以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：94

、訪客IP：3.149.244.45

姓名	李世揚(Shih-Yang Lee)  查詢紙本館藏	畢業系所	電機工程學系
論文名稱	三軸壓電致動平台之智慧型精密運動控制系統 (Intelligent Precise Motion Control System for Three-Axis Piezo-Flexure Stage)
相關論文	★ 機場地面燈光更新工程 -以桃園國際機場南邊跑滑道為例 ★ 多功能太陽能微型逆變器之研製 ★ 應用於儲能系統之智慧型太陽光電功率平滑化控制 ★ 利用智慧型控制之三相主動式電力濾波器的研製 ★ 應用於內藏式永磁同步馬達之智慧型速度控制及最佳伺服控制頻寬研製 ★ 新型每安培最大轉矩控制同步磁阻馬達驅動系統之開發 ★ 同步磁阻馬達驅動系統之智慧型每安培最大轉矩追蹤控制 ★ 利用適應性互補式滑動模態控制於同步磁阻馬達之寬速度控制 ★ 具智慧型太陽光電功率平滑化控制之微電網電能管理系統 ★ 高性能同步磁阻馬達驅動系統之 寬速度範圍控制器發展 ★ 智慧型互補式滑動模態控制系統實現於X-Y-θ三軸線性超音波馬達運動平台 ★ 智慧型同動控制之龍門式定位平台及應用 ★ 利用智慧型滑動模式控制之五軸主動式磁浮軸承控制系統 ★ 智慧型控制雙饋式感應風力發電系統之研製 ★ 無感測器直流變頻壓縮機驅動系統之研製 ★ 應用於模組化輕型電動車之類神經網路控制六相永磁同步馬達驅動系統
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載] 本電子論文使用權限為同意立即開放。 已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。 請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。
摘要(中)	本論文之目標為發展以個人電腦為控制核心之智慧型奈米精密定位控制技術之研製，設計出適用於半導體工程、掃描探針顯微鏡及微機電系統的智慧型精密定位之控制技術。 在本論文中，首先針對壓電效應作詳細說明，並介紹壓電元件的種類與壓電奈米定位平台之優缺點。接著介紹近年來相關文獻中各種壓電奈米定位平台之磁滯模型。此外，提出磁滯摩擦力模型來描述壓電奈米定位平台之磁滯現象，並利用LuGre磁滯摩擦力模型與機械動態模型來建立壓電奈米定位平台之動態模型。由於磁滯摩擦力模型有許多參數於實際應用無法獲得且壓電奈米定位平台動態模型亦存在許多不確定項，因此，提出了遞迴式類神經網路(Recurrent Neural Network, RNN)之智慧型積分步階迴歸滑動控制(Intelligent Integral Backstepping Sliding-Mode Control, IIBSMC)來估測包括系統參數和外部干擾之線上總集不確定量，藉以提高壓電奈米定位平台的控制性能和強健性。接著，利用計算力控制技巧發展非對稱型函數連結放射狀基底函數網路計算力控制系統(Functional Link Radial Basis Function Network with an Asymmetric Membership Function, FLRBFN-AMF) 用來估測非線性函數包含壓電奈米位移平台的動態總集不確定量以提高追隨多樣的參考軌跡之性能。另外，使用Elman類神經網路(Elman Neural Network, ENN)之智慧型非奇異點終端滑動模式控制(Intelligent Nonsingular Terminal Sliding-Mode Control, INTSMC)系統，採用多輸入多輸出(Multi-Input-Multi-Output, MIMO)之ENN來估測以增加壓電奈米定位平台之控制性及強健性。Elman類神經網路估測器是用來估測線上壓電奈米定位平台的磁滯現象及包含系統參數和外部干擾等等的總集不確定量範圍。 由於上述之智慧型控制系統皆利用類神經網路理論近似壓電致動器動態模型之非線性函數，且線上調整智慧型控制系統之參數，故使得系統在磁滯現象、參數變化和外來干擾等因素下仍然具有相當良好的強健性。本論文最後以實測驗證上述所提出之智慧型控制系統的有效性。
摘要(英)	The objective of this dissertation is to develop a high performance intelligent control system for the PC-based piezo-flexural nanopositioning stage (PFNS) that has widely utilized in the industries for many applications such as high-density semiconductors, scanning probe microscopy, and micro electromechanical systems (MEMS), etc. In this dissertation, first, we examined and explained in detail of the piezoelectric effect, different types of piezoelectric components, and the advantages and disadvantages of PFNS. Then, we introduced hysteresis models for various PFNS mentioned in the relevant literature in recent years. Furthermore, we proposed a hysteresis friction model to describe the hysteresis effect in the PFNS. In order to establish a dynamic model for the PFNS, we adopted LuGre hysteresis friction model and Mechanical model to derive the dynamic model of the PFNS. Due to the hysteresis friction model parameters and dynamic model of the PFNS are unknown, we proposed an intelligent integral backstepping sliding-mode control (IIBSMC) system, where an recurrent neural network (RNN) estimator is proposed to improve the control performance and robustness of the PFNS. In this approach, an RNN estimator is proposed to estimate online the