博碩士論文 955401017 詳細資訊




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:8 、訪客IP:52.204.98.217
姓名 陳瑄易(Syuan-Yi Chen)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 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
(Intelligent Sliding-Mode Control for Five-DOF Active Magnetic Bearing Control System)
相關論文
★ 感光式觸控面板設計★ 單級式直流無刷馬達系統之研製
★ 機場地面燈光更新工程 -以桃園國際機場南邊跑滑道為例★ 單級高功因LLC諧振電源轉換器之研製
★ 多頻相位編碼於穩態視覺誘發電位之大腦人機介面系統設計★ 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
★ 類神經網路於切換式磁阻馬達轉矩漣波控制之應用★ 感應馬達無速度感測之直接轉矩向量控制
★ 具自我調適導通角度功能之切換式磁阻馬達驅動系統---DSP實現★ 感應馬達之低轉速直接轉矩控制策略
★ 加強型數位濾波器設計於主動式噪音控制之應用★ 非匹配不確定可變結構系統之分析與設計
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]
  1. 本電子論文使用權限為同意立即開放。
  2. 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。
  3. 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) 近年來,主動式磁浮軸承(active magnetic bearing, AMB)由於具備非接觸(noncontact)與無摩擦(frictionless)特性,已經成功且廣泛地實現在各種應用之中,包括非旋轉裝置與旋轉裝置。而對於非旋轉裝置最重要之控制目標為受控體必須能達到精密定位或精確追隨預先設定之軌跡;另一方面,對於旋轉裝置最重要之控制目標為轉軸必須能夠精準地被調節與維持在狹小孔隙內的中心位置。有鑑於此,本論文之目標即為發展一個包含兩個徑向主動式磁浮軸承(radial AMBs)與一個推力型主動式磁浮軸承(thrust AMB)的全懸浮五軸主動式磁浮軸承控制系統(fully suspended five degree-of-freedom AMB control system),以同時建立旋轉裝置與非旋轉裝置之控制技術,並滿足實際的應用需求。
論文中首先詳述磁浮軸承的系統架構、動態分析與實驗設計,再針對推力型主動式磁浮軸承提出不同的追隨控制方法包括無模型控制(model free control)法如以赫米特多項式為基礎之遞迴式類神經網路(Hermite polynomial-based recurrent neural network, HPBRNN)控制;結合滑動模式控制(sliding-mode control, SMC)、適應控制(adaptive control)、遞迴式赫米特類神經網路(recurrent Hermite neural network, RHNN)優點之智慧型滑動模式控制(intelligent SMC)法如適應性互補式滑動模式控制(adaptive complementary SMC, ACSMC)與強健性無奇異終端滑動模式控制(robust non-singular terminal SMC, RNTSMC)等,以實現轉軸在軸向之精密軌跡追隨控制。而針對五軸主動式磁浮軸承系統的五軸同時控制,本論文先提出分散式比例-積分-微分類神經網路控制器(decentralized proportional-integral-derivative neural network controller, PIDNN),使控制器具備線上控制增益調整能力。再提出一解耦動態模型(decoupled dynamic model),將原本各軸耦合之五軸主動式磁浮軸承系統轉換為五個獨立之子系統,使系統可以分散式控制概念進行控制系統之設計。基於該解耦動態模型,本論文再提出分散式智慧型雙重積分滑動模式控制系統(decentralized intelligent double integral SMC system, IDISMC)以進一步提高控制品質,其中所設計之雙重積分滑動面使控制律具有積分控制特性,且控制律中各項控制增益及系統不確定性可同時藉由本論文所提出之改良型比例積分微分類神經網路估測器(modified PIDNN observer, MPIDNN)分別進行線上調整與估測,可有效減少穩態誤差,適合用於高度非線性、易受外來干擾影響與精度要求高之五軸主動式磁浮軸承系統。
本論文以個人電腦(personal computer, PC)為實驗平台,並設計多種測試條件與性能量測,對使用不同控制方法之推力型主動式磁浮軸承系統與全懸浮五軸主動式磁浮軸承系統分別進行可行性之驗證。由實驗結果可知,使用本論文所提出之控制方法,推力型主動式磁浮軸承控制系統在外加0.38kg負載時,追隨誤差平均值仍可維持於1μm以內;而五軸主動式磁浮軸承系統在轉速達到4800RPM時,各軸偏差量之均方根值仍可穩定調節於0.1mm以內,亦即1/4氣隙中。因此,本論文所發展之推力型主動式磁浮軸承控制系統與全懸浮五軸主動式磁浮軸承控制系統,確實均具備優異之控制特性與強健性。
摘要(英) In recent years, active magnetic bearings (AMBs) with noncontact and frictionless characteristics have been successfully and widely implemented in various kinds of applications including the non-rotating and rotating devices. The most important control object for the non-rotating devices is the controlled devices should be positioned or tracked to the pre-defined trajectories precisely. On the other hand, the most important