臺灣博碩士論文加值系統

行波式超音波馬達和一般電磁馬達相較有顯著的優點，例如低轉速時高轉矩、體積小、高精確度、快速的動態響應、結構簡單、體積小、可忽略磁場干擾、運轉安靜、無需消耗功率的定位維持…等等。因此可適用於辦公室、醫院、精密定位設備、照相機自動對焦系統、機械手臂或是容易受到電磁干擾的地方，如核磁共振設備，以及…等等，而且超音波馬達的設計相當多樣化，可見超音波馬達在未來有著相當廣泛的應用前景。
然而行波式超音波馬達應用於調相控制時具有死區這項缺陷，且在不同的驅動條件下發生變動。由此可知行波式超音波馬達的特性複雜且高度非線性，其馬達參數易受溫度上升、驅動頻率、驅動電壓等變數所影響。為了能更深入分析跟控制超音波馬達，本論文針對行波式超音波馬達的模型化和控制器兩大方向來作研究。模型廣義回歸類神經網路是以調頻、調相、加載的實驗結果為根據所建立的超音波馬達模型。新穎的模糊遞迴類神經網路控制器，在陌生或是不明確的系統裡，能夠達到及時控制之目的以及產生良好的性能。而超音波馬達的死區則藉由廣義回歸類神經網路控制器補償，使系統能維持良好的響應。
最後，藉著數位訊號處理器實現全數位元化之超音波馬達控制系統。由模擬結果可以大致瞭解超音波馬達的動態響應，實驗結果可以看出本論文所提出的控制器可獲得良好的控制性能及精確的速度響應，並且驗證了本新穎控制器在超音波馬達應用上，有著相當良好的效能與高實用性。

The traveling-wave ultrasonic motor (TWUSM) is a new type of actuator with significant features such as high holding torque at low speed range, high precision, fast dynamics, simple structure, compactness in size, no electromagnetic interference, silent drive, reversible controllability, and ability to maintain angle without electric power. Therefore, the TWUSM has been used in many practical areas such as industrial, medical, robotic, and automotive applications.
However, the dynamic model of the TWUSM motor has the nonlinear characteristic and dead-zone problem which varies with many driving conditions. The USM parameters are nonlinear with time varying due to the temperature increasing and different motor drive operating conditions, such as driving frequency, source voltage, and load torque. In this thesis, both modeling and control aspects of the TWUSM are investigated. Based on experimental measurements with different conditions: driving frequency and input phase differences, a model general regression neural networks(MGRNN) structure is obtained. Moreover, the novel controller, recurrent fuzzy neural networks(RFNN), provides a way to derive uncertain dynamics with on-line learning. The GRNN is adopted to determine the dead-zone compensating input and decouple the frequency and phase commands via the FRNN.
The experimental results of this thesis are shown a superior performance for the TWUSM. Furthermore, the results are provided to demonstrate the effectiveness of the proposed controller.

摘要 I
Abstract II
Acknowledgements III
Contents IV
List of Tables VII
List of Figures VII
Symbols XI
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Outline of this thesis 5
Chapter 2 Modeling of Traveling-Wave Ultrasonic Motor 7
2.1 The Background of the Ultrasonic Motor 7
2.2 Basic Structure of the TWUSM 8
2.3 Equivalent Circuit for TWUSM 11
2.4 TWUSM Modeling 14
2.4.1 Modeling General Regression Neural Networks Scheme 15
2.4.2 Modeling General Regression Neural Networks System Design 17
Chapter 3 General Regression Neural Networks Based On Recurrent Fuzzy Neural Networks Controller Design 19
3.1 The Control Scheme 20
3.2 The Principle of Recurrent Fuzzy Neural Networks Controller 21
3.3 Recurrent Fuzzy Neural Networks Control System Design 23
3.3.1 Stability Derivation 24
3.3.2 Convergence Analysis of RFNN 29
3.4 General Regression Neural Networks Control System Design 33
Chapter 4 Computer Simulations 35
4.1 Brief of the Simulations 35
4.1.1 Simulations of General Regression Neural Networks based on Recurrent Fuzzy Neural Networks Controller 35
4.1.2 Simulations of Recurrent Fuzzy Neural Networks Controller 36
4.1.3 Simulations of PI controller 37
4.2 Reference Model Design 38
4.3 Simulation Results 38
4.3.1 Simulation with a periodic angle command 39
4.3.2 Comparison of 100 rpm speed command with 0.5N-m Load 41
Chapter 5 Experimental Results 56
5.1 Digital Signal Processor 58
5.2 Motor Driving Circuit 60
5.2.1 Voltage-controlled Oscillator 61
5.2.2 Voltage-controlled Phase Shifter 63
5.2.3 Power Amplifiers Circuit and Transformer 64
5.3 Experimental Results 65
5.3.1 Experiments with a periodic Square angle command 66
5.3.2 Experiment with a sinusoidal angle command 67
5.3.3 Experimental Results for a Speed Command of 100 rpm with 0.5N-m Load 68
Chapter 6 Conclusions and Suggestions 83
6.1 Conclusions 83
6.2 Suggestions 84
References 85
Vita 88