lumped uncertainty, including the system parameters and external disturbances. Moreover, we developed the computed force control system using a functional link radial basis function network with an asymmetric membership function (FLRBFN-AMF) controller to estimate the lumped uncertainty including the parameter variations and external disturbance of the system directly. Furthermore, an intelligent nonsingular terminal sliding-mode control (INTSMC) system using a multi-input-multi-output (MIMO) Elman neural network (ENN) estimator is proposed to improve the control performance and robustness of the PFNS. The ENN estimator is proposed to estimate the hysteresis phenomenon and lumped uncertainty including the system parameters and external disturbance of the PFNS online. In addition, the parameters of the intelligent control systems are learning on line. Using the proposed control systems, the robustness to hysteresis, parameter variations and external disturbances can be obtained. Finally, the effectiveness of the proposed control schemes are demonstrated by some experimental results.
關鍵字(中)	★ 壓電奈米定位平台 ★ 智慧型積分步階迴歸滑動控制模式 ★ 遞迴式類神經網路 ★ 非對稱型函數連結放射狀基底函數網路 ★ 非奇異點終端滑動模式模式控制 ★ 非對稱型隸屬函數 ★ 計算力控制	關鍵字(英)	★ Piezo-flexural nanopositioning stage ★ integral backstepping sliding-mode control ★ recurrent neural network ★ functional link radial basis function network ★ nonsingular terminal sliding-mode control ★ asymmetric membership function ★ computed force control
論文目次	摘 要 I Abstract III Acronyms V 誌 謝 VI Contents VIII List of Figures X List of Tables XVII Chapter 1 INTRODUCTION 1 1.1 Motivations and Historical Background 1 1.2 Literature Review 2 1.3 Organization 8 Chapter 2 PIEZO-FLEXURAL NANOPOSITIONING STAGE 10 2.1 Overview 10 2.2 Piezoelectric Effect 10 2.3 Piezoelectric Components 13 2.4 Advantages and Disadvantages of the PFNS 16 2.5 Hysteresis Model 18 Chapter 3 PRECISE MOTION CONTROL SYSTEM FOR THREE-AXIS PIEZO-FLEXURE STAGE 28 3.1 Overview 28 3.2 PFNS Dynamic Model 28 3.3 System Description 33 3.4 Performance Measuring 34 Chapter 4 PRECISE MOTION CONTROL OF PFNS SYSTEM USING INTELLIGENT INTEGRAL BACKSTEPPING SLIDING-MODE CONTROL 36 4.1 Overview 36 4.2 Intelligent Integral Backstepping Sliding-Mode Control Strategy 37 4.2.1 IBSMC System 37 4.2.2 RNN Estimator 42 4.2.3 IIBSMC System 44 4.3 Experimental Results 46 4.4 Summary 75 Chapter 5 PRECISE MOTION CONTROL OF PFNS SYSTEM USING COMPUTED FORCE CONTROL 76 5.1 Overview 76 5.2 Computed Force Control Strategy 77 5.2.1 Computed Force Control System 77 5.2.2 Design of FLRBFN-AMF 79 5.2.3 Robust Compensator 82 5.3 Experimental Results 86 5.4 Summary 97 Chapter 6 PRECISE MOTION CONTROL OF PFNS SYSTEM USING INTELLIGENT NONSINGULAR TERMINAL SLIDING-MODE CONTROL 99 6.1 Overview 99 6.2 Nonsingular Terminal Sliding-Mode Control Strategy 100 6.2.1 NTSMC System 100 6.2.2 ENN Estimator 104 6.2.3 INTSMC System 107 6.3 Experimental Results 110 6.4 Summary 121 Chapter 7 DISCUSSIONS, CONCLUSIONS, AND SUGGESTIONS FOR FUTURE WORKS 123 7.1 Discussions 123 7.2 Conclusions 126 7.3 Suggestions for Future Works 127 REFERENCE 129 VITA…… 139
