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研究生: 邵皓勇
Hao-Yong Shao
論文名稱: 模擬退火演算法於區間第二類模糊類神經網路控制器設計
Design of Interval Type-2 Fuzzy-neural Network Controllers Using Simulated Annealing Algorithms
指導教授: 呂藝光
Leu, Yih-Guang
學位類別: 碩士
Master
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2010
畢業學年度: 98
語文別: 中文
論文頁數: 86
中文關鍵詞: 模擬退火演算法區間第二類模糊類神經網路適應控制非線性控制
英文關鍵詞: Simulation Annealing (SA), Type-2 Fuzzy-Neural Network, Adaptive Control, Nonlinear Control
論文種類: 學術論文
相關次數: 點閱:152下載:11
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  • 本文提出利用模擬退火演算法(SA)於區間第二類模糊類神經網路控制器設計,該模擬退火演算法可以適當的調整模糊類神經系統內部的參數,並應用於函數近似與非線性系統之適應控制設計。此模擬退火演算法應用在適應控制器設計,不需要事先的離線學習程序和複雜的數學運算,相較於傳統的非線性系統適應控制器,可以有效減少適應控制器需要的複雜數學運算,模擬退火演算法的參數調整機制主要是依據閉迴路系統的穩定性,這樣一來可增加線上系統之穩定性。傳統上在非線性系統的適應控制過程中,模糊類神經控制器的權重値是透過模擬退火演算法做即時的線上調整,產生所想要的控制輸入。為了立即評估閉迴路系統穩定的趨勢,我們從Lyapunov函數的推導過程中,提出一個能量成本函數於模擬退火最佳演算法中,藉此來獲得更好的閉迴路系統的穩定度。此外,為了防止模擬退火法可能在控制過程中使系統狀態進入不穩定不安全的區域,我們加入監督控制器來限制,使閉迴路系統的狀態維持在安全穩定的區域。
    本文利用電腦來模擬與實驗所提出方法的可行性與效果。最後將模擬退火演算法於區間第二類模糊類神經網路應用於具有直流轉換器之馬達控制實驗。

    We propose a framework that can be applied into the controller design by integrating simulation annealing (SA) algorithm into interval type-2 fuzzy-neural network control. The SA algorithm can adjust the parameters of a fuzzy-neural network (FNN) so that the FNN system would be more adapted to functional approximation and adaptive controller design. Typically, the weighting parameters of an FNN controller are on-line adjusted by SA algorithm. Compared with traditional nonlinear adaptive controllers, the proposed SA-based adaptive controller design has a lower computational time complexity because it requires no more beforehand off-line learning processes and the relevant complicated operations. Because the adaptation mechanism of SA algorithm relies on the stability of the close-loop system, the stability of the proposed on-line system can then be enhanced. Moreover, in order to simultaneously evaluate the tendency of the stability of a close-loop system, a cost function which is derived from Lyapunov function is proposed and is applied to SA to obtain a better close-loop performance. Also, in order to prevent circumstance that SA algorithm might make our system unstable, an additional controller which acts as a supervisor is added to confine the close-loop system in a stable working state.
    Finally, the feasibility and efficacy of the proposed method are measured by computer simulations. This SA-based type-2 FNN control strategy is also experimented on the motors with DC-DC buck converter.