1.T. Sashida and T. Kenjo, An introduction to ultrasonic motors, Clarendon Press, Oxford, 1993.
2.S. Ueha and Y. Tomikawa, Ultrasonic motors theory and applications, Clarendon Press, Oxford, 1993.
3.K. Uchino, Piezoelectric actuators and ultrasonic motors, Kluwer Academic Publishers, 1997.
4.T. Senjyu, T. Kashiwagi, and K. Uezato, “A study on high efficiency drive of ultrasonic motors,” Electric Power Components and Systems, vol. 29, pp. 179-189, 2001.
5.K. Uchino, “Piezoelectric ultrasonic motors: overview,” Smart Materials and Structures, vol. 7, pp. 273-285, 1998.
6.T. S. Glrnn, “Mixed-Domain performance model of the piezoelectric traveling-wave motor and the development of a two-sided device,” PhD, Massachusetts Institute of Technology, 2002.
7.N. W. Hagood and A. J. Mcfarland, “Modeling of a piezoelectric rotary ultrasonic motor,” IEEE Trans. on Ultrasonics, Ferroelectrics, and Frequency control, vol. 42, no. 2, pp. 210-224, March 1995.
8.Y. Izuno, R. Takeda, and M. Nakaoka, “New fuzzy reasoning-based high performance speed/position servo control schemes incorporating ultrasonic motor,” IEEE Trans. on Industry Applications, vol. 28, no. 3, pp. 613-618, May 1992.
9.T. Izumi, H. Yasutsune, Y. J. Kim, and M. Nakaoka, “New inverter-fed power ultrasonic motor for speed tracking servo application and its feasible evaluations,” IEEE Trans. on Power Electronics. Drive Systems, pp. 766-773, 1995.
10.G. Bal and E. Bekiroglu, “Servo speed control of traveling-wave ultrasonic motor using digital signal processor,” Sensor and Actuators A 109, pp. 212-219, 2004.
11.G. Bal and E. Bekiroglu, “A highly effective load adaptive servo drive system for speed control of traveling-wave ultrasonic motor,” IEEE Trans. on Power Electronics, vol. 20, no. 5, pp. 1143-1149, September 2005.
12.A. Alessandri, C. Cervellera, and M. Sanguineti, “Design of asymptotic estimators: an approach based on neural networks and nonlinear programming,” IEEE Trans. on Neural Networks, vol. 18, no. 1, pp. 86-96, January 2007.
13.M. Liu, “Delayed standard neural network models for control systems,” IEEE Trans. on Neural Networks, vol. 18, no. 5, pp. 1376-1391,September 2007.
14.V. M. Becerra, F. R. Garces, S. J. Nasuto, and W. Holderbaum, “An efficient parameterization of dynamic neural networks for nonlinear system identification,” IEEE Trans. on Neural Networks, vol. 16, no. 4, pp. 983-988, July 2005.
15.C. M. Lin, and C. F. Hsu, “Recurrent neural network based adaptive -backstepping control for induction servomotors,” IEEE Trans. on Industrial Electronics, vol. 52, no. 6, pp. 1677-1684, December 2005.
16.C. C. Ku and K. Y. Lee, “Diagonal recurrent neural networks for dynamic systems control,” IEEE Trans. on Neural Networks, vol. 6, no. 1, pp. 144-156, 1995.
17.M. A. Brdys and G. J. Kulawski, “Dynamic neural controllers for induction motor,” IEEE Trans. on Neural Networks, vol. 10, no.2, pp. 340-355, 1999.
18.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. on Industrial Electronics, vol. 45, no. 1, pp. 151-161, 1998.
19.C. J. Lin and C. H. Chen, “Identification and prediction using recurrent compensatory neuro-fuzzy systems,” Fuzzy Sets and Systems, vol. 150, no. 2, pp. 307-330, 2005.
20.T. Senjyu, T. Kashiwagi, and K. Uezato, “Position control of ultrasonic motors using MRAC with deadzone compensation,” IEEE Trans. on Power Electronics, vol. 17, no. 2, pp. 265-272,March 2002.