Directed Time Series Regression for Control
Machine Learning
2012-07-02 v1 Systems and Control
Machine Learning
Abstract
We propose directed time series regression, a new approach to estimating parameters of time-series models for use in certainty equivalent model predictive control. The approach combines merits of least squares regression and empirical optimization. Through a computational study involving a stochastic version of a well known inverted pendulum balancing problem, we demonstrate that directed time series regression can generate significant improvements in controller performance over either of the aforementioned alternatives.
Cite
@article{arxiv.1206.6141,
title = {Directed Time Series Regression for Control},
author = {Yi-Hao Kao and Benjamin Van Roy},
journal= {arXiv preprint arXiv:1206.6141},
year = {2012}
}