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.
Calafiore, G.c., Possieri, C. (2021). Output Feedback Q-Learning for Linear-Quadratic Discrete-Time Finite-Horizon Control Problems. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 32(7), 3274-3281 [10.1109/TNNLS.2020.3010304].
Output Feedback Q-Learning for Linear-Quadratic Discrete-Time Finite-Horizon Control Problems
Possieri C.
2021-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