    目 錄 中文摘要…………………………………………………………… i 英文摘要…………………………………………………………… ii 誌謝………………………………………………………………… iv 目錄…………………………………………………………………… v 表目錄……………………………………………………………… vii 圖目錄……………………………………………………………… viii 第一章 緒論………………………………………………………………1 1.1 研究動機與目的…………………………………………………1 1.2 相關研究的發展…………………………………………………3 1.3 論文架構…………………………………………………………8 第二章 模擬退火演算法……………………………………………… 10 2.1 簡介模擬退火演算法………………………………………… 10 2.2 發展歷史與文獻……………………………………………… 15 2.3 本章結論……………………………………………………… 16 第三章 模糊類神經網路……………………………………………… 17 3.1 模糊神經網路的相關發展…………………………………… 17 3.2 Type-1的模糊類神經網路的系統架構……………………… 19 3.3 本章結論……………………………………………………… 21 第四章 利用SA應用於Type-2的模糊類神經網路……………… 22 4.1 Type-2模糊類神經網路系統的架構………………………… 22 4.2 Type-2的模糊類神經網路於SA應用在非線性系統控制…… 26 4.3 Type-2模糊類神經網路應用於馬達控制………………… 37 4.4 本章結論…………………………………………………… 44 第五章 SA於Type-2的模糊類神經網路應用在具有直流轉換器之馬達控制實驗…………………………………………………………… 45 5.1 直流壓降轉換器之文獻………………………………………45 5.1.1 連續導通模式……………………………………………48 5.1.2 非連續導通模式…………………………………………51 5.2 PID控制器之文獻………………………………………………52 5.3 實驗硬體架構介紹……………………………………………56 5.3.1 馬達硬體架構介紹………………………………………57 5.3.2 硬體架構操作說明………………………………………59 5.4 測試結果………………………………………………………61 5.5 本章結論………………………………………………………74 第六章 結論與未來展望………………………………………………75 6.1 結論…………………………………………………………75 6.2 未來展望……………………………………………………75 參考文獻…………………………………………………………………76 自傳………………………………………………………………………85

    參考文獻

    [1] K. Hornik, M. Stinchcombe and H. White, “Multilayer feedforward networks are universal approximators, ” Neural Networks, no. 2, pp. 359-366, 1989.
    [2] L. X. Wang, Adaptive Fuzzy Systems and Control, Prentice Hall, 1994.
    [3] C. H. Wang, W. Y. Wang, T. T. Lee and P. S. Tseng, “Fuzzy B-spline membership function (BMF) and its applications in fuzzy-neural control, ” IEEE Transactions on Systems Man and Cybernetics, vol. 25, no. 5, pp. 841-851, May 1995.
    [4] W. Y. Wang, Y. H. Chien and I. H. Li, “ An On-Line Robust and Adaptive T-S Fuzzy-Neural Controller for More General Unknown Systems,” International Journal of Fuzzy Systems, vol. 10, no. 1, pp. 33-43, 2008.
    [5] C. T. Lin and L. Siana, “An Efficient Human Detection System Using Adaptive Neural Fuzzy Networks,” International Journal of Fuzzy Systems, vol. 10, no. 3, pp. 150-160, 2008.
    [6] S. Wu, M. J. Er and Y. Gao, “A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks,” IEEE Transactions on Fuzzy Systems, vol. 9, pp.578-594, 2001.
    [7] Y. G. Leu, W. Y. Wang and T. T. Lee, “Robust Adaptive Fuzzy-Neural Controllers for Uncertain Nonlinear Systems,” IEEE Transactions On Robotics and Automation, vol. 15, no. 5, pp. 805-817, October 1999.
    [8] 張斐章、張麗秋,類神經網路,臺灣東華書局,2005。
    [9] Y. G. Leu, T. T. Lee and W. Y. Wang, “On-line tuning of fuzzy neural network for adaptive control of nonlinear dynamic systems,” IEEE Transactions on Systems, Man and Cybernetics, Part B, vol. 27, pp. 1034–1043, Dec. 1997.
    [10] T. Y. Kim and J. H. Han, “Edge representation with fuzzy sets in blurred images,” Fuzzy Sets Syst, vol. 100, pp. 77–87, 1998.
    [11] Kecman V. “Learning and Soft Computing: Support Vector Machines, Neural Networks and Fuzzy Logic Models,” Cambridge, MA: MIT press. 2001.
    [12] M. Hojati and S. Gazor, “Hybrid Adaptive Fuzzy dentification and Control of Nonlinear Systems,” IEEE Transactions On Fuzzy Systems, vol. 10, no. 2, April 2002
    [13] J. Y. Choi and J. A. Farrell, “Nonlinear adaptive control using networks of piecewise linear approximators,” Proceedings of 38th Conference on Decision & Control Phoenix, Arizona USA. December 1999.
    [14] W. Y. Wang, Y. G. Leu and T. T. Lee, "Output-feedback control of nonlinear systems using direct adaptive fuzzy-neural controller,” Fuzzy Sets and Systems 140, pp. 341-358, 2003.
