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arXiv · 1911.07767

Data-driven Linear Quadratic Regulation via Semidefinite Programming

Monica Rotulo·Claudio De Persis·Pietro Tesi
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Abstract

This paper studies the finite-horizon linear quadratic regulation problem where the dynamics of the system are assumed to be unknown and the state is accessible. Information on the system is given by a finite set of input-state data, where the input injected in the system is persistently exciting of a sufficiently high order. Using data, the optimal control law is then obtained as the solution of a suitable semidefinite program. The effectiveness of the approach is illustrated via numerical examples.

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Monica Rotulo, Claudio De Persis, Pietro Tesi. 2019-11-18. Data-driven Linear Quadratic Regulation via Semidefinite Programming. https://arxiv.org/abs/1911.07767

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