結合模糊控制與類神經網路探討非線性結構控制的穩定性

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姓名

陳震武(Cheng-Wu Chen) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

結合模糊控制與類神經網路探討非線性結構控制的穩定性
(Stability of Nonlinear Structural Control via Fuzzy Control and Neural Network)

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摘要(英)

In this dissertation, several new stability analysis techniques and systematic design procedures for the Takagi-Sugeno (T-S) model-based fuzzy control and neural-network-based approach are proposed.
This paper also investigates the effectiveness of a passive Tuned Mass Damper (TMD) and fuzzy controllers in reducing the structural responses under the external force. In general, TMD is good for linear system. We proposed here an approach of Takagi-Sugeno (T-S) fuzzy controller to deal with the nonlinear system. In this dissertation, the nonlinear part is concerned with the nonlinear stiffness but not the field of nonlinear plastic behavior of the structural response.
To overcome the effect of modeling error between nonlinear systems and T-S fuzzy models, a robustness design of fuzzy control via model-based approach is proposed in this work. A stability criterion in terms of Lyapunov’’s direct method is derived to guarantee the stability of nonlinear interconnected systems. Based on the decentralized control scheme and this criterion, a set of model-based fuzzy controllers is then synthesized via the technique of parallel distributed compensation (PDC) to stabilize the nonlinear interconnected system and the control performance is achieved at the same time. Also, several asymptotically stability conditions via linear matrix inequalities (LMI) approaches are derived for multiple time-delay nonlinear systems.
In this dissertation, neural network (NN) model is employed to approximate the nonlinear systems. Then, the dynamics of each NN model is converted into LDI (linear differential inclusion) representation. Next, a robustness design of fuzzy control via NN-based approach is proposed to overcome the effect of modeling error between nonlinear systems and NN models. Meanwhile, NN model approach is better than T-S fuzzy model to approximate the nonlinear systems.
Finally, the developed theory is illustrated by an example of a nonlinear TMD system throughout this paper. Several illustrative examples and simulations are used to demonstrate that the proposed approaches are effective. However, in chapter 6, the practical application in structural system does not discuss the influence of the time delay. Besides, the designing procedures for the T-S fuzzy model and NN model are systematic and simplified.

關鍵字(中)

★ 結構系統
★ 非線性系統
★ 迷糊神經

關鍵字(英)

