Robust Model Predictive Longitudinal Position Tracking Control for an Autonomous Vehicle Based on Multiple Models

TL;DR

This paper develops a robust model predictive control strategy for longitudinal vehicle positioning that uses multiple linear models to handle nonlinear dynamics and uncertainties, ensuring constraint satisfaction and stability.

Contribution

It introduces a method to identify multiple local linear models from data, integrates robust MPC with model switching, and addresses computational challenges for autonomous vehicle control.

Findings

Effective multiple-model switching improves control accuracy.

Robust MPC guarantees constraint satisfaction under model uncertainties.

Simulation demonstrates feasibility and computational considerations.

Abstract

The aim of this work is to control the longitudinal position of an autonomous vehicle with an internal combustion engine. The powertrain has an inherent dead-time characteristic and constraints on physical states apply since the vehicle is neither able to accelerate arbitrarily strong, nor to drive arbitrarily fast. A model predictive controller (MPC) is able to cope with both of the aforementioned system properties. MPC heavily relies on a model and therefore a strategy on how to obtain multiple linear state space prediction models of the nonlinear system via input/output data system identification from acceleration data is given. The models are identified in different regions of the vehicle dynamics in order to obtain more accurate predictions. The still remaining plant-model mismatch can be expressed as an additive disturbance which can be handled through robust control theory. Therefore modifications to the models for applying robust MPC tracking control theory are described. Then a controller which guarantees robust constraint satisfaction and recursive feasibility is designed. As a next step, modifications to apply the controller on multiple models are discussed. In this context, a model switching strategy is provided and theoretical and computational limitations are pointed out. Lastly, simulation results are presented and discussed, including computational load when switching between systems.