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Data‐Driven Adaptive Critic Approach for Nonlinear Optimal Control via Least Squares Support Vector Machine
Author(s) -
Sun Jingliang,
Liu Chunsheng,
Liu Nian
Publication year - 2018
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1517
Subject(s) - support vector machine , least squares support vector machine , computer science , nonlinear system , artificial neural network , least squares function approximation , control theory (sociology) , function (biology) , optimal control , mathematical optimization , adaptive control , algorithm , control (management) , artificial intelligence , mathematics , statistics , physics , quantum mechanics , estimator , evolutionary biology , biology
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.