Please use this identifier to cite or link to this item:
Type: Journal article
Title: Neural network based stochastic optimal control for nonlinear Markov jump systems
Author: Luan, X.
Liu, F.
Shi, P.
Citation: International Journal of Innovative Computing Information and Control, 2010; 6(8):3715-3723
Publisher: ICIC International
Issue Date: 2010
ISSN: 1349-4198
Statement of
Xiaoli Luan, Fei Liu and Peng shi
Abstract: This paper deals with the problem of stochastic optimal control for a class of nonlinear systems subject to Markovian jump parameters. The nonlinearities in the different jump modes are initially parameterized by multilayer neural networks (MNNs), which lead to neural Markovian jump systems. A stochastic neural Lyapunov function (NLF) is used to analyze the stability of the resulting neural control MJSs. Then, based on this stochastic NLF and the neural model, a linear state feedback controller is designed to stabilize the closed-loop nonlinear system and guaranteed an upper bound of the system performance for all admissible approximation errors of the MNNs. The control gains can be derived by solving a set of linear matrix inequalities. Finally, a single link robot arm is demonstrated to show the effectiveness of the proposed design techniques.
Keywords: Markovian jump systems
Multilayer neural networks
Stochastic optimal control
Linear matrix inequalities (LMIs)
Rights: ICIC International © 2010
Appears in Collections:Aurora harvest
Electrical and Electronic Engineering publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.