Performance Enhanced Predictive Control for Adaptive Cruise Control System Considering Road Elevation Information

For road transportation, adaptive cruise control (ACC) is a widely used driver assistance technique with partially longitudinal automation. The existing ACC systems usually ignore the road elevation information, which often ends up with large tracking error and poor fuel economy on the roads with up and down slopes. This paper presents a model-based predictive controller (MPC) for fuel-saving ACC to improve the performances on tracking accuracy and fuel consumption by simultaneously considering the road elevation information, nonlinear powertrain dynamics, and spatiotemporal constraint from preceding vehicle. The feasibility of MPC is enhanced by softening nonsafety related hard constraints, and its asymptotical stability is proved by considering the monotonicity property of a properly chosen Lyapunov function. To efficiently compute the optimal control sequence, a pseudospectral discretization technique, instead of evenly spaced discretization, is adopted to transform the continuous-time optimal control problem into a nonlinear programming. Simulation results demonstrate that the proposed method could reduce fuel consumption by 2.65% in a typical driving scenario and significantly improve the tracking accuracy compared to the one without road slope consideration.