臺灣博碩士論文加值系統

本論文是以小腦模型控制器為主控制架構，提出以步階迴歸控制法則設計之強健性小腦模型控制系統，在此控制系統中是以小腦模型控制器(CMAC)用來學習理想的步階迴歸(Ideal Backstepping)控制法則，並且設計一補償控制器來補強小腦模型控制器與理想的步階迴歸控制法則之間的差異，此外，泰勒線性化技巧也將運用到小腦模型控制器上，並以Lyapunov穩定定理為基礎推導出控制系統之適應性規則(Adaptive Law)，所以控制系統的穩定度是可以被證實的。最後，為了證實所提出控制系統的控制性能，所提出的控制系統將應用於線型壓電陶瓷馬達之位移控制。經由實驗結果證實所提出的控制系統能順利達到所需的高精密度之位移控制。接著以車輛追蹤防撞控制作為探討的主題，利用 控制技巧提出一強健性 遞迴式小腦模型控制器(RCMAC)，在提出的強健性遞迴式小腦模型控制系統(RCMAC)其中遞迴式小腦模型控制器將被用來學習理想的反饋線性化控制法則，而強健控制器 則被設計用來縮小因不確定的動態系統。外來的干擾和近似誤差產生的影響。在強健性所推導遞迴式小腦模型控制器中適應性規則是利用 控制技巧和Lyapunov穩定定理，所以控制系統的穩定度是可以被證實的。最後，模擬的結果證實所提出的強健性 遞迴式小腦模型控制器系統對於車輛追蹤的防撞控制能達到優秀的追蹤目的。

In this thesis the cerebellar model articulation controller (CMAC) is the main controller. First , since the dynamic characteristics of the linear piezoelectric ceramic motor (LPCM) are highly nonlinear and time varying, it is difficult to design a suitable motor position controller to achieve high-precision position control at all time. An robust cerebellar model articulation controller (CMAC) via the backstepping control technique is proposed. In the robust CMAC backstepping control system, an adaptive CMAC is used to mimic an ideal backstepping control law and a robust controller is designed to compensate for the difference between the ideal backstepping control law and the adaptive CMAC. The adaptation laws of the control system are derived in the sense of Lyapunov stability analysis, so that the stability of the system can be guaranteed. the controlled LPCM possesses the advantages of good tracking control performance and robustness to uncertainties under wide operating ranges. The effectiveness of the proposed control system is verified with hardware experiments under the occurrence of uncertainties.
Then, an adaptive recurrent cerebellar model articulation controller (RCMAC) with a guaranteed performance is proposed for the car-following collision prevention systems to track periodic reference trajectories. In this control scheme, the proposed dynamic structure of RCMAC has superior capability to the conventional static cerebellar model articulation controller (CMAC) in efficient learning mechanism and dynamic response. The control laws for the robust RCMAC control system are derived based on the control technique and the Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. Finally, the proposed robust RCMAC control system is applied for the car-following collision prevention control. Simulation results show that this method can achieve favorable tracking performance for a safe car-following control.

中文摘要 i
英文摘要 iii
誌謝 v
目錄 vi
圖目錄 viii
第一章 緒論 1
1.1　研究背景與動機 1
1.2　研究方法 2
1.3　論文大綱 3
第二章 小腦模型控制器 5
2.1　原始小腦模型的歷史 5
2.1.1原始小腦模型控制架構 7
2.2　小腦模型控制器 11
2.3　遞迴式小腦模型控制器 15
第三章 步階迴歸之強健性小腦模型控制器於線型壓電陶瓷馬達之位置控
制 18
3.1　簡介 18
3.2　線型壓電陶瓷馬達 19
3.2.1線型壓電陶瓷馬達動態數學模型 21
3.3　線型壓電陶瓷馬達之強健性小腦模型控制系統 22
3.3.1步階迴歸控制 24
3.3.2小腦模型控制器調整演算法 27
3.4　線型壓電陶瓷馬達驅動系統與實驗結果 31
第四章 強健性 遞迴小腦模型控制器之車輛追蹤控制 46
4.1　簡介 46
4.2　車輛排列模型 47
4.3　車輛動態模型 47
4.4　強健性遞迴式小腦模型控制器 49
4.4.1 控制理論概念 50
4.4.2 控制器設計 51
4.5　模擬結果 55
第五章 結論 73
參考文獻 75
簡歷 79

