臺灣博碩士論文加值系統

本研究將TSK模糊規則(Takagi-Sugeno-Kang fuzzy rules)導入遞迴小腦模型控制器(recurrent cerebellar model articulation controller, RCMAC)中，再結合自組織RCMAC (self-organizing RCMAC) 及改良型補償控制器來設計適應性TSK模糊自組織遞迴小腦模型控制器(adaptive TSK fuzzy self-organizing recurrent cerebellar model articulation controller, ATFSORC)。新穎的設計觀點是採用自組織小腦模型控制器(self-organizing cerebellar model articulation controller, SOCMAC)、遞迴小腦模型控制器(RCMAC)架構與適應性法則，使原本靜態且聯想記憶體層數固定的傳統小腦模型控制器，具有動態記憶性能與層數修正能力。其中聯想記憶體層數依據層數決策機制(layer decision-making mechanism)進行增加或減少，以降低ATFSORC之架構複雜度，提升控制性能。並且引入TSK模糊邏輯的概念，提升小腦模型控制器的學習速率與準確度。所提出的ATFSORC控制器以積分誤差函數作為輸入，再將其引入TSK模糊自組織遞迴小腦模型控制器中，且以改良型補償控制器來補償理想控制器和TSK模糊自組織遞迴小腦模型控制器間的誤差。另外，本研究以Lyapunov定理推導小腦模型控制器權重值、TSK模糊規則參數值、遞迴權重值、高斯函數中心點及高斯函數標準差之適應性法則，以確保系統的穩定度。
為了驗證所設計控制器之性能及可行性，本研究將所設計的適應性TSK模糊自組織遞迴小腦模型控制器分別應用於切換式磁阻馬達直接轉矩控制驅動系統之速度控制以及混沌系統之同步與穩定控制。經由模擬結果證明，切換式磁阻馬達於不同轉速命令或是穩態加載下皆能有良好的轉速響應，且聯想記憶體層數會根據層數決策機制而調整，達到節省記憶體之效果。另外也將本研究所提出之ATFSORC應用於混沌系統之同步控制與穩定控制，在混沌系統不同參數變化或是改變切入控制時間下皆能有良好的控制效果。本研究使用方均根誤差、最大誤差及平均誤差做為性能評估指標並與傳統小腦模型控制器及模糊小腦模型控制器比較，所提出之ATFSORC展現了更佳的控制性能。

In this study, an adaptive Takagi-Sugeno-Kang fuzzy self-organizing recurrent cerebellar model articulation controller (ATFSORC) is proposed for speed control of switched reluctance motor (SRM) drive systems and for the control of chaotic systems. The proposed ATFSORC is composed of a set of TSK fuzzy rules, a cerebellar model articulation controller (CMAC), a recurrent CMAC (RCMAC), a self-organizing cerebellar model articulation controller (SOCMAC) and a compensation controller. The novel design is that the association memory layers of ATFSORC will be adjusted systematically by the self-organizing mechanism, in order to reduce the structure complexity and improve control performance of ATFSORC.
In addition, the concept of Takagi-Sugeno-Kang fuzzy rules is introduced to increase the learning speed of ATFSORC. A integrated error function is used as input to ATFSORC. Furthermore, the improved compensating controller is designed to dispel the errors between an ideal controller and the TFSORC. Moreover, the adaptive laws of TSK parameters, recurrent weights, the Gaussian function mean parameters and
the Gaussian function standard deviation are online tuned, and the Lyapunov function is applied to guarantee the stability of the system.
Finally, simulation studies show that the proposed ATFSORC can achieve favorable control performance when the SRM drive systems is operated at different speed command and the chaotic systems are operated at different parameters. In this study, the root-mean-square error (RMSE), average error and max error are used as performance indexing. According to simulation result, the proposed ATFSORC can achieve faster convergence of the tracking error than fuzzy CMAC (FCMAC) and CMAC.