control object for the rotating devices is the controlled rotor should be regulated and stabilized in the centers within the narrow aperture perfectly. For this reason, the purpose of this dissertation is to develop a fully suspended five degree-of-freedom (DOF) AMB control system, which is composed of two radial AMBs (RAMBs) and one thrust AMB (TAMB), to build up the technologies for both non-rotating and rotating devices and fulfill the requirements of the practical applications.
In the beginning of this dissertation, the system dissections, dynamic analyses, and experimental designs of the AMB systems are presented in detail. Then, various tracking controllers including model-free control methods such as Hermite polynomial-based recurrent neural network (HPBRNN) control; intelligent sliding-mode control (SMC) methods, which combine the merits of SMC, adaptive control, and recurrent Hermite neural network (RHNN), such as adaptive complementary SMC (ACSMC) and robust non-singular terminal SMC (RNTSMC) and so on are proposed to control the rotor in the axial direction of the TAMB system for the tracking of various reference trajectories. Moreover, to control the five-axes of the five-DOF AMB system simultaneously, a decentralized proportional-integral-derivative neural network (PIDNN) controller with on-line tuning control gains is proposed first. Then, a decoupled dynamic model is proposed to transfer the original coupled five-DOF AMB system into five independent subsystems for the purpose of decentralized control. Based on the decoupled dynamic model, a decentralized intelligent double integral SMC (IDISMC) system is further proposed to improve the control performance of the five-DOF AMB control system. In the control law of the IDISMC, the designed double integral sliding surface reinforces the control law with integral control feature. Furthermore, the control gains of the IDISMC can be adjusted on-line and the system uncertainty can also be observed simultaneously by using of a modified PIDNN (MPIDNN) observer. Thus, the proposed IDISMC system with reduced steady-state error is suitable for the highly nonlinear, highly sensitive to the external disturbance, and high precision requirement five-DOF AMB system.
In this dissertation, the validities of the TAMB and five-DOF AMB control systems using different control methods are verified by various testing conditions and performance measures in personal computer-based experimentation. According to the experimental results, the average tracking error of the TAMB system with 0.38kg load can be kept within 1μm and the root mean square of regulating error of the five-DOF AMB system operated at 4800RPM can be maintained within 0.1mm, i.e. 1/4 air gap. Obviously, both the developed TAMB and five-DOF AMB control systems possess good control performances and robustness.
