Federated Learning for Data-Driven Feedforward Control: A Case Study on Vehicle Lateral Dynamics

Abstract

In many control systems, tracking accuracy can be enhanced by combining (data-driven) feedforward (FF) control with feedback (FB) control. However, designing effective data-driven FF controllers typically requires large amounts of high-quality data and a dedicated design-of-experiment process. In practice, relevant data are often distributed across multiple systems, which not only introduces technical challenges but also raises regulatory and privacy concerns regarding data transfer. To address these challenges, we propose a framework that integrates Federated Learning (FL) into the data-driven FF control design. Each client trains a data-driven, neural FF controller using local data and provides only model updates to the global aggregation process, avoiding the exchange of raw data. We demonstrate our method through simulation for a vehicle trajectory-tracking task. Therein, a neural FF controller is learned collaboratively using FL. Our results show that the FL-based neural FF controller matches the performance of the centralized neural FF controller while reducing communication overhead and increasing data privacy.