Robust Tracking Control of the Euler-Lagrange System Based on Barrier Lyapunov Function and Self-Structuring Neural...

Area of Science:

• Robotics and Control Systems
• Nonlinear Control Theory
• Artificial Intelligence in Control

Background:

• Euler-Lagrange systems are widely used but susceptible to uncertainties and disturbances.
• Robust tracking control is crucial for maintaining system performance under adverse conditions.
• Existing methods may face computational burdens or limitations in handling complex dynamics.

Purpose of the Study:

• To develop a robust tracking control scheme for Euler-Lagrange systems with uncertainties.
• To enhance system robustness and reduce communication load.
• To ensure stability and convergence of tracking errors.

Main Methods:

• Utilized an asymmetric tan-type barrier Lyapunov function (ATBLF) for dynamic constraint position tracking errors.
• Developed a self-structuring neural network (SSNN) to estimate unknown system dynamics, reducing computational load.
• Designed a robust compensator to address neural network approximation errors and external disturbances.
• Implemented a relative threshold event-triggered strategy to conserve communication resources.

Main Results:

• The proposed control scheme enables robust tracking despite system uncertainties and disturbances.
• Stability analysis confirmed that all signals in the closed-loop system remain bounded.
• Tracking errors were shown to converge to a small neighborhood around the origin.
• Numerical simulations validated the effectiveness and performance of the developed control strategy.

Conclusions:

• The integration of ATBLF, SSNN, and event-triggering provides an effective solution for robust tracking in EL systems.
• The approach significantly enhances control robustness while optimizing communication efficiency.
• The study demonstrates a viable method for handling complex dynamics and uncertainties in robotic and mechanical systems.