Optimization-Based Iterative Learning Speed Control for Vehicle Test Procedures

Procedures for measuring the emissions of automotive vehicles typically include a speed trace that the driver has to track within prescribed tolerances. For development purposes, following this trace by means of automatic control is desirable in order to minimize costs. In this contribution, an iterative learning scheme is proposed that iteratively improves a feed-forward control signal. This is done by means of an optimization problem that takes the speed tolerances into account in the form of constraints. Experimental results obtained with a vehicle on a Road-to-Rig (R2R) test bed for a part of the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) are presented and compared to results of a pure PI control scheme. After very few iterations, both tolerance violations and sudden changes of the pedal position are eliminated, yielding a significantly improved driving behavior.