# Speed Control of DC Motor Using Fuzzy PID Controller

**Authors:** Messaadi Nassim, Amroun Abdelkader  
**arXiv:** [2108.05450](https://arxiv.org/abs/2108.05450)  
**Date:** 2021-08-11  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2108.05450v1/

## Key points

- Messaadi Nassim and Amroun Abdelkader designed a DC motor speed control system using a Fuzzy PID controller
- Proportional, integral, and derivative gains (KP, KI, KD) were adjusted based on Fuzzy logic for robustness
- Implemented a Fuzzy Logic Controller (FLC) with 25 rules for auto-tuning each parameter of the PID controller
- FLC had two inputs: motor speed error between reference setpoint and actual speed, and variation of speed error
- Output of FLC determined parameters of PID controller used to control DC motor's speed
- Self-adaptive PID controller achieved excellent dynamic behavior with perfect speed tracking, short rise and settling times, zero overshoot, and minimal steady-state error compared to conventional PID controller
- Fuzzy PID controller offered better compromise between rise time and response time compared to classical PID controllers while providing highly accurate output

## Summary

In this project, Messaadi Nassim and Amroun Abdelkader successfully designed a DC motor speed control system using a Fuzzy PID controller. The controller's proportional, integral, and derivative gains (KP, KI, KD) were adjusted based on Fuzzy logic to ensure robustness. To achieve this, they implemented a Fuzzy Logic Controller (FLC) with 25 rules for auto-tuning each parameter of the PID controller. The FLC had two inputs: the motor speed error between the reference setpoint and actual speed, and the variation of the speed error. The output of the FLC determined the parameters of the PID controller used to control the DC motor's speed. The study demonstrated that by combining precise characteristics of traditional PID controllers with flexible features of fuzzy controllers through their self-tuning approach, they were able to effectively control the DC motor's speed. Simulation results obtained through MATLAB/SIMULINK showed that their self-adaptive PID controller achieved excellent dynamic behavior with perfect speed tracking, short rise and settling times, zero overshoot, and minimal steady-state error compared to a conventional PID controller. To validate their theoretical results in practical applications, they also modeled the fuzzy PID using simple code on Arduino IDE and conducted experiments. Through these experiments, they confirmed that the fuzzy PID controller offered a better compromise between rise time and response time compared to classical PID controllers while providing highly accurate output. From their comparison between step responses of different systems in Figure 17, it was evident that operating only with a DC motor led to unreliable performance. While classical PID controllers were faster in rise time than fuzzy PIDs, they took longer to reach their final value. On the other hand, fuzzy PIDs exhibited optimal balance between rise time and response time along with precise output accuracy. Overall, this research showcased how integrating Fuzzy logic into traditional control systems like PID controllers can significantly enhance performance in effectively controlling DC motors' speeds.