Learning and Uncertainty Compensation in Robotic Motion Systems using Li-Slotine Adaptive Tracking and Intelligent Adaptive Control

Abstract

This paper examines adaptive control strategies for stabilizing robots, focusing on Li-Slotine adaptive control and Iterative Learning Control (ILC). Both methods handle uncertainties through learning and compensation, ensuring stability and precision. Li-Slotine control, based on Lyapunov theory, dynamically adjusts parameters for asymptotic stability in uncertain systems. ILC improves performance in repetitive tasks by refining control inputs using tracking errors, making it suitable for robotics and manufacturing. While Li-Slotine excels in real-time adaptation and robustness to disturbances, its computational demands challenge high-degree-of-freedom systems. ILC enhances accuracy through iterative learning but is sensitive to noise and requires careful tuning. MATLAB simulations and experimental results demonstrate the effectiveness of both approaches. Future work will explore hybrid frameworks that combine the adaptability of Li-Slotine with the data-driven refinement of ILC to provide robust solutions for complex, dynamic robotic systems.