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系統識別號 U0002-1807201310442800
中文論文名稱 運用T-S模糊小腦位置追蹤在下肢復健系統
英文論文名稱 Tracking Control for Lower-Limb Rehabilitation System using Takagi-Sugeno Cerebellar Model Articulation Control
校院名稱 淡江大學
系所名稱(中) 電機工程學系碩士班
系所名稱(英) Department of Electrical Engineering
學年度 101
學期 2
出版年 102
研究生中文姓名 陳柏宏
研究生英文姓名 Po-Honh Chen
學號 600470180
學位類別 碩士
語文別 英文
口試日期 2013-06-27
論文頁數 46頁
口試委員 指導教授-劉寅春
委員-江東昇
委員-邱謙松
中文關鍵字 追蹤, T-S模糊  線性矩陣不等式  小腦模型控制器  復健 
英文關鍵字 Tracking  T-S fuzzy  LMI  CMAC  Rehabilitation 
學科別分類 學科別應用科學電機及電子
中文摘要 近年來,技術性輔助運用在功能性復健器材受到非常大的關注,許多類型的復健器材之設計被相繼的提出,並運用各種控制器加以實現。在本篇論文中,我們使用一個名為T-S小腦模糊控制器的方法,用來對於我們的下肢復健系統進行追蹤控制。而我們提出的控制架構有兩個部分,第一個部份是追蹤產生器,第二個部分T-S小腦模糊控制器。在模擬的結果中,我們的系統表現出良好的追蹤性能。
英文摘要 In recent year, Technical assistance to functional rehabilitation device has attracted great interest. Many kind of rehabilitation device have been designed and implemen-tation by varied control theorem. In this study, we introduce an adaptive Tak-agi-Sugeno cerebellar model articulation control (T-S CMAC) for tracking control of lower-limb rehabilitation system. The proposed control structure is based on two parts. The first part is trajectory generator, and second part is T-S CMAC controller. In the simulation results, the tracking performance can be proved.
論文目次 Contents
Abstract in Chinese I
Abstract in English II
Contents III
List of Figures V
List of Tables VII
1 Introduction 1
1.1 Research Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 Rehabilitation Techniques . . . . . . . . . . . . . . . . . . . . . 2
1.1.2 Fuzzy System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1.3 Linear Matrix Inequalities . . . . . . . . . . . . . . . . . . . . . 5
1.1.4 Cerebellar model articulation controller . . . . . . . . . . . . . . 6
1.1.5 Cerebellar Model Articulation Controller with T-S Fuzzy Model 7
1.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Concept of System 14
2.1 Rehabilitation strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2 Trajectory generator design . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Dynamic model of the device . . . . . . . . . . . . . . . . . . . . . . . 16
3 Dynamic Output Feedback Controller 20
3.1 Fuzzy model-based robust H1 control design . . . . . . . . . . . . . . . 20
3.2 TS-SCMAC network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3 Constraint of Generalized Kinematics . . . . . . . . . . . . . . . . . . . 32
4 Numerical Simulations 34
4.1 Trajectory Generator test . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.2 Mobile Support Tracking test . . . . . . . . . . . . . . . . . . . . . . . 35
4.3 Simulation of Whole System . . . . . . . . . . . . . . . . . . . . . . . . 36
5 Conclusions 41
5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
References 31
List of Figures
1.1 Exercise in OMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Exercise in CMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 CMAC basic structure . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4 CMAC separate each input to each memory region . . . . . . . . . . . 9
1.5 CMAC structure with T-S fuzzy model . . . . . . . . . . . . . . . . . . 9
2.1 External efforts which affected system’s flexion-extension . . . . . . . . 14
2.2 External efforts which affected system’s internal-external rotation . . . 15
2.3 Trajectory generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4 Mechanical principle from the rehabilitation device . . . . . . . . . . . 16
3.1 The configuration of TS-SCMAC . . . . . . . . . . . . . . . . . . . . . 26
3.2 The overall structure of the TS fuzzy model based VDV via TS-SCMAC
control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.1 Plant of simulation 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.2 (a) Patient efforts on q1(b) Desired trajectory(c) Desired velocity . . . . 35
4.3 Plant of simulation 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.4 (a) Output of q1 and desired q1r(b) Output of q˙1 and desired q˙1r(c) Patient
effort and control signal of 40 ∗ CM1 . . . . . . . . . . . . . . . . . . . 37
4.5 (a) Output of q2 and desired q2r(b) Output of q˙2 and desired q˙2r(c) Patient
effort and control signal of CM2 . . . . . . . . . . . . . . . . . . . . . . 37
4.6 Plant of simulation 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.7 (a) Output of q1 and desired q1r(b) Error of q1(c) Control signal of CM1 38
4.8 (a) Output of q˙1 and desired q˙1r(b) Error of q˙1(c) Control signal of CM1 39
4.9 (a) Output of q2 and desired q2r(b) Error of q2(c) Control signal of CM2 39
4.10 (a) Output of q˙2 and desired q˙2r (b) Error of q˙2 (c) Control signal of CM2 40
List of Tables
2.1 Numerical parameters of the dynamical model of the rehabilitation device 19
參考文獻 [1] S. Jezernik, G. Colombo, and M. Morari, “Automatic gait-pattern adaptation
algorithms for rehabilitation with a 4-dof robotic orthosis,” IEEE Transactions,
Robotics and Automation, vol. 20, no. 3, pp. 574 – 582, june 2004.
