A Multi Model Neural Network Approach for Longitudinal Model Predictive Control of a Passenger Vehicle

The system architecture of an autonomous vehicle consists of several parts. One of those is motion control, responsible for following a desired trajectory or path. To fulfill the task of trajectory following using a future trajectory reference while considering constraints, model predictive control (MPC) is especially suitable. MPC in general is however computationally heavy and the control performance mainly relies on the accuracy of the model used. This is why it is important to find an accurate model of the considered dynamics which is still relatively easy to evaluate online. In this paper we present a nonlinear MPC approach based on neural network models. Our aim is to control the longitudinal position of an autonomous passenger vehicle with a complex conventional drivetrain. Different network outputs and topologies are discussed and compared in terms of their ability to accurately predict the longitudinal position of the vehicle. The modeling approaches investigated are three different three layer perceptron (TLP) models. Among those, a multi model neural network approach provides the best results and is therefore used as a prediction model within a nonlinear MPC. Simulation results using a complex high fidelity vehicle model as a plant are given and discussed.