Tuning of PID Controller Using Particle Swarm Optimization Method for Speed Control of DC Motor

Abstract

The Proportional-Integral-Derivative (PID) controller is one of the most commonly used control techniques in industrial systems due to its simple structure and efficient control performance. It is particularly popular for the speed control of DC motors. However, conventional PIDtuningmethodssuchasZiegler-Nicholsortrial-and-error oftenresultinsuboptimalsystemresponses,includinglonger settling time, higher overshoot, and slower rise time. To address these limitations, this study explores the use of Particle Swarm Optimization (PSO), a population-based stochastic optimization technique inspired by the social behavior of birds and fish, for tuning PID parameters. The PSO algorithm is employed to optimize the proportional, integral, and derivative gains of the PID controller with the objective of minimizing the error in the motor\'s speed response.TheoptimizedPIDcontrolleristhensimulatedand itsperformanceiscomparedwiththatofatraditionallytuned PID controller. Simulation results reveal that the PSO-tuned PID controller significantly improves the dynamic performance of the system by reducing rise time, settling time, and overshoot, thereby providing more accurate and stable speed control of the DC motor. This demonstrates the effectiveness ofPSOinenhancingclassicalcontrolstrategies.

Introduction

DC motors are widely used in industries due to their precise speed control and reliability. The Proportional-Integral-Derivative (PID) controller is a common method for motor speed regulation, but its performance depends heavily on tuning the controller gains (Kp, Ki, Kd). Traditional tuning methods like Ziegler-Nichols often fail to achieve optimal results, especially in complex or disturbed systems.

This study focuses on using Particle Swarm Optimization (PSO), an intelligent, population-based optimization technique inspired by natural flocking behavior, to automatically tune PID gains for DC motor speed control. PSO efficiently searches the parameter space to minimize control errors such as Integral of Squared Error (ISE).

Simulation results comparing PSO-tuned PID controllers with conventionally tuned ones show that PSO achieves faster rise times, shorter settling times, significantly lower overshoot, and reduced steady-state errors. This leads to smoother, more precise motor operation, improving efficiency and reliability in industrial applications.

PSO’s advantages include simplicity, robustness, quick convergence from random initial values, and no need for gradient information, making it suitable for adaptive or real-time control. The study highlights PSO as a practical, superior alternative for PID tuning in DC motor control, with potential applications in robotics, electric vehicles, conveyor systems, and more.

Conclusion

This paper presents the implementation of a Particle Swarm Optimization (PSO) algorithm for tuning a Proportional-Integral-Derivative (PID) controller used in the speed control of a DC motor. Conventional PID tuning methods,suchasZiegler-Nicholsormanualtuning,oftenlead tosuboptimalperformance,includinghighovershoot,slowerrise time, and longer settling time. The PSO algorithm, inspired by the social behavior of birds and fish, effectively searches the solution space to determine optimal PID gain values. Simulation results demonstrate that the PSO-tuned PID controller outperforms the conventionally tuned controller by significantly reducing overshoot, improving rise time, and shortening settling time. These improvements enhance both transient and steady-state system responses, making the control system more accurate and reliable. Due to its adaptability and efficiency, the PSO-based tuning method can be extended to various industrial applications that involve electric motor control, offering a scalable and intelligent alternative to traditional control strategies.

References

[1] Ziegler,J.G.,&Nichols,N.B.(1942).\"Optimumsettings forautomaticcontrollers.\"Trans.ASME,64(11),759-768. [2] Kennedy, J., &Eberhart, R. (1995). \"Particle swarm optimization.\" Proceedings of ICNN\'95 - International Conference on Neural Networks, 1942-1948. [3] Astrom, K. J., & Hagglund, T. (1995). PID Controllers: Theory, Design, and Tuning. ISA. [4] Pandian,A.R.,&Paramasivam,S.(2012).\"Optimization of PID controller parameters using PSO for speed control of DC motor.\" International Journal of Engineering Research & Technology (IJERT), 1(5), 1-6. [5] Eberhart, R., & Shi, Y. (2001). \"Particle swarm optimization:Developments,applications,andresources.\" Proceedings of IEEE, 81(1), 81-86.

Copyright

Copyright © 2025 Dr. Shilpa Kalambe, Mr. Jayjeet Meshram. 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.