[2] S. Moughamir, J. Zaytoon, N. Manamanni, and L. Afilal, “A system approach
for control development of lower-limbs training machines,” Control Engineering
Practice, vol. 10, no. 3, pp. 287–299, 2002.
[3] P. Metrailler, V. Blanchard, I. Perrin, R. Brodard, R. Frischknecht, C. Schmitt,
J. Fournier, M. Bouri, and R. Clavel, “Improvement of rehabilitation possibilities
with the motionmaker tm,” in Biomedical Robotics and Biomechatronics, 2006.
BioRob 2006. The First IEEE/RAS-EMBS International Conference on, feb. 2006,
pp. 359 –364.
[4] G. Colombo, R. Schreier, A. Mayr, H. Plewa, and R. Rupp, “Novel tilt table with
integrated robotic stepping mechanism: design principles and clinical application,”
in Rehabilitation Robotics, 2005. ICORR 2005. 9th International Conference on,
june-1 july 2005, pp. 227 – 230.
[5] K. Xing, J. Huang, Y. Wang, J. Wu, Q. Xu, and J. He, “Tracking control of
pneumatic artificial muscle actuators based on sliding mode and non-linear disturbance
observer,” IET, Control Theory Applications, vol. 4, no. 10, pp. 2058 –2070,
october 2010.
[6] L. Zadeh, “Fuzzy sets,” Information and control, vol. 8, no. 3, pp. 338–353, 1965.
[7] R. Isermann, “On fuzzy logic applications for automatic control, supervision, and
fault diagnosis,” Systems, Man and Cybernetics, Part A: Systems and Humans,
IEEE Transactions on, vol. 28, no. 2, pp. 221–235, 1998.
[8] F. S. Lin, “Integral fuzzy control and application on power converter,” Master’s
thesis, CYCU, 2003.
[9] T. Takagi and M. Sugeno, “Fuzzy identification of system and its applications to
modelling and control,” IEEE Trans. Syst., Man, and Cyber, vol. 15, pp. 116–132,
1985.
[10] K. Tanaka and M. Sugeno, “Stability analysis and design of fuzzy control systems,”
Fuzzy sets and systems, vol. 45, no. 2, pp. 135–156, 1992.
[11] S. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan, Linear matrix inequalities
in system and control theory. Society for Industrial Mathematics, 1994, vol. 15.
[12] T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and Its Applications
to Modeling and Control,” IEEE Transactions on Systems, Man, and
Cybernetics, vol. 15, no. 1, pp. 116–132, Feb. 1985. [Online]. Available:
http://www.hi.cs.meiji.ac.jp/ {}takagi/paper/TS-MODEL.tar.gz
[13] K.-Y. Lian, C.-S. Chiu, T.-S. Chiang, and P. Liu, “Lmi-based fuzzy chaotic synchronization
and communications,” Fuzzy Systems, IEEE Transactions on, vol. 9,
no. 4, pp. 539 –553, aug 2001.
[14] H. Wang, K. Tanaka, and M. Griffin, “An approach to fuzzy control of nonlinear
systems: stability and design issues,” IEEE Trans. Fuzzy Systems, vol. 4, no. 1,
pp. 14 –23, feb 1996.