★ structural system
★ nonlinear system
★ fuzzy neural

論文目次

Chapter 1 Introduction
1.1 Background and Motivation 1
1.2 Review of Previous Works 11
1.2.1 Fuzzy Control 11
1.2.1.1 Themes in Design 11
1.2.1.2 Themes in Analysis 13
1.2.2 Neural Network 17
1.2.3 Controller Design via T-S Fuzzy Model and NN 19
1.3 Organization of This Dissertation 25
Chapter 2 Lyapunov Theory
2.1 Introduction 27
2.2 Nonlinear Systems and Equilibrium Points 28
2.2.1 Nonlinear Systems 28
2.2.2 Equilibrium Points 30
2.3 Concepts of Stability 31
2.3.1 Stability and Instability 32
2.3.2 Asymptotic Stability and Exponential Stability 32
2.3.3 Local and Global Stability 34
2.4 Lyapunov Direct Method 34
2.5 Positive Definite Function and Lyapunov Functions 35
Chapter 3 Problem Formulations
3.1 Introduction 40
3.2 T-S Fuzzy Model and The Stability Conditions 41
3.2.1 T-S Fuzzy Continuous Model and PDC Control 41
3.2.2 T-S Fuzzy Discrete Model 45
3.3 Example 47
3.4 Conclusion 49
Chapter 4 Robustness Design of T-S Fuzzy Controllers for Nonlinear multiple Time-Delay Interconnected TMD Systems
4.1 Introduction 52
4.2 Large Scale System and Chaotic Vibration 53
4.2.1 Large Scale System 53
4.2.1.1 Formulation of Large Scale System 54
4.2.1.2 Weakly Connected and Strongly Connected 55
4.2.2 Chaotic Vibration 55
4.2.2.1 Some Characteristics of Chaotic Vibrations 56
4.2.2.2 Nonlinear Vibration Theory 58
4.3 System Description 59
4.4 Parallel Distributed Compensation 61
4.5 H Infinity Control Design via Fuzzy Control 62
4.6 Robustness Design of Fuzzy Control 63
4.6.1 Modeling Error 63
4.6.2 Stability in The Presence of Modeling Error 67
4.7 Algorithm 69
4.8 Example 69
4.9 Conclusions 77
Chapter 5 Stability Analysis via Neural Network
5.1 Back Propagation Network 99
5.1.1 Introduction 99
5.1.2 BPN Operation 99
5.2 Stability Analysis of Neural Network for Nonlinear Systems 101
5.3 System Description and Stability Analysis 101
5.4 Example 106
5.4.1 Example 1 107
5.4.2 Example 2 107
5.5 Conclusions 116
Chapter 6 Model-Based Fuzzy Control for Structural Systems
6.1 Summary 126
6.2 Introduction 127
6.3 Fuzzy System Modeling 130
6.3.1 Two Representative Fuzzy Model Structures 131
6.3.2 Fuzzy Modeling of Structural System 134
6.3.3 Stable Controllers Design via Linear Matrix Inequalities 140
6.4 Neural Network Modeling 141
6.5 Numerical Examples 146
6.6 Conclusions 156
Chapter 7 Conclusions
7.1 Summary of Research 172
7.2 Future Research 174
Reference 175
Appendix 193
List of Figures and Tables
Fig. 1.1 Functional Diagram of a Fuzzy Controller 26
Fig. 1.2 Basic architecture of a fuzzy logic controller (FLC). 26
Fig. 2.1 Concepts of stability 38
Fig. 2.2 Interpreting positive definite functions using contour curves 38
Fig. 2.3 Illustrating Definition 2.7 for n=2 39
Fig. 3.1 The block diagram of T-S fuzzy model-based control system 50
Fig. 3.2 Parallel-distributed-compensation (PDC) design 50
Fig. 3.3 The state response of fuzzy system. 51
Fig. 3.4 The control force of fuzzy system. 51
Fig. 4.1 The jth subsystem of the nonlinear large-scale system. 85
Fig. 4.2 Structure of the composite system. 85
Fig. 4.3 The jth subsystem of the fuzzy large-scale system. 86
