A neural flexible PID controller for task-space control of robotic manipulators

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Robotic manipulators require precise task-space position-tracking control for effective operation.
• Traditional controllers like Proportional-Integral-Derivative (PID) may struggle with internal and external dynamic disturbances.
• Ensuring controller robustness across varied working conditions is a significant challenge in robotics.

Purpose of the Study:

• To propose an adaptive robust Jacobian-based controller for enhanced task-space position-tracking in robotic manipulators.
• To develop a controller that compensates for dynamic uncertainties and external disturbances.
• To improve the robustness and adaptability of robotic control systems.

Main Methods:

• A Jacobian-based control framework is employed for task-space control.
• A Proportional-Integral-Derivative (PID) structure forms the controller's foundation.
• A novel neural control signal is synthesized using a non-linear learning law.
• An adaptive gain learning feature is integrated to automatically adjust PID gains.
• Lyapunov stability constraints are utilized to guarantee closed-loop system stability.

Main Results:

• The proposed controller demonstrates effective compensation for internal and external disturbances.
• The adaptive gain learning feature enhances robustness across different operating conditions.
• Intensive simulation results verify the controller's effectiveness and stability.
• The integration of neural control significantly improves tracking performance.

Conclusions:

• The adaptive robust Jacobian-based controller offers a promising solution for precise robotic manipulator control.
• The controller's adaptive nature and robustness make it suitable for complex and dynamic environments.
• This approach advances the field of intelligent control for robotic systems.