Control-Oriented Identification for the Linear Quadratic Regulator: Technical Report
<|reference_start|>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 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.<|reference_end|>
@article{anderson2024control-oriented,
    title={Control-Oriented Identification for the Linear Quadratic Regulator:
  Technical Report},
    author={Sean Anderson and Jo~ao Pedro Hespanha},
    journal={arXiv preprint arXiv:2403.05455},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05455},
    primaryClass={eess.SY cs.SY}
}