摘 要 i
ABSTRACT iii
誌 謝 v
目 錄 vi
表目錄 ix
圖目錄 x
第一章 緒論 1
1.1研究動機 1
1.2研究目的 2
1.3文獻探討 2
1.4大綱 5
第二章 小腦模型控制器理論 6
2.1前言 6
2.2小腦模型控制器之架構 7
2.3小腦模型控制器之工作原理 8
2.3.1小腦模型控制器之回想階段 8
2.3.2小腦模型控制器之學習階段 13
2.4模糊小腦模型控制器 14
2.5 TSK模糊系統 15
2.6 TSK模糊自組織遞迴小腦模型控制器設計 17
2.7函數學習比較 19
2.8本章結論 20
第三章 切換式磁阻馬達 22
3.1前言 22
3.2切換式磁阻馬達基本構造與特性 22
3.3切換式磁阻馬達驅動原理 25
3.4切換式磁阻馬達數學模型 30
3.4.1電壓與電流方程式 30
3.4.2轉矩方程式 32
3.5本章結論 34
第四章 適應性TSK模糊自組織遞迴小腦模型控制器設計 35
4.1前言 35
4.2適應性TSK模糊自組織遞迴小腦模型控制器架構 35
4.3 TSK模糊自組織遞迴小腦模型控制器設計 36
4.4 ATFSORC適應性法則與穩定度推導 40
4.5改良型補償控制器設計 48
4.6切換式磁阻馬達直接轉矩控制驅動系統 49
4.6.1轉矩分配策略 50
4.6.2實際轉矩計算 53
4.6.3換相機制與轉矩控制器 54
4.6.4電壓脈波寬度調變與轉換器 55
4.7模擬結果與分析 56
4.8本章結論 76
第五章 混沌系統 77
5.1混沌系統介紹 77
5.2混沌的定義 77
5.2.1吸引子 78
5.2.2奇異吸引子 80
5.2.3分岔 81
5.3本章結論 83
第六章 適應性TSK模糊自組織遞迴小腦模型控制器於混沌系統之設計 84
6.1前言 84
6.2混沌系統之同步控制 84
6.2.1兩個間隙連接耦合FHN神經元之動態行為 86
6.2.2穩定度推導 87
6.2.3改良型補償器 95
6.2.4混沌同步控制模擬結果與分析 96
6.3混沌系統之穩定控制 121
6.3.1 穩定度推導 121
6.3.2 改良型補償器 130
6.3.3 混沌同步控制模擬結果與分析 131
6.4本章結論 139
第七章 結論與未來研究方向 140
7.1 結論 140
7.2 本研究之貢獻 140
7.3 未來研究方向 141
參考文獻 143
附錄A 147
附錄B 148
符號彙編 149

[1]E. N. Lorenz, “Deterministic nonperiodic flow,” Journal of The Atmospheric Sciences, vol. 20, 1963, pp. 130-141.
[2]J. Liu, J. C. Sprott, S. Wang and Y. Ma, “Simplest chaotic system with a hyperbolic sine and its applications in DCSK scheme,” IET Commun. The Institution on Engineering and Technology, vol. 12, iss. 7, 2018, pp. 809-815.
[3]Y. Liu, J. H. Park, B. Z. Guo, and Y. Shu, “Security-Enhanced Electro-Optic Feedback Phase Chaotic System Based on Nonlinear Coupling of Two Delayed Interfering Branches,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, 2018, pp. 1040-1045
[4]C. Wang, Y. Ji, H. Wang and L. Bai, “Further Results on Stabilization of Chaotic Systems Based on Fuzzy Memory Sampled-Data Control,” IEEE Transactions on Photonics Journal, vol. 10, no. 4, 2018, pp. 0655-0663
[5]Z. Hua, B. Zhou and Y. Zhou, “Sine-Transform-Based Chaotic System With FPGA Implementation,” IEEE Transactions on Industrial Electronica, vol. 65, no. 3, 2018, pp. 2557-2566
[6]H. T. Tao, J. Wang, B. Deng, X. L. Wei and Y. Y. Chen, “Adaptive synchronization control of coupled chaotic neurons in an external electrical stimulation,” Chin. Phys. B, vol. 22, no. 5, 2013, pp. 058701.
[7]C. F. Hsu, “Hermite-neural-network-based adaptive control for a coupled nonlinear chaotic system,” Neural Comput and Applic, vol. 22, 2013, pp. 421-433.
[8]F. J. Lin, K. C. Lu and B. H. Yang, “Recurrent Fuzzy Cerebellar Model Articulation Neural Network Based Power Control of a Single-Stage Three-Phase Grid-Connected Photovoltaic System During Grid Faults,” IEEE Transactions on Industrial Electronics, vol. 64, no. 2, 2017, pp. 1258-1268
[9]Y. Q. Che, J. Wang, S. S. Zhou and B. Deng, “Robust synchronization control of coupled chaotic neurons under external electrical stimulation,” ScienceDirect Chaos, Solutions and Fractals, vol. 40, 2009, pp. 1333-1342
[10]C. W. Wu, C. F. Hsu and C. K. Hwang, “Master–slave chaos synchronization using adaptive TSK-type CMAC neural control,” ScienceDirect Journal of the Franklin Institute, vol. 348, 2011, pp. 1847-1868
[11]C. M. Wen and M. Y. Cheng, “Development of a recurrent fuzzy CMAC with adjustable input space quantization and self-tuning learning rate for control of a dual-axis piezoelectric actuated micromotion stage,” IEEE Transactions on Industrial Electronics, vol. 60, no. 11, 2013, pp. 5105-5115.
[12]C. M. Lin and H. Y. Li, “TSK fuzzy CMAC-based robust adaptive backstepping control for uncertain nonlinear systems,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 6, 2012, pp. 1147-1154.

