Development of Explicit Neural Predictive Control Algorithm Using Particle Swarm Optimisation

Maciej Ławryńczuk

Abstract

This paper describes development of a nonlinear Model Predictive Control (MPC) algorithm. The algorithm is very computationally efficient because for control signal calculation an explicit control law is used, no on-line optimisation is necessary. The control law is implemented by a neural network which is trained off-line by means of a particle swarm optimisation algorithm. Inefficiency of a classical gradient-based training algorithm is demonstrated for the polymerisation reactor. Moreover, the discussed MPC algorithm is compared in terms of accuracy and computational complexity with two suboptimal MPC algorithms with model linearisation and MPC with full nonlinear optimisation.
Author Maciej Ławryńczuk (FEIT / AK)
Maciej Ławryńczuk,,
- The Institute of Control and Computation Engineering
Pages130-139
Publication size in sheets0.5
Book Rutkowski Leszek, Korytkowski Marcin, Scherer Rafal, Tadeusiewicz Ryszard, Zadeh Lotfi A., Zurada Jacek (eds.): Artificial Intelligence and Soft Computing. Part I, Lecture Notes in Artificial Intelligence, vol. 7894, 2013, Springer, ISBN 978-3-642-38657-2, [978-3-642-38658-9], 637 p., DOI:10.1007/978-3-642-38658-9
Keywords in EnglishProcess control Model Predictive Control neural networks optimisation particle swarm optimisation soft computing
DOIDOI:10.1007/978-3-642-38658-9_12
URL http://link.springer.com/chapter/10.1007%2F978-3-642-38658-9_12
Languageen angielski
Score (nominal)15
Score sourceconferenceIndex
ScoreMinisterial score = 10.0, 01-02-2020, BookChapterSeriesAndMatConfByConferenceseries
Ministerial score (2013-2016) = 15.0, 01-02-2020, BookChapterSeriesAndMatConfByConferenceseries
Publication indicators Scopus Citations = 0; WoS Citations = 0
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