| 引用本文: | 郭红霞, 吴 捷, 王春茹.基于强化学习的模型参考自适应控制[J].控制理论与应用,2005,22(2):291~294.[点击复制] |
| GUO Hong-xia, WU Jie, WANG Chun-ru.Model reference adaptive control based on reinforcement learning[J].Control Theory and Technology,2005,22(2):291~294.[点击复制] |
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| 基于强化学习的模型参考自适应控制 |
| Model reference adaptive control based on reinforcement learning |
| 摘要点击 4393 全文点击 4342 投稿时间：2003-05-08 修订日期：2004-06-29 |
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| DOI编号 10.7641/j.issn.1000-8152.2005.2.024 |
| 2005,22(2):291-294 |
| 中文关键词 强化学习 模型参考自适应控制 联想搜索单元 自适应评价单元 |
| 英文关键词 reinforcement learning model reference adaptive control associative search element adaptive critic elements |
| 基金项目 国家自然科学基金资助项目(60174025); 国家重点基础研究专项经费资助项目(G1998020308). |
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| 中文摘要 |
| 提出了一种基于强化学习的模型参考自适应控制方法,控制器采用自适应启发评价算法,它由两部分组成:自适应评价单元及联想搜索单元.由参考模型给出系统的性能指标,利用系统反馈的强化信号在线更新控制器的参数.仿真结果表明:基于强化学习的模型参考自适应控制方法可以实现对一类复杂的非线性系统的稳定控制和鲁棒控制,该控制方法不仅响应速度快,而且具有较高的学习速率,实时性较强. |
| 英文摘要 |
| 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. |
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