Dictionary-free Koopman Predictive Control for Autonomous Vehicles in Mixed Traffic

Abstract

Koopman Model Predictive Control (KMPC) and Data-EnablEd Predictive Control (DeePC) use linear models to approximate nonlinear systems and integrate them with predictive control. Both approaches have recently demonstrated promising performance in controlling Connected and Autonomous Vehicles (CAVs) in mixed traffic. However, selecting appropriate lifting functions for the Koopman operator in KMPC is challenging, while the data-driven representation from Willems' fundamental lemma in DeePC must be updated to approximate the local linearization when the equilibrium traffic state changes. In this paper, we propose a dictionary-free Koopman model predictive control (DF-KMPC) for CAV control. In particular, we first introduce a behavioral perspective to identify the optimal dictionary-free Koopman linear model. We then utilize an iterative algorithm to compute a data-driven approximation of the dictionary-free Koopman representation. Integrating this data-driven linear representation with predictive control leads to our DF-KMPC, which eliminates the need to select lifting functions and update the traffic equilibrium state. Nonlinear traffic simulations show that DF-KMPC effectively mitigates traffic waves and improves tracking performance.