參考文獻	[1] N. C. Bird, T. J. Stephenson, B. Ross, and A. G. Johnson, “Effects of piezoelectric lithotripsy on human DNA,” Ultrasound in Medicine & Biology, vol. 21, no. 3, pp. 399-403, 1995. [2] D. Dalecki, C. H. Raeman, S. Z. Child, and E. L. Carstensen, “Thresholds for intestinal hemorrhage in mice exposed to a piezoelectric lithotripter,” Ultrasound in Medicine & Biology, vol. 21, no. 9, pp. 1239-1246, 1995. [3] A. Katsuki, H. Onikura, T. Sajima, T. Takei, and D. Thiele, “Development of a high-performance laser-guided deep-hole boring tool: Optimal determination of reference origin for precise guiding,” Precision Engineering, vol. 24, no, 1, pp. 9-14, 2000. [4] J. F. Cuttino, Jr. A. C. Miller, and D. E. Schinstock, “Performance optimization of a fast tool servo for single-point diamond turning machines,” IEEE/ASME Trans. Mechatronics, vol. 4, no, 2, pp. 169-179, 1999. [5] M. A. Paesler, and P. J. Moyer, Near-Field Optics: Theory, Instrumentation, and Applications. New York: John Wiley & Sons, 1996. [6] T. R. Hsu, MEMS and Microsystems: Design and Manufacture. New York: McGraw-Hill, 2002. [7] J. W. Gardner, V. K. Varadan, and O. O. Awadekarim, Microsensors, MEMS and Smart Devices. New York: John Wiley & Sons, 2001. [8] X. Chen and T. Hisayama, “Adaptive sliding-mode position control for piezo-actuated stage,” IEEE Trans. Ind. Electron., vol. 55, no. 11, pp. 3927-3934, 2008. [9] S. Bashash and N. Jalili, “Robust multiple frequency trajectory tracking control of piezoelectrically driven micro/nanopositioning systems,” IEEE Trans. Control Syst. Technol., vol. 15, no. 5, pp. 867-878, 2007. [10] I. D. Mayergoyz, “Dynamic Preisach models of hysteresis,” IEEE Trans. Magnetics, vol. 24, no. 6, pp. 2925-2927, 1988. [11] Y. Bernard, E. Mendes, and F. Bouillault, “Dynamic hysteresis modeling based on Preisach model,” IEEE Trans. Magnetics, vol. 38, no. 2, pp. 885-888, 2002. [12] P. N. Sreeram, G. Salvady, and N. G. Naganatham, “Hysteresis prediction for a piezoceramic material system,” in Proc. ASME Winter Annual Meeting, vol. 35, pp. 35-42, 1993. [13] D. Song and C. J. Li, “Modeling of piezo actuator’s nonlinear and frequency dependent dynamics,” Mechatronics, vol. 9, no. 4, pp. 391-410, 1999. [14] S. Mittal and C. H. Menq, “Hysteresis compensation in electromagnetic actuators through Preisach model inversion,” IEEE/ASME Trans. Mechatronics, vol. 5, no. 4, pp. 394-409, 2000. [15] A. Reimers and E. D. Torre, “Fast preisach-based magnetization model and fast inverse hysteresis model,” IEEE Trans. Magnetics, vol. 34, no. 6, pp. 3857-3866, 1998. [16] M. Goldfarb and N. Celanovic, “Modeling piezoelectric stack actuators for control of micromanipulation,” IEEE Control Systems Magazine, vol. 17, no. 3, pp. 69-79, 1997. [17] D. Croft and S. Devasia, “Hysteresis and virbration compensation for piezoactuators,” Journal of Guidance, Control and Dynamic, vol. 21, no. 5, pp. 710-717, 1998. [18] J. D. Kim and S. R. Nam, “A piezoelectrically driven micro-positioning system for the ductile-mode grinding of brittle materials,” Journal of Materials Processing Technology, vol. 