關鍵字(中) ★ 五軸主動式磁浮軸承系統
★ 推力型主動式磁浮軸承
★ 滑動模式控制
★ 互補式滑動模式控制
★ 無奇異終端滑動模式控制
★ 赫米特多項式
★ 分散式控制
★ 比例-積分-微分類神經網路
關鍵字(英) ★ decentralized control
★ Hermite polynomial
★ proportional-integral-derivative neural network
★ non-singular terminal sliding-mode control
★ complementary sliding-mode control
★ Five-DOF active magnetic bearing system
★ sliding-mode control
★ thrust active magnetic bea
論文目次 摘 要 I
Abstract III
Acronyms V
謝 誌 VII
List of Figures XI
List of Tables XXI
Chapter 1 INTRODUCTION 1
1.1 Historical Background 1
1.2 Previous Work Reviews 9
1.3 Motivations 11
1.4 Organization 13
Chapter 2 SYSTEM DISSECTION, DYNAMIC ANALYSES, AND EXPERIMENTAL DESIGNS OF FIVE-DOF AMB SYSTEM 15
2.1 Overview 15
2.2 System Dissection of Five-DOF AMB System 17
2.2.1 System Structure 17
2.2.2 Drive System 19
2.3 Dynamic Analyses of Five-DOF AMB System 26
2.3.1 Dynamic Model of TAMB System 27
2.3.2 Dynamic Model of Five-DOF AMB System 27
2.4 Experimental Design for TAMB Control System 34
2.4.1 Experimental Setup 35
2.4.2 Reference Trajectories Planning 36
2.4.3 Performance Measures and Comparisons 36
2.5 Experimental Design for Five-DOF AMB Control System 37
2.5.1 Experimental Setup 37
2.5.2 Operating Conditions Planning 38
2.5.3 Performance Measures and Comparisons 39
Chapter 3 PRECISE TRACKING CONTROL OF TAMB SYSTEM USING MODEL-FREE CONTROL METHODS 40
3.1 Overview 40
3.2 Model-Free Control Methods 41
3.2.1 PID Control 41
3.2.2 RNN Control 42
3.2.3 HPBRNN Control 45
3.3 Experimental Results 50
3.4 Summary 56
Chapter 4 PRECISE TRACKING CONTROL OF TAMB SYSTEM USING ADAPTIVE COMPLEMENTARY SLIDING-MODE CONTROL 57
4.1 Overview 57
4.2 Adaptive Complementary Sliding-Mode Control Strategy 58
4.2.1 Sliding-Mode Control 59
4.2.2 Complementary Sliding-Mode Control 61
4.2.3 MIMO RHNN Estimator 65
4.2.4 Adaptive Complementary Sliding-Mode Control 67
4.3 Experimental Results 70
4.4 Summary 75
Chapter 5 PRECISE TRACKING CONTROL OF TAMB SYSTEM USING ROBUST NON-SINGULAR TERMINAL SLIDING-MODE CONTROL 77
5.1 Overview 77
5.2 Robust Non-Singular Terminal Sliding-Mode Control Strategy 78
5.2.1 Terminal Sliding-Mode Control 78
5.2.2 Non-Singular Terminal Sliding-Mode Control 81
5.2.3 MISO RHNN Estimator 83
5.2.4 Robust Non-Singular Terminal Sliding-Mode Control 84
5.3 Experimental Results 87
5.4 Summary 93
CHAPTER 6 ROBUST CONTROL OF FULLY SUSPENDED FIVE-DOF AMB SYSTEM USING PID NEURAL NETWORK CONTROL 94
6.1 Overview 94
6.2 Decentralized PIDNN Control Scheme 95
6.3 Experimental Results 101
6.4 Summary 113
Chapter 7 ROBUST CONTROL OF FULLY SUSPENDED FIVE-DOF AMB SYSTEM USING INTELLIGENT DOUBLE INTEGRAL SLIDING-MODE CONTROL 114
7.1 Overview 114
7.2 Decentralized Sliding-Mode Control Approaches 115
7.2.1 Decentralized Integral Sliding-Mode Control 116
7.2.2 Decentralized Intelligent Double Integral Sliding-Mode Control 117
7.3 Experimental Results 123
7.4 Summary 135
Chapter 8 DISCUSSIONS, CONCLUSIONS, AND SUGGESTIONS FOR FUTURE WORKS 136
8.1 Discussions 136
8.2 Conclusions 144
8.3 Suggestions for Future Works 144
REFERENCE 146
VITA 152
參考文獻 [1] D. M. Rote and Y. Cai, “Review of dynamic stability of repulsive-force maglev suspension systems,” IEEE Trans. Magnetics, vol. 38, no. 2, pp. 1383-1390, Mar. 2002.
[2] M. Ono, S. Koga, and H. Ohtsuki, “Japan’s superconducting Maglev train,” IEEE Instrumentation & Measurement Magazine, vol. 5, no. 1, pp. 9-15, Mar. 2002.
