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姓名 許安仁(An-Jan Hsu)  查詢紙本館藏   畢業系所 機械工程學系
論文名稱 自調式類神經PID控制於超音波馬達之應用
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摘要(中) PID控制器是目前產業界應用最多的控制器,但其控制器參數調整不易,大多依賴專家調整,非常不便。本文提出一個自調式類神經PID控制架構,應用倒傳遞類神經網路理論,對於系統模型參數未知的情況下,使用兩個類神經網路分別進行系統鑑別與PID控制器參數調整。由電腦模擬結果可知,本控制架構能在很短的時間內調整出極佳的控制器參數。最後將此控制架構實際應用於超音波馬達位置控制上,實驗結果則顯示,本控制架構確實可以在實際控制應用上實現,其調整結果亦相當快速良好。
摘要(英) This thesis presents a self-tuning PID controller based on the neural network theories. There are two multilayer neural networks within the self-tuning PID controller, one for system identification for unknown controlled systems, and the other for the PID gains determination. Back-propagation method is adopted to perform both the neural networks training.
  The results of computer simulation show that the neural based PID control scheme can tune suitable PID gains within a short period. In addition, the controller was implemented to the position control of an ultrasonic motor. The experimental results have shown that the control scheme is also practically successful.
關鍵字(中) ★ 自調式PID控制
★ 類神經網路
★ 系統鑑別
★ 超音波馬達
關鍵字(英) ★ self-tuning PID control
★ neural network
★ system identification
★ ultrasonic motor
論文目次 中文摘要............................I
Abstract...........................II
致 謝............................III
目 錄.............................IV
圖目錄.............................VI
第一章 緒論.......................1
1.1 研究動機...................1
1.2 研究方法...................2
1.3 文獻回顧...................3
1.4 論文大綱...................4
第二章 類神經網路與控制理論.......6
2.1 類神經網路理論.............6
2.1.1 神經元的模型...............6
2.1.2 類神經網路的連接方式......12
2.1.3 類神經網路的學習與回想....13
2.1.4 倒傳遞類神經網路..........14
2.2 類神經網路控制架構........20
2.2.1 串列控制架構..............21
2.2.2 平行類神經網路控制架構....23
2.2.3 自調式類神經PID控制架構...24
2.3 PID控制理論...............25
第三章 自調式類神經PID控制器.....29
3.1 前言......................29
3.2 系統鑑別網路..............30
3.2.1 系統特性..................30
3.2.2 鑑別方法..................33
3.2.3 系統鑑別網路設定..........35
3.3 PID參數自調類神經網路.....38
第四章 電腦模擬與實作結果........42
4.1 電腦模擬與結果............42
4.1.1 系統鑑別..................44
4.1.2 自調式PID控制器...........47
4.1.3 模擬結果討論..............49
4.2 實作與結果................50
4.2.1 超音波馬達簡介............50
4.2.2 實驗架構..................51
4.2.3 系統鑑別..................55
4.2.4 自調式PID控制器...........59
4.2.5 實作結果討論..............61
第五章 結論與展望................63
5.1 結論......................63
5.2 未來展望..................64
參考文獻...........................65
參考文獻 [1] Sashida, T. and Kenjo, T. An Introduction to Ultrasonic Motors. Oxford: Clarendon Press, 1993.
[2] Ueha, S., Tomikawa, Y., Kurosawa, M. and Nakamura, N. Ultrasonic Motors Theory and Applications. Oxford:Clarendon Press, 1993.
[3] Segawa, S., Ushioda, T. and Inada, H. "Ultrasonic piezomotor equipped with a piezoelectric rotary encoder," IEEE Ultrasonics Symposium, Vol. 3, pp.1205-1209, 1990.
[4] Nanomotion User manual for the AB1.
[4] Nanomotion User manual for the AB1.
[4] Nanomotion User manual for the AB1.
