Online and Model-Free Supplementary Learning Control Based on Approximate Dynamic Programming

摘要：

　　An approximate dynamic programming(ADP)based supplementary learning control method is developed to online improve the performance of existing controllers.The proposed supplementary learning structure can make full use of the prior knowledge of the pre-designed controller and endow the controller with learning ability.Moreover,by introducing the action dependent value function for policy evaluation,the supplementary learning control can work in a model-free manner.The policy iteration algorithm is employed to train the actor-critic structure of the ADP supplementary controller.Simulation studies are carried out on the cart-pole system to validate the optimization and the adaptation capability of the proposed methodology.

关键词： Approximate Dynamic Programming Model-Free Online Supplementary Control

作者: Wentao Guo Feng Liu Jennie Si Shengwei Mei

作者单位: State Key Laboratory of Power Systems,Department of Electrical Engineering,Tsinghua University,Beiji Department of Electrical Engineering,Arizona State University,Tempe,AZ 85287,USA

会议类型: 国际会议

会议名称: 第26届中国控制与决策会议(2014 CCDC)

会议地点: 长沙

会议语种:英文

页码: 1316-1321

在线出版日期: 2014-05-31（万方平台首次上网日期，不代表论文的发表时间）