Development of Explicit Neural Predictive Control Algorithm Using Particle Swarm Optimisation
AbstractThis 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.
|Publication size in sheets||0.5|
|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 English||Process control Model Predictive Control neural networks optimisation particle swarm optimisation soft computing|
|Score|| = 10.0, 01-02-2020, BookChapterSeriesAndMatConfByConferenceseries|
= 15.0, 01-02-2020, BookChapterSeriesAndMatConfByConferenceseries
|Publication indicators||= 0; = 0|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.