The objective of this article is to introduce a novel data-driven iterative linear quadratic (LQ) control method for solving a class of nonlinear optimal tracking problems. Specifically, an algorithm is proposed to approximate the Q-factors arising from LQ stochastic optimal tracking problems. This algorithm is then coupled with iterative LQ-methods for determining local solutions to nonlinear optimal tracking problems in a purely data-driven setting. Simulation results highlight the potential of this method for field applications.
Possieri, C., Incremona, G.p., Calafiore, G.c., Ferrara, A. (2021). An iterative data-driven linear quadratic method to solve nonlinear discrete-time tracking problems. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 66(11), 5514-5521 [10.1109/TAC.2021.3056398].
An iterative data-driven linear quadratic method to solve nonlinear discrete-time tracking problems
Possieri C.;
2021-01-01
Abstract
The objective of this article is to introduce a novel data-driven iterative linear quadratic (LQ) control method for solving a class of nonlinear optimal tracking problems. Specifically, an algorithm is proposed to approximate the Q-factors arising from LQ stochastic optimal tracking problems. This algorithm is then coupled with iterative LQ-methods for determining local solutions to nonlinear optimal tracking problems in a purely data-driven setting. Simulation results highlight the potential of this method for field applications.| File | Dimensione | Formato | |
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