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Performance improvement of DC motor control system using PID controller with Kookaburra and Red Panda optimization

G Saravanan1, C Pazhanimuthu1, Palanichamy Naveen2

  • 1Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore, 641407, India.

Scientific Reports
|June 6, 2025
PubMed
Summary
This summary is machine-generated.

New metaheuristic algorithms, Kookaburra Optimization Algorithm (KOA) and Red Panda Optimization Algorithm (RPOA), were used to tune Proportional-Integral-Derivative (PID) controllers for DC motor speed control. KOA demonstrated superior rise time and bandwidth, while RPOA achieved a lower Integral Time Absolute Error (ITAE).

Keywords:
DC motorFrequency responseKookaburra Optimization Algorithm (KOA)Proportional–integral–derivative (PID) controllerRed panda optimization algorithm (RPOA)Time response

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Area of Science:

  • Control Systems Engineering
  • Computational Intelligence
  • Robotics and Automation

Background:

  • Precise speed control of DC motors is crucial for industrial applications.
  • Proportional-Integral-Derivative (PID) controllers are commonly used for DC motor speed control.
  • Tuning PID controller gains is challenging, especially in complex systems, often requiring metaheuristic algorithms.

Purpose of the Study:

  • To investigate the effectiveness of Kookaburra Optimization Algorithm (KOA) and Red Panda Optimization Algorithm (RPOA) for tuning PID controller gains in a DC motor speed control system.
  • To compare the performance of KOA and RPOA in terms of time and frequency response, robustness, and convergence characteristics.

Main Methods:

  • The study applied KOA and RPOA, swarm intelligence-based metaheuristic algorithms, to optimize PID controller gains for a DC motor speed control system.
  • Performance was evaluated using the Integral Time Absolute Error (ITAE) objective function.
  • Time response (rise time, settling time) and frequency response (bandwidth) analyses were conducted.
  • Robustness was assessed under parameter variations from ±20% to ±50%.

Main Results:

  • KOA achieved a faster rise time (9.2-12.8% improvement) and larger bandwidth (15-16.4% improvement) compared to RPOA.
  • RPOA yielded a lower Integral Time Absolute Error (ITAE).
  • Both algorithms demonstrated convergence around the 70th iteration with similar time complexity.
  • Robustness analysis indicated improved system responses under parameter variations.

Conclusions:

  • KOA and RPOA are effective metaheuristic algorithms for PID controller tuning in DC motor speed control.
  • KOA excels in dynamic performance metrics like rise time and bandwidth.
  • RPOA demonstrates superior performance in minimizing Integral Time Absolute Error (ITAE).
  • Both algorithms offer robust solutions for DC motor speed control applications.