61, no. 3, pp.309-319, 1996. [19] H. J. M. T. S. Adriaens, W. De Koning, and R. Banning, “Modeling piezoelectric actuators,” IEEE/ASME Trans. Mechatronics, vol. 5, no. 4, pp. 331-341, 2000. [20] J. J. Tzen, S. L. Jeng, and W. H. Chieng, “Modeling of piezoelectric actuator for compensation and controller design,” Precision Engineering, vol. 27, no. 1, pp. 70-86, 2003. [21] T. S. Low and W. Guo, “Modeling of a three-layer piezoelectric bimorph beam with hysteresis,” IEEE Journal of Microelectromechanical Systems, vol. 4, no. 4, pp. 230-237, 1995. [22] B. M. Chen, T. H. Lee, C. C. Hang, Y. Guo, and S. Weerasooriya, “An H∞ almost disturbance decoupling robust controller design for a piezoelectric bimorph actuator with hysteresis,” IEEE Trans. Control Systems Technology, vol. 7, no. 2, pp. 160-174, 1999. [23] M. Todd and K. Johnson, “A model for coulomb torque hysteresis in ball bearings,” International Journal of Mechanical Science, vol. 29, no. 5, pp. 339-354, 1987. [24] B. A. Awaddy, W. C. Shih, and D. M. Auslander, “Nanometer positioning of a linear motion stage under static loads,” IEEE/ASME Trans. Mechatronics, vol. 3, no. 2, pp. 113-119, 1998. [25] D. Helmick and W. Messner, “Higher order modeling of hysteresis in disk drive actuators,” in Proc. IEEE International Conference Decision and Control, vol. 4, pp. 3712-3716, 2003. [26] C. C. De Wit, H. Olsson, K. J. Åström, and P. Lischinsky, “A new model for control of systems with friction,” IEEE Trans. Automatic Control, vol. 40, no. 3, pp. 419-425, 1995. [27] S. Bashash and N. Jalili, “Robust adaptive control of coupled parallel piezo-flexural nanopositioning stages,” IEEE/ASME Trans. Mechatronics, vol. 14, no. 1, pp. 11-20, 2009. [28] F. J. Lin, P. H. Shieh, and P. H. Chou, “Robust adaptive backstepping motion control of linear ultrasonic motors using fuzzy neural network,” IEEE Trans. Fuzzy Sys., vol. 16, no. 3, pp. 676-692, 2008. [29] J. S. Bang, H. Shim, S. K. Park, and J. H. Seo, “Robust tracking and vibration suppression for a two-inertia system by combining backstepping approach with disturbance observer,” IEEE Trans. Ind. Electron., vol. 57, no. 9, pp. 3197-3206, 2010. [30] F. J. Lin, H. J. Shieh, and P. K. Huang, “Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism,” IEEE Trans. Neural Networks, vol. 17, no. 2, pp. 432-444, 2006. [31] T. W. S. Chow and Y. Fang, “A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics,” IEEE Trans. Ind. Electron., vol. 45, no. 1, pp. 151-161, 1998. [32] B. Xu, Z. Shi, C. Yang, and F. Sun, ”Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form,” IEEE Trans. on Cybernetics, vol. pp, no. 99, pp. 1, 2014. [33] B. Xu, C. Yang, and Z. Shi, “Reinforcement learning output feedback NN control using deterministic learning technique,” IEEE Trans. on Neural Networks and Learning Systems, vol. 25, no. 3, pp. 635-641, 2014. [34] A. U. Levin and K. S. Narendra, “Control of nonlinear dynamical systems using neural networks-Part II: observability, identification, and control,” IEEE Trans. Neural Networks, vol. 7, no. 1, pp. 30-42, 1996. [35] C. F. Hsu, “Intelligent