(3.239.159.107) 您好!臺灣時間:2021/03/08 21:22
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:趙少甫
研究生(外文):Shao-Fu Chao
論文名稱:應用模糊模式為基礎的強健性及適應性可變結構控制於機械手臂軌跡追蹤之研究
論文名稱(外文):Trajectory Tracking of Manipulator Robot Using Fuzzy-Model-Based Robust and Adaptive Variable Structure Controls: Theory and Experiments
指導教授:黃志良黃志良引用關係
指導教授(外文):Chih-Lyang Hwang
學位類別:碩士
校院名稱:大同大學
系所名稱:機械工程研究所
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:2001
畢業學年度:89
語文別:英文
論文頁數:97
中文關鍵詞:機械手臂滑動模式控制模糊線性基礎模式
外文關鍵詞:Robot armSliding-mode controlFuzzy-linear-based model
相關次數:
  • 被引用被引用:0
  • 點閱點閱:93
  • 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
應用模糊模式為基礎的強健性及適應性可變結構控制於機械手臂軌跡追蹤之研究
摘要
在這篇論文中包含兩個部分。第一部為鉛直面二軸機械手臂之電腦模擬; 第二部為水平面二軸機械手臂之實驗。在第一部中,所提出機械手臂之未模式化動態系統是由N個模糊模式為基礎的線性狀態空間次系統來近似。而這N個次系統是我們依循cosine之正斜率及負斜率線性化得來。然後,相同模糊規則之模糊集合來建立所追蹤振幅及相位的數學參考模式。
如果所建立之模糊模式與未模式化的動態系統誤差不大的話,則以模糊模式為基礎的強健性可變結構控制就可以達到可接受之結果。相反的,如果所建立之模糊模式與未模式化的動態系統誤差很大,我們會再用類神經網路來建立模式。而有e修正之學習法則是可以確保學習權重之範圍而沒有暫態之現象。我們也會使用相同模糊規則之模糊模式來建立以模糊模式為基礎之適應性可變結構控制控制。在每一個控制器中都含有等效控制及切換控制。整個系統的穩定性可由李雅普諾夫穩定性定理得到證明。本論文所提之控制器對於機械手臂之控制問題提供了一個系統化之設計而得到可保證之結果。而電腦模擬也證明所提出控制器之效果。
在第二部中,依據第一部中的理論,我們將實驗系統之未模式動態系統也由n個以模糊模式為基礎的狀態空間次系統依循cosine之正斜率與負斜率線化性來近似。我們使用相同模糊規則之模糊集合來建立所追蹤振幅及相位的數學參考模式。在模式化之後,所設計之模糊模式為基礎的強健性滑動模式控制就可達到可接受之結果。所提出之控制器也含有等效控制及切換控制。模糊等效控制是應付在數學模式後之系統控制; 而模糊切換控制是用來加強應付不確定量的控制。我們所提出之控制器提供了系統化及簡單化的設計而能達到確定之效果。最後,水平面上的實驗也加上負載來證明控制器的有效性。

Trajectory Tracking of Manipulator Robot Using Fuzzy-Model-Based Robust and Adaptive Variable Structure Controls: Theory and Experiments
Abstract --- This thesis contains two parts. Part I is the simulation of two-joint robot in vertical plane, and Part II is the experiment of the two-joint robot in horizontal plane. In Part I, a robotic system in the presence of unmodeled dynamics is approximated by N fuzzy-based linear state-space subsystems those are obtained from the linearization of robotic system about a cosine trajectory with positive and negative slopes. Then, the same fuzzy sets of the system rule are applied to establish reference models with desired amplitude and phase properties. If approximation error of the fuzzy-model and unmodeled dynamics are not very large, a fuzzy-model-based robust variable structure model reference control (FRVSMRC) is designed to obtain an acceptable performance. On the contrary, if approximation error of the fuzzy-model or unmodeled dynamics is huge, they are modeled by radial-basis-function neural-networks (RBFNNs). A learning law with e-modification is given to ensure a boundedness of learning weight without the persistent excitation (PE) condition. Then, the same fuzzy sets of the system rule are employed to design fuzzy-model-based adaptive variable structure model reference control (FAVSMRC). Each control includes a fuzzy equivalent control and a fuzzy switching control. The stability of overall system is verified by Lyapunov stability theory. The proposed control provides a systematic design for the control problem of a class of robots to obtain a guaranteed performance. The simulations are also given to verify the usefulness of the proposed control.
In the Part II, according to the theory of Part I, a robotic arm in the presence of unmodeled dynamics is also approximated by N fuzzy-based linear state-space subsystems obtained from its linearization about a cosine trajectory. The same fuzzy sets of the plant rule are applied to establish reference models with desired amplitude and phase features. After a modeling verification, a fuzzy-model-based robust sliding-mode control is then designed to obtain an acceptable performance. The proposed control includes a fuzzy equivalent control and a fuzzy switching control. The fuzzy equivalent control is employed to obtain the desired control behavior of the nominal system; the fuzzy switching control is applied to reinforce the performance of robot arm as it is subject to uncertainty. The proposed control provides a systematic and easy design for the robot arms to attain a guaranteed performance. Finally, experiments of two-joint in horizontal plane with (or without) payload are arranged to verify the usefulness of the proposed control.

