An Iterative Data-Driven Linear Quadratic Method to Solve Nonlinear Discrete-Time Tracking Problems

被引：9
|
作者：
Possieri, Corrado [1 ]
Incremona, Gian Paolo [2 ]
Calafiore, Giuseppe C. [3 ,4 ]
Ferrara, Antonella [5 ]
机构：
[1] CNR, Ist Anal Sistemi Informat A Ruberti, I-00185 Rome, Italy
[2] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, I-20133 Milan, Italy
[3] Politecn Torino, Dipartimento Elettron & Telecomunicaz, I-10129 Turin, Italy
[4] IEIIT CNR Torino, I-10129 Turin, Italy
[5] Univ Pavia, Dipartimento Ingn Ind & Informaz, I-27100 Pavia, Italy
来源：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2021年 / 66卷 / 11期
关键词：
Optimal control; Heuristic algorithms; Dynamic programming; Approximation algorithms; Q-factor; Stochastic processes; Mathematical model; Data-driven control design; dynamic programming; linear quadratic (LQ) control; optimal control; FEEDBACK-CONTROL; REINFORCEMENT; DESIGN;
D O I：
10.1109/TAC.2021.3056398
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：5514 / 5521
页数：8
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