position tracking control for LCM drive using stable online self-constructing recurrent neural network controller with bound architecture,” Control Engineering Practice, vol. 17, no. 6, pp. 714-722, 2009. [36] C. H. Lu, “Design and application of stable predictive controller using recurrent wavelet neural networks,” IEEE Trans. Ind. Electron., vol. 56, no. 9, pp. 3733-3742, 2009. [37] C. F. Juang, Y. C. Chang, and C. M. Hsiao, “Evolving gaits of a hexapod robot by recurrent neural networks with symbiotic species-based particle swarm optimization,” IEEE Trans. Ind. Electron., vol. 58, no. 7, pp. 3110-3119, 2011. [38] F. J. Lin, H. J. Shieh, P. H. Shieh, and P. H. Shen, “An adaptive recurrent-neural-network motion controller for x–y table in CNC machine,” IEEE Trans. Sys., Man, Cybern. B, Cybernetics, vol. 36, no. 2, pp. 286-299, 2006. [39] F. J. Lin, H. J. Shieh, L. T. Teng and P. H. Shieh, “Hybrid controller with recurrent neural network for magnetic levitation system,” IEEE Trans. Magnetics, vol. 41, no. 7, pp. 2260 – 2269, 2005. [40] J. S. R. Jang, C. T. Sun, and E. Mizutani, “Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence,” IEEE Trans. Autom. Control, vol. 42, no. 10, pp. 1482-1484, 1997. [41] J. S. R. Jang and C. T. Sun, “Functional equivalence between radial basis function networks and fuzzy inference systems,” IEEE Trans. Neural Networks, vol. 4, no, 1, pp. 156-159, 1993. [42] P. H. Shen and F. J. Lin, “Intelligent backstepping sliding-mode control using RBFN for two-axis motion control system,” IEE Proc. Electric Power Appl., vol. 152, no. 5, pp. 1321-1342, 2000. [43] S. Chen, X. Hong, and C. J. Harris, “Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design,” IEE Proc. Control Theory Appl., vol. 150, no. 2, pp. 136-146, 2003. [44] Y. Hao, X. Tiantian, S. Paszczynski, and B. M. Wilamowsk, “Advantages of radial basis function networks for dynamic system design,” IEEE Trans. Ind. Electron., vol. 58, no. 12, pp. 5438-5450, 2011. [45] H. Y. Pan, C. H. Lee, F. K. Chang, and S. K. Chang, “Construction of asymmetric type 2 fuzzy membership function and application in time series prediction,” in Proc. Int. Conf. Machine Learning and Cybernetics, pp. 2024-2030, 2007. [46] K. H. Cheng, C. F. Hsu, C. M. Lin, T. T. Lee, and C. Li, “Fuzzy neural sliding mode control for dc-dc converters using asymmetric gaussian membership functions,” IEEE Trans. Ind. Electro., vol. 54, no. 3, pp. 1528-1536, 2004. [47] F. J. Lin and P. H. Chou, “FPGA based functional link radial basis function network control for PMLSM servo drive system,” in Proc. IEEE Conf. Power Electro., pp. 1377-1382, 2010. [48] J. C. Patra and R. N. Pal, “A functional link artificial neural network for adaptive channel equalization,” Signal Processing, vol. 43, pp. 181-195, 1995. [49] J. C. Patra and A. C. Kot, “Nonlinear dynamic system identification using chebyshev functional link artificial neural networks,” IEEE Trans. Syst. Man Cybern. B, vol. 32, no. 4, pp. 505-511 , 2002. [50] M. Li, J. Liu, Y. Jiang, and W. Feng, “Complex-chebyshev functional link neural network behavioral model for