Intelligent robust control for uncertain nonlinear time-varying systems and its application to robotic systems

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Robotics

Background:

• Designing robust controllers for uncertain nonlinear time-varying systems is challenging due to perturbations.
• Existing methods may struggle with high-degree uncertainties and external disturbances.

Purpose of the Study:

• To develop an intelligent robust control scheme for uncertain nonlinear systems.
• To achieve robust stabilization and tracking control for robotic systems.
• To demonstrate the effectiveness of adaptive fuzzy or neural network-based controllers.

Main Methods:

• Integration of nonlinear H(infinity) control, adaptive control, and variable structure control (VSC).
• Utilizing fuzzy-logic or neural network-based adaptive mechanisms.
• Employing a control design requiring solution of a linear algebraic matrix inequality.

Main Results:

• Successful design of an intelligent robust stabilization controller achieving H(infinity) control.
• Demonstrated effectiveness in robust tracking control for uncertain robotic systems.
• Guaranteed satisfactory transient and asymptotic tracking performance.

Conclusions:

• The developed control scheme provides an effective solution for robust tracking in uncertain robotic systems.
• The approach simplifies implementation by requiring only a linear algebraic matrix inequality solution.
• Simulation results confirm the performance and robustness of the proposed control algorithms.