Fig. 4.4a Period-2 motion for forced motion of a buckled beam in the phase planes. 86
Fig. 4.4b Chaotic trajectory for forced motion of a buckled beam. 87
Fig. 4.5 (a) Frequency spectrum of buckled elastic beam for low-amplitude
excitation-linear periodic response. 87
Fig. 4.5 (b) Frequency spectrum of buckled elastic beam for larger
excitation-broad-band response of beam due to chaotic vibration. 87
Fig. 4.6 Experimental bifurcation diagram for a periodically forced nonlinear
circuit systems. 88
Fig. 4.7 Introduction the complete design procedure 88
Fig. 4.8 Two-DOF structure-TMD system. 89
Fig. 4.9 The effectiveness of a TMD system. 89
Fig. 4.10 The effectiveness of a TMD system with linear stiffness k(x). 90
Fig. 4.11 Dynamic magnification factor of a TMD system with nonlinear
stiffness k(x). 90
Fig. 4.12 Dynamic magnification factor of a TMD system with nonlinear
stiffness k(x). 91
Fig. 4.13 Dynamic magnification factor of a TMD system with nonlinear
stiffness k(x). 91
Fig. 4.14 Phase-plane trajectory of the chaotic system. 92
Fig. 4.15 Chaotic behavior of a nonlinear system with no control force. 92
Fig. 4.16 The plots of
(dashed line) and (solid line). 93
Fig. 4.17 The plots of
(dashed line) and (solid line). 93
Fig. 4.18 The plots of
(dashed line) and (solid line). 94
Fig. 4.19 The plots of
(dashed line) and (solid line). 94
Fig. 4.20 The plots of
(dashed line) and (solid line). 95
Fig. 4.21 The plots of
(dashed line) and (solid line). 95
Fig. 4.22 The plots of
(dashed line) and (solid line). 96
Fig. 4.23 The plots of
(dashed line) and (solid line). 96
Fig. 4.24 The state response of system 1. 97
Fig. 4.25 The state response of system 2. 97
Fig. 4.26 The control force of system 1. 98
Fig. 4.27 The control force of system 2. 98
Fig. 5.1 Neural Network of Back propagation. 121
Fig. 5.2 The jth isolated NN subsystem. 121
Fig. 5.3 Original function. 122
Fig. 5.4 2-2-1 Neural network. 122
Fig. 5.5 2-5-1 neural network. 123
Fig. 5.6 The first isolated NN subsystem. 123
Fig. 5.7 The second isolated NN subsystem. 123
Fig. 5.8 The third isolated NN subsystem. 124
Fig. 5.9 The state of subsystem 1. 124
Fig. 5.10 The state of subsystem 2. 125
Fig. 5.11 The state of subsystem 3. 125
Fig. 6.1 The Chi Chi earthquake. 148
Table. 6.1 Maximum response with and without input control. 148
Fig. 6.2 Time histories of response quantities of the first floor. 148
Fig. 6.3 Time histories of response quantities of the second floor. 149
Fig. 6.4 Time histories of response quantities of the third floor. 149
Fig. 6.5 Time histories of response quantities of the fourth floor. 149
Figs. 6.6-6.9 Time histories of response quantities of the first-fourth floor via decentralized
control. 150-151
Figs. 6.10-6.13 Time histories of control force of the first-fourth floor via decentralized
control. 151-152
Table. 6.2 Maximum response via T-S fuzzy model and NN model. 152
Figs. 6.14-6.17 Time histories of response quantities of the first-fourth floor.153-154
Fig. 6.18 The model error of the overall subsystems without decentralized control. 154
Figs. 6.19-22 The model error of the first-fourth subsystem with decentralized control. 154-155
Fig. 6.23 The model error of the overall subsystems via NN model. 156

