Hierarchical Framework for Adaptive Cruise Control with Model Predictive Control Method 2017-01-1963
Adaptive cruise control (ACC), as one of the advanced driver assistance systems (ADAS), has become increasingly popular in improving both driving safety and comfort. Since the objectives of ACC can be multi-dimensional, and often conflict with each other, it is a challenging task in its control design. The research presented in this paper takes ACC control design as a constrained optimization problem with multiple objectives. A hierarchical framework for ACC control is introduced, aimed to achieve optimal performance on driving safety and comfort, speed and/or distance tracking, and fuel economy whenever possible. Under the hierarchical framework, the operational mode is determined in the upper layer, in which a model predictive control (MPC) based spacing controller is employed to deal with the multiple control objectives. On the other hand, the lower layer is for actuator control, such as braking and driving control for vehicle longitudinal dynamics. Actuator delay, combined with vehicle longitudinal dynamics, is converted into a delay-free system by augmenting the system dimension. Then a quadratic cost function is developed to obtain an ideal control output by solving an optimal control problem. Driving safety is guaranteed by constraining the inter-vehicle distance within a safe range. Other objectives are considered by their corresponding performance indexes. The low-level controller serves as the actuator control unit, which controls the powertrain and braking systems to ensure desired acceleration be tracked based on the inverse longitudinal dynamics model. Finally, the proposed ACC is simulated and evaluated under PanoSim®, a virtual experimental environment for development, testing and verification of ADAS and intelligent driving in general. Simulation results have demonstrated satisfactory performance with the proposed ACC system.