broadband wireless power amplifiers,” IEEE Trans. Micro. Theory Tech., vol. 60, no. 6, pp. 1979-1989, 2012. [51] C. H. Chen, C. T. Lin, and C. J. Lin, “A functional-link-based fuzzy neural network for temperature control,” Proc. IEEE Foundations of Computational Intelligence Conf., pp. 53-58, 2007. [52] J. L. Elman, “Finding structure in time,” Cogn. Sci., 14, pp. 179–211, 1990. [53] F. J. Lin and Y. C. Hung, “FPGA-based Elman neural network control system for linear ultrasonic motor,” IEEE Trans. Ultrason. Ferroelect., Freq. Control., vol. 56, no. 1, pp. 101–113, 2009. [54] X. Li, G. Chen, Z. Chen, and Z. Yuan, “Chaotifying linear Elman networks,” IEEE Trans. Neural Netw., vol. 13, no. 5, pp. 1193–1199, 2002. [55] D. T. Pham and X. Liu, “Training of Elman networks and dynamic system modelling,” Int. J. Syst. Sci., vol. 27, no. 2, pp. 221–226, 1996. [56] J. B. Mbede, X. Huang, and M. Wang, “Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators,” IEEE Trans. Fuzzy Syst., vol. 11, no. 2, pp. 249–261, 2003. [57] H. Liu, S. Wang, and P. Ouyang, ”Fault diagnosis based on improved Elman neural network for a hydraulic servo system,” Int. Conf. on Robotics, Automation and Mechatronics, pp. 1–6, 2006. [58] Y. Tan, J. Chang, H. Tan, and J. Hu, “Integral backstepping control and experimental implementation for motion system,” IEEE Proc. Control Appl. Conf., pp. 367-372, 2000. [59] H. J. Shieh and C. H. Hsu, “An adaptive approximator-based backstepping control approach for piezoactuator-driven stages,” IEEE Trans. Ind. Electron., vol. 55, no. 4, pp. 1729-1738, 2008. [60] C. C. Hua, P. X. Liu, and X. P. Guan, “Backstepping control for nonlinear systems with time delays and applications to chemical reactor systems,” IEEE Trans. Ind. Electron., vol. 56, no. 9, pp. 3723- 732, 2009. [61] J. J. E. Slotine and W. Li, Applied Nonlinear Control. Englewood Cliffs, NJ: Prentice-Hall, 1991. [62] B. Xu, X. Huang, D. Wang, and F. Sun, “Dynamic surface control of constrained hypersonic flight models with parameter estimation and actuator compensation,” Asian Journal of Control, vol. 16, no.1, pp. 162-174, 2014. [63] B. Xu, F. Sun, H. Liu, and J. Ren, “Adaptive kriging controller design for hypersonic flight vehicle via back-stepping,” IET Control Theory and Applications, vol. 6, no. 4, pp. 487-497, 2012. [64] G. Bartolini, A. Ferrara, L. Giacomini, and E. Usai, “Properties of a combined adaptive/second-order sliding mode control algorithm for some classes of uncertain nonlinear systems,” IEEE Trans. Autom. Control, vol. 45, no. 7, pp. 1334-1341, 2000. [65] G. Foo and M. F. Rahman, “Sensorless sliding-mode MTPA control of an IPM synchronous motor drive using a sliding-mode observer and HF signal injection,” IEEE Trans. Ind. Electron., vol. 57, no. 4, pp. 1270-1278, 2010. [66] S. J. Zhang and Y. Xia, “Design of static output feedback sliding mode control for uncertain linear systems,” IEEE Trans. Ind. Electron., vol. 57, no. 6, pp. 2161-2170, 2010. [67] B. Veselic, B. Perunicic-Drazenovic, and C. Milosavljevic, “Improved discrete-time sliding-mode position control using Euler