Identification and Predictive Control Using RecurrentNeural Networks
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
In this thesis, a special class of Recurrent Neural Networks (RNN) is employed
for system identification and predictive control of time dependent systems.
Fundamental architectures and learning algorithms of RNNs are studied
upon which a generalized architecture over a class of state-space represented
networks is proposed and formulated. Levenberg-Marquardt (LM) learning
algorithm is derived for this architecture and a number of enhancements are
introduced. Furthermore, using this recurrent neural network as a system identifier,
a Model Predictive Controller (MPC) is established which solves the optimization
problem using an iterative approach based on the LM algorithm.
Simulation results show that the new architecture accompanied by LM learning
algorithm outperforms some of existing methods. The third approach which
utilizes the proposed method in on-line system identification enhances the identification/
control process even more.
Place, publisher, year, edition, pages
2012. , 103 p.
IdentifiersURN: urn:nbn:se:oru:diva-21762ISRN: ORU-NAT/DAT-AS-2012/0003--SEOAI: oai:DiVA.org:oru-21762DiVA: diva2:505101
Subject / course
Kalaykov, Ivan, Professor
Dimitrov, Dimitar, ForskarePecora, Federico, Universitetslektor