Error Tolerant Predictive Control Based on Recurrent Neural Models

  • Petia Georgieva
  • Sebastião Feyo de Azevedo
Part of the Studies in Computational Intelligence book series (SCI, volume 561)


This chapter is focused on developing a feasible model predictive control (MPC) based on time dependent recurrent neural network (NN) models. A modification of the classical regression neural models is proposed suitable for prediction purposes. In order to reduce the computational complexity and to improve the prediction ability of the neural model, optimization of the NN structure (lag space selection, number of hidden nodes), pruning techniques and identification strategies are discussed. Furthermore a computationally efficient modification of the general nonlinear MPC is proposed termed Error Tolerant MPC (ETMPC). The NN model is imbedded into the structure of the ETMPC and extensively tested on a dynamic simulator of an industrial crystalizer. The results demonstrate that the NN-based ETMPC relaxes the computational burden without losing closed loop performance and can complement other solutions for feasible industrial real time control.


Hide Node Neural Model Proportional Integral Derivative Prediction Horizon Sampling Instant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work was partially funded by the Portuguese National Foundation for Science and Technology (FCT) in the context of the project FCOMP-01-0124-FEDER-022682 (FCT reference PEst-C/EEI/UI0127/2011) and the Institute of Electrical Engineering and Telematics of Aveiro (IEETA), Portugal.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Signal Processing Lab, Department of Electronics Telecommunications and Informatics (DETI), Institute of Electronics Engineering and Telematics of Aveiro (IEETA)University of AveiroAveiroPortugal
  2. 2.Faculdade de Engenharia de Universidade do PortoPortoPortugal

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