Title of Paper:Data-Driven Adaptive Critic Approach for Nonlinear Optimal Control via Least Squares Support Vector Machine

Hits:

Affiliation of Author(s):自动化学院

Journal:ASIAN JOURNAL OF CONTROL

Key Words:data-driven adaptive critic LS-SVM optimal control nonlinear

Abstract: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 No.:1561-8625

Translation or Not:no

Date of Publication:2018-01-01

Co-author:Sun, Jingliang,Liu, Nian

Correspondence Author:lcs