Dual MPC for Adaptive Cruise Control with Unknown Road Profile

Abstract

Inspired by the recent work on dual control for exploration and exploitation (DCEE), this paper presents a solution to adaptive cruise control problems via a dual control approach. Different from other adaptive controllers, the proposed dual model predictive control not only uses the current and future inputs to keep a constant headway distance between the leading vehicle and the ego vehicle but also tries to reduce the uncertainty of state estimation by actively learning the surrounding environment as well, which leads to faster convergence of the estimated parameters and better reference tracking performance. The simulation results demonstrate that the proposed dual control framework outperforms a conventional model predictive controller with disturbance observer for adaptive cruise control with unknown road grade.