參考文獻

1. Barnard, E. “Optimization for training neural nets,” IEEE Trans. Neural Networks, vol. 3, pp. 232-240, 1992.
2. Boyd, S., Ghaoui, L. E., Feron, E., and Balakrishnan, V., Linear matrix inequalities in system and control theory, Philadelphia, PA: SIAM, 1994.
3. Braae, M. and Rutherford, D. A., “Selection of parameters for a fuzzy logic controller,” Fuzzy Sets Syst., vol. 2, no. 3, pp. 185-199, 1979a.
4. Braae, M., and Rutherford, D. A., “Theoretical and linguistic aspects of the fuzzy logic controller,” Automatic, vol. 15, no. 5, pp. 553-577, 1979b.
5. Cao, S. G., Rees, N. W., and Feng, G., “Analysis and design for a class of complex control systems Part II: Fuzzy controller　design,” Automatica, vol. 33, no. 6, pp. 1029-1039, 1997.
6. Cao, S. G., Rees, N. W., and Feng, G., “Analysis and design of fuzzy control systems using dynamic fuzzy-state space　models,” IEEE Trans. Fuzzy Syst., vol. 7, no. 2, pp. 192-200, 1999.
7. Cao, S. G., Rees, N. W., and Feng, G., “Lyapunov-like stability theorems for continuous-time fuzzy control systems,” Int. J. Control, vol. 1, no. 1, pp. 49-64, 1998.
8. Cao, S. G., Rees, N. W., and Feng, G., “Quadratic stability analysis and design of continuous-time fuzzy control systems,” Int. J. Syst. Science, vol. 27, no. 2, pp. 193-203, 1996a.
9. Cao, S. G., Rees, N. W., and Feng, G., “Stability analysis and　design for a class of continuous-time fuzzy control systems,” Int. J. Control, vol. 64, no. 6, pp. 1069-1087, 1996b.
10. Cao, Y. Y. and Frank, P. M., “Robust disturbance attenuation for a class of uncertain discrete-time fuzzy systems,” IEEE Trans. Fuzzy Systems, vol. 8, pp. 406-415, 2000.
11. Cao, Y. Y. and Lin, Z., “Robust stability analysis and fuzzy-scheduling control for nonlinear systems subject to actuator saturation,” IEEE Trans. Fuzzy Systems, vol. 11, pp. 57-67, 2003.
12. Chang, S. S. L. and Zadeh, L. A., “On fuzzy mapping and control”, IEEE Trans. Syst., Man and Cybern., SMC, vol. 2 no. 1, pp. 30-34,1972.
13. Chen, B. S. and Wang, W. J., “Robust stabilization of nonlinearly perturbed large-scale systems by decentralized observer-controller compensators,” Automatica, vol. 26, pp.　 1035-1041, 1990.
14. Chen, B. S., Lee, C. H., and Chang, Y. C., “H infinity tracking design of linear system: Adaptive fuzzy approach,” IEEE Trans. Fuzzy Syst., vol. 4, pp. 32-43, 1996.
15. Chen, B. S., Tseng, C. S., and Uang, H. J., “Mixed H2/H infinity fuzzy output feedback control design for nonlinear dynamic systems: An LMI approach,” IEEE Trans. Fuzzy Syst., vol. 8, pp. 249-265, 2000.
16. Chen, B. S., Tseng, C. S., and Uang, H. J., “Robustness design of nonlinear dynamic systems via fuzzy linear control,” IEEE Trans. Fuzzy Syst., vol. 7, pp. 571-585, 1999.
17. Chen, C. T., Linear System Theory and Design, Oxford University, 1984.
18. Chen, G. and Dong, X., From Chaos to Order-Methodologies, Perspectives, and Applications. Singapore: World Scientific, 1998.
19. Chen, G., “Intelligent identification and control of chaotic dynamics,” in IEEE Symp. Circuits Syst., Atlanta, GA, May 1996, pp. 5-8.
20. Chen, J., Xu, D., and Shafai, B., “On sufficient conditions for stability independent of delay,” IEEE Trans. Automatic Control, vol. 40, pp. 1675-1680, 1995.
21. Chiang, W. L., and Yeh, K., “Application of Adaptive Fuzzy Sliding Mode Control Using Output Feedback for Bridges” J. Advanced Computing Intelligence, vol.5, no.3, pp. 172-179, 2001.
22. Chiang, W. L., Chen, C. W., Yeh, K., and Lu, L. T., “An approach to stability criteria of fuzzy-neural control by using linear differential inclusion state-space representation,” The 2002 Structure Engineering Proceedings, F10, pp. 1-7, 2002a.
23. Chiang, W. L., Chen, T. W., Liu, M. Y., and Hsu, C. J., “Application and robust H infinity control of PDC fuzzy controller for nonlinear systems with external disturbance,” J. Marine Sci. and Tech., vol. 9, pp. 84-90, 2001.
24. Chiang, W. L., Chen, C. W., Lu, L. T., and Yeh, K., “robustness design of fuzzy controllers for nonlinear interconnected TMD systems with external forces,” The 15th ASCE Engineering Mechanics Division Conference, 2002b.
