Output Feedback Q-Learning for Linear-Quadratic Discrete-Time Finite-Horizon Control Problems.

Calafiore, Giuseppe C; Possieri, Corrado · IEEE Trans Neural Netw Learn Syst · 2021

basic_science · Level V

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Abstract

An algorithm is proposed to determine output feedback policies that solve finite-horizon linear-quadratic (LQ) optimal control problems without requiring knowledge of the system dynamical matrices. To reach this goal, the Q -factors arising from finite-horizon LQ problems are first characterized in the state feedback case. It is then shown how they can be parameterized as functions of the input-output vectors. A procedure is then proposed for estimating these functions from input/output data and using these estimates for computing the optimal control via the measured inputs and outputs.