcontroller, for the speed control of a Brushless direct current (BLDC) motor. Fuzzy logic is used to update the gains of the PID controller online. The results prove the superiority of the Fuzzy-tuned PID controller over its conventional counterpart as it has lower steady state errors and more robust to parameter variations and load disturbances. Keywords: BLDC motor, Fuzzy Tuned PID Controller, PIC18F252 Microcontroller, Speed Control DOI: 10.7176/CTI/8-08

Highlights

Introduction Brushless Direct CurrentMotors (BLDC) are gradually taken over the usage of DC Motors [1]
One unique feature that make Brushless direct current (BLDC) motors robust over the conventional DC motors is that switching of current in the armature coil is done with the help of electronic circuit, which reduces mechanical losses and improves efficiency [3]
The IF part of the rule refers to the antecedent and the part refers to the consequent Since the output of the fuzzy logic is to tuned the gains of the PID KP, KI and KD through the output membership function called ‘Positive Very Small’, ‘Positive Small’, ‘Positive Medium Small’, ‘Positive Medium’, ‘Positive Medium Large’, ‘Positive Large’ and ‘Positive Very Large’ as error and change in error varies between fuzzy sets : ‘Positive Very Small’, ‘Positive Small’, ‘Positive Medium Small’, ‘Positive Medium’, ‘Positive Medium Large’, ‘Positive Large’, and ‘Positive Very Large’ respectively

Summary

Design of PID controller

For the PID control presented in Figure 2: The output of the controller to the motor u is [5]:. 4.1 Fuzzification This performs the conversion of the point-wise (Crip) value of the process variable into fuzzy set in order to make it compatible with the fuzzy set representation of the process variable in the rule antecedent [6]. This conversion is based on the membership function so assigned and shown in figure 4. From figure 4 above each membership function was resolved using the equation of a straight line.

Data base

For the ‘positive small’ MF the area is given as

Driver Unit

Control unit

Experimental Setup

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