Author： Sivakumaran N. Radhakrishnan T. Babu J.
Publisher： Taylor & Francis Ltd
ISSN： 1568-5543
Source： Composite Interfaces, Vol.34, Iss.6, 2006-11, pp. : 635-651
Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.
Abstract
A nonlinear model predictive control (NMPC) strategy based on recurrent neural networks (RNN) is proposed for a single‐input single‐output system (SISO) to control the uncertain nonlinear process. The automatic configuration and modeling of the networks is carried out using a recurrent Elman network using back propagation through time (BPTT) with MATLAB. Identification of the process is performed with a RNN based nonlinear autoregressive with exogenous input (NARX) model and the incorporation of the developed model in the formulation of NMPC is presented. Further, the results of the NMPC is compared with a well tuned IMC based PI controller, which shows a better performance based on the rise time and settling time of the proposed NMPC scheme for the control of an unstable bioreactor.
Related content
Identification of fuzzy dynamic systems using Max-Min recurrent neural networks
By Blanco A. Delgado M. Pegalajar M.C.
Fuzzy Sets and Systems, Vol. 122, Iss. 3, 2001-09 ,pp. :
Access to resources
Recommend
Adaptive Control of Multivariable Process Using Recurrent Neural Networks
Composite Interfaces, Vol. 37, Iss. 6, 2009-11 ,pp. :
Adaptive Control of Neutralization Process Using Recurrent Neural Networks
Composite Interfaces, Vol. 37, Iss. 4, 2009-07 ,pp. :
By Yu W.
Information Sciences, Vol. 158, Iss. unknown, 2004-01 ,pp. :
L2–L∞ nonlinear system identification via recurrent neural networks
By Ahn Choon
Nonlinear Dynamics, Vol. 62, Iss. 3, 2010-11 ,pp. :