Comparative Analysis on ANN based Fuzzy and PI Controller based Speed Control of DC Motor

Abstract

It concerns the study of intelligent control techniques and its application to speed control of DC motors fed by DC-DC converters, which can be implemented in an embedded system. The intelligent techniques include Fuzzy Logic Control (FLC) and Artificial Neural Network (ANN). Implementation of closed loop control for power converters and drives requires efficient controllers and in turn requires high computation rate and so embedded system implementation is advantageous. The ease of design of intelligent controllers can be well understood only if the complexities in the conventional controller design are explained. Initially, conventional PT controller design based on the small signal model of the motor with buck type DC-to-DC power converter is designed. This controller implementation on a PC using data acquisition card is presented. Then a fuzzy controller was designed with the embedded systems implementation in mind and the analysis of response of the motor speed is compared with conventional Proportional and Integration (P1) controller. Fuzzy controller gives a better performance compared to P1 controller. The simulated fuzzy controller was implemented experimentally in an 8051-based embedded system. A buck type DC-to-DC power converter fed DC drive was constructed and the fuzzy controller performance was studied experimentally. The entire system is found to be cost effective and has more advantages in terms of steady state error and rise time.

Introduction

The proposed system uses a DC-DC power converter with a closed-loop control strategy, consisting of two control loops:

• An inner ON/OFF current control loop
• An outer fuzzy logic speed control loop

In this system, the motor’s actual speed is continuously compared with the desired speed. The error and its rate of change are processed by a fuzzy logic controller, which adjusts the duty cycle of the converter to regulate the motor speed. A four-quadrant converter is used to allow both forward and reverse operation, enabling better handling of speed changes and load variations while reducing torque ripple.

The methodology incorporates fuzzy logic and neural network techniques, which are more effective than traditional controllers for handling complex, nonlinear systems. Implementation is done using an embedded system (8051 microcontroller), with sensors for speed (tacho-generator) and current feedback.

Experimental and simulation results show that:

• Fuzzy and ANN-based controllers outperform traditional PI controllers
• They reduce steady-state error and improve response time
• ANN controllers, in particular, provide faster rise time and better dynamic performance

Overall, the study demonstrates that intelligent control methods (fuzzy logic and neural networks) significantly improve motor performance, efficiency, and responsiveness compared to conventional control techniques.

Conclusion

The study of Fuzzy and ANN control techniques and its application to control the speed of separately exc motors has been taken up in this thesis. The DC motor drive system uses Buck type DC-DC converters and microcontroller based embedded system for intelligent control algorithm implementation. Initially, the conventional controller design starting from mathematical modeling of the DC motor and the related power electronic converters were discussed. Instead of the general time averaged model, the small signal model was developed and based on the small signal model; the conventional P1 controller was designed. The small signal model is advantageous as it takes care of the dynamics of the system under study.

References

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Copyright

Copyright © 2026 Prashant Nayak, Ankit Tiwari, Ankit Kumar Sharma, Devansh Tiwari, Suneel Patel, MD Asif. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.