    [15] W. Y. Wang, M. L. Chan, T. T. Lee and C. H. Liu, “Recursive Back-stepping Design of Adaptive Fuzzy Controller for Strict Output Feedback Nonlinear Systems,” Asian Journal of Control, vol. 4, no.3, Sept. 2002.
    [16] W. Y. Wang, M. L. Chan, C. C. Hsu and T. T. Lee, “ Tracking-Based Sliding Mode Control for Uncertain Nonlinear Systems via an Adaptive Fuzzy-Neural Approach,” IEEE Transactions. on System Man and Cybernetics-Part B, vol. 32, no.4, pp.483-492. 2002.
    [17] L. X. Wang, “A Supervisory Controller for Fuzzy Control Systems that Guarantees Stability,” IEEE Trans. On Automatic Control, vol. 39, no. 9, pp.1845-1847, 1994.
    [18] Y. G. Leu, T. T. Lee and W. Y. Wang, “Observer-based Adaptive Fuzzy-Neural Control for Unknown Nonlinear Dynamical Systems, ” IEEE Trans. Syst. Man, Cyber. Part B: Cybernetics, vol. 29, no. 5, pp.583-591, Oct., 1999.
    [19] C. H. Wang, H. L. Liu, T. C. Lin, “Direct adaptive fuzzy-neural control with state observer and supervisory controller for unknown nonlinear dynamical systems,” IEEE Transactions on Fuzzy Systems, vol. 10, no.1, pp.39-49, 2002.
    [20] K. Hornik, M. Stinchcombe and H. White, “Multilayer feedforward networks are universal approximators, ” Neural Networks, no. 2, pp. 359-366, 1989.
    [21] Li-Xin Wang, Adaptive Fuzzy Systems and Control, Prentice Hall, 1994.
    [22] 張碩、張益,自動控制系統,鼎茂圖書出版,2000。
    [23] Z. Yang, T. Hachino and T. Tsuji, “Model reduction with time delay combining the least-squares method with the genetic algorithm, ” IEE Proc. Control Theorem Appl, vol. 143, no. 3, 1996.
    [24] 王進力,感應機向量控制驅動器之PID控制器調適,淡江大學電機工程學系,2000。
    [25] S. Kirkpatrick, C. D. Gelatt Jr, and MP Vecchi, “Optimization by Simulated Annealing,” Science, vol. 220. no. 4598, pp. 671 – 680, 1983.
    [26] J. Sheild, ‘”Partitioning concurrent VLSI simulation programs onto a multiprocessor by simulated annealing,”IEE Proceedings, vol.134, Pt.E, no.1, JANURAY, 1987.
    [27] K. Kurbel, B. Schneider and K. Singh, “Solving Optimization Problems by Parallel Recombinative Simulated Annealing on a Parallel Computer-An Application to Standard Cell Placement in VLSI Design, ” IEEE Transactions. on System Man and Cybernetics-Part B, vol. 28, on,3, pp. 454-461, JUNE, 1998.
    [28] M. Gao and J. Tian, “Path Planning for Mobile Robot Based on Improved Simulatded Annealing Artificial Neural Network,” Third International Conference on Natural Computation, 2007.
    [29] P. Lucidarme and A. Liegeois, “Learning Reactive Neuroconrtollers using Simulated Annealing for Mobile Robots,” Intf. Conference on Intelligent Robots and Systems, Oct. 2003.
    [30] Y. Wang, W. Yan and G. Zhang, “Adaptive Simulated Annealing for the optimal of Electromagnetic Devices,” IEEE Transactions on Magnetics, vol. 32, no.3, pp.1214–1217, May 1996.
    [31] A. F. Atiya, A. G. Parlos and L. Ingber, “A Reinforcement Learning Method Based on Adaptive Simulated Annealing,” IEEE Circuits and Systems , vol. 1, pp.121–124, Dec. 2003.
    [32] L. Ingber “Very fast simulated reannealing,” Mathematical Computer Modelling, vol.12, no.8, pp. 967-973, 1989.
