Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

Area of Science:

• Robotics
• Control Systems Engineering
• Computational Intelligence

Background:

• Manipulator fixed-point tasks are susceptible to external disturbances, parameter variations, and noise, necessitating improved controller robustness and accuracy.
• Existing control strategies often struggle to adapt dynamically to changing environmental conditions and system uncertainties.

Purpose of the Study:

• To design a self-tuning particle swarm optimization (PSO) fuzzy PID positioning controller for manipulators.
• To optimize fuzzy PID control parameters using PSO for enhanced robustness and accuracy.
• To improve the transient response speed, tracking accuracy, and follower characteristics of manipulator systems.

Main Methods:

• Development of a mathematical model for the manipulator system.
• Implementation of a PD control strategy with gravity compensation for positioning control.
• Dynamic tuning of PID controller parameters using a fuzzy controller, optimizing quantization and scaling factors via PSO.
• Online self-tuning strategy for quantization and proportion factors using a closed-loop control system.

Main Results:

• The proposed PSO-based fuzzy PID controller significantly enhances manipulator control performance.
• Demonstrated improvements in transient response speed, tracking accuracy, and follower characteristics compared to conventional methods.
• The self-tuning mechanism effectively adapts controller parameters online for optimal performance.

Conclusions:

• The self-tuning PSO fuzzy PID controller offers a robust and accurate solution for manipulator positioning tasks.
• The developed strategy effectively mitigates the adverse effects of disturbances and parameter variations.
• This approach represents a significant advancement in adaptive control for robotic manipulators.