Disturbance Observer-Based Robust Cooperative Adaptive Cruise Control Approach under Heterogeneous Vehicle

Abstract

Cooperative adaptive cruise control (CACC) is one of the control methods that improves fuel efficiency by allowing multiple vehicles to drive in groups. In this paper, we propose a robust CACC with a heterogeneous vehicle using a disturbance observer. The longitudinal vehicle dynamics, including the engine dynamics, have been modeled as a first-order model using a time constant. However, the simplified first-order model varies in accuracy depending on the dynamic driving situation due to engine performance and air drag force. Designing a more accurate higher-order model might be a solution, but this has a high computational cost. Thus, we propose an augmented state observer for model uncertainties and disturbances. The proposed method makes it possible to design a CACC using nominal parameters without considering dynamic changes to the model parameters. Also, the proposed method can directly compensate for disturbances, compared to the adaptation technique, while also satisfying string stability. The proposed method was validated via computational simulations for heterogeneous traffic and experimental evaluation.