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研究生:張凱勝
研究生(外文):Kai-Sheng Chang
論文名稱:二足機器人之模式追隨適應模糊控制器設計
論文名稱(外文):Model Following Adaptive Fuzzy Control Design for a Biped Robot
指導教授:游文雄
指導教授(外文):Wen-Shyong Yu
學位類別:碩士
校院名稱:大同大學
系所名稱:電機工程學系(所)
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:102
中文關鍵詞:機器人適應模糊
外文關鍵詞:adaptive fuzzyrobot
相關次數:
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在本論文要探討一個適應模糊控制器針對於這個多輸入多輸出參數未知的二足機器人。對於控制機構去追縱一個追蹤軌跡的目的去建立一個適應模糊控制器從依據一些適應法則及時調整參數的 IF-THAN 規則的模糊集合。使用模糊邏輯系統去近似雙足機器人的非線性系統函數然而提出適應法則即時調整模糊控制器的參數。然後由使用Lyapunov的穩定性方法來證實這個所提出來的強健適應模糊控制器方法的穩定性和強健性。並由模擬的研究以及實例來驗證性能。在本篇裡的模擬研究討論兩種不同的研究。第一個研究, 我們討論系統的性能當使用不同的期望追蹤軌跡。第二個研究, 我們去追蹤對於個一個雙足機器人的連續行走的軌跡路進架構。透過計算,我們可以得到所有連桿的幾何關係。 利用連桿的幾何關係,我們可以得到所有連桿的角度變化。這些角度當作輸入應用到我們提出的控制器來作模擬。再將控制器輸出的資料,當作機器人的馬達控制的資料。然後藉由馬達控制介面輸入機器人,達到控制機器人行走。在這個實驗研究, 我們將使用一個雙足行走機器人來驗證電腦模擬的結果及證實所提出來方法的效能。
In this thesis, model following adaptive fuzzy tracking control is presented for multi-input multi-output (MIMO) biped robot.An adaptive fuzzy controller is constructed from a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given trajectory. The fuzzy logic systems are used to approximate the unknown nonlinear system functions of biped robot while the adaptive law is proposed to adjust the parameters of the fuzzy controller on-line.The stability and robustness properties of the proposed adaptive fuzzy control scheme are established by using Lyapunov stability tools. The performance of the proposed control scheme is verified by computer simulation and experimental studies.The simulation studies discuss two kind of cases in this thesis.The first case is to discuss the system performance using different desired trajectories. The second case is to track a series trajectory plans of walking for the biped robot.In simulation, we use the robot's hip and leg trajectory. We can find the relation of geometry by computing the trajectories. Using the relation, we can get the angles of the links. We apply the angles as inputs to the propose controller for simulation.
We use the data from the output of the controller for the data of the motor of the robot. By using the robot control interface, we apply the data to the robot such that make the robot walk. Finally, we will use a biped walking robot to verify the simulation results and effectiveness of the proposed method.
ACKNOWLEDGEMENTS
ABSTRACT (IN ENGLISH) II
ABSTRACT (IN CHINESE) III
TABLES OF CONTENTS IV
LIST OF FIGURES V
CHAPTER
1 INTRODUCTION 1
2 PROBLEM FORMULATION AND FUZZY DESCRIPTION 5
3 STABILITY ANALYSIS 13
4 SIMULATIONS 16
4.1 Adaptive Fuzzy Control Track Different Desired Trajectories for The
Biped Robot 24
4.2 Adaptive Fuzzy Control Approach Track Series Trajectory Plan of
Walking for The Biped Robot 65
4.2.1 Hardware description 65
4.2.2 Discuss series trajectory plan of walking design 71
4.2.3 The controller design and simulation 74
5 CONCLUSIONS 84
REFERENCES 85
[1] S. Tzafestas, M. Raibert and C. Tzafestas, “Robust sliding-mode control applied to a 5-Link biped robot,” Journal Intellgent and Robotic Systems pp.
67-133, 1996.
[2] J.W. Grizzle, G. Abba, and F. Plestan, “Asymptotically stable walking for
biped robots: Analysis via systems with mpulse effects,” IEEE Trans. Automat
Contr, vol. 46, pp. 51-64, 2001.
[3] S. Ha, Y. Han, and H. Hahn, “Adaptive gait pattern generation of biped robot based on human’s gait pattern analysis,” Journal Mechanical Systems Science and Engineering, vol. 1, no. 2, 2008.
