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Paper detail

Data-driven Linear Quadratic Regulation via Semidefinite Programming

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.

preprint2020arXivOpen access
Monica RotuloClaudio De PersisPietro Tesi
Systems and Controleess.SYmath.OC
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Monica RotuloClaudio De PersisPietro Tesi

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