    [33] S. J. Ho, L. S. Shu and S. Y. Ho, “Optimizing Fuzzy Neural Networks for Tuning PID Controllers Using an Orthogonal Simulated Annealing Algorithm OSA,” IEEE Tranactions on Fuzzy Systems, vol.14, no.3, JUNE 2006.
    [34] W. K. Ho, C. C. Hang and J. Zhou,“Self-tuning PID control of a plant with under-dampd response with specifications on gain and phase margins, ”IEEE Trans. Control Syst. Technol , vol.5, no.4, pp.446-452, Jul.1997.
    [35] S. J. Ho, S. Y. Ho and L. S. Shu, “OSA:orthogonal simulated annealing algorithm and its application to designing mixed optimal controllers,” IEEE Trans.Syst., Man, Cybern. A, Syst. Humans, vol. 34, no. 5, pp.588-600, Sep. 2004.
    [36] S. Y. Ho, S. J. Ho, Y. k. Lin, and C. C. W. Chu, “An orthogonal simulated annealing algorithm for large floorplanning problems,” IEEE Trans. Very Large Scale(VLSI)Syst, vol. 12, no. 8, pp.874-876,Aug. 2004.
    [37] W. Y. Wnag and Y. H. Li, “Evolutionary learning of BMF fuzzy-neural networks using a reduced-form genetic algorithm,” IEEE Trans. on Systems, Man and Cybernetics, part B, vol. 33, pp.966-976, 2003.
    [38] R. A. Krohling and J. P. Ray, “Design of optimal disturbance rejection PID controllers using genetic algorithms,” IEEE Trans. Evol.Comput, vol. 5, no. 1, pp.78-82, Feb. 2001.
    [39] N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller and E. Teller, “Equation of state calculations by fast computing machines,” J. Chem. Phys, vol. 21, no. 6, pp. 1087-1092, 1953.
    [40] T. Renyuan, S. Jianzhong and L. Yan,”Optimization of electromagnetic devices by using intelligent simulated annealing algorithm,” IEEE Trans. Magn, vol.34, no.5, pp.2992-2995, Sep. 1998.
    [41] H. Szu and R. Hartley, “Fast simulated annealing,” Phys.Lett, vol.122, pp. 157-162, 1987.
    [42] S. Yang, J. M. Machado, G. Ni, S. L. Ho, and P. Zhou,”A self-learning simulated annealing algorithm for global optimizations of electromagnetic devices,” IEEE Trans. Magn, vol.36, no.7, pp. 1004-1008, Jul. 2000.
    [43] M. Zhaung and D. P. Atherton, “Automatic tuning of optimal PID controllers,” Proc. Inst. Elect. Eng.D, vol. 140, pp. 216-224, 1993.
    [44] Y. S. Ahmed, S. Tomonobu, M. Tsukasa, U. Naomitsu and F. Toshihisa, ”Fuzzy Unit Commitment Scheduling Using Absolutely Stochastic Simulated Annealing,” IEEE Transactions on power systems, vol. 21, no. 2, May 2006.
    [45] T. Satoh and K. Nara, “Maintenance scheduling by using simulated annealing method for power plants ,” IEEE Trans. Power Syst, vol. 6, no.2, pp. 850-857, May 1991.
    [46] Y. W. Wang, “An enhanced simulated annealing approach to unit commitment,” Elect. Power Energy Syst, vol. 20, pp. 359-368, May 1998.
    [47] F. Zhuang and F. D. Galiana, ”Unit commitment by simulated annealing,” IEEE Trans. Power Syst, vol. 5, no.1, pp. 311-318, Feb. 1990.
    [48] A. H. Mantawy, Y. L. Abel-Mogid, and S. Z. Selim, “A simulated annealing algorithm for unit commitment,” IEEE Trans. Power Syst, vol. 13, no.1, pp. 197-204, Feb. 1998.
    [49] C. P. Cheng, C. W. Liu, and C. C. Liu,” Unit commitment by simulated annealing algorithms,” Elect. Power Energy Syst, vol. 24, PP. 149 –158, 2000.
