車削系統一般都具有複雜和非線性的特點, 使得難以確切設計出以模式為基礎的控制器,來改善車削系統的控制性能。 為了解決這個問題， 本研究發展出自組織模糊滑動模式類神經網路控制器(SFSRBNC) 以保持車削系統之定力切削，SFSRBNC 不僅消除了自組織模糊控制器 (SOFC) 和自組織模糊滑動模式控制器 (SFSC), 在參數選擇的問題也決定了模糊控制器之適當的隸屬函數與模糊規則表，
並且由 SFSRBNC 解決了自組織模糊類神經網路控制器 (SFRBNC) 穩定性的問題。經由模擬結果證實, SFSRBNC 實現之控制性能優於 SOFC, SFSC, SFRBNC, 以控制車削系統之定力切削和定力切削與固定金屬移除率。

Turning systems generally have nonlinear and complex characteristics,
so the design of model-based controllers to manipulate such systems to improve their control performances is impractical.
To address this problem, this study developed a self-organizing fuzzy sliding-mode radial basis-function neural-network
controller (SFSRBNC) for the control of turning systems. The SFSRBNC not only eliminates the problem caused by the
inappropriate selection of parameters in both a self-organizing fuzzy controller (SOFC) and
a self-organizing fuzzy sliding-mode controller (SFSC) and by the determination of the inappropriate membership functions and
fuzzy rules for the design of a fuzzy logic controller,
but also solves the stability problem of a self-organizing fuzzy radial basis-function neural-network controller (SFRBNC) application.
Simulation results indicated that the SFSRBNC achieved better control performance than the SFSC,
SFRBNC, and SOFC for the control of the constant cutting force, with or without fixed material removal rate, in turning.

中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . .i
英文摘要. . . . . . . . . . . . . . . . . . . . . . . . . ii
誌謝 . . . . . . . . . . . . . . . . . . . . . . . . . . iii
目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . iv
圖目錄. . . . . . . . . . . . . . . . . . . . . . . . . . viii
第一章緒論 . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 研究動機與目的 . . . . . . . . . . . . . . . . . . . . 1
1.2 文獻回顧 . . . . . . . . . . . . . . . . . . . . . 1
1.3 本研究的貢獻 . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 本文架構 . . . . . . . . . . . . . . . . . . . . . . . 3
第二章系統的數學模式 . . . . . . . . . . . . . . . . . . . 4
2.1 車削系統簡介 . . . . . . . . . . . . . . . . . .4
2.2 定力切削與固定金屬移除率之數學模式. . . . . . . . . . . . . . . .5
第三章控制器的設計. . . . . . . . . . . . . . . . . . . . 7
3.1 模糊控制理論. . . . . . . . . . . . . . . . . . . . . 7
3.2 自組織模糊控制器的設計. . . . . . . . . . . . . . . . 11
3.2.1 自組織模糊演算法 . . . . . . . . . . . . . . . . . .11
3.2.2 車削系統之自組織模糊控制器的設計 . . . . . . .14
3.3 自組織模糊滑動模式控制器的設計. . . . . . . . . . . . 15
3.3.1 滑動模式控制理論 . . . . . . . . . . . . . . . . . .15
3.3.2 車削系統之自組織模糊滑動模式控制器的設計. . . 18
3.4 自組織模糊徑向基類神經網路控制器的設計. . . . . . . . 19
3.4.1 徑向基底函數類神經網路. . . . . . . . . . . . . . . 19
3.4.2 車削系統之自組織模糊徑向基類神經網路控制器
的設計. . . . . . . . . . . . . . . . . . . . . . . 23
3.5 車削系統之自組織模糊滑動徑向基類神經網路控制
器的設計. . . . . . . . . . . . . . . . . . . . . . . 24
第四章數值模擬結果與分析. . . . . . . . . . . . . . . . . 25
4.1 車削系統之定力切削控制的數值模擬分析. . . . . . . . . . 25
4.1.1 SOFC 與 SFRBNC 定力切削控制比較. . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.1.2 SFSC 與 SFSRBNC 定力切削控制比較œ. . . . . . . . . . . . . . . . . . . . . . . . . . . .38
4.1.3 SFRBNC 與 SFSRBNC 定力切削控制比較. . . . . . . . . . . . . . . . . . . . . . . . 48
4.2 車削系統之定力切削與固定金屬移除率控制的數值模擬分析. . . . . . . . . . . 60
4.2.1 SOFC 與 SFRBNC 定力切削與固定金屬移除率控制比較. . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.2.2 SFSC 與 SFSRBNC 定力切削與固定金屬移除率控制比較œ. . . . . . . . . . . . . . . . . . . . . . . . . . . .79
4.2.3 SFRBNC 與 SFSRBNC 定力切削與固定金屬移除率控制比較. . . . . . . . . . . . . . . . . . . . . . . . 96
第五章結論. . . . . . . . . . . . . . . . . . . . . . . . 115
5.1 本文重要結論. . . . . . . . . . . . . . . . . . . . . 115
5.2 未來展望. . . . . . . . . . . . . . . . . . . . . . . 116
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . 117
著作發表. . . . . . . . . . . . . . . . . . . . . . . . . 120

