Neural network based stochastic optimal control for nonlinear Markov jump systems

Full text for this resource is not available from the Research Repository.

Luan, Xiaoli, Liu, Fei and Shi, Peng (2010) Neural network based stochastic optimal control for nonlinear Markov jump systems. International Journal of Innovative Computing, Information and Control, 6 (8). pp. 3715-3723. ISSN 1349-4198

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.

Item type Article
URI https://vuir.vu.edu.au/id/eprint/7461
Subjects Historical > Faculty/School/Research Centre/Department > Institute for Logistics and Supply Chain Management (ILSCM)
Current > FOR Classification > 0802 Computation Theory and Mathematics
Historical > SEO Classification > 970108 Expanding Knowledge in the Information and Computing Sciences
Keywords ResPubID19965, Markovian jump systems, nonlinearities, multilayer neural networks, stochastic optimal control, linear matrix inequalities (LMIs)
Citations in Scopus 62 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login