Simulation Validation of Three Nonlinear Model-Based Controllers in the Adaptive Cruise Control System

In this paper, the simulation validation of the hierarchical two-loop Adaptive Cruise Control (ACC) system is considered as a candidate for the application in the Advanced Driver Assistance Systems (ADAS), which aims to ensure driving safety and comfort as well as to improve fuel efficiency. Three different nonlinear model-based approaches for the inner-loop controllers are investigated for this system: the conventional Proportional-Integral Gain Scheduling controller (PI + GS) and two other strategies based on the simplified modelling of the vehicle dynamics: Balance-Based Adaptive Controller (B-BAC) and Nonlinear Model Predictive Controller (NMPC). The performance of each considered ACC system is tested by simulation under the same realistic scenarios for distance tracking mode and switching mode. The comparative criteria include driving safety, comfort of the driver and passengers and the fuel-economy aspect defined as BSFC (Brake Specific Fuel Consumption) index. The simulation results demonstrate that all the considered control algorithms meet the safety and car-following requirements while they provide slightly different level of driving comfort and fuel consumption, depending on the traffic situation and operating mode.