(3.238.7.202) 您好!臺灣時間:2021/03/03 23:53
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:陳建志
研究生(外文):Chien-Chih Chen
論文名稱:結合類神經網路與反應曲面法於多軸轉向聯結車轉向控制
論文名稱(外文):Combine Neural Network and Response Surface Method in Control of a Multi-Axle-Steering Tractor and Trailer
指導教授:劉思正劉思正引用關係
指導教授(外文):Hsu-Jeng Liu
學位類別:碩士
校院名稱:國立屏東科技大學
系所名稱:機械工程系所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:63
中文關鍵詞:多軸轉向聯結車類神經網路反應曲面法
外文關鍵詞:Tractor-Full TrailerArtificial neural networkResponse surface Method
相關次數:
  • 被引用被引用:1
  • 點閱點閱:128
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本文之目的在研究多軸轉向聯結車轉向控制器,針對車輛於載重變化、高低速轉彎及其他參數變化下,對車輛穩定性之影響進行分析。採用的全聯結車為曳引車及拖曳車皆具可轉向之前輪軸,與傳統全聯結車轉向方法不同。依四輪可轉向轎車研究結果,首先推導多軸轉向全聯結車數學模式,接著進行最佳控制法則的推導;當前述控制因素改變或參數發生變動時,藉由訓練類神經網路使得控制器參數在強健穩定範圍調整,確保車輛控制器之強健穩定性,並結合反應曲面法求控制器參數最佳解。最後利用MATLAB軟體進行模擬分析,研究結果顯示,此自調強健控制器可在短時間內縮減因變數所造成的誤差,且調整至符合系統規範要求。將此控制器與最佳控制法則控制簡化的系統比較後,系統偏差率從9.7589到8.7375,有效改善10.5%。
The purpose of this content was studying The controller of a multi-axle-steering tractor and trailer that analyzed effects of a vehicle’s roadability which aimed the vehicle’s changes at loading, high and low speedy turning and other parameters. The tractor-full trailer adopted was formed by a tractor and a trailer what both had the front axles that were able to make turning different form the way of traditional tractor-full trailer. According to the consequence of investigating a four-wheel-turning compact, deriving firstly the multi-axle-steering model, and then proceeding the derivation of the optimal control method which used the trained artificial neural network to make the parameter of the controller adjust within the range of the robust stability for assuring the robust stability of a vehicle’s controller and combining the response surface methodology to request the best solution of the controller when the control factors or parameters stated above changed. At last, applying MATLAB software to proceed simulation analysis. The initial studying result showed that the automatic adjusted robust controller could shorten in short time the error caused by variables, and it adjusted to qualify the request from the system. Comparing this controller with the system of simplified control of the optimal control method, the error of the system became 8.7375 from 9.7589, it improved 10.5%.
摘要 I
Abstract II
謝誌 III
目錄 IV
表目錄 VII
圖目錄 VIII
符號索引 X
第1章 緒論 1
1.1研究動機與目的 1
1.2文獻回顧 2
1.3論文結構 7
第2章 聯結車數學模式 9
2.1系統數學模式推導 9
2.2輪胎側向力分析 11
2.3簡化系統模式 13
2.4最佳控制法則推導 18
第3章 控制器設計 21
3.1前言 21
3.2 -次佳 控制器 22
3.2.1 -次佳 控制設計 22
3.2.2性能指標 25
3.2.3強健穩定 25
3.3類神經網路 26
3.3.1倒傳遞類神經網路模式 27
3.4反應曲面法 29
3.4.1數學模式 31
3.4.2搜尋過程 32
3.4.3反應曲面法的優點 33
第4章 結合類神經網路與反應曲面法的設計 35
4.1前言 35
4.2系統模型架構 35
4.3強健穩定邊界設計 37
4.4反應曲面設計 37
4.4.1設計步驟 38
4.4.2三水準因子設計 40
4.4.3選用控制因子 40
4.4.4結果分析 41
第5章 模擬結果與比較 44
5.1模擬方法 44
5.2頻域響應分析 46
5.3時域響應分析 53
第6章 結果討論與未來展望 55
6.1結果討論 55
6.2聯結車的結果討論 55
6.3未來展望 56
參考文獻 57
附錄 曳引車與拖曳車參數 61
作者簡介 63
1. 劉思正、吳德和、王家仁,“多軸轉向聯結車轉向控制─最佳控制“,國立屏東技術學院學報第十卷第一期,2000,3。
2. 劉思正,“多軸轉向聯結車轉向穩態轉角比法控制”,p511-514,1997自動控制研討會暨兩岸機電及控制技術交流學術研討會,1997,3。
3. 江柏興”量化回授理論之研究與應用—多軸轉向聯結車轉向控制” ,碩士論文,屏東科技大學/機械工程系,民國89年。
4. 杜孟奇 "應用RBF類神經網路於超音波馬達之位置控制",碩士論文,國立中央大學,民國89年。
5. 林楨喨、劉聰仁、嚴成文 "以線性迴歸的技巧加強RBF類神經網路的引申能力" 第六屆人工智慧與應用研討會,高雄,國立中山大學,277-282頁,2001年11月。
6. 曾彥翔 "藉由類神經網路不確定非線性系統之適應輸出追蹤控制器設計",碩士論文,大同大學,民國87年。
7. Der-Ho Wu, “A Theoretical Study for Yaw/Roll Motions of Multiple Steering Articulated Vehicle”.
8. D. de Bruin and P.P.J van den Bosch, “Modeling and control of a double articulated vehicle with four steerable axes”, Proceedings of the American Control Conference San. Diego. California, June 1999.
