Stochastic model predictive control of Markov jump linear systems based on a two-layer recurrent neural network

Zheng Yan, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

This paper presents a stochastic model predictive control approach to constrained Markov jump linear systems based on neurodynamic optimization. The stochastic model predictive control problem is formulated as a nonlinear convex optimization problem, which is iteratively solved by using a two-layer recurrent neural network in real-time. The applied neural network can globally converge to the exact optimal solution of the optimization problem. Simulation results are provided to demonstrate the effectiveness and characteristics of the proposed approach.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Information and Automation, ICIA 2013
Pages564-569
Number of pages6
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 IEEE International Conference on Information and Automation, ICIA 2013 - Yinchuan, China
Duration: 26 Aug 201328 Aug 2013

Publication series

Name2013 IEEE International Conference on Information and Automation, ICIA 2013

Conference

Conference2013 IEEE International Conference on Information and Automation, ICIA 2013
Country/TerritoryChina
CityYinchuan
Period26/08/1328/08/13

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