Prescribed Performance Control of Uncertain Euler-Lagrange Systems Subject to Full-State Constraints

Area of Science:

• Robotics
• Control Theory
• Artificial Intelligence

Background:

• Euler-Lagrange systems are widely used in robotics and control applications.
• Tracking control is essential for precise system operation.
• Full-state constraints and nonparametric uncertainties pose significant challenges in control design.

Purpose of the Study:

• To develop a zero-error tracking control strategy for Euler-Lagrange systems.
• To address challenges posed by full-state constraints and nonparametric uncertainties.
• To ensure the safe and effective integration of neural networks in the control loop.

Main Methods:

• Development of a neural adaptive tracking control scheme.
• Integration of error transformation with barrier Lyapunov functions.
• Utilization of Nussbaum gain for enhanced performance.
• Application of Lyapunov analysis for stability proofs.

Main Results:

• Continuous and smooth control action.
• Finite-time convergence of full-state tracking error to a prespecified compact set.
• Asymptotic convergence of tracking error to zero.
• Guaranteed safety of neural network operation within its training input domain.
• Semiglobal uniform ultimate boundedness of all closed-loop system signals.

Conclusions:

• The proposed neural adaptive control scheme effectively solves the zero-error tracking problem for constrained Euler-Lagrange systems.
• The method ensures finite-time convergence, asymptotic zero error, and robust performance.
• Lyapunov analysis confirms the stability and boundedness of the closed-loop system, validating the approach through simulations.