  This paper proposes an adaptive tube-based nonlinear model predictive control
(AT-NMPC) approach to the design of autonomous cruise control (ACC) systems.
The proposed method utilizes two separate models to define the constrained
receding horizon optimal control problem. A fixed nominal model is used to
handle the problem constraints based on a robust tube-based approach. A
separate adaptive model is used to define the objective function, which
utilizes least square online parameter estimators for adaption. By having two
separate models, this method takes into account uncertainties, modeling errors
and delayed data in the design of the controller and guaranties robust
constraint handling, while adapting to them to improve control performance.
Furthermore, to be able implement the designed AT-NMPC in real-time, a
Newton/GMRES fast solver is employed to solve the optimization problem.
Simulations performed on a high-fidelity model of the baseline vehicle, the
Toyota plug-in Prius, which is a plug-in hybrid electric vehicle (PHEV), show
that the proposed controller is able to handle the defined constraints in the
presence of uncertainty, while improving the energy cost of the trip. Moreover,
the result of the hardware-in-loop experiment demonstrates the performance of
the proposed controller in real time application.
