Black box nonlinear model predictive control using recurrent neural network

Hasan, Muhammad and Idres, Moumen and Abdelrahman, Mohammad (2013) Black box nonlinear model predictive control using recurrent neural network. In: 2nd International Conference on Mechanical, Automotive and Aerospace Engineering (ICMAAE 2013), 2-4 July 2013, Kuala Lumpur, Malaysia.

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Abstract

A black box Nonlinear Model Predictive Control (NMPC) based on a Recurrent Neural Network (RNN) is implemented to solve two nonlinear benchmark examples: a Continuous Stirred Tank Reactor (CSTR) and Quadruple Tank Process (QTP). The RNN model is trained by a set of input and output data from the plant. A nonlinear observer based on Extended Kalman Filter (EKF) is used for the state estimation process. The implementation of successive linearization technique in NMPC shows an improvement in handling the plant nonlinearity and in the same time preserves all the important features of linear model predictive control (LMPC) with quadratic optimization function. To demonstrate the improvement, the NMPC performance is compared with LMPC based on identified state space model. Both examples show the superiority of the NMPC over LMPC.

Item Type:	Conference or Workshop Item (Full Paper)
Additional Information:	5827/32567
Uncontrolled Keywords:	Nonlinear Model predictive Control; NMPC; Recurrent Neural Network; RNN; Extended Kalman Filter; EKF; Successive Linearization; Continuous Stirred Tank Reactor; Quadruple Tank
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ163.13 Power resources
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechanical Engineering
Depositing User:	Dr. Mohammad Abdelrahman
Date Deposited:	11 Nov 2013 16:31
Last Modified:	11 Nov 2013 16:31
URI:	http://irep.iium.edu.my/id/eprint/32567

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