臺灣博碩士論文加值系統

本研究加入動態篩選機制、遞迴小腦模型控制器(recurrent cerebellar model articulation controller, RCMAC)、自組織小腦模型控制器(self-organizing CMAC)及TSK模糊系統之架構概念，來設計具動態篩選機制之適應性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 with Dynamic Screening Mechanism, ATSKSORC-DSM)。
此控制器新穎的設計概念是加入了動態誤差篩選機制，並採用自組織遞迴小腦模型控制器架構、TSK模糊規則和適應性學習法則，來優化受控系統輸出響應之目的。另外，可加速在暫態時的收斂，且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，具有動態記憶的性能以及修正記憶體層數的能力。其中聯想記憶體層數依據層數決策機制(layer decision-making mechanism)來做增減，以獲得更佳的控制性能。所提出的ATSKSORC以積分誤差函數作為輸入，再將其引入自組織遞迴小腦模型控制器中，並且以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定理推導所提出之控制器權重值、遞迴權重值、TSK模糊規則參數、高斯函數中心點及標準差之適應性學習法則，以確保系統的穩定度。
本研究將所設計的具動態篩選機制之適應性TSK模糊自組織遞迴小腦模型控制器植入混沌系統中作為同步控制器及穩定控制器，以驗證所設計控制器之性能及可行性。
在混沌系統中做同步控制及穩定控制時，本研究將傳統小腦模型控制器、模糊小腦模型控制器和所提出的ATSKSORC三者做比較，從模擬結果中可以明顯比較出三種控制器的差異。從模擬結果可知，所提出之ATSKSORC於各個模擬條件下都具有較佳的強健性及控制性能。

This thesis organizes the concepts of dynamic screening mechanism, self-organizing recurrent cerebellar model articulation controller and TSK fuzzy system architecture to fulfill an adaptive TSK Fuzzy Self-organizing Recurrent Cerebellar Model Articulation Controller with Dynamic Screening Mechanism (ATSKSORC-DSM).
The novel design concept of this controller is to add dynamic error screening mechanism, adopts cerebellar model controller, self-organizing recurrent cerebellar model controller architecture, TSK fuzzy rules and adaptive learning rules, in order to optimize the output response of controlled systems and improves the acceleration of the convergence in the transient state. Simultaneously, the CMAC, which is originally static and fixed number of associative memory layers, has the performance of dynamic memory and the ability to correct the number of associative memory layers. According to the layer decision-making mechanism, the number of associative memory layers will be adjusted. In addition, ATSKSORC takes the integral error function as input and introduces it into the self-organizing recurrent cerebellar model controller.
Furthermore, the compensating controller is designed to dispel to the errors between an ideal controller and the TSK fuzzy self-organized recurrent cerebellar model controller. To ensure the stability of the control system, the Lyapunov's stability theorem is applied to derive the adaptive learning laws of TSK parameters, recurrent weights, the mean parameters and the standard deviation of Gaussian function, respectively.
In this study, the adaptive TSK fuzzy self-organized recurrent cerebellar model controller with dynamic screening mechanism is used in chaotic systems as the synchronous controller and stabilizing controller.
In synchronizing and stabilizing the chaotic systems, experiments for comparisons among the conventional CMAC, FCMAC, and ATSKSORC are performed. The experimental results reveal the proposed ATSKSORC has better robustness and control performance in different simulation conditions.

