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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
