Green ILC: A Novel Energy-Efficient Iterative Learning Control Approach

Abstract

In this paper, we introduce Green Iterative Learning Control (Green ILC), an innovative hybrid control method that addresses the critical need for energy-efficient control in dynamic, repetitive-task environments. By integrating the iterative refinement capabilities of traditional Iterative Learning Control (ILC) with the optimization strengths of gradient descent, Green ILC achieves a balanced trade-off between tracking accuracy and energy consumption. This novel approach introduces a cost function that minimizes both tracking errors and control effort, enabling the system to adaptively optimize performance over iterations. Theoretical analysis and simulation results demonstrate that Green ILC not only achieves faster convergence but also provides significant energy savings compared with traditional ILC methods. Notably, Green ILC reduces energy consumption by prioritizing efficiency, making it particularly suitable for energy-intensive applications such as robotics, manufacturing, and process control. While a slight decrease in tracking accuracy is observed, this trade-off is acceptable for scenarios where energy efficiency is paramount. This work establishes Green ILC as a promising solution for modern industrial systems requiring robust and sustainable control strategies.

Keywords

• iterative learning control
• gradient descent optimization
• energy-efficient control systems
• industrial energy optimization
• hybrid control methodologies