[15] J. Joh, Y.-H. Chen, and R. Langari, “On the stability issues of linear takagi-sugeno
fuzzy models,” IEEE Trans. Fuzzy Systems, vol. 6, no. 3, pp. 402 –410, aug 1998.
[16] R. Jain, N. Sivakumaran, and T. Radhakrishnan, “Design of self tuning fuzzy
controllers for nonlinear systems,” Expert Systems with Applications, vol. 38, no. 4,,
pp. 4466–4476, 2011.
[17] Y.-H. Chang, C.-W. Chang, C.-W. Tao, H.-W. Lin, and J.-S. Taur, “Fuzzy slidingmode
control for ball and beam system with fuzzy ant colony optimization,” Expert
Systems with Applications, vol. 39, no. 3,, pp. 3624–3633, 2012.
[18] R.-E. Precup, M.-B. Radac, M. L. Tomescu, E. M. Petriu, and S. Preitl, “Stable
and convergent iterative feedback tuning of fuzzy controllers for discrete-time SISO
systems,” Expert Systems with Applications, vol. 40, no. 1,, pp. 188–199, 2013.
[19] K. Tanaka, T. Ikeda, and H. Wang, “A unified approach to controlling chaos via
an lmi-based fuzzy control system design,” Circuits and Systems I: Fundamental
Theory and Applications, IEEE Transactions on, vol. 45, no. 10, pp. 1021 –1040,
oct 1998.
[20] P. Liu, W.-T. Yang, and C.-E. Yang, “Robust observer-based output feedback
control for fuzzy descriptor systems,” Expert Systems with Applications, vol. 40,
no. 11,, pp. 4503–4510, 2013.
[21] H. Han, C.-Y. Su, and Y. Stepanenko, “Adaptive control of a class of nonlinear
systems with nonlinearly parameterized fuzzy approximators,” IEEE Transactions
on Fuzzy Systems, vol. 9, no. 2, pp. 315–323, 2001.
[22] C.-H. Wang, H.-L. Liu, and T.-C. Lin, “Direct adaptive fuzzy-neural control with
state observer and supervisory controller for unknown nonlinear dynamical systems,”
IEEE Transactions on Fuzzy Systems, vol. 10, no. 1, pp. 39–49, 2002.
[23] H. Jin-quan and F. Lewis, “Neural-network predictive control for nonlinear dynamic
systems with time-delay,” IEEE Transactions on Neural Networks, vol. 14,
no. 2, pp. 377–389, 2003.
[24] F. Hong, S. S. Ge, and T.-H. Lee, “Practical adaptive neural control of nonlinear
systems with unknown time delays,” IEEE Transactions on Systems, Man, and
Cybernetics, Part B: Cybernetics, vol. 35, no. 4, pp. 849–854, 2005.
[25] J. S. Albus, “A new approach to manipulator control: the cerebellar model articulation
controller (cmac),” Journal of Dynamic Systems, Measurement, and Control,
vol. 97, pp. 220–227, 1975.
[26] H.-C. Lu and C.-Y. Chuang, “Robust parametric {CMAC} with self-generating
design for uncertain nonlinear systems,” Neurocomputing, vol. 74, no. 4, pp. 549 –
562, 2011. [Online]. Available: http://www.sciencedirect.com/science/article/pii/
S0925231210003681
[27] M.-F. Yeh and M.-S. Leu, “Art-type {CMAC} network classifier,” Neurocomputing,
vol. 74, no. 5, pp. 783 – 791, 2011. [Online]. Available: http://www.sciencedirect.
com/science/article/pii/S0925231210003942
[28] Y.-F. Peng and C.-M. Lin, “Adaptive recurrent cerebellar model articulation
controller for linear ultrasonic motor with optimal learning rates,” Neurocomputing,
vol. 70, no. 168, pp. 2626 – 2637, 2007. [Online]. Available: http:
//www.sciencedirect.com/science/article/pii/S0925231207000884
[29] C.-F. Hsu and K.-H. Cheng, “Recurrent fuzzy-neural approach for nonlinear
control using dynamic structure learning scheme,” Neurocomputing, vol. 71, no.