Table of contents
PART I : Trajectory Tracking of Manipulator Robot Using Fuzzy-Model-Based Robust and Adaptive Variable Structure Model Reference Controls
CHAPTER I
INTRODUCTION……………………………………………………………………………1
CHAPTER II
MATHEMATCIAL PRELIMINARIES………………………………………………….5
CHAPTER III
PROBLEM FORMULATION……………………………………………………………..6
CHAPTER IV
CONTROLLER DESIGN………..………………………….……………………………10
CHAPTER V
ILLUSTRATIVE EXAMPLES……………………………………………………………15
CHAPTER VI
CONCLUSIONS……………….………………………...……………………………….22
APPENDIXES
Appendix A
Proof of Theorem 1…………………………………………………………………………23
Appendix B
Proof of Theorem 3…………………………………………………………………………………..24
Appendix C
The system parameters of 36 fuzzy-model………………………………………………….26
List of Figures and Table……………………………………………………………………28
Fig.1. Control block diagram……………………………………………………………………..29
Fig.2. Two-joint robot in vertical plane……………………………………………………………30
Fig.3. The location and number of center of membership function for angular position…………30
Fig.4. The membership functions…………………………………………………………………31
Fig.5. Comparison of sinusoidal responses between physical system (…) and mathematical (-)
for input …………….……………………………….37
Fig.6. The responses of FRVSMRC………………………………………………………………39
Fig.7. The responses of Figure 6 case under the subjection of uncertainty (34)……………………41
Fig.8. The responses of Figure 7 case using FAVSMRC…………………………………………44
Fig.9. The output response for the trajectory ………………..45
Fig.10. The output response of for tradition fuzzy control (i.e., the proposed
control without switching control)………………………………………………………….46
Table 1. Maximum steady-state tracking errors relative to amplitude of reference input for
different conditions, controllers and reference inputs……………………………………47
PART II: A Fuzzy-Model-Based Robust Sliding-Mode Control for Robot Arms: Theory and Experiments
CHAPTER I
INTRODUCTION…………………………………………………………………………48
CHAPTER II
PROBLEM FORMULATION……………………………………………………………51
CHAPTER III
CONTROLLER DESIGN…………………………………………………………………54
CHAPTER IV
EXPERIMENTAL RESULTS………………………………………………………………58
CHAPTER V
CONCLUSIONS………………………………………………………………………….65
APPENDIX
Appendix A (The system parameters of 64 fuzzy-model)
Captions of Figures and Tables……………………………………………………………………..66
Fig.1. Control block diagram………………………………………………………………………..70
Fig.2. Experimental setup.(a)Photograph. (b)Block diagram…………….………………71
Fig.3. The location and number of center of membership function for angular position…………70
Fig.4. The membership functions…………………………………………………………………71
Fig.5. The sinusoidal responses of robotic arm and T-S fuzzy model ……………………81
Fig.6. The responses of for the reference input using the
proposed control…………………………………………………………………………….83
Fig.7. The output responses for Fig. 6 case with 0.5kg payload on the tip of second link…………84
Fig.8. The output responses for ……………………85
Table1. Steady-state output bias of peak-to-peak for different sinusoidal inputs………………….86
Table2. Maximum steady-state tracking errors for and
for last two columns (The column with
notation # denotes the same case with 0.5 kg payload on the tip of second link)………….86
REFERENCES……………………………………………………………………………………87

REFERENCES
[1] J. J. E. Slotine and S. S. Sastry, “Tracking control of nonlinear systems using sliding surface, with application to robot manipulators,” Int. J. Contr., vol. 38, no. 2, pp. 465-492, 1983.
[2] C. Y. Su and T. P. Leung, “A sliding mode controller with bounded estimation for robot manipulators,” IEEE Trans. Robotics Automat., vol. 9, no. 2, pp. 208-214, 1993.
[3] J. J. Craig, P. Hsu and S. S. Sastry, “Adaptive control of mechanical manipulators,” Int. J. Robotics Res., vol. 6, no. 2, pp. 6-28, 1987.
[4] A. Laib, “Adaptive output regulation of robot manipulators under actuator constraints,” IEEE Trans. Robotics Automat., vol. 16, no. 1, pp. 29-35, 2000.
[5] S. P. Chan, “ A neural network compensator for uncertainties in robotic assembly,” Int. J. Robotics Res., vol. 13, no. , pp. 127-141, 1995.
[6] F. L. Lewis, K. Liu and A. Yesildirek, “Neural net robot controller with guaranteed tracking performance,” IEEE Trans. Neural Networks, vol. 6, no. 3, pp. 703-715, 1995.
