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.
Data-driven linear quadratic regulation via semidefinite programming / Rotulo M.; de Persis C.; Tesi P.. - ELETTRONICO. - 53:(2020), pp. 3995-4000. (Intervento presentato al convegno 21st IFAC World Congress 2020 tenutosi a deu nel 2020) [10.1016/j.ifacol.2020.12.2264].
Data-driven linear quadratic regulation via semidefinite programming
Tesi P.
2020
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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