Adaptive robust synchronized control for cooperative robotic manipulators with uncertain base coordinate system.

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Cooperative robotic manipulators are crucial for complex tasks.
• Uncertainties in the base coordinate system introduce significant dynamic biases.
• Existing literature has largely overlooked base coordinate uncertainties in cooperative robotics.

Purpose of the Study:

• To develop an adaptive robust controller for synchronized control of two cooperative robotic manipulators.
• To address the challenge of unknown system dynamics and base coordinate uncertainties.
• To ensure stable trajectory and internal force control.

Main Methods:

• An adaptive neural network with a base coordinate parameter adaptation law is proposed for system parameter estimation.
• A synchronization-factor-based robust slide mode controller is designed for stabilization.
• Mathematical proofs and numerical experiments are conducted to validate the controller.

Main Results:

• The proposed controller effectively estimates cooperative system parameters under uncertainty.
• The controller achieves satisfactory and effective convergence of cooperative robotic trajectories.
• Stable synchronization of internal forces between manipulators is demonstrated.

Conclusions:

• The adaptive robust controller successfully compensates for base coordinate uncertainties in cooperative robotic systems.
• The developed method provides a robust solution for synchronized control tasks.
• This research contributes to advancing the reliability of cooperative robots in uncertain environments.