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An off‐policy approach for model‐free stabilization of linear systems subject to input energy constraint and its application to spacecraft rendezvous
Author(s) -
Gu Juan,
Zhou Jianzhong
Publication year - 2020
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2579
Subject(s) - rendezvous , constraint (computer aided design) , spacecraft , control theory (sociology) , computer science , mathematical optimization , energy (signal processing) , subject (documents) , riccati equation , linear system , system dynamics , mathematics , control (management) , engineering , artificial intelligence , differential equation , aerospace engineering , mathematical analysis , statistics , geometry , library science
Summary This note is concerned with the problem of stabilizing a class of linear continuous‐time systems with completely unknown system dynamics subject to input energy constraint. To deal with this problem, a model‐based low gain feedback law is designed firstly by establishing a special algebraic Riccati equation. Such a low gain feedback law can semiglobally stabilize the linear systems subject to input energy constraint with the exact system model. In order to relax the assumption that the system model is exactly known, an off‐policy reinforcement learning approach is designed to solve the same problem without requiring the completely knowledge of the system dynamics. Finally, in order to verify the effectiveness of the proposed model‐free approach, simulation result on the spacecraft rendezvous problem is introduced.