25. Chiang, W. L., Yeh, K., and M. Y. Liu, “Adaptive fuzzy sliding mode control for vase-isolated buildings,” Int. J. Artificial Intelligence Tools, vol. 9, pp. 493-508, 2000.
26. Chou, J. H., and Chen, S. H., “Stability analysis of the discrete Takagi-Sugeno fuzzy model,” Proceedings of Fuzzy Systems Symposium, 11-14 Dec, Asian, pp. 400 – 405, 1996.
27. Clough, R. W. and Penzien, J., Dynamics of Structures, Mcgraw-Hill, New York, 1983.
28. Cuesta, F., Gordillo, F., Aracil, J., and Ollero, A., “Stability analysis of nonlinear multivariable Takagi-Sugeno fuzzy control systems,” IEEE Trans. Fuzzy Systems, vol. 7, no. 5, pp. 508 – 520, 1999.
29. Czogala, E. and Pedrycz, W., “Fuzzy rule generation for fuzzy control”, Cybernetics and Syst., vol. 13, no. 3, pp. 275-294, 1982.
30. Czogala, E. and Pedrycz, W., “Some problems concerning the construction of algorithms of decision making in fuzzy systems”, Int. J. Man-Machine Studies, vol. 15, no. 2, pp. 210-211, 1981.
31. Czogala, E., “A generalized concept of a fuzzy probabilistic controller,” Fuzzy Sets and Syst., vol. 11, no. 3, pp. 287-297, 1983.
32. deGlas, M., “A mathematical theory of fuzzy systems”, In: Fuzzy Information and Decision Processes, Gupta M. M. and Sanchez E. (eds), North-Holland, Amsterdam, 1982.
33. Feng, G., Cao, S. G., Rees, N. W., and Chak, C. K., “Design of fuzzy control systems with guaranteed stability,” Fuzzy Sets Syst., vol. 85, pp. 1-10, 1997.
34. Frahm, H. “Device for Damping Vibrations of Bodies,” U.S. Patent No. 989-958, 1911.
35. Gorzalczany, M. B., Kiszka J. B., and Stachowicz, M. S., “Some problems of studying adequacy of fuzzy models”, In Fuzzy Set and Possibility Theory, Yager R. R. (ed), Pergammon Press, New York, 1982.
36. Gutman, S., “Uncertain dynamic systems- a Lyapunov min-max approach,” IEEE Trans Automatic Control, vol. 24, pp. 438-443, 1979.
37. Hirota, K., and Pedrycz, W., “Analysis and synthesis of fuzzy systems by the use of fuzzy sets”, Fuzzy Sets Syst., vol. 10, no. 1, pp. 1-14, 1983.
38. Hsiao, F. H. and Hwang, J. D., “Criterion for asymptotic stability o uncertain multiple time-delay systems”, Electronics Letters, vol. 32, no. 4, pp. 410-411, 1996.
39. Hsiao, F. H., “Stability analysis of nonlinear multiple time-delay systems with a periodic input”, Control Theory and Advanced Technology, vol. 10, no. 4, part 2, pp. 1223-1233, 1995.
40. Hwang, C. C., Chow, H. Y., and Wang, Y. K., “A new feedback control of a modified Chua’s circuit system,” Physica D., vol. 92, pp. 95-100, 1996.
41. Ikeda, M. and Ashida, T., “Stabilization of linear systems with time-varying delay,” IEEE Trans. Automatic Control, vol. 24, pp. 369-370, 1979
42. Ikeda, M. and Siljak, D. D., “Decentralized stabilization of linear time-varying systems,” IEEE Trans. Automatic Control, vol. AC-25, pp. 106-107, 1980.
43. Jain, R., “Outline of an approach for the analysis of fuzzy systems”, Int. J. Control, vol. 23, pp. 627-640, 1976.
44. James M. and David R., Parrel Distributed Processing, Vol. 1&2, MIT Press, Cambridge, MA, 1986.
45. James, A., Freeman and David M. Skapura, Neural Networks: Algorithms, applications, and programming techniques, Addison Wesley, 1991.
46. James, M. and David, R., Explorations in Parallel Distributed processing, MIT Press, Cambridge, MA, 1986.
47. Jamshidi, M., Large-Scale System Modeling and Control, Elsevier, North- Holland, New York, 1983.
1. Jenkins, D. F. and K. M. Passino, "An introduction to　nonlinear analysis of fuzzy systems," Journal of Intelligent　and Fuzzy Systems, Vol. 7, pp. 75- 103, 1999.
49. Joh, J., Chen, Y. H., and Langari, R., “On the stability issues of linear Takagi-Sugeno fuzzy models,” IEEE Trans. Fuzzy Systems, vol. 6, no. 3, pp. 402-410, 1998.
50. Johansson, M., Rantzer, A., and Arzen, K. E., “Piecewise quadratic stability of fuzzy systems,” IEEE Trans. Fuzzy Systems, vol. 7, no. 6, pp. 713-722, 1999.
51. Johnson, M. A., and Leahy, M. B., Jr., “Adaptive model-based neural network control,” in Proc. IEEE Int. Conf. Robot. Automat., pp. 1704-1709, 1990.
