Speed Control of DC Motor Using Fuzzy PID Controller

TL;DR

This paper presents a fuzzy logic-based self-tuning PID controller for DC motor speed control, demonstrating improved dynamic response and accuracy through simulation and practical Arduino experiments.

Contribution

It introduces a fuzzy auto-tuning method for PID parameters, enhancing control performance and robustness over conventional PID controllers.

Findings

Fuzzy self-tuning PID achieves better dynamic response.

Simulation and practical tests confirm improved speed control.

Zero overshoot and steady-state error achieved.

Abstract

In this project, we designed a DC motor whose speed can be controlled by a PID controller. The proportional, integral and derivative gains (KP, KI, KD) of the PID controller are adjusted according to Fuzzy logic. First of all, the fuzzy logic controller is designed according to rules so that the systems is basically robust. There are 25 rules for the auto-tuning of each parameter of the PID controller. The FLC (fuzzy logic controller) has two inputs. The first is the motor speed error between the reference (setpoint) and the actual speed. The second is the variation of the speed error (derivative of the speed error). Secondly the output of the FLC is the parameters of the PID controller which are used to control the speed of the DC motor. The study shows that both the precise characters of PID controllers and the flexible characters of fuzzy controllers are present in the fuzzy self-tuning PID controller. The fuzzy auto-tuning approach implemented on a conventional PID structure was able to control the speed of the DC motor. It also improved the dynamic and static response of the system. The comparison between the conventional response and the fuzzy self-tuning response was performed based on the simulation result obtained by MATLAB/SIMULINK. The simulation results show that the designed self-adaptive PID controller achieves good dynamic behavior of the DC motor, perfect speed tracking with short rise and settling times, zero overshoot and steady state error and thus gives better performance compared to the conventional PID controller. We then model the fuzzy PID using simple code on Arduino IDE and perform a practical experiment, to confirm our theorical results.