[3] M. Y. Chen, M. J. Wang, and C. L. Fu, “A novel dual-axis repulsive maglev guiding system with permanent magnet: modeling and controller design,” IEEE Trans. Mechatronics, vol. 8, no. 1, pp. 77-86, Mar. 2003.
[4] H. M. Gutierrez and P. I. Ro, “Magnetic servo levitation by siding-mode control of nonaffine systems with algebraic input invertibility,” IEEE Trans. Ind. Electron., vol. 52, no. 5, pp. 1449-1455, Oct. 2005.
[5] J. H. Park and Y. S. Baek, “Design and analysis of a maglev planar transportation vehicle,” IEEE Trans. Magn., vol. 44, no. 7, pp. 1830-1836, Jul. 2008.
[6] C. Samiappan, N. Mirnateghi, B. E. Paden, and J. F. Antaki, “Maglev apparatus for power minimization and control of artificial hearts,” IEEE Trans. Contr. Syst. Technol., vol. 16, no. 1, pp. 13-18, Jan. 2008.
[7] G. Schweitzer, H. Bleuler, and A. Traxler, Active Magnetic Bearings: Basics, Properties, and Applications of Active Magnetic Bearings. Zurich, Switzerland: vdf Hochschulverlag, 1994.
[8] A. T. A. Peijnenburg, J. P. M. Vermeulen, and J. v. Eijk, “Magnetic levitation systems compared to conventional bearing systems,” Micro Electronic Engineering, vol. 83, pp. 1372-1375, 2006.
[9] H. Stoelting, E. Kallenbach, and W. Eberhard, Handbook of Fractional-Horsepower Drives. Springer Berlin Heidelberg, 2008.
[10] G. Schweitzer and E. H. Maslen, Magnetic Bearings - Theory, Design and Application to Rotating Machinery. Springer-Verlag, 2009.
[11] E. A. Knoth and J. P. Barber, “Magnetic repulsion bearings for turbine engines,” IEEE Trans. Magn., vol. 24, no. 6, pp. 3141-3143, Nov. 1998.
[12] M. A. Pichot, J. P. Kajs, B. R. Murphy, A. Ouroua, B. M. Rech, R. J. Hayes, J. H. Beno, G. D. Buckner, and A. B. Palazzolo, “Active magnetic bearings for energy storage systems for combat vehicles,” IEEE Trans. Magn., vol. 37, no. 1, pp. 318-323, Jan. 2001.
[13] M. D. Noh, S. R. Cho, J. H. Kyung, S. K. Ro, and J. K. Park, “Design and implementation of a fault-tolerant magnetic bearing system for turbo-molecular vacuum pump,” IEEE Trans. Mechatronics, vol. 10, no. 6, pp. 626-631, Dec. 2005.
[14] N. Miyamoto, T. Enomoto, M. Amada, J. Asama, A. Chiba, T. Fukao, S. Iwasaki, and M. Takemoto, “Suspension characteristics measurement of a bearingless motor ” IEEE Trans. Magn., vol. 45, no. 6, pp. 2795-2798, June 2009.
[15] T. Ohji, S. Ichiyama, K. Amei, M. Sakui, and S. Yamada, “Conveyance test by oscillation and rotation to a permanent magnet repulsive-type conveyor,” IEEE Trans. Magn., vol. 40, no. 4, pp. 3057-3059, Jul. 2004.
[16] R. L. Fittro, A high speed machining spindle with active magnetic bearings: control theory, design, and application. Doctoral dissertation, University of Virginia, 1998.
[17] O. S. Kim, S. H. Lee, and D. C. Han, “Positioning performance and straightness error compensation of the magnetic levitation stage supported by the linear magnetic bearing,” IEEE Trans. Ind. Electron., vol. 50, no. 2, pp. 374-378, Apr. 2003.
[18] J. H. Lee, P. E. Allaire, G. Tao, J. A. Decker, and X. Zhang, “Experimental study of sliding mode control for a benchmark magnetic bearing system and artificial heart pump suspension,” IEEE Trans. Control Syst. Technol., vol. 11, no. 1, pp. 128-138, Jan. 2003.
