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

References: Balkenius, C., & Moren, J. (2000). Emotional learning: a computational model of the amygdale Brandner, J. J., & Schubert, K. (2005). Fabrication and testing of microstructure heat exchangers for thermal applications Eppler, W., & Beck, H. N. (1999). Piecewise linear networks (PLN) for function approximation Fatourechi, M., Lucas, C., & Khaki Sedigh, A. (2001a). Reducing control effort by means of emotional learning conference on electrical engineering (ICEE2001), May, Tehran, Iran, pp Fatourechi, M., Lucas, C., & Khaki Sedigh, A. (2001b). Reduction of maximum overshoot by means of emotional learning Fatourechi, M., Lucas, C., & Khaki Sedigh, A. (2003). Emotional learning as a new tool for development of agent based system Fink, A., Fischer, M., Nelles, O., & Isermann, R. (2000). Supervision of nonlinear adaptive controllers based on fuzzy models Fink, A., Topfer, S., & Isermann, R. (2003). Nonlinear model-based control with local linear neuro-fuzzy models Hafner, M., Schukler, M., Nelles, O., & Isermann, R. (2001). Fast neural networks for diesel engine control design Practice, 8, 1211–1221. Henning, T., Brandner, J. J., & Schubert, K. (2004). Characterization of electrically powered micro-heat exchangers Inoue, K., Kawabata, K., & Kobayashi, H. (1996). On a decision making system with emotion Jang, J. S. R. (1993). Adaptive-network-based fuzzy inference system. Lucas, C., Shahmirzadi, D., & Sheikholeslami, N. (2004). Introducing BELBIC: brain emotional learning based intelligent controller Moren, J. (2002). Emotion and learning: a computational model of the amygdala, PhD Thesis, Lund university, Lund, Sweden. Neese, R. (1998). Emotional disorders in evolutionary perspective. British Journal of Medical Psycology, 71, 397–415. Nelles, O. (1997). Orthonormal basis functions for nonlinear system identification with local linear model trees (LoLiMoT) Nelles, O. (2001). Nonlinear system identification: From classical approaches to neural networks and fuzzy models Savinov, A. A. (1999). An algorithm for induction of possibilities setvalued rules by finding prime disjunctions. In Proceedings of the fourth on-line world conference on soft computing in industrial applications Sugeno, M., & Kang, G. T. (1988). Structure identification of fuzzy model.