Directed Time Series Regression for Control

TL;DR

This paper introduces directed time series regression, a novel method for estimating model parameters in control systems, combining least squares and empirical optimization to improve controller performance.

Contribution

It presents a new regression approach tailored for control applications, demonstrating its effectiveness through a stochastic inverted pendulum case study.

Findings

Significant performance improvements over traditional methods

Effective in stochastic control scenarios

Applicable to model predictive control

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.