[19] A. A. Hussien, S. Yamada, M. Iwahara, T. Okada, and T. Ohji, “Application of the repulsive-type magnetic bearing for manufacturing micromass measurement balance equipment,” IEEE Trans. Magn., vol. 41, no. 10, pp. 3802-3804, Oct. 2005.
[20] M. N. Sahinkaya and A. E. Hartavi, “Variable bias current in magnetic bearings for energy optimization,” IEEE Trans. Magn., vol. 43, no. 3, pp. 1052-1060, Mar. 2007.
[21] Y. C. Chen and C. C. Teng, “A model reference control structure using a fuzzy neural network,” Fuzzy Sets and Systems, vol. 73, pp. 291-312, 1995.
[22] J. T. Jeng and T. T. Lee, “Control of magnetic bearing systems via the Chebyshev polynomial-based unified model (CPBUM) neural network,” IEEE Trans. Sys., Man, Cybern. B, Cybernetics, vol. 30, no. 1, pp. 85-92, Feb. 2000.
[23] F. J. Lin, R. J. Wai, and C. M. Hong, “Recurrent neural network control for LCC-resonant ultrasonic motor drive,” IEEE Trans. Ultrason., Ferroelect., Freq. Contr., vol. 47, no. 3, pp. 737-749, May 2000.
[24] K. W. E. Cheng, H. Y. Wang, and D. Sutanto, “Adaptive directive neural network control for three-phase AC/DC PWM converter,” IEE Proc. Electric Power Applications, vol. 148, no. 5, pp. 425-430, Sept. 2001.
[25] 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, Apr. 2006.
[26] F. J. Lin, L. T. Teng, and H. Chu, “Modified Elman neural network controller with improved particle swarm optimisation for linear synchronous motor drive,” IET Electr. Power Appl., vol. 2, no. 3, pp. 201-214, 2008.
[27] R. J. Wai and C. M. Liu, “Design of dynamic Petri recurrent fuzzy neural network and its application to path-tracking control of nonholonomic mobile robot,” IEEE Trans. Ind. Electron., vol. 56, no. 7, pp. 2667-2683 July 2009.
[28] F. J. Lin, Y. C. Hung, and S. Y. Chen, “FPGA-based computed force control system using Elman neural network for linear ultrasonic motor,” IEEE Trans. Ind. Electron., vol. 56, no. 4, pp. 1238-1253, April 2009.
[29] Z. Li, “Robust control of PM spherical stepper motor based on neural networks,” IEEE Trans. Ind. Electron., vol. 56, no. 8, pp. 2945-2954, Aug. 2009.
[30] L. Ma and K. Khorasani, “Constructive feedforward neural networks using hermite polynomial activation functions,” IEEE Trans. Neural Networks, vol. 16, no. 4, pp. 821-833, July 2005.
[31] L. Rutkowski, “Adaptive probabilistic neural networks for pattern classification in time-varying environment,” IEEE Trans. Neural Networks, vol. 15, no. 4, pp. 811-827, July 2004.
[32] C. Pan, W. Chen, and Y. Yun, “Fault diagnostic method of power transformers based on hybrid genetic algorithm evolving wavelet neural network ” IET Electr. Power Appl., vol. 2, no. 1, pp. 71-76, 2008.
[33] S. H. Ling, H. H. C. Iu, F. H. F. Leung, and K. Y. Chan, “Improved hybrid particle swarm optimized wavelet neural network for modeling the development of fluid dispensing for electronic packaging,” IEEE Trans. Ind. Electron., vol. 55, no. 9, pp. 3447-3460, Sept. 2008.
[34] H. Deng, R. Oruganti, and D. Srinivasan, “Neural controller for UPS inverters based on B-spline network,” IEEE Trans. Ind. Electron., vol. 55, no. 2, pp. 899-909, Feb. 2008.
