A reference-decoupled reformulation makes direct data-driven LQT equivalent to certainty-equivalence solutions and supports convergent offline and online DeePO algorithms.
author Wang, R
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A variant of the stochastic fundamental lemma for LTI systems that enables trajectory prediction without past disturbance data in Hankel matrices via polynomial chaos expansions and known disturbance distributions.
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Direct Data-Driven Linear Quadratic Tracking via Policy Optimization
A reference-decoupled reformulation makes direct data-driven LQT equivalent to certainty-equivalence solutions and supports convergent offline and online DeePO algorithms.
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A Stochastic Fundamental Lemma with Reduced Disturbance Data Requirements
A variant of the stochastic fundamental lemma for LTI systems that enables trajectory prediction without past disturbance data in Hankel matrices via polynomial chaos expansions and known disturbance distributions.