Data-Driven Model-Free Robust Predictive Control for Autonomous Vehicle Motion Control Using Signal-Based System Representation

Motion control is a fundamental task in autonomous vehicle systems, and data-driven approaches offer the advantage of independence from explicit vehicle dynamics modeling. This paper presents a novel data-driven, model-free predictive control method that operates within a receding horizon framework, eliminating the need for pre-identified system models. First, we establish a signal-based system representation and provide a theoretical validation demonstrating its capability to encapsulate vehicle dynamics without requiring explicit model approximation. This representation is then seamlessly integrated into the predictive control framework, ensuring real-time adaptability to dynamic uncertainties. Unlike conventional methods, the proposed approach continuously updates vehicle dynamics using historical driving data, thereby enhancing robustness against time-varying parameters. Moreover, the method is designed to function effectively even when only partial state measurements are available, addressing a key challenge in real-world applications where full state observability is often impractical. To validate its performance, Rapid Control Prototype (RCP) experiments are conducted under both fully and partially measurable state conditions. Results confirm that the proposed approach achieves superior tracking performance compared to traditional model predictive control (MPC) methods, particularly in scenarios where vehicle state estimation is incomplete.