High-speed trajectory following of a heavy-duty vehicle via adaptive nonlinear model predictive controller

In this paper, a controller based on novel discrete-time adaptive nonlinear model predictive control (AN-MPC) is proposed to enhance the trajectory tracking performance of a heavy-duty vehicle preventing the wheel lift-off and lateral slip, increasing maximum NATO double lane change (DLC) speed. An eight-DOF nonlinear vehicle model is designed as a system model to obtain the realistic behaviour of the vehicle. This model is experimentally validated by using the data obtained in NATO DLC tests. The proposed controller is based on a two-DOF nonlinear single-track vehicle model and configured to be adaptive in two aspects: linearisation of the base model at each sampling instant and online tuning of the controller parameters. The tuning process is achieved by an artificial-neural-network structure. The simulated results revealed that the DLC speeds can be significantly improved due to the predictive capability of the proposed controller, compared to the classical PID controllers or human drivers.