[1] K. H. Hajikolaei, H. Moradi, G. Vossoughi, M. R. Movahhedy, ”Spindle speed variation
and adaptive force regulation to suppress regenerative chatter in the turning
process,” Journal of Manufacturing Processes, Volume 12, Issue 2, 2010, pp. 106-
115.
[2] S. Yaldiz, F. Unsacar, H. Saglam, ”Comparison of experimental results obtained
by designed dynamometer to fuzzy model for predicting cutting forces in turning,”
International Journal of Materials in Engineering Applications, Volume 27, Issue
10, 2006, pp. 1139-1147.
[3] N. Wang, ”Neuro-fuzzy control for turning processes,” IEEE American Control
Conference, Volume 3, 2003, pp. 2477-2482.
[4] T. J. Procyk , E. H. Mamdani, ”A linguistic self-organizing process controller,”
Automatica, volume 15, no. 1, 1979, pp. 15-30.
[5] M. H. Chang , H. C. Lu , ”Self-organizing fuzzy neural network controller design,”
IEEE International Conference, 2011, pp. 2273-2278.
[6] S. W. Tung , C. Quek, C. Guan , ”SaFIN: A Self-Adaptive Fuzzy Inference Network,”
IEEE Transactions, Volume 22, Issue 12, 2011, pp. 1928-1940.
[7] C. S. Chen , ”Robust Self-Organizing Neural-Fuzzy Control With Uncertainty Observer
for MIMO Nonlinear Systems,” IEEE Transactions on Fuzzy Systems., Volume
19 , Issue 4 , 2011, pp. 694-706.
[8] 楊之廷, “混和自組織模糊與徑向基底函數類神經網路控制器的應用”, 碩士論文, 國立臺北科技大學機電整合研究所, 臺北, 2008。
[9] O. Cerman, P. Husek, ”Adaptive fuzzy sliding mode control for electro-hydraulic
servo mechanism,” International Journal of Expert Systems with Applications, Volume
39, Issue 11, 2012, pp. 10269-10277.
[10] Y. H. Chang , C.W. Chang ,W.C. Chan ,”Fuzzy sliding-mode consensus control for
multi-agent systems,” IEEE American Control Conference, 2011, pp. 1636-1641.
[11] V. I. Utkin, ”Variable structure systems with slidingmode: a survey,” IEEE Transaction
on Automatic Control,vol. 22, 1977,pp.212-22.
[12] W. C. Pitstra, J. K. Pieper, ”Controller designs for constant cutting force turning
machine control,” The International Society of Automation, Volume 39, Issue 2,
2000, pp 191-203
[13] Y. Guo, H. Long, ”Self organizing fuzzy sliding mode controller for the position
control of a permanent magnet synchronous motor drive,” Ain Shams Engineering
Journal, Volume 2, Issue 2, 2011, pp. 109-118.
[14] K. H. Fuh and C. T. Chen, “Constant turning force operation with a fixed metal
removal rate via a prior fuzzy controller system,” Journal of Materials Processing
Technology,vol.70,1997, pp.116-121.
[15] J. H. Chou, S. H. Chen and J.J.Li, “Application of the taguchi-genetic method to
design an optimal grey-fuzzy controller of a constant turning force system,” Journal
of Materials Processing Technology, vol.105,2000, pp.333-343.
[16] C. H. Hsieh, J. H. Chou and Y.J.Wu, “Optimal predicted fuzzy controller of a constant
turning force system with fixed metal removal rate,” Journal of Materials Processing
Technology,vol.123,2002, pp.22-30.
[17] S. H. Chen,J.H.Chou and J.J.Li ,“Optimal grey-fuzzy controller design for a constant
turning force system,” International Journal of Machine Tools & Manufacyure,
vol.42, 2002,pp.343-355.
[18] L. A. Zadeh, ”Fuzzy sets,” Informat. Control, vol. 8, 1965, pp.338-353.
[19] D. Driankow, H. Hellendoorn, M. Reinfrank, ”An introduction to fuzzy control,”
Springer-Verlag Berlin Heidelberg, 1993.
[20] E. H. Mandani, N. Baaklini, ”Descriptive method for deriving control policy in a
fuzzy logic controller,” Elec. Letters 11 25/6, 1975, pp. 625-626.
[21] D.Driankov, H.Hellendoorn and M.Reinfrank, ”An Introduction to Fuzzy Control,”
Springer-Verlag Berlin Heidelberg, 1993.
[22] E.H. Mandani, ”Application of fuzzy algorithms for control of simple dynamic
plants,” Proc. IEEE, Vol. 121, No. 12, 1974, pp. 1585-1588.
[23] L. Ljung, ”System identification: theory for the user,” Information and System Sciences
Series. Prentice-Hall, New York, 1987.
[24] J. J. E. Slotine, W. Li, ”Applied nonlinear control,” New York, London: Prentice-
Hall Inc, 1991.
[25] D. Driankov, H. Hellendoorn, M. Reinfrank, ”An introduction to fuzzy control,”
Berlin: Springer-Verlag, 1993.
[26] G. C. Hwang, S. C. Lin, ”A stability approach to fuzzy control design for nonlinear
systems,” Fuzzy Sets and Systems, 48(3), 1992,pp. 279-287.
[27] C. L. Phillips and H. T. Nagle, Digital Control System Analysis and Design, 3rd ed.
Englewood Cliffs, NJ: Prentice-Hall, 1994.