[1]王惠美,「適應性步階迴歸控制之電力系統穩定器設計」,淡江大學,碩士論文,民國92年.
[2]史尚錫,「具非匹配性干擾系統之控制器設計」,成功大學,碩士論文,民國89年.
[3]朱政威,「動態系統理論應用於跟車模式之研究」,嘉義大學,碩士論文,民國92年.
[4]沈柏宏,「利用李亞普諾夫穩定度證明之類神經網路控制永磁線型同步馬達伺服驅動系統」,中原大學,碩士論文,民國91年.
[5]沈孟樵,「線性超音波馬達共振驅動電路與智慧型控制」,中原大學,碩士論文,民國90年.
[6]林立任,「線性馬達平台誤差補償之類神經網路補償器設計」,交通大學,碩士論文,民國92年.
[7]林法正、魏榮宗、段柔勇，超音波馬達之驅動與智慧型控制,滄海書局,民國88年.
[8]林育瑞 ,「利用類神經網路構建機車車流模式之研究」,成功大學,碩士論文,民國90年.
[9]徐書鵬 ,「以步階迴歸控制器為基礎之線型感應馬達驅動系統」,中原大學,碩士論文,民國89年.
[10]張力祥,「可微分小腦模型用於函數逼近及馬達控制可微分小腦模型用於函數」,中原大學,碩士論文,民國91年.
[11]張簡子介,「用小腦模型在FPGA上作車牌辨識」,台灣師範大學,碩士論文,民國91年.
[12]陳宏謀,「植基於小腦模型補償之狀態回授控制器設計」,台灣師範大學,碩士論文,民國91年.
[13]陳芳雄,「機械手臂系統之非線性遞回步階控制設計」,暨南大學,碩士論文,民國92年.
[14]陳盈男,「多變數適應性模糊 控制器之研究」,成功大學,碩士論文,民國89年.
[15]莊宏祥,「伺服控制系統之強健設計與實現」,成功大學,博士論文,民國90年.
[16]揚智仁,「具區域性極點配置之強韌多目標控制器分析與設計」,高雄應用科技大學,碩士論文,民國92年.
[17]楊博森,「線性馬達系統推導與控制模擬研究」,逢甲大學,碩士論文,民國88年.
[18]龔書暉,「內嵌內容可定址記憶體之 CMAC 控制器」,中正大學,碩士論文,民國89年.
[19]Albus, J. S., “A new approach to manipulator Control: The Cerebellar Model Articulation Controller (CMAC) ,” Journal of Dynamic Systems, Measurement, and Control, Trans. of ASME, p.220-227, 1975.
[20]Albus, J. S., “Data storage in the Cerebellar Model Articulation Controller (CMAC) ,” Journal of Dynamic Systems, Measurement and Control, Trans. of ASME, pp. 228-233, 1975.
[21]B. S. Chen, C. S. Tseng, and H. J. Uang “Robustness of nonlinear dynamic systems
via fuzzy linear control”, IEEE Trans. on Fuzzy Systems, vol. 7, no. 5,
pp. 571-585, 1999.
[22]Bor-Sen Chen, Ching-Hsiang Lee, Yeong-Chan Chang,” tracking design of uncertain nonlinear SISO systems adaptive fuzzy approach,” Fuzzy Systems, IEEE Trans. vol. 4, pp.32 – 43,1996
[23]C. Y. Chen, and C. C. Teng, “A mode reference control structure using a fuzzy neural
networks”, Fuzzy sets and Syst. vol. 73, pp. 291-312, 1995.
[24]C. T. Chang and C. S. Lin, “CMAC with general basis functions,” Neural Network, vol. 9, no. 7, pp. 1199-1211, 1996.
[25]C. M. Lin, Y. F. Peng and C. F. Hsu, “Robust cerebellar model articulation controller design for unknown nonlinear systems,” IEEE Trans. on Circuit and systems: II: Express Briefs, vol. 51, no. 7, pp. 354-358, 2004.
[26]D. Marr, “A theory of cerebellar cortex,” J. Physiol., vol. 202, pp. 437-470, 1969.
[27]F. J. Lin, “Real-time IP position controller design with torque feedforward control for PM synchronous motor” IEEE Trans. Ind. Electron.,vol. 44, pp. 398-407, 1997
[28]M. Zhihong, H. R. Wu, and M. Palaniswami, “An adaptive tracking
controller using neural networks for a class of nonlinear systems,
” IEEE Trans. Neural Networks, vol. 9, no. 5, pp. 947-1031, 1998.
[29]L. Xie, “Output feedback control of systems with parameter uncertainties,” Int. J. Contr., vol. 63, no. 4, pp. 741-750, 1996
[30]R. J. Wai, C. M. Lin and Y. F. Peng, “Intelligent hybrid control for linear piezoelectric ceramic motor,” R.O.C. Symposium on Electrical Power Engineering, pp. 1326-1330, 2002.
[31]R. J. Wai, C. M. Lin and Y. F. Peng, ”Robust CMAC neural network control for LLCC-resonant driving linear piezoelectric ceramic motor,” IEE Proc., Contr. Theory Appl., vol. 150, no. 3, pp. 221-232, 2003.
[32]T. Fukuda, and T. Shibata, “Theory and applications of neural networks for industrial control systems”, IEEE Trans Industrial Electronics, pp. 472-489, 1992.
[33]T. Takagi, and M. Sugeno, “Fuzzy identification of systems and its applications to
modeling and control”, IEEE Trans. Systems, Man and Cybernetics, vol. 15, no. 1,
pp. 116-132, 1985.
[34]W. T. Miller, F. H. Glanz, and L. G. Kraft, “CMAC: An associative neural network alternative to backpropagation,” Proc. of the IEEE, vol. 78, no. 10, pp. 1561-1567, 1990.
[35]Y. F. Peng, C. F. Hsu, C. M. Lin and C. J. Kao “Robust CMAC backstepping longitudinal control of vehicle platoons,” IEEE International Conference on Networking, Sensing and Control (ICNSC), vol. 1, pp. 571-576, 21-23 2004.
[36]Yeong-Chan Chang, Bor-Sen Chen,”A nonlinear adaptive tracking control design in robotic systems via neural networks,” IEEE Trans. Control Systems Technology vol. 5, pp.13 – 29,1997
[37]Y. H. Kim and F. L. Lewis, “Optimal design of CMAC neural-network controller for robot manipulator,” IEEE Trans. on Systems, mans and Cybernetics, vol. 30, no. 1, pp. 2231, 2000