Diffeomorphism-transformed adaptive robust control for dual-arm collaborative robot systems with inequality...

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Dual-arm collaborative robot systems face challenges with uncertainties and inequality constraints.
• Existing control strategies may not adequately address both issues simultaneously.

Purpose of the Study:

• To develop an adaptive robust control strategy for dual-arm collaborative robots.
• To effectively manage system uncertainties and inequality constraints.
• To ensure practical stability and optimize control performance.

Main Methods:

• Utilized diffeomorphism technique to incorporate inequality constraints.
• Characterized system uncertainties using fuzzy set theory.
• Developed an adaptive robust control scheme for practical stability.
• Optimized control parameters based on fuzzy uncertainty description.

Main Results:

• The proposed adaptive robust control strategy effectively addresses uncertainty and inequality constraints.
• Numerical simulations validated the scheme's effectiveness in ensuring practical stability.
• Achieved a balance between system performance and control cost through optimized parameters.

Conclusions:

• The adaptive robust control strategy offers a robust solution for dual-arm collaborative robots.
• The integration of diffeomorphism and fuzzy set theory provides a systematic approach to constraint and uncertainty management.
• The proposed method enhances the reliability and efficiency of collaborative robot systems.