Data-Driven Adaptive Critic Approach for Nonlinear Optimal Control via Least Squares Support Vector Machine
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所属单位：自动化学院
发表刊物：ASIAN JOURNAL OF CONTROL
关键字：data-driven adaptive critic LS-SVM optimal control nonlinear
摘要：This paper develops an online adaptive critic algorithm based on policy iteration for partially unknown nonlinear optimal control with infinite horizon cost function. In the proposed method, only a critic network is established, which eliminates the action network, to simplify its architecture. The online least squares support vector machine (LS-SVM) is utilized to approximate the gradient of the associated cost function in the critic network by updating the input-output data. Additionally, a data buffer memory is added to alleviate computational load. Finally, the feasibility of the online learning algorithm is demonstrated in simulation on two example systems.
ISSN号：1561-8625
是否译文：否
发表时间：2018-01-01
合写作者：Sun, Jingliang,Liu, Nian
通讯作者：刘春生