velocity estimation,” IEEE Trans. Indust. Electron., vol. 57, no. 11, pp. 3840-3847, 2010. [68] F. Esfandiari and H. K. Khalil, “Stability analysis of a continuous implementation of variable structure control,” IEEE Trans. Automatic Control, vol. 36, no. 5, pp. 616-620, 1991. [69] S. N. Huang, K. K. Tan, and T. H. Lee, “Sliding-mode monitoring and control of linear drives ” IEEE Trans. Indust. Electron., vol. 56, no. 9, pp. 3532-3540, 2009. [70] X. Yu and O. Kaynak, “Sliding-mode control with soft computing: a survey,” IEEE Trans. Indust. Electron., vol. 56, no. 9, pp. 3275-3285, 2009. [71] B. Beltran, T. Ahmed-Ali, and M. Benbouzid, “High-order sliding-mode control of variable-speed wind turbines,” IEEE Trans. Indust. Electron., vol. 56, no. 9, pp. 3314-3321, 2009. [72] M. Zhihong and X. H. Yu, “Terminal sliding mode control of MIMO linear systems,” IEEE Trans. Circuits Syst. I, Fundam. Theory Appl., vol. 44, no. 11, pp. 1065–1070, Nov. 1997. [73] S. Yua, X. Yub, B. Shirinzadehc, and Z. Mand, “Continuous finite-time control for robotic manipulators with terminal sliding mode,” Automatica, vol. 41, pp. 1957–1964, 2005. [74] Y. Feng, X. Yu, and Z. Man, “Nonsingular terminal sliding mode control of rigid manipulators,” Automatica, vol. 38, pp. 2159–2167, 2002. [75] C. K. Lin, “Nonsingular terminal sliding mode control of robot manipulators using fuzzy wavelet networks,” IEEE Trans. Fuzzy Syst., vol. 14, no. 6, pp. 849–859, Dec. 2006. [76] C. W. Tao, J. S. Taur, and M. L. Chan, “Adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 1, pp. 255–262, Feb. 2004. [77] S. H. Lee, J. B. Park, and Y. H. Choi, “Terminal sliding mode control of nonlinear chaotic systems using self-recurrent wavelet neural network,” in Proc. Int. Conf. Control, Autom. Syst., Seoul, Korea, pp. 1671–1676, 2007. [78] S. Qiang, S. Hu, G. Chen, and J. Junpeng, “Research on the cusp electromagnetic field in single crystal furnace,” Power and Energy Engineering Conf., pp. 1-4, 2011. [79] M. Paul, “ A self-calibrating mathematical model for the direct piezoelectric effect of a new mens tilt sensor,” Eensors Journal, 2011.。 [80] W. G. Cady, Piezoelectricity, Dover Publications, New York, vol. 1, pp. 177-235, 1964. [81] F. J. Lin, S. Y. Lee, and P. H. Chou, “Intelligent nonsingular terminal sliding-mode control using MIMO elman neural network for piezo-flexural nanopositioning stage,” IEEE Trans. Ultra. Ferro. and Freq. Ctrl., vol. 59, no.12, pp. 2716-2730, 2012. [82] C. H. Lee and C. C. Teng, “Identification and control of dynamic systems using recurrent fuzzy neural networks,” IEEE Trans. Fuzzy Sys., vol. 8, no. 4, pp. 349-366, 2000. [83] 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 micro motion stage,” IEEE Trans. Indus. Electro., vol. 60, no. 11, pp. 5105-5115, 2013. [84] C. Laufer and G. Coghill, “Regularization for the kernel recursive least squares CMAC,” IEEE International Joint Conference Neural network, pp.1-8, 2012.
指導教授	林法正(Faa-Jeng Lin)	審核日期	2015-8-17
推文	facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu
網路書籤	Google bookmarks   del.icio.us   hemidemi   myshare