Decentralized Prescribed-Time Control of Robotic Arm-Finger Systems for Grasping and Moving Tasks

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Humanoid robots require precise control for grasping and manipulation.
• Synchronizing multi-finger movements is complex due to joint convergence times and dynamic uncertainties.
• Existing control methods may lack robustness or require extensive prior knowledge.

Purpose of the Study:

• To develop decentralized prescribed-time tracking control strategies for a humanoid robot's arm-finger system.
• To enhance the synchronization of finger movements for secure object grasping.
• To improve the robot's ability to manipulate objects through robust and adaptive control.

Main Methods:

• Design of decentralized prescribed-time tracking control strategies.
• Development of a linear controller based on the maximum eigenvalue for simplified analysis.
• Establishment of a new technical lemma for stability analysis and actuator effort reduction.
• Creation of robust and decentralized adaptive control schemes for arm and fingers.

Main Results:

• Successful validation of controller performance through 3-D kinematic simulations of grasping and moving tasks.
• Demonstration of effective tracking performance in the joint space via numerical simulations.
• Achieved robust and decentralized adaptive control with improved transient performance, reduced prior knowledge, and lower computation costs.

Conclusions:

• The proposed decentralized control strategies effectively manage the complexities of humanoid robot grasping and manipulation.
• The novel control approach ensures precise finger synchronization, preventing object slippage.
• The methods offer robust performance, adaptability, and computational efficiency for robotic applications.