Hybrid Adaptive Control for Robotic Arm Using PID-ILC Approach

Abstract

This study presents a hybrid control system that combines Iterative Learning Control (ILC) and Proportional-Integral-Derivative (PID) control to improve the joint control of the SCORBOT ER4u robotic arm. The aim is to improve the accuracy, stability, and efficiency of repeated motion tasks in lowcost instructional robots. PID offers fast response and inherent stability, and ILC progressively refines control performance through repetition. By integrating PID and ILC, the system utilizes their complementary strengths. The controller's performance was evaluated using multiple metrics, including Root Mean Square Error (RMSE), Root Mean Square Percentage Error (RMSPE), Integral of Time- Weighted Absolute Error (ITAE), and Integral of Time-Weighted Squared Error (ITSE), to capture both transient and long-term error characteristics. The hybrid PID-ILC approach reduced steady-state errors by 35% under no-load conditions and by 71% with a 1 kg payload. The system exhibited exceptional stability, with no oscillations or divergence, demonstrating apparent convergence and a bounded response across iterations. These results confirm that the proposed method significantly improves motion precision, reliability, and robustness, making it well-suited for tasks requiring accurate and repetitive control in educational and industrial contexts.