52. Joo, Y. H., Shie,h L. S., and Chen, G. “Hybrid state-space fuzzy model-based controller with dual-rate sampling for digital control of chaotic systems,” IEEE Trans. Fuzzy Syst., vol. 7, pp. 394-408, 2000.
53. Juang, C. F. and Lin, C. T., “An on-line self-constructing neural fuzzy inference network and its applications,” IEEE Trans. Fuzzy Systems, vol. 6, pp. 12-32, 1998.
54. Kacprzyk, J., Multistage Decision-Making Under Fuzziness, Verlag TUV Rheinland, Koln, 1983.
55. Kapitaniak, T., Kocarev, L. J., and Chua, L. O., “Controlling chaos without feedback and control signals,” Int. J. Bifurcation Chaos, vol. 3, pp. 459-468, 1993.
56. Kawato, M., et al., “A hierarchical model for voluntary movement and its application to robotics,” in Proc. 1987 IEEE Int. Cong. Neural Network, vol. 4, pp.573-582, 1987.
57. Kaynia, A. M., Veneziano D., and Biggs, J. M., “Seismic effectiveness of tuned mass dampers,” J. Structural Division, ASCE, vol. 107, pp. 1465-1484, 1981.
58. Khalil, H. K., Nonlinear Systems. London, U.K.:Macmilllan, 1992.
59. Kickert, W. J. M., “Towards and analysis of linguistic modeling”, Fuzzy Sets Syst., vol. 2, pp. 293-307, 1979.
60. Kickert, W. J. M., and Mamdani, E. H., “Analysis of a fuzzy logic controller,” Fuzzy Sets Syst., vol. 1, no. 1. pp. 29-44, 1978.
61. Kiriakidis, K., “Fuzzy model-based control of complex plants,” IEEE Trans. Fuzzy Syst., vol. 6, pp. 517-529, 1998.
62. Kiriakidis, K., “Nonlinear control system design via fuzzy　modeling and LMIs,” Int. J. Control, vol. 72, no. 7/8, pp. 676-685, 1999.
63. Kiszka, J. B., On the Convergence of Fuzzy Control Processes, Dept. EECS, Univ. California at Berkeley, ERL memorandum UCB/ERL-M81/43, 1981.
64. Klir,,G. J., and Yuan, B., Fuzzy Sets and Fuzzy Logic, Prentice-Hall: New Jersey, 1995.
65. Kloeden, P. E., “Fuzzy dynamical systems,” Fuzzy Sets Syst., vol. 7, pp. 275-296, 1982.
66. Kung, S. Y. and Hwang, J. N., “Neural network architechture’s for robotic application,” IEEE Trans. Robot. Automat., vol. 5, pp. 641-647, 1989.
67. Kwakernaak, H. and Sivan, R., Linear Optimal Control Systems, John Wiley & Sons, Inc. 1972.
68. Lee, C. C., “Fuzzy Logic in Control Systems: Fuzzy Logic Controller, Part I,” IEEE Trans. Syst., Man, and Cybernetics, vol. 20, no. 2, pp. 404- 418, 1990.
69. Lee, C. H., Li, T. H., and Kung, F. C., “On the robust stability for continuous large-scale uncertain systems with time delays in interconnections,” J. Chinese Institute Eng., vol. 17, pp. 577-584, 1994.
70. Lee, H. J., Park, J. B., and Chen, F., “Robust fuzzy control of nonlinear systems with parametric uncertainties,” IEEE Trans. Fuzzy Systems, vol. 9, no. 2, pp. 369-379, April 2001.
71. Leung, F. H. F., Lam, H. K., and Tam, P. K. S. “Design of fuzzy controllers for uncertain nonlinear systems using　stability and robustness analysis,” Syst. Control Letters, vol. 35, pp. 237-243, 1998.
72. Leung, F. H. F., Wong, L. K., and Tam, P. K. S., “Fuzzy model based controller for an inverted pendulum,”' Electronics Letters, vol. 32, no. 18, pp. 1683-1685, 1996.
73. Li, X., and Souza, C. E. de, “Criteria for robust stability and stabilization of uncertain linear systems with state delay,” Automatica, vol. 33, pp. 1657-1662, 1997.
74. Limanond S., and Si, J., “Neural-network-based control design: An LMI approach,” IEEE Trans. Neural Networks, vol. 9, pp. 1422-1429, 1998.
75. Lin, C. C., Hu, C. M., Wang, J. F. and Hu, R. Y., “Vibration control effectiveness of passive tuned mass dampers,” J. Chinese Institute Eng., vol. 17, no. 3, pp. 367-376, 1994.
76. Lin, W., and Byrnes, C. I., “ -control of discrete-time nonlinear systems,” IEEE Trans. Automatic Control, vol. 41, pp. 494-510, 1996.
77. Lu, L. T., Chiang, W. L., and Tang, J. P., “Application of model reduction and LQG/LTR robust control methodology in active structure control,” J. Engineering Mechanics, ASCE, vol. 124, no. 4, pp. 446-454, 1998.
78. Lu, L. T., Chiang, W. L., Tang, J. P., Liu, M. Y., and Chen, C. W., “Active control for a benchmark building under wind excitations” J. Wind Engineering Industrial Aerodynamics, vol. 9, no. 4, pp. 469-493, 2003.
