Elman neural network for modeling and predictive control of delayed dynamic systems

Antoni Wysocki , Maciej Ławryńczuk


The objective of this paper is to present a modified structure and a training algorithm of the recurrent Elman neural network which makes it possible to explicitly take into account the time-delay of the process and a Model Predictive Control (MPC) algorithm for such a network. In MPC the predicted output trajectory is repeatedly linearized on-line along the future input trajectory, which leads to a quadratic optimization problem, nonlinear optimization is not necessary. A strongly nonlinear benchmark process (a simulated neutralization reactor) is considered to show advantages of the modified Elman neural network and the discussed MPC algorithm. The modified neural model is more precise and has a lower number of parameters in comparison with the classical Elman structure. The discussed MPC algorithm with on-line linearization gives similar trajectories as MPC with nonlinear optimization repeated at each sampling instant.
Author Antoni Wysocki IAiIS
Antoni Wysocki,,
- The Institute of Control and Computation Engineering
, Maciej Ławryńczuk IAiIS
Maciej Ławryńczuk,,
- The Institute of Control and Computation Engineering
Journal seriesArchives of Control Sciences, ISSN 1230-2384, e-ISSN 2300-2611
Issue year2016
Publication size in sheets1.25
Keywords in Englishdynamic models, process control, model predictive control, neural networks, Elman neural network, delayed systems
URL http://www.degruyter.com/dg/viewarticle.fullcontentlink:pdfeventlink/$002fj$002facsc.2016.26.issue-1$002facsc-2016-0007$002facsc-2016-0007.pdf?t:ac=j$002facsc.2016.26.issue-1$002facsc-2016-0007$002facsc-2016-0007.xml
projectDevelopment of methodology of control, decision support and production management. Project leader: Zieliński Cezary, , Phone: 5102, start date 19-05-2015, end date 31-12-2016, 504/02233/1031, Completed
WEiTI Działalność statutowa
Languageen angielski
Wysocki Lawrynczuk acsc-2016-0007-1.pdf 3.23 MB
Score (nominal)15
ScoreMinisterial score [Punktacja MNiSW] = 15.0, 27-03-2017, ArticleFromJournal
Ministerial score (2013-2016) [Punktacja MNiSW (2013-2016)] = 15.0, 27-03-2017, ArticleFromJournal
Citation count*3 (2018-06-16)
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