Self Learning Fuzzy Logic-Based Robust Control of Robotic Manipulators Driven With BLDC Motors: A Task Space Control...

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Robot manipulators driven by brushless DC motors (BLDC) often face challenges with model uncertainties.
• Accurate trajectory tracking is crucial for manipulator performance, requiring robust control strategies.
• Incorporating actuator dynamics (AD) is essential for improving positioning sensitivity and overall reliability.

Purpose of the Study:

• To develop a control strategy enabling robot end-effectors to accurately track desired trajectories despite uncertainties in both the robot model and AD.
• To enhance the tracking performance and reliability of robot manipulators by considering BLDC motor dynamics.
• To improve the efficiency and stability of the closed-loop control system.

Main Methods:

• Utilized a self-organized adaptive fuzzy logic (AFL) framework to estimate uncertainties in the dynamic model and AD.
• Implemented online updates of membership function means and variances within the AFL for precise uncertainty estimation.
• Developed a novel Lyapunov function to rigorously prove the uniform ultimate boundedness of the closed-loop system.

Main Results:

• The AFL framework successfully estimated model and actuator dynamics uncertainties.
• The proposed controller demonstrated enhanced trajectory tracking performance in the presence of uncertainties.
• The Lyapunov analysis confirmed the stability and uniform ultimate boundedness of the closed-loop system.

Conclusions:

• The developed adaptive fuzzy logic controller effectively addresses model uncertainties in robot manipulators.
• The controller enhances trajectory tracking accuracy and system reliability for BLDC-driven robots.
• Experimental validation on a two-DOF planar robot confirms the controller's practical applicability and effectiveness.