Article ID Journal Published Year Pages File Type
406717 Neurocomputing 2013 8 Pages PDF
Abstract

In this paper, the adaptive dynamic programming (ADP) approach is utilized to design a neural-network-based optimal controller for a class of unknown discrete-time nonlinear systems with quadratic cost function. To begin with, a neural network identifier is constructed to learn the unknown dynamic system with stability proof. Then, the iterative ADP algorithm is developed to handle the nonlinear optimal control problem with convergence analysis. Moreover, the single network dual heuristic dynamic programming (SN-DHP) technique, which eliminates the use of action network, is introduced to implement the iterative ADP algorithm. Finally, two simulation examples are included to illustrate the effectiveness of the present approach.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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