GWO-Based Multi-Stage Algorithm for PMDC Motor Parameter Estimation.

Area of Science:

• Robotics
• Control Systems Engineering
• Electrical Engineering

Background:

• Accurate motor controller tuning is crucial for mobile robot performance.
• Permanent Magnet Direct Current (PMDC) motor parameter identification is key to precise control.
• Optimization algorithms, like genetic algorithms, are increasingly used for parameter identification but can be inefficient without proper search ranges.

Purpose of the Study:

• To introduce an effective method for determining Permanent Magnet Direct Current (PMDC) motor parameters.
• To address the challenge of inefficient parameter identification in optimization algorithms due to wide search ranges.
• To improve the time efficiency and solution-finding capabilities of bio-inspired optimization algorithms for motor parameter estimation.

Main Methods:

• Developing a novel approach for the initial estimation of parameter search ranges for PMDC motors.
• Utilizing optimization-based techniques, specifically genetic algorithms, for parameter identification.
• Implementing a method that refines search ranges to accelerate the optimization process.

Main Results:

• The proposed method successfully estimates the required parameter ranges for PMDC motors.
• Initial estimation of parameter ranges significantly reduces the computation time for genetic algorithms.
• The approach enhances the efficiency and effectiveness of parameter identification for robot motor controllers.

Conclusions:

• The developed method for estimating PMDC motor parameter ranges optimizes the performance of bio-inspired algorithms.
• This technique leads to more precise controller tuning and improved mobile robot dynamics.
• The findings contribute to more efficient and effective robot design and control system development.