[13]R. J. Lian, “Enhanced adaptive self-organizing fuzzy sliding-mode controller for active suspension systems,” IEEE Transactions on Industrial Electronics, vol. 60, no. 3, 2013, pp. 958-968.
[14]C. M. Lin and H. Y. Li, “Self-organizing adaptive wavelet CMAC backstepping control systemdesign for nonlinear chaotic systems,” Nonlinear Analysis: Real World Applications, vol. 14, no. 1, 2013, pp. 206-223.
[15]C. S. Chen, “TSK-type self-organizing recurrent-neural-fuzzy control of linear microstepping motor drives,” IEEE Transactions on Power Electronics, vol. 25, no. 9, 2010, pp. 2253-2265.
[16]Y. Q. Che, S. G. Cui, J. Wang, B. Deng and X. L. Wei, “Chaos Synchronization of Coupled FitzHugh-Nagumo Neurons via Adaptive Sliding Mode Control,” Third International Conference on Measuring Technology and Mechatronics Automation, 2011.
[17]Y. F. Peng and C. M. Lin, “Adaptive recurrent cerebellar model articulation controller for linear ultrasonic motor with optimal learning rates,” Neurocomputing, vol. 70, no. 16-18, 2007, pp. 2626-2637.
[18]Y. C. Hung, F. J. Lin and J. C. Hwang, “Wavelet Fuzzy Neural Network With Asymmetric Membership Function Controller for Electric Power Steering System via Improved Differential Evolution,” IEEE Transactions on Power Electronics, vol. 30, no. 4, 2015, pp. 2350 - 2362.
[19]C. M. Lin and H. Y. Li, “A novel adaptive wavelet fuzzy cerebellar model articulation control system design for voice coil motors,” IEEE Transactions on Industrial Electronics, vol. 59, no. 4, 2012, pp. 2024-2033.
[20]S. Li, M. Zhou and X. Yu, “Design and implementation of terminal sliding mode control method for PMSM speed regulation system,” IEEE Transactions on Industrial Informatics, vol. 9, no. 4, 2013, pp. 1879-1891
[21]R. J. Lian, “Enhanced adaptive self-organizing fuzzy sliding-mode controller for active suspension systems,” IEEE Transactions on Industrial Electronics, vol. 60, no. 3, 2013, pp. 958-968.
[22]F. J. Lin, K. H. Tan and C. H. Tsai,“Improved differential evolution-based Elman neural network controller for squirrel-cage induction generator system, ” IET Renewable Power Generation, vol. 10, iss 7, 2016, pp. 988-1001.
[23]F. J. Lin, S. Y. Lee and P. H. Chou, “Computed force control system using functional link radial basis function network with asymmetric membership function for piezo-flexural nanopositioning stage,” IET Control Theory and Appliactions, vol. 7, iss. 18, 2013, pp. 2128-2142.
[24]F. J. Lin, K. C. Lu, T. H. Ke, B. H. Yang and Y. R. Chang,“ Reactive Power Control of Three-Phase Grid-Connected PV System During Grid Faults Using Takagi–Sugeno–Kang Probabilistic Fuzzy Neural Network Control,” IEEE Transactions on Industrial Electronics, vol. 62, no. 9, 2015, pp. 5516-5528.
[25]J. S. Albus, “New approach to manipulator control: the cerebellar model articulation controller (CMAC),” Transactions of the ASME Journal of Dynamic Systems, Measurement, and Control, vol. 97, no. 3, 1975, pp. 220-227.
[26]Z. Q. Wang, J. L. Schiano and M. Ginsberg, “Hash-coding in CMAC neural networks,” IEEE International Conference on Neural Networks, vol. 3, 1996, pp. 1698-1703.
[27]S. K. Sahoo, S. K. Panda and J. X. Xu, “Iterative learning-based high-performance current controller for switched reluctance motors,” IEEE Transactions on Energy Conversion, vol. 19, no. 3, 2004, pp. 491-497.
[28]C. H. Chuang, Y. H. Lin, C. H. Wang and C. F. Hsu, “SYNCHRONIZATION OF TWO COUPLED NEURONS USING CMAC NEURAL NETWORKS,” Proceedings of the 2011 International Conference on Machine Learning and Cybernetics, Guilin, 10-13, July, 2011, pp. 789-794.