    [50] M. M. El-Saadawi, M. A. Tantawi and E. Tawfik, “A fuzzy optimization based approach to large scale thermal unit commitment,” Elect. Power Syst. Res, vol. 72, pp.245–252, 2004.
    [51] D. W. Jeffrey, “ A simulated annealing algorithm for optimizing RF power efficiency in coupled-cavity traveling-wave tubes,” IEEE Transactions onelectron devices, vol. 44, no. 12, Dec. 1997.
    [52] S. Kirkpatrick, C. D. Gelatt Jr and M. P. Vecchi, “ Optimization by simulated annealing,” Science, vol.220, pp. 671-680, May 1983.
    [53] E. Aarts and J. Korst, Simulated Annealing and Boltzmann Machines.New York: Wiley, pp. 88-91, 1989
    [54] S. S. Sastry and A. Isidori, “Adaptive control of linearization systems,” IEEE Transactions Automat. Contr, vol. 34, pp. 1123–1131, 1989.
    [55] R. Marino and P. Tomei, “Globally adaptive output-feedback control of nonlinear systems, part I: Linear parameterization,” IEEE Transactions Automat. Contr, vol. 38, pp. 17–32, Jan. 1993.
    [56] R. Marino and P. Tomei, “Globally adaptive output-feedback control of nonlinear systems, part II: Nonlinear parameterization,” IEEE Transactions Automat. Contr, vol. 38, pp. 33–48, Jan. 1993.
    [57] I. Kanellakopoulos, P. V. Kokotovic and A. S. Morse, “Systematic design of adaptive controllers for feedback linearizable systems,” IEEE Transactions Automat. Contr, vol. 36, pp. 1241–1253, Nov. 1991.
    [58] A. Isidori, Nonlinear Control System. New York: Springer-Verlag, 1989.
    [59] M. Krstic, I. Kanellakopoulos and P.V.Kokotovic, Nonlinear and Adaptive Control Design. New York: Wiley, 1995.
    [60] I. Kanellakopoulos, P. V. Kokotovic and A. S. Morse, “Systematic design of adaptive controller for feedback linearizable system,” IEEE Transactions Automat. Contr, vol. 36, pp. 1241–1253, 1991.
    [61] C. Kwan and F. L. Lewis, “Robust backstepping control of nonlinear systems using neural networks,” IEEE Transactions Syst., Man, Cybern. A, vol. 30, pp. 753–765, 2000.
    [62] T. Knohl and H. Unbehauen, “ANNNAC—extension of adaptive backstepping algorithm with artificial neural networks,” Inst. Elect. Eng. Proc. Contr. Theory Appl, vol. 147, pp. 177–183, 2000.
    [63] C. M. Kwan and F. L. Lewis, “Robust backstepping control of induction motors using neural networks,” IEEE Transactions Neural Networks, vol. 11, pp. 1178–1187, 2000.
    [64] J. Y. Choi and J. A. Farrell, “Adaptive observer backstepping control using neural networks,” IEEE Transactions Neural Networks, vol. 12, pp. 1103–1112, 2001.
    [65] Y. Zhang, P. Y. Peng and Z. P. Jiang, “Stable Neural Controller Design for Unknown Nonlinear Systems Using Backstepping,” IEEE Transactions on Neural Networks, vol. 11, no. 6, November 2000.
    [66] C. F. Hsu, C. M. Lin and T. T. Lee, “Wavelet Adaptive Backstepping Control for a Class of Nonlinear Systems,” IEEE Transactions on Neural Networks, vol. 17, no. 5, September 2006.
    [67] S. Kirkpatrick et.al., “Optimization by simulated annealing”, Science, vol.220, pp. 671-80, 1983.
