Model Predictive Control with Uncertainty in Human Driven Systems

Abstract

Human driven systems present a unique optimization challenge for robot control. Generally, operators of these systems behave rationally given environmental factors and desired goals. However, information available to subsystem controllers is often incomplete, and the operator becomes more difficult to model without this input information. In this work we present a data-driven, nonparametric model to capture both expectation and uncertainty of the upcoming duty for a subsystem controller. This model is a modified k-nearest neighbor regressor used to generate weighted samples from a distribution of upcoming duty, which are then exploited to generate an optimal control. We test the model on a simulated heterogeneous energy pack manager in an Electric Vehicle operated by a human driver. For this domain, upcoming load on the energy pack strongly affects the optimal use and charging strategy of the pack. Given incomplete information, there is a natural uncertainty in upcoming duty due to traffic, destination, signage, and other factors. We test against a dataset of real driving data gathered from volunteers, and compare the results other models and the optimal upper bound.