Online Dynamic Mode Decomposition Based Adaptive Control for Lane-Keeping System

This study investigates the application of Online Dynamic Mode Decomposition (Online DMD) for real-time system identification and control in an autonomous vehicle lane-keeping system. The Online DMD algorithm dynamically updates a linear state-space model of lateral vehicle dynamics, enabling continuous adaptation to changing road conditions. To test the robustness and predictive capabilities of these models, Model Predictive Control (MPC) and Linear Quadratic Regulator (LQR) strategies are designed and implemented in MATLAB/Simulink. The system is evaluated under constant longitudinal velocity across diverse road sections in simulation environment. The results demonstrate that combining data-driven system identification with optimal control frameworks achieves robust lane tracking and adaptability, while also revealing that the short-term prediction capability of Online DMD may pose limitations in certain dynamic scenarios.