Towards Transparent Control Systems: The Role of Explainable AI in Iterative Learning Control
Abstract
This paper presents a novel approach to improving the performance and interpretability of Iterative Learning Control (ILC) systems through the integration of Explainable Artificial Intelligence (XAI) techniques. ILC is a powerful method used across various domains, including robotics, process control, and traffic management, where it iteratively refines control inputs based on past performance to minimize errors in system output. However, traditional ILC methods often operate as "black boxes," making it difficult for users to understand the decision-making process. To address this challenge, we incorporate XAI, specifically SHapley Additive exPlanations (SHAP), into the ILC framework to provide transparent and interpretable insights into the algorithm's behavior. The study begins by detailing the evolution of ILC, highlighting key advancements such as predictive optimal control and adaptive schemes, and then transitions into the methodology for integrating XAI into ILC. The integrated system was evaluated through extensive simulations, focusing on robotic arm trajectory tracking and traffic flow management scenarios. Results indicate that the XAI-enhanced ILC not only achieved rapid convergence and high control accuracy but also maintained robustness in the face of external disturbances. SHAP analyses revealed that parameters such as the proportional gain (Kp) and derivative gain (Kd) were critical in driving system performance, with detailed visualizations providing actionable insights for system refinement. A crucial metric for control precision was the root mean square error (RMSE), which was reduced to as low as 0.02 radians in the robotic arm case, indicating extremely precise tracking of the intended route. Similarly, the ILC algorithm effectively maintained the ideal traffic density within the predetermined bounds in the traffic management scenario, resulting in a 40% reduction in congestion compared to baseline control measures. The resilience of the ILC algorithm was also examined by introducing changes to the system model, external disturbances, and sensor noise. The algorithm demonstrated a high degree of stability and accuracy in the face of these disruptions. For instance, in the robotic arm case, adding noise to the sensor readings had a negligible effect on the algorithm's performance, increasing the RMSE by less than 5%. This integration of XAI into ILC addresses a significant gap in control system design by offering both high performance and transparency, particularly in safety critical applications. The findings suggest that future research could further enhance this approach by exploring additional XAI techniques and applying the integrated system to more complex, real-world scenarios.
Keywords
References
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