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February 19, 20260 citationsOpen Access

Metaheuristic Optimization of Fractional Order Pid Controller Parameters Using Ant Colony Algorithm for Dc Motor Drives

RARajesh Verma Ankit

Key Points

  • The aim is to optimize the parameters of a FOPID controller for DC motor speed control using ant colony optimization.
  • Developed a simulation model in Simulink for a DC motor with a FOPID controller.
  • Reduced the integral time absolute error using ant colony optimization.
  • Utilized the Ziegler-Nichols tuning method for controller parameter adjustment.
  • Achieved significant improvements in rise time and settling time compared to traditional methods.
  • Showed enhanced steady and transient response characteristics.
  • Minimized the integral time absolute error effectively.

Abstract

This paper deals with the use of a FOPID Controller for the direct current motor speed controlling process. FOPID Controller consists of fractional integral-derivative terms along with the integer order proportional terms. It is a specific controller in which orders of derivative and integral lie in between fractions of 0 and 1. Mathematical model of DC motor and controller is presented whose field has been excited by an external source. In this paper, the simulation part of a DC motor for controlling its speed using a FOPID Controller has been performed. There are five degrees of freedom in FOPID controller contrary to traditional PID controller which have only three. The values of the five parameters (Kp, Ki, Kd, λ, µ) of a FOPID Controller have been improved by reducing the ITAE (Integral Time Absolute Error) cost to best possible value using the ACO i.e. Ant Colony Optimization Technique. The closed loop ZNT (Ziegler-Nichols Tuning) method used for the tuning of DC motor. Simulink model of proposed system has been developed and simulated to find out the minimum cost. The intensification in the steady and transient behaviors of the system. The results also exhibit significant improvement in the rise time, settling time and peak overshoot as compared to the other optimization methods

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Cite This Study

Rajesh Verma Ankit (2025) studied this question.

synapsesocial.com/papers/6996a7e3ecb39a600b3edf8bhttps://doi.org/10.5281/zenodo.18661697
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