Advanced control parameter optimization in DC motors and liquid level systems

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.