Trajectory Tracking Control for Flexible-Joint Robot Based on Extended Kalman Filter and PD Control

Area of Science:

• Robotics
• Control Systems Engineering
• Mechatronics

Background:

• Flexible robot arms offer advantages in environmental adaptability and human-robot interaction.
• Traditional controllers often rely on extensive sensor data, making them susceptible to external interference and reducing control precision.
• Incomplete state feedback poses a significant challenge for precise control of flexible joint robots.

Purpose of the Study:

• To propose an effective control method for flexible robot arms with incomplete state feedback.
• To reduce reliance on multiple sensors and mitigate external interference in robot arm control.
• To improve the trajectory tracking performance of flexible robot arms.

Main Methods:

• Developed a control system model based on the non-linear dynamics of the flexible robot.
• Designed a state prediction observer utilizing the Extended Kalman Filter (EKF).
• Employed Lyapunov stability analysis to ensure system stability.
• Implemented a closed-loop Proportional-Derivative (PD) controller integrated with the EKF observer.

Main Results:

• The EKF-based observer effectively estimates the system's state using motor data, reducing sensor requirements and external interference.
• Lyapunov stability analysis confirmed the theoretical stability of the proposed control system.
• Experimental comparisons demonstrated superior trajectory tracking performance compared to traditional sensor-based PD control.

Conclusions:

• The proposed control algorithm, combining a closed-loop PD controller and EKF state observer, is effective for flexible robot arm trajectory control.
• The method significantly improves control precision and robustness against external disturbances.
• This approach offers a viable solution for controlling flexible robots under conditions of incomplete state feedback.