    [68] 李世炳,鄒忠毅,簡介導引模擬退火法及其應用,物理雙月刊二十四卷二期,2002。
    [69] 陳信諭,模擬退火演算法在土壤採樣佈點之應用,國立臺灣大學生物環境系統工程學系暨研究所,2003。
    [70] 童慶斌,模擬退火演算法在地下水參數分區與抽水量率定之應用,國立臺灣大學農業工程學研究所,2000。
    [71] 林基興,模擬退火的人工智慧研究(二),科學月刊雜誌社,1997。
    [72] 汪秉宏,類神經網路,海洋大學應用科學研究所,2006。
    [73] 林易俊,應用模糊類神經網路於積體電路之微影製程機台故障診斷分析,國立成功大學工業與資訊管理學系研究所,2004。
    [74] 林基興,有趣的人工智慧研究(一),科學月刊全文資料庫,1997。
    [75] 徐正育,應用FPGA於電動機車驅動系統之分析及設計,私立大葉大學機電自動化研究所碩士論文,2004。
    [76] 楊文魁,切換式降壓型DC-DC轉換器之滑動模態控制器設計,國立中興大學電機工程學系2001。
    [77] 梁適安,交換式電源供應器之理論與實務設計,全華科技圖書有限公司,1995。
    [78] 王醴,工業電子學,全威圖書有限公司,2002。
    [79] 廖東成、王順忠,電力電子學,滄海書局,2004。
    [80] 楊建宏,適應性倒階類神經濾波控制器與其在伺服馬達控制上之應用,國立台灣師範大學工業教育所碩士論文,2009。
    [81] 廖建豪,簡化退火演算法基於模糊類神經網路控制器於非線性系統之控制,國立台灣師範大學工業教育所碩士論文,2009。
    [82] 鍾義順,應用粒子群演算法於電流基礎之預防式安全限制最佳化電力潮流,國立中山大學電機工程系,2007。
    [83] Hisao, Ishibuchi, shinta and Hideo Tanaka,“Modified simulated annealing algorithms for the flow shop sequencing problem, ”European Journal of Operational Research, vol.81, pp. 388-398, 1995.
    [84] Adenso-Diaz, Belarmino, “An SA/TS mixture algorithms for the scheduling tardiness problem, ” European Journal of Operational Research, vol.88, pp. 516-524, 1996.
    [85] M. Karakose and E. Akin, “Type-2 fuzzy activation function for multilayer feedforward neural networks,” in Proc. IEEE Int. Conf. Syst., Man Cybern, vol. 4, pp. 3762–3767. Oct. 10–13, 2004.
    [86] L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning—I,” Inf. Sci, vol. 8, no. 3, pp. 199–249, 1975.
    [87] N. N. Karnik and J. M. Mendel, “Centroid of a type-2 fuzzy set,” Inf. Sci, vol. 132, no. 1, pp. 195–220, Feb. 2000.
    [88] Q. Liang and J. M. Mendel, “Interval type-2 fuzzy logic systems: Theory and design, ” IEEE Trans. Fuzzy Syst, vol. 8, no. 5, pp. 535–550, Oct. 2000.
    [89] 邵皓勇,呂藝光,廖建豪,模擬退火法基於模糊類神經網路於非線性系統控制,中華民國系統科學與工程會議,2009。
    [90] M. Hojati and S. Gazor, “Hybrid Adaptive Fuzzy dentification and Control of Nonlinear Systems,” IEEE Transactions On Fuzzy Systems, vol. 10, no. 2, April 2002.
    [91] L. X. Wang, “Stable adaptive fuzzycontrollers with application to Inverted pendulum tracking” IEEE Transactions on Fuzzy Systems, Man and Cybernetics-Part B: Cybernetics, vol. 26, no. 5, October 1996.
    [92] M. Krstic, I. Kanellakopoulos and P. V. Kokotovic, Nonlinear and Adaptive Control Design. New York: Wiley, 1995.
    [93] S. Sastry and M. Bodson, Adaptive Control: Stability, Convergence and Robustness, Englewood Cliffs, NJ: Prentice-Hall, 1989.
    [94] 王立新,模糊理論與應用,全威圖書有限公司,2006。
    [95] 李承洲,黃克勤,DC/DC轉換器控制器設計之研究,國立臺灣師範大學工業教育系,2010。
    [96] 林進燈,模糊類神經網路,國立交通大學控制與工程研究所,2010。
    [97] 潘建宏,王偉彥,呂藝光,適應倒階控制基於區間第二類模糊類神經網路於非線性系統控制,中華民國系統科學與工程會議,2009。

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