%0 Journal Article
%T Model reference adaptive control based on reinforcement learning<br>基于强化学习的模型参考自适应控制
%A GUO Hong-xi
%A WU Jie
%A WANG Chun-ru
%A <br>郭红霞
%A 吴　捷
%A 王春茹
%J 控制理论与应用
%D 2005
%I 
%X Aiming at adaptive control problems of a sort of nonlinear system,model reference adaptive control based on reinforcement learning is proposed.The controller uses adaptive heuristic critic algorithm,which consists of two elements:adaptive critic element,associative search element.The desired performance index is presented by the reference model,and the controller parameters are updated by reinforcement signal given by system.The simulation shows that the proposed method is efficient for a class of complex nonlinear system,and it has a high learning rate,which is important to online learning.
%K reinforcement learning
%K model reference adaptive control
%K associative search element
%K adaptive critic elements<br>强化学习
%K 模型参考自适应控制
%K 联想搜索单元
%K 自适应评价单元
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=F5564F40F5BBED74&yid=2DD7160C83D0ACED&vid=BC12EA701C895178&iid=0B39A22176CE99FB&sid=6490F0E20C4B41AD&eid=E39A3F4E3A67639B&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=3&reference_num=6