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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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High precision experimentally validated adaptive neuro fuzzy inference system controller for DC motor drive system.

Muhammad Akram Bhayo¹, Javed Ahmed Laghari¹, Mohammad Aljaidi²

¹Department of Electrical Engineering, Quaid-e-Awam University of Engineering, Science & Technology, Nawabshah, 67450, Sindh, Pakistan.

Scientific Reports

|April 25, 2025

Summary

Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers eliminate overshoot and reduce settling time in DC motor drives. This advanced control method outperforms traditional Proportional-Integral (PI) controllers, achieving superior performance.

Keywords:

ANFIS Cascaded control structure DC motor drive DS1104 R&D control board PI controller

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Area of Science:

Electrical Engineering
Control Systems
Artificial Intelligence

Background:

DC motor drives commonly use Proportional-Integral (PI) controllers for speed and torque regulation.
Tuning PI controllers presents challenges, often resulting in significant overshoot and long settling times.
Eliminating overshoot without compromising speed in DC motor control is a persistent issue.

Purpose of the Study:

To present an experimental implementation of Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers for high-precision DC motor drive control.
To eliminate overshoot and simultaneously improve settling time in DC motor speed and torque control.
To compare the performance of ANFIS controllers against traditionally tuned PI controllers.

Main Methods:

Utilized a 2-quadrant DC motor drive experimental setup with a dSPACE DS1104 R&D controller board.
Employed the Bode Plot method for optimal tuning of Proportional-Integral (PI) controller gains (Kp and Ki).
Designed, implemented, and validated Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers through both simulation and experimental testing.

Main Results:

ANFIS controllers successfully eliminated overshoot, achieving 0% in experimental results.
ANFIS controllers significantly improved settling time, reducing it to 0.18 seconds.
Experimental and simulation results demonstrated that ANFIS controllers outperform tuned PI controllers.

Conclusions:

Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers offer a superior solution for DC motor drive control compared to PI controllers.
ANFIS-based control effectively addresses the limitations of PI controllers, namely overshoot and settling time.
The proposed ANFIS controllers provide high-precision control for DC motor drives, validated through rigorous simulation and experimentation.