[35] T. H. Linh, S. Osowski, and M. Stodolski, “On-line heart beat recognition using Hermite polynomials and neuro-fuzzy network,” IEEE Trans. Instrumentation and Measurement, vol. 52, no. 4, pp. 1224-1231, Aug. 2003.
[36] A. I. Rasiah, R. Togneri, and Y. Attikiouzel, "Modeling 1-d signals using hermite basis functions," in Proc. Inst. Elect Eng. Vis. Image Signal Process, 1997, pp. 345-354
[37] J. Crowe, PID Control: New Identification and Design Methods. London, U.K.: Springer-Verlag, 2005.
[38] T. Yamamoto, K. Takao, and T. Yamada, “Design of a data-driven PID controller,” IEEE Trans. Control Syst. Technol., vol. 17, no. 1, pp. 29-39, Jan. 2009.
[39] K. K. Tan, S. Huang, and R. Ferdous, “Robust self-tuning PID controller for nonlinear systems,” J. Process Contr., vol. 12, no. 7, pp. 753-761, Oct. 2002.
[40] S. Parvez and Z. Gao, “A wavelet-based multiresolution PID controller,” IEEE Trans. Industry Applications, vol. 41, no. 2, pp. 537-543, Mar./Apr. 2005.
[41] V. Parra-Vega, S. Arimoto, Y. H. Liu, G. Hirzinger, and P. Akella, “Dynamic sliding PID control for tracking of robot manipulators: theory and experiments,” IEEE Trans Robot. Automat., vol. 19, no. 6, pp. 967-976, Dec. 2003.
[42] T. J. Ren and T. C. Chen, “Motion control for a two-wheeled vehicle using a self-tuning PID controller,” Control Engineering Practice, vol. 16, no. 3, pp. 365-375, Mar. 2008.
[43] S. Cong and Y. Liang, “PID-like neural network nonlinear adaptive control for uncertain multivariable motion control systems,” IEEE Trans. Ind. Electron., vol. 56, no. 10, pp. 3872-3879, Oct. 2009.
[44] J. J. E. Slotine and W. Li, Applied Nonlinear Control. Englewood Cliffs, NJ: Prentice-Hall, 1991.
[45] 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, May 1991.
[46] S. N. Huang, K. K. Tan, and T. H. Lee, “Sliding-mode monitoring and control of linear drives ” IEEE Trans. Ind. Electron., vol. 56, no. 9, pp. 3532-3540, Sept. 2009.
[47] X. Yu and O. Kaynak, “Sliding-mode control with soft computing: a survey,” IEEE Trans. Ind. Electron., vol. 56, no. 9, pp. 3275-3285, Sept. 2009.
[48] J. P. Su and C. C. Wang, “Complementary sliding control of non-linear systems,” Int. J. Control, vol. 75, no. 5, pp. 360-368, 2002.
[49] C. Y. Liang and J. P. Su, “A new approach to the design of a fuzzy sliding mode controller,” Fuzzy Sets and Systems, vol. 139, pp. 111-124, 2003.
[50] M. Zhihong and X. H. Yu, “Terminal sliding mode control of MOMO linear systems,” IEEE Trans. Circuits Syst. I, Fundam. Theory Appl., vol. 44, no. 11, pp. 1065-1070, Nov. 1997.
[51] 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.
[52] Y. Feng, X. Yu, and Z. Man, “Non-singular terminal sliding mode control of rigid manipulators,” Automatica, vol. 38, pp. 2159-2167, 2002.
[53] C. K. Lin, “Nonsingular terminal sliding mode control of robot manipulators using fuzzy wavelet networks,” IEEE Trans. Fuzzy Sys., vol. 14, no. 6, pp. 849-859, Dec. 2006.
[54] 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. Sys., Man, Cybern. B, Cybernetics, vol. 34, no. 1, pp. 255-262, Feb. 2004.
[55] 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. on Control, Automation and Systems, Seoul, Korea, 2007, pp. 1671-1676
[56] S. C. Tan, Y. M. Lai, and C. K. Tse, “Indirect sliding mode control of power converters via double integral sliding surface,” IEEE Trans. Power Electronics, vol. 23, no. 2, pp. 600-611, Mar. 2008.