[7] F. Sun, Z. Sun and P. Y. Woo, “Neural network-based adaptive controller design of robotic manipulators with an observer,” IEEE Trans. Neural Network, vol. 12, no. 1, pp. 54-67, 2001.
[8] B. K. Yoo and W. C. Ham, “Adaptive control of robot manipulator using fuzzy compensator,” IEEE Trans. Fuzzy Syst., vol. 8, no. 2, pp. 186-199, 2000.
[9] X. Tang, L. Cai and W. Huang, “A learning controller for robot manipulators using Fourier series,” IEEE Trans. Robotics Automat., vol. 16, no. 1, pp. 36-45, 2000.
[10] C. L. Hwang, “Neural-network-based variable structure control of electrohydraulic servosystems subject to huge uncertainties without the persistent excitation,” IEEE/ASME Trans. Mechatronics, vol. 4, no.1, pp. 50-59, 1999.
[11] C. L. Hwang and C. H. Lin, “A discrete-time multivariable neuro-adaptive control for nonlinear unknown dynamic systems,” IEEE Trans. Syst., Man, and Cybern. B, vol. 30, no. 6, pp. 865-877, 2000.
[12] H. O. Wang, K. Tanaka and M. F. Griffin, “An approach to fuzzy control of nonlinear systems: stability and design issue,” IEEE Trans. Fuzzy Syst., vol. 4, no. 1, pp. 14-23, 1996.
[13] F. Cuesta, F. Gordillo, J. Aracil and A. Ollero, “Stability analysis of nonlinear multivariable Takagi-Sugeno fuzzy control systems,” IEEE Trans. Fuzzy Syst., vol. 7, no. 5, pp. 508-520, 1999.
[14] B. S. Chen, C. S. Tsen and H. J. Uang, “Robustness design of nonlinear dynamic systems via fuzzy linear control,” IEEE Trans. Fuzzy Syst., vol. 7, no. 5, pp. 571-585, 1999.
[15] S. J. Wu and C. T. Lin, “Optimal fuzzy controller design: local concept approach,” IEEE Trans. Fuzzy Syst., vol. 8, no. 2, pp. 171-185, 2000.
[16] J. J. E. Slotine and W. Li, Applied Nonlinear Control. Englewood Cliffs, NJ: Prentice-Hall, 1991.
[17] R. M. Sanner and J.-J. E. Slotine, “Gaussian networks for direct adaptive control,” IEEE Trans. Neural Networks, vol. 3, no. 6, pp. 837-863, 1992.
[18] R. J. Schilling, J. J. Carroll and A. F. Al-Ajlouni, “Approximation of nonlinear systems with radial basis function neural networks,” IEEE Trans. Neural Networks, vol. 12, no. 1, pp. 1-15, 2001.
[19] C. L. Hwang, “Takagi-Sugeno-based robust and adaptive fuzzy sliding-mode control systems,” IECON-2000, Nagoya, Japan, pp. 530-535, Oct. 22-28, 2000.
[20] C. L. Hwang and C. Y. Kuo, “A stable adaptive fuzzy sliding-mode control for nonlinear systems with application to four-bar-linkage systems,” IEEE Trans. Fuzzy Systems, vol. 9, no. 2, pp. 238-252, 2001.
[21] G. Prokop and F. Pfeiffer, “Synthesis of robot dynamic behavior for environmental interaction,” IEEE Trans. Robotics Automat., vol. 14, no. 5, pp. 718-731, 1998.
[22] A. Jaritz and M. W. Spong, “An experimental comparison of robust control algorithms on a direct drive manipulator,” IEEE Trans. Contr. Syst. Technol., vol. 4, no. 6, pp. 627-640, 1996.
[23] P. R. Pagilla and M. Tomizuka, “An adaptive output feedback controller for robot arms: stability and experiments,” Automatica, vol. 37, pp. 983-995, 2001.
[24] R. G. Berstecher, R. Palm and H. D. Unbehauen, “An adaptive fuzzy siding-mode controller,” IEEE Trans. Ind. Electron., vol. 48, no. 1, pp. 18-31, 2001.
[25] L. A. Dessaint, S. B. Hebert and K. Al-Haddad, “An adaptive controller for a direct-drive Scara robot,” IEEE Trans. Ind. Electron., vol. 39, no. 2, pp. 105-111, 1992.
[26] C. L. Hwang and C. W. Hsu, “A thin and deep hole drilling using a fuzzy discrete sliding mode control with a woodpeckering strategy,” IME Proc.-I, J. Syst. Contr. Eng., vol. 209, pp. 281-292,1995.
[27] C. S. Tseng, B. S. Chen, and H. J. Uang, “Fuzzy tracking control design for nonlinear dynamic systems via T-S fuzzy model,” IEEE Trans. Fuzzy Syst., vol. 9, no. 3, pp. 381-392, 2001.

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