[4] E. Westervelt, J. Grizzle, and C. Canudas-de-Wit, “Switching and PI control of walking motions of planar biped walkers,” IEEE Trans. Automat Contr, vol. 48, no. 2, pp. 308-312, 2003.
[5] Y. Guo and P. Y. Woo, “An adaptive fuzzy sliding mode controller for robotic manipulators,” IEEE Trans. Systems, vol.33, no.2, 2003.
[6] J.W. Grizzle, Gabriel Abba, and F. Plestan, “Asymptotically stable walking for biped robots: analysis via system with impulse effects,” IEEE Trans. Automat Contr, vol. 46, no. 1, 2001.
[7] W. S. Yu, “H∞ Tracking-based adaptive fuzzy-neural control for MIMO uncertain robotic systems with time delays,” Elsvier Fuzzy Set and Systems, pp.375-401, 2004
[8] C. Makkar, G. Hu, W. G. Sawyer and W. E. D ixon, “Lyapunov-based tracking
control in the presence of uncertain nonlinear parameterizable friction,” IEEE
Trans. Automat Contr, vol. 52, no. 10, 2007.
[9] P. Tomei, “Robust adaptive cintrol of robots with arbitrary transient performance and disturbance attenuation,” IEEE Trans. Automat Contr, vol. 44, no. 3, 1999.
[10] D. L. Tsay, H. Y. Chung and Ching-Jung Lee, “The adaptive control of nonlinear systems using the sungeno-type of fuzzy logic,” Elsvier Fuzzy Set and Systems, vol. 7, no. 2, 1999.
[11] B. Yao and M. Tomizula, “Adaptive Rrobust control of MIMO nonlinear systems in semi-strict feedback forms,” Pergamon Automat Contr, pp. 1305-1321,
2001.
[12] P. A. Phan and T.Gale, “Two-mode Adaptive control with approximation error estimator,” IEEE Trans. Fuzzy Systems, vol. 15, no. 5, 2007.
[13] L. Zuo, J. E. Slotine and S. A. Nayfeh, “Model reaching adaptive control for vibration isolation,” IEEE Trans. Contr. Syst. Technology, vol. 13, no. 4, pp. 611-617, 2005.
[14] D. J. Krusienski and W. K. Jenkins, “Design and performance of adaptive
systems based on structured stochastic optimization strategies,” IEEE Circuits
and Systems, pp. 1531-636 2005
[15] T. Shaocheng, C. Bin and W. Yongfu, “Fuzzy adaptive output feedback control for MIMO nonlinear systems,” Elsvier Fuzzy Set and Systems, pp. 285-289, 2005.
[16] H. chekired, M. Tadjine and D. Bouchaffra, “Direct adaptive fuzzy control of nonlinear system class with applications,” Control Intell. Syst., vol. 31, no. 2, pp. 113-121, 2003.
[17] B. Yoo and W. Ham, “Adaptive fuzzy sliding mode control of nonlinearsystem,”IEEE Trans. Fuzzy Systems, vol. 6, no. 2, 1998.
[18] S. Tong and H. X. Li, “Fuzzy adaptive sliding-mode control forMIMO nonlinear systems,” IEEE Trans. Fuzzy Systems, vol. 11, no. 3, 2003.
[19] C. W. Tao, J. S. Taur and M. Chan, “Adaptive fuzzy terminal sliding mode
controller for nonlinear system with mismatched time-varying uncertainties,”
IEEE TRANS. Syst, vol. 34, no. 1, 2004.
[20] S. Janardhanan and B. Bandyopadhyay, “On discretization of continuous-time terminal sliding mode,” IEEE Trans. Automat Contr, vol. 51, no. 9, 2006.
[21] B. S. Chen, H. J. Uang and C. S. Tesng, “Robust tracking enhancement of robot systems including motor dynamics: a fuzzy-based dynamic game approach,”
IEEE Trans. Fuzzy Systems, vol. 6, no. 4, 1998.
[22] C. F. Hsu, “Self-organizing adaptive fuzzy neural control for a class of nonlinear systems,” IEEE Trans. Neural Networks, vol. 18, no. 4, 2007.
[23] C. Y. Lee and J. J. Lee “Multiple neuro-adaptive control of robot manipulators susing visual cues,” IEEE Trans. Industria Electronics, vol. 52, no. 1, 2005.
[24] B. Chen, X. Liu, and S. Tong “Adaptive fuzzy output tracking control of MIMO nonlinear uncertain systems,” IEEE Trans. Fuzzy Systems, vol. 15, no. 2, 2007.
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