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 3
1.4大綱 6
第二章 小腦模型控制器理論 7
2.1前言 7
2.2小腦模型控制器之架構 8
2.3小腦模型控制器之工作原理 9
2.3.1小腦膜型控制器之回想階段 9
2.3.2小腦模型控制器之學習階段 14
2.4模糊小腦模型控制器 16
2.5 TSK模糊系統 18
2.6 具動態篩選機制之TSK模糊自組織遞迴小腦模型控制器設計 20
2.7函數學習模擬比較 22
2.8本章結論 24
第三章 具動態篩選機制之TSK模糊自組織遞迴小腦模型控制器設計 25
3.1前言 25
3.2具動態篩選機制之TSK模糊自組織遞迴小腦模型控制器之控制系統 26
3.3具動態篩選機制之TSK模糊自組織遞迴小腦模型控制器設計 27
3.4本章結論 31
第四章 混沌系統 32
4.1混沌現象介紹 32
4.2混沌現象定義與特徵 33
4.3吸引子介紹 35
4.4本章結論 37
第五章 ATSKSORC應用於混沌系統 38
5.1前言 38
5.2混沌系統Duffing介紹 38
5.3 混沌系統同步控制之ATSKSORC設計 39
5.3.1混沌系統同步控制之ATSKSORC穩定度推導 41
5.4改良型補償控制器設計 51
5.5模擬結果與分析 52
5.5.1混沌系統同步控制之模擬結果與分析 52
5.6混沌系統之穩定控制 84
5.6.1混沌系統穩定控制之ATSKSORC穩定度推導 84
5.6.2改良型補償控制器設計用於混沌系統之穩定 94
5.6.3混沌系統穩定控制模擬結果與分析 95
5.7 本章結論 107
第六章 結論與未來研究方向 108
6.1結論 108
6.2本研究之貢獻 108
6.3未來研究方向 109
參考文獻 110
符號彙編 113

[1]C. E. C. Souza , D. P. B. Chaves and C. Pimentel, “Digital Communication Systems Based on Three-Dimensional Chaotic Attractors,” IEEE Access, vol. 7, 2019, pp. 10523-10532.
[2]Z. Wu , X. Zhang and X. Zhong, “Generalized Chaos Synchronization Circuit Simulation and Asymmetric Image Encryption,” IEEE Access, vol. 7, 2019, pp. 37989-38008.
[3]M. G. Bosque, A. P. Resa and C.S.Azqueta, “Chaos-Based Bitwise Dynamical Pseudorandom Number Generator on FPGA,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no.1, 2019, pp. 291-293.
[4]F. J. Escribano, J. S.Landete and A.Wagemakers, “Chaos-Based Multicarrier VLC Modulator With Compensation of LED Nonlinearity,” IEEE Transactions on Communications, vol. 67, no.1, 2019, pp. 590-598.
[5]Y. Liu, J.H. Park , B. Z. Guo and Y. Shu, “Further Results on Stabilization of Chaotic Systems Based on Fuzzy Memory Sampled-Data Control,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 2, 2018, pp. 1140-1045.
[6]Y. Y. Wang , H. R. Karimi and H. C. Yan,“An Adaptive Event-Triggered Synchronization Approach for Chaotic Lur’e Systems Subject to Aperiodic Sampled Data,” IEEE Transactions on Circuits and Systems, vol. 66, no. 1, 2019, pp. 442-446.
[7]Z. Hua, B. Zhou and Y. Zhou, “Sine Chaotification Model for Enhancing Chaos and Its Hardware Implementation,” IEEE Transactions on Industrial Electronics, vol. 66, no. 2, 2019.
[8]J. Ye , J. Yang and D. Xie, “Strong Robust and Optimal Chaos Control for Permanent Magnet Linear Synchronous Motor,” IEEE Access, vol. 7, 2019, pp. 57907-57916.
[9]D. Xie, J. Yang and H. Cai, “Blended Chaos Control of Permanent Magnet Linear Synchronous Motor,” IEEE Access, vol. 7, 2019.
[10]E. N. Lorenz, “Deterministic Nonperiodic Flow,” Journal of The Atmospheric Sciences, vol. 20, 1963, pp. 130-141.
[11]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.
[12]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.
[13]C. M. Lin and H. Y. Li, “Self-organizing Adaptive Wavelet CMAC Backstepping Control System design for Nonlinear Chaotic Systems,” Nonlinear Analysis: Real World Applications, vol. 14, no. 1, 2013, pp. 206-223.
[14]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.
[15]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.
[16]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.
[17]C. M. Lin and H. Y. Li, “Dynamic Petri Fuzzy Cerebellar Model Articulation Controller Design for a Magnetic Levitation System and a Two-Axis Linear Piezoelectric Ceramic Motor Drive System,” IEEE Transactions on Control Systems Technology, vol. 23, no. 2, 2015, pp. 693-699.
[18]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.
[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]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.
[21]K. A. Abuhasel and F. M. El-Sousy, “Adaptive RCMAC Neural Network Dynamic Surface Control for Permanent-Magnet Synchronous Motors Driven Two-Axis X-Y Table,” IEEE Access, vol. 7, 2019.
[22]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, 1975, pp. 220-227.
[23]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.