Control-Oriented Identification for the Linear Quadratic Regulator: Technical Report

Data-driven control benefits from rich datasets, but constructing such datasets becomes challenging when gathering data is limited. We consider an offline experiment design approach to gathering data where we design a control input to collect data that will most improve the performance of a feedback...

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Bibliographic Details
Published in:arXiv.org 2024-05
Main Authors: Anderson, Sean, João Pedro Hespanha
Format: Article
Language:English
Subjects:
Data collection
Datasets
Design of experiments
Feedback control
Linear quadratic regulator
Performance enhancement
Online Access:Get full text
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Summary:Data-driven control benefits from rich datasets, but constructing such datasets becomes challenging when gathering data is limited. We consider an offline experiment design approach to gathering data where we design a control input to collect data that will most improve the performance of a feedback controller. We show how such a control-oriented approach can be used in a setting with linear dynamics and quadratic objective and, through design of a gradient estimator, solve the problem via stochastic gradient descent. We show our formulation numerically outperforms an A- and L-optimal experiment design approach as well as a robust dual control approach.
ISSN:2331-8422