168, pp. 3447 – 3459, 2008. [Online]. Available: http://www.sciencedirect.com/
science/article/pii/S0925231207003645
[30] C.-H. Chen, C.-M. Lin, and M.-C. Li, “Development of {PI} training algorithms
for neuro-wavelet control on the synchronization of uncertain chaotic systems,”
Neurocomputing, vol. 74, no. 17, pp. 2797 – 2812, 2011. [Online]. Available:
http://www.sciencedirect.com/science/article/pii/S0925231211003067
[31] S.-F. Su, Z.-J. Lee, and Y.-P. Wang, “Robust and fast learning for fuzzy cerebellar
model articulation controllers,” IEEE Transactions on Systems, Man, and
Cybernetics, Part B: Cybernetics, vol. 36, no. 1, pp. 203–208, 2006.
[32] C.-T. Chiang, T.-S. Chiang, and C.-K. Li, “A simple and converged structure of
addressing technique for cmac,” in IEEE International Conference on Systems,
Man and Cybernetics, vol. 7, 2004, pp. 6097–6101 vol.7.
[33] Y.-Y. Cao, Y.-X. Sun, and C. Cheng, “Delay-dependent robust stabilization of
uncertain systems with multiple state delays,” IEEE Transactions on Automatic
Control, vol. 43, no. 11, pp. 1608 –1612, nov 1998.
[34] C.-Y. Lu, J. S.-H. Tsai, G.-J. Jong, and T.-J. Su, “An lmi-based approach for robust
stabilization of uncertain stochastic systems with time-varying delays,” IEEE
Transactions on Automatic Control, vol. 48, no. 2, pp. 286 – 289, feb. 2003.
[35] R. Luo and L.-Y. Chung, “Stabilization for linear uncertain system with time
latency,” IEEE Transactions on Industrial Electronics, vol. 49, no. 4, pp. 905 –
910, aug 2002.
[36] W.-H. Chen, Z.-H. Guan, and X. Lu, “Delay-dependent guaranteed cost control
for uncertain discrete-time systems with delay,” IEE Proceedings - Control Theory
and Applications, vol. 150, no. 4, pp. 412 – 416, july 2003.
[37] T.-J. Su, C.-Y. Lu, and J.-H. Tsai, “Lmi approach to delay-dependent robust
stability for uncertain time-delay systems,” IEE Proceedings - Control Theory and
Applications, vol. 148, no. 3, pp. 209 –212, may 2001.
[38] L. Guo, “H infin; output feedback control for delay systems with nonlinear and
parametric uncertainties,” IEE Proceedings Control Theory and Applications, vol.
149, no. 3, pp. 226 –236, may 2002.
[39] Y.-Y. Cao and P. Frank, “Analysis and synthesis of nonlinear time-delay systems
via fuzzy control approach,” Fuzzy Systems, IEEE Transactions on, vol. 8, no. 2,
pp. 200 –211, apr 2000.
[40] S. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan, Linear Matrix Inequalities
in System and Control Theory, ser. Studies in Applied Mathematics. Philadelphia,
PA: SIAM, Jun. 1994, vol. 15.
[41] K.-Y. Lian, C.-H. Chiang, and H.-W. Tu, “Lmi-based sensorless control of
permanent-magnet synchronous motors,” IEEE Transactions on Industrial Elec-
tronics, vol. 54, no. 5, pp. 2769 –2778, oct. 2007.
[42] I. Masubuchi, Y. Kamitane, A. Ohara, and N. Suda, “H∞ control for descriptor
systems: A matrix inequalities approach,” Automatica, vol. 33, no. 4, pp. 669 –
673, 1997. [Online]. Available: http://www.sciencedirect.com/science/article/pii/
S0005109896001938
[43] C.-M. Lin and Y.-F. Peng, “Adaptive cmac-based supervisory control for uncertain
nonlinear systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part
B: Cybernetics, vol. 34, no. 2, pp. 1248–1260, 2004.
[44] T.-S. Chiang, C.-S. Chiu, and P. Liu, “Adaptive ts-fnn control for
a class of uncertain multi-time-delay systems: The exponentially stable
sliding mode-based approach,” International Journal of Adaptive Control and
Signal Processing, vol. 23, no. 4, pp. 378–399, 2009. [Online]. Available:
http://dx.doi.org/10.1002/acs.1052
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