[57] M. Defoort, T. Floquet, W. Perruquetti, and S. V. Drakunov, “Integral sliding mode control of an extended Heisenberg system,” IET Control Theory Appl., vol. 3, no. 10, pp. 1409-1424, 2009.
[58] S. L. Chen, S. H. Chen, and S. T. Yan, “Experimental validation of a current-controlled three-pole magnetic rotor-bearing system,” IEEE Trans. Magn., vol. 41, no. 1, pp. 99-112, Jan. 2005.
[59] T. Minihan, “Large motion tracking control for thrust magnetic bearings with fuzzy logic, sliding mode, and direct linearization,” J. Sound Vib., vol. 263, pp. 549-567, 2003.
[60] T. J. Yeh, Y. J. Chung, and W. C. Wu, “Sliding control of magnetic bearing systems,” Journal of Dynamic Systems, Measurement, and Control, vol. 123, pp. 353-362, 2001.
[61] J. Y. Hung, N. G. Albritton, and F. Xia, “Nonlinear control of a magnetic bearing system,” Mechatronics, vol. 13, pp. 621-637, 2003.
[62] N. C. Tsai, C. H. Kuo, and R. M. Lee, “Regulation on radial position deviation for vertical AMB systems,” Mechanical Systems and Signal Processing, vol. 21, pp. 2777-2793, 2007.
[63] S. Sivrioglu, “Adaptive backstepping for switching control active magnetic bearing system with vibrating base,” IET Control Theory Appl., vol. 1, no. 4, pp. 1054-1059, 2007.
[64] L. Li and J. Mao, “Feedback linearisation of magnetic bearing actuators for a uniform upper bound of force slew rate,” IEE Proc. Electric Power Applications, vol. 146, no. 4, pp. 378-382, July 1999.
[65] L. C. Lin and T. B. Gau, “Feedback linearization and fuzzy control for conical magnetic bearings,” IEEE Trans. Contr. Syst. Technol., vol. 5, no. 4, pp. 417-426, Jul. 1997.
[66] M. S. Queiroz and D. M. Dawson, “Nonlinear control of active magnetic bearings: a backstepping approach,” IEEE Trans. Control Syst. Technol., vol. 4, no. 5, pp. 545–552, Sep. 1996.
[67] C. T. Hsu and S. L. Chen, “Exact linearization of a voltage-controlled 3-pole active magnetic bearing system,” IEEE Trans. Contr. Syst. Technol., vol. 10, no. 4, pp. 618-625, 2002.
[68] C. Bi, D. Wu, Q. Jiang, and Z. Liu, “Automatic learning control for unbalance compensation in active magnetic bearings,” IEEE Trans. Magn., vol. 41, no. 7, pp. 2270-2280, July 2005.
[69] B. Lu, H. Choi, G. D. Buckner, and K. Tammi, “Linear parameter-varying techniques for control of a magnetic bearing system,” Control Engineering Practice, vol. 16, no. 10, pp. 1161-1172, 2008.
[70] T. R. Grochmal and A. F. Lynch, “Experimental comparison of nonlinear tracking controllers for active magnetic bearings,” Control Engineering Practice, vol. 15, pp. 95-107, 2007.
[71] K. Y. Chen, P. C. Tung, M. T. Tsai, and Y. H. Fan, “A self-tuning fuzzy PID-type controller design for unbalance compensation in an active magnetic bearing,” Expert Systems with Applications, vol. 36, pp. 8560–8570, 2009.
[72] M. Liu, “Decentralized control of robot manipulators: nonlinear and adaptive approaches,” IEEE Trans. Automat. Contr., vol. 44, no. 2, pp. 357-363 1999.
[73] F. J. Lin and P. H. Chou, “Adaptive control of two-axis motion control system using interval type-2 fuzzy neural network,” IEEE Trans. Ind. Electron., vol. 56, no. 1, pp. 178-193, Jan. 2009.
指導教授 徐國鎧、林法正
(Kuo-Kai Shyu、Faa-Jeng Lin)
審核日期 2010-7-27
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   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明