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研究生:張婷雅
研究生(外文):Ting-ya Zhang
論文名稱:非線性系統之自組織大腦情緒控制系統設計
論文名稱(外文):Self-Organizing Brain Emotional Control System Design for Nonlinear Systems
指導教授:林志民林志民引用關係
指導教授(外文):Chih-Min Lin
口試委員:莊季高許駿飛
口試委員(外文):Jih-Gau JuangChun-Fei Hsu
口試日期:2015-07-14
學位類別:碩士
校院名稱:元智大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:103
語文別:英文
論文頁數:39
中文關鍵詞:自組織大腦情緒控制系統雙倒單擺系統磁浮系統陶瓷壓電馬達
外文關鍵詞:Brain emotional controllerinverted double pendulum systemmagnetic levitation systempiezoelectric ceramic motor system
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  • 下載下載:3
  • 收藏至我的研究室書目清單書目收藏:0
本論文提出新型的自組織大腦情緒控制系統,其目的在於解決複雜非線性的控制系統問題。該控制系統包括一個自組織大腦情緒控制器和補償控制器。大腦情緒學習控制器是一種模仿大腦的判斷與情感的數學模型。大腦有眶兒皮層與杏仁核,所以大腦情緒學習控制器有包含兩個神經網路,前者是一種感官神經網路,後者是一種情感神經網路。此控制系統應用於非線性系統控制能有效地降低了追蹤誤差,並迅速調整學習誤差。
  本論文分別舉出三個應用例子,雙倒單擺系統和磁浮系統模擬,以及線性陶瓷壓電馬達實作,這三個例子都表現出以自組織大腦情緒控制方法的有效性。模擬和實驗結果顯示,良好的追蹤性能可以通過使用所提出的控制系統來實現。

This thesis presents a self-organizing control system based on Brain Emotional Controller (BEC) for a class of nonlinear systems. The control system consists of a self-organizing BEC (SOBEC) and a robust controller. BEC is a mathematical model that approximates judgment and emotion of a brain. As well as the sensing algorithm, the emotional algorithm allows fast learning for the BEC. The BEC contains a prefrontal cortex and an amygdala that effectively reduces the tracking error and adjusts the learning error quickly. Three examples, simulation of an inverted double pendulum system and a magnetic levitation system, experiment of a linear piezoelectric ceramic motor system, are demonstrated to illustrate the effectiveness of the proposed control method. The simulation and an experimental results show that favorable tracking performance can be achieved by using the proposed control system.
書名頁 i
論文口試委員審定書 ii
授權書 iii
摘要 vi
Abstract vii
誌謝 viii
Contents ix
List of Figures x
List of Tables xii
Nomenclature xiv
Chapter 1 1
Introduction 1
Chapter 2 3
Problem Formulation 6
Chapter 3 6
Self-Organizing Brain Emotional Control System Design 6
3.1 Overview 6
3.2 Self-Organizing Brain Emotional Control System Design 6
3.3 Self-Organizing of Brain Emotional Controller 8
3.4 Robust Controller 10
3.5 The SOBEC with Robust Controller 12
Chapter 4 14
Simulation and Experimental Results 14
4.1 Overview 14
4.2 Inverted Double Pendulum System 14
4.3 Magnetic Levitation System 24
4.4 Linear Piezoelectric Ceramic Motor Drive 30
Chapter 5 35
Conclusions and Future Researches 35
5.1 Conclusions 35
5.2 Future Researches 35
Reference 36
Autobiography 37



[1] J. E. LeDoux, “The Amygdala: Neurobiological Aspects of Emotion,” Wiley-Liss, New York, pp. 339-351, 1992.
[2] C. Balkenius and J. Moren, “Emotional Learning: A Computational Model of the Amygdala,” Cybernetics and Systems, vol. 32, no. 6, pp. 611-636, 2001.
[3] J. Moren. “Emotion and Learning-A Computational Model of the Amygdala,” PhD dissertation, Lund University, 2002.
[4] H. A. Zarchi, E. Daryabeigi, G. R. A. Markadeh and J. Soltani, “Emotional Controller (BELBIC) Based DTC for Encoderless Synchronous Reluctance Motor Drives,” Drive Systems and Technologies Conference, 2011 2ndPower Electronics,pp. 478-483, 2011.
[5] M. Roshanaei, E. Vahedi and C. Lucas, “Adaptive Antenna Applications by Brain Emotional Learning Based on Intelligent Controller,”IET Microwaves, Antennas &;Propagation, vol. 4, no. 12, pp. 2247-2255, 2010.
[6] C. Lucas, D. Shahmirzadi and N. Sheikholeslami. “Introducing BELBIC: Brain Emotional Learning Based Intelligent Controller,” International Journal of Intelligent Automation and Soft Computing, vol. 10, no. 1, pp. 11-22, 2004.
[7] G. Yang, Y. Cao and L. Zhang, “Design of Brain Emotional Learning Model Based Hydraulic Servo System,” 2011 Second International Conference on Mechanic Automation and Control Engineering (MACE), pp. 4874 - 4876, 2011.
[8]C. M. Lin and T. Y. Chen,” Self-Organizing CMAC Control for a Class of MIMO Uncertain Nonlinear Systems” IEEE Transaction on Neural Networks, vol. 20, no. 9, pp.1377-1384, 2009.
[9] C. M. Lin, M. H. Lin and H.Y. Li, “Adaptive Wavelet Cerebellar Model Articulation Controller Design for MIMO Nonlinear Systems,” 2010 IEEE Interactional Conference on System Man and Cybernetics, pp. 1358-1363, 2010.
[10] C. M. Lin and C.C. Chung, “ Fuzzy Brain Emotional Learning Control System Design or Nonlinear Systems” Fuzzy Setstems and Association and Springer-Verlag-Berlin Heidelberg,pp.117-128, 2015.
[11] S. Zhou, G. Feng and C. B. Feng, “Robust Control for A Class of Uncertain Nonlinear System: Adaptive Fuzzy Approach Based on Backstepping, ”Fuzzy Sets and System, vol. 151, no. 1, pp. 1-20, 2005.
[12] C. M. Lin, M.H. Lin and C.W. Chen, “SoPC-Based Adaptive PID Control System Design for Magnetic Levitation System” IEEE Systems Journal, vol.5, no.2, pp.278-287, 2011.
[13] M. B. Naumovic, “Modeling of a Didactic Magnetic Levitation System for Control Education,” IEEE Conference Telecommunications in Modern Satellite, Cable and Broadcasting Services, vol. 2, pp. 783–786, 2003.
[14] F. J. Lin, S. Y. Lee, and P. H. Chou, “Intelligent Nonsingular Terminal Sliding-Mode Control using MIMO Elman Neural Network for Piezoflexural Nanopositioning stage,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 59, no. 12, December 2012
[15] P. K. Huang, P. H. Shieh, F. J. Lin, and H. J. Shieh, “Sliding-Mode Control for a Two-Dimensional Piezo-Positioning stage,” IET Control Theory Applications., vol. 4, no. 1, pp. 1104–1113, 2007.
[16] C. M. Lin and H.Y. Li, “Intelligent Control Using the Wavelet Fuzzy CMAC Backstepping Control System for Two-Axis Linear Piezoelectric Ceramic Motor Drive Systems” IEEE Transactions on Fuzzy Systems, vol. 22, no. 4, pp.791-802,2014.


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