[7] Rumelhart, D. E., Hinton, G. E. and Williams, R. J. "Learning internal representation by error propagation," Parallel Distributed Processing, Vol. 1, pp. 318-362, 1986.
[7] Rumelhart, D. E., Hinton, G. E. and Williams, R. J. "Learning internal representation by error propagation," Parallel Distributed Processing, Vol. 1, pp. 318-362, 1986.
[7] Rumelhart, D. E., Hinton, G. E. and Williams, R. J. "Learning internal representation by error propagation," Parallel Distributed Processing, Vol. 1, pp. 318-362, 1986.
[7] Rumelhart, D. E., Hinton, G. E. and Williams, R. J. "Learning internal representation by error propagation," Parallel Distributed Processing, Vol. 1, pp. 318-362, 1986.
[11] Visioli, A. "Fuzzy logic based set-point weight tuning of PID controllers," IEEE Transactions on Systems, Man and Cybernetics, Part A, Vol. 29, No. 6, pp. 587-592, November 1999.
[12] Lai, L. Y. and Lee, M. Y. "Fuzzy tuning of integrator outputs of PID controllers for a DC motor system," The Chung Yuan Journal, Vol. XXII, pp. 126-137, December 1993.
[13] Natarajan, K. and Gilbert, A. F. "On direct PID controller tuning based on finite number of frequency response data," ISA Transactions, Vol. 36, No. 2, pp. 139-149, 1997.
[14] Ho, W. K., Hang, C. C. and Cao, L. S., "Tuning of PID controllers based on gain and phase margin specifications," Automatica, Vol. 31, No. 3, pp. 497-502, March 1995.
[15] Porter, B. and Jones, A. H. "Genetic tuning of digital PID controllers," Electronics Letters, Vol. 28, No. 9, pp. 843-844, 23 April 1992.
[16] Wu, C. J. and Huang, C. H. "A hybrid method for parameter tuning of PID controllers," Journal of the Franklin Institute, Vol. 334, No. 4, pp. 547-562, July 1997.
[16] Wu, C. J. and Huang, C. H. "A hybrid method for parameter tuning of PID controllers," Journal of the Franklin Institute, Vol. 334, No. 4, pp. 547-562, July 1997.
[16] Wu, C. J. and Huang, C. H. "A hybrid method for parameter tuning of PID controllers," Journal of the Franklin Institute, Vol. 334, No. 4, pp. 547-562, July 1997.
[19] Hemerly, E. M. and Nascimento Jr, C. L. "An NN-based approach for tuning servocontrollers," Neural Networks, Vol. 12, No. 3, pp. 513-518, April 1999.
[20] 葉怡成, 類神經網路模式應用與實作, 儒林, 1995.
[21] 林昇甫、洪成安, 神經網路入門與圖樣辨識, 全華科技, 1993.
[22] 王進德、蕭大全, 類神經網路與模糊控制理論入門, 全華科技, 1994.
[23] 焦李成, 神經網路系統理論, 儒林, 1991.
[24] 陳燕慶、鹿浩, 神經網路理論及其在控制工程中的應用, 儒林, 1992.
[25] Psaltis, D., Sideris, A. and Yamamura, A. "A multilayered neural network controller," IEEE Control Systems Magazine, Vol. 8, No. 2, pp. 17-21, 1989.
[26] 林錦龍, 類神經網路控制器之設計,國立台灣科技大學碩士論文, 1998.
[27] Ogata, K., Discrete-time control systems. Prentice-Hall, 1995.
[28] Narendra, K. S. and Annaswamy, A. M. Stable adaptive systems. Englewood Cliffs, NJ: Prentice-Hall, 1989.
[29] Funahashi, K. "On the approximate realization of continuous mapping by neural networks," IEEE Transactions on neural networks, Vol. 2 No. 1, pp. 193-192, 1989.
指導教授 莊漢東(Han-tung Chuang) 審核日期 2000-7-12
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