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Advanced control parameter optimization in DC motors and liquid level systems.

Serdar Ekinci1, Davut Izci1,2, Mohammad H Almomani3

  • 1Department of Computer Engineering, Batman University, Batman, 72100, Turkey.

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|January 9, 2025
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Summary
This summary is machine-generated.

The novel mountain gazelle optimizer (MGO) effectively tunes proportional-integral-derivative (PID) controller parameters for dynamic systems. This approach enhances control performance and stability in industrial applications.

Keywords:
DC motor speed regulationLiquid level controlMountain Gazelle optimizerPID controllerParameter estimation

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Area of Science:

  • Control Systems Engineering
  • Optimization Algorithms
  • Industrial Automation

Background:

  • Dynamic systems require effective control for optimal industrial functionality.
  • Optimizing control parameters is crucial for enhancing controller performance.
  • The mountain gazelle optimizer (MGO) mimics natural behaviors for optimization.

Purpose of the Study:

  • To introduce and apply the mountain gazelle optimizer (MGO) for optimizing control parameters.
  • To fine-tune proportional-integral-derivative (PID) controller parameters in DC motor and liquid level systems.
  • To evaluate the MGO's effectiveness against other optimization algorithms.

Main Methods:

  • Implementation of the mountain gazelle optimizer (MGO) algorithm.
  • Optimization of PID controller parameters for a DC motor system.
  • Optimization of PID controller parameters for a three-tank liquid level system.
  • Comparative analysis with Grey Wolf Optimizer and Particle Swarm Optimization.
  • Introduction of a new performance indicator, ZLG, for control quality assessment.

Main Results:

  • MGO achieved a rise time of 0.0478 s, zero overshoot, and settling time of 0.0841 s for the DC motor.
  • The liquid level system showed improved control with a rise time of 11.0424 s and settling time of 60.6037 s.
  • MGO demonstrated superior performance compared to Grey Wolf Optimizer and Particle Swarm Optimization.
  • MGO-based approach consistently yielded lower ZLG values, indicating enhanced control quality.

Conclusions:

  • The MGO is a robust and adaptable method for dynamic system control and parameter optimization.
  • MGO offers a dependable and efficient optimization methodology for advancing control systems.
  • This research contributes to improved stability and efficiency in diverse industrial applications.