Belbic
Expert Systems with Applications
Expert Systems with Applications 32 (2007) 911–918 www.elsevier.com/locate/eswa Brain emotional learning based intelligent controller applied to neurofuzzy model of micro-heat exchanger
Hossein Rouhani a,*,1, Mahdi Jalili b,2, Babak N. Araabi b,
Wolfgang Eppler c, Caro Lucas b b a
Mechanical Engineering Department, University of Tehran, Tehran, Iran
Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department,
University of Tehran, Tehran, Iran c Institute of Data Processing and Electronics, Forschungszentrum Karlsruhe, Germany
Abstract
In this paper, an intelligent controller is applied to govern the dynamics of electrically heated micro-heat exchanger plant. First, the dynamics of the micro-heat exchanger, which acts as a nonlinear plant, is identified using a neurofuzzy network. To build the neurofuzzy model, a locally linear learning algorithm, namely, locally linear mode tree (LoLiMoT) is used. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. The intelligent controller is based on a computational model of limbic system in the mammalian brain. The brain emotional learning based intelligent controller (BELBIC) based on PID control is adopted for the micro-heat exchanger plant. The contribution of BELBIC in improving the control system performance is shown by comparison with results obtained from classic PID controller without BELBIC. The results demonstrate excellent improvements of control action, without any considerable increase in control effort for PID + BELBIC.
Ó 2006 Elsevier Ltd. All rights reserved.
Keywords: Intelligent control; Emotion based learning; Neurofuzzy models; Locally linear models; Nonlinear system identification; Heat exchanger
1. Introduction
Although industrial processes usually contain complex nonlinearities, most of the conventional control algorithms are based on a
Expert Systems with Applications 32 (2007) 911–918 www.elsevier.com/locate/eswa Brain emotional learning based intelligent controller applied to neurofuzzy model of micro-heat exchanger
Hossein Rouhani a,*,1, Mahdi Jalili b,2, Babak N. Araabi b,
Wolfgang Eppler c, Caro Lucas b b a
Mechanical Engineering Department, University of Tehran, Tehran, Iran
Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department,
University of Tehran, Tehran, Iran c Institute of Data Processing and Electronics, Forschungszentrum Karlsruhe, Germany
Abstract
In this paper, an intelligent controller is applied to govern the dynamics of electrically heated micro-heat exchanger plant. First, the dynamics of the micro-heat exchanger, which acts as a nonlinear plant, is identified using a neurofuzzy network. To build the neurofuzzy model, a locally linear learning algorithm, namely, locally linear mode tree (LoLiMoT) is used. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. The intelligent controller is based on a computational model of limbic system in the mammalian brain. The brain emotional learning based intelligent controller (BELBIC) based on PID control is adopted for the micro-heat exchanger plant. The contribution of BELBIC in improving the control system performance is shown by comparison with results obtained from classic PID controller without BELBIC. The results demonstrate excellent improvements of control action, without any considerable increase in control effort for PID + BELBIC.
Ó 2006 Elsevier Ltd. All rights reserved.
Keywords: Intelligent control; Emotion based learning; Neurofuzzy models; Locally linear models; Nonlinear system identification; Heat exchanger
1. Introduction
Although industrial processes usually contain complex nonlinearities, most of the conventional control algorithms are based on a
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