Optimal robust constraint following control design and experimental validation for fuzzy robotic manipulator system

Area of Science:

• Robotics
• Control Systems Engineering
• Fuzzy Logic

Background:

• Robotic manipulators are complex mechanical systems prone to uncertainties and external interference.
• Existing control methods may struggle with parameter variations and unpredictable environmental factors.

Purpose of the Study:

• To propose a robust constraint following control algorithm for robotic manipulators.
• To address system uncertainties using fuzzy set theory.
• To guarantee uniform boundedness (UB) and uniform ultimate boundedness (UUB) performance.

Main Methods:

• Developed a second-order servo constraint-based robust control algorithm.
• Utilized fuzzy set theory to model and manage system uncertainty.
• Established a fuzzy information-based system performance index function.
• Optimized controller gain parameters by minimizing the performance index.

Main Results:

• The proposed algorithm effectively controls robotic manipulators despite system uncertainties.
• Guaranteed deterministic performance of uniform boundedness (UB) and uniform ultimate boundedness (UUB).
• Numerical simulations and experimental results validated the controller's efficacy and optimization method.

Conclusions:

• The robust constraint following control algorithm provides a reliable solution for uncertain robotic manipulator systems.
• Fuzzy set theory integration enhances control performance and stability.
• The controller gain optimization method ensures effective and robust robotic operation.