9. Lu Qiang and Wang Huiyi, “Identification and Control of Four-Wheel-Steering Vehicles Based on Neural Network”, IEEE.
10. C. Y. Seong and B. Widrow, “Neural dynamic optimization for control systems- Part I: Background,” IEEE Trans. Syst., Man, Cybern. B, vol. 31, pp. 482-489, Aug. 2001.
11. C. Y. Seong and B. Widrow, “Neural dynamic optimization for control systems- Part II: Theory,” IEEE Trans. Syst., Man, Cybern. B, vol. 31, pp. 490-501, Aug. 2001.
12. C. Y. Seong and B. Widrow, “Neural dynamic optimization for control systems- Part II: Applications,” IEEE Trans. Syst., Man, Cybern. B, vol. 31, pp. 502-513, Aug. 2001.
13. A. E. Bryson, Dynamic Optimization. Menlo Park, CA: Addison-Wesley-Longman, 1999.
14. F. L. Lewis, Optimal Control, 2nd ed. New York: Wiley, 1995.
15. R. F. Stengel, Optimal Control and Estimation. New York: Dover, 1994.
16. S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 1999.
17. B. Widrow and M. A. Lehr, “30 years of adaptive neural networks: Perceptron, Madaline, and backpropagation,” Proc. IEEE, vol. 78, pp.1415-1442, Sept. 1990.
18. Oh and Yim, “Modeling of Vehicle Dynamics from Real Vehicle Measurements Using a Neural Network with Two-Stage Hybrid Learning for Accurate Long-Term Prediction”, IEEE.
19. S. N. Brennan, “Modeling and Control Issues Associated with Scaled Vehicles”, Thesis, University of Illinois at Urbana-Champaign, 1999.
20. J. E. Slotine and W. Li, Applied Nonlinear Control. Englewood Cliffs,NJ: Prentice-Hall, 1991.
21. A. Isidori, Nonlinear Control Systems: An Introduction. New York: Springer-Verlag, 1989.
22. H. K. Khalil, Nonlinear Systems. New York: Macmillan, 1992.
23. Ellis, J. R., ”The Ride and Handling of Semi-Trailer Articulated Vehicles”, Automobile Eng., Vol. 56, pp. 523-529, Dec. 1966.
24. Wong, J.Y., THEORY OF GROUND VEHICLES, John Wiley & Sons, Inc, 1993.
25. Gahinet, P., and Apkarian, P., “A Linear Matrix Inequality Approach to H∞ Control”, Int. J. Robust and Nonlinear Control, Vol. 4, pp. 421-448. 1994.
26. Boyd, S., Ghaoui, L. E., Feron, E., and Balakrishnan, V., Linear Matrix Inequalities in System and Control Theory, Studies in Applied Mathmatics, 1994.
27. Gahinet, P., Nemirovski, A., Laub, A. J., and Chilali, M., LMI Control Toolbox For Use with Matlab, The MathWorks, 1995.
28. T. Chen and B. Francis, Optimal sampled-data control Systems, Spring-Verlag, Reading, 1996.
29. Doyle, J. C., Glover, K., Khargonekar, P. P., and Francis, B. A., “State-space Solutions to Standard and Control Problems”, IEEE Transactions on Automatic Control, Vol. 34, No. 8, pp. 831-847, 1989.
30. Mario Sznaier, “An exact solution to general siso mixed problems via convex optimization,”IEEE Trans. Automat. Contr., vol. 39, no. 12, pp. 2511-2517, Dec, 1994.
31. H. Demuth and M. Beale, Neural Network ToolBox User’s Guide, The MathWorks Inc., Reading, MA, 1998.
32. A. I. Khuri and J. A. Cornell, Response surfaces: design and analyses, Marcel Dekker, Reading, NY, 1996.
33. J. Doyle, K. Glover, P. Khargonekar, and B. Francis, “State-space solutions to standard and control problems,” IEEE Trans. Automat, Contr., vol. 34, no. 8, pp. 831-846, Aug, 1981.
34. S. Boyd et al., “A new CAD method and associated architectures for linear controllers,” IEEE Trans. Automat. Contr., vol. 33, no. 3, pp. 268-283, 1988.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