79. Luoh, L., Liaw, Y. K., and Wang, W. J., “A novel method to solve fuzzy relation equations” Proceedings of 2000 Eighth National Conference on Fuzzy Theory and Its Applications, Taipei, Taiwan.
80. Luoh, L., Stability Analysis and Design for Fuzzy Control Systems, Ph.D. Dissertation, National Central University, 2001.
81. Ma, X. J., Sun, Z. O., and He, Y. Y., “Analysis and design of fuzzy controller and fuzzy observer,” IEEE Trans. Fuzzy Syst., vol. 6, pp. 41-51, 1998.
82. MacGregor, R. J., Neural and Brain Modeling, Academic Press, San Diego, CA, 1987.
83. Mahmoud, M. S., Hassan, M. F., and Darwish, M. G., Large-scale Control Systems (New York: Marcel Dekker), 1985.
84. McNamara, R. J., “Tuned mass dampers for buildings,” J. Structural Division, ASCE, vol. 103, pp. 1785-1798, 1977.
85. Michel, A. N. and Farrell, J. A., “Associative memories via artificial neural networks,” IEEE Control System Magazine, April, pp. 6-17, 1990.
86. Michel, A. N. and Miller, R. K., Qualitative Analysis of Large Scale System. Academic Press: New York, 1977.
87. Minsky, M., and Papert, S, Perceptrons. MIT Press, Cambridge, MA, 1969.
88. Mohammad, R. E., Turksen, I. B., and Andrew, A. G., “Development of a systematic methodology of fuzzy logic modeling,” IEEE Trans. Fuzzy Syst., vol. 6, pp. 346-360, 1998.
89. Moon, F. C., Chaotic Vibrations. New York, John Wiley & Sons, 1987.
90. Mori, T., “Criteria for asymptotic stability of linear time delay systems,” IEEE Trans. Automatic Control, vol. 30, pp. 158-162, 1985.
91. Mori, T., Fukama, N., and Kumahara, M., “Simple stability criteria for single and composite linear systems with time delays,” Int. J. Control, vol. 34, pp. 1175-1184, 1981.
92. Narenda, K. S., and Parthasarathy, K., “Identification and control of dynamical system using neural networks,” IEEE Trans. Neural Networks, vol. 1, pp. 4-27, 1990.
93. Nesterov, Y., and Nemirovsky, A., Interior-Point Polynomial Methods in Convex Programming, SIAM, Philadelphia, PA, 1994.
94. Nguyen, D. and Widrow, B., “The truck backer-upper: An example of self-learning in neural networks,” in Proc. Int. Joint Conf. Neural Networks (IJCNN-89), vol. 2, June 1989, pp. 357-363.
95. Palm, R., Driankov, D., and Hellendoorn, H., Model Based Fuzzy Control, Springer-Verlag Berlin Heidelberg, 1997.
96. Pasltis, D. et al., “Neural controller,” in Proc. IEEE Int. Conf. Neural Networks, vol. 4, 1987, pp. 551-558.
97. Pedrycz, W., “An approach to the analysis of fuzzy systems,” Int. J. of Control, vol. 34, no. 3, pp. 403-421, 1981.
98. Proeyk, T. J. and Mamdani, E. H., “A linguistic self-organizing process controller”, Automatica, vol. 15, no. 1, pp. 15-30, 1979.
99. Psaltis, D., Sideris, A., and Yamamura, A. A., “A multilayer neural network controller,” IEEE Control Syst. Magazine, vol. 8, pp. 17-21, 1988.
100. Sezer, M. E. and Huseyin, O., “Stabilization of linear time-invariant interconnected systems using local state feedback,” IEEE Trans. Syst., Man, Cybern., vol. SMC-8, pp. 751-756, 1978.
101. Shoulie, X., Lihua, X., and Changyun, W., “Decentralized output feedback control of interconnected time-delay systems,” Proc. America Contr. Conf., pp. 824-828, 2000.
102. Singer, J., Wang, Y. T., and Bau, H. H., “Experimental control of chaos,” Phys. Rev. Letters, vol. 66, pp. 1123-1125, 1991.
103. Sladek, J. R. and Klingner, R. E., “Effect of tuned-mass dampers on seismic response,” J. Structural Eng., ASCE, vol. 109, no. 8, pp. 2004-2009, 1983.
104. Sltine, J. J. E. and Li, W., Applied Nonlinear Control, Englewoo Cliffs, New Jersey: Prentice-Hall, 1991.
105. Specht, D. F. “A general regression neural network,” IEEE Trans. Neural Networks, vol. 2, pp. 568-576, 1991.
106. Su, J. J., “Further results on the robust stability of linear systems with a single time delay,” Syst. Control Letters, vol. 23, pp.375-379, 1984.
107. Sugeno, M. and Yasukawa, T., “A fuzzy logical based approach to qualitative modeling,” IEEE Trans. Fuzzy Syst., vol. 1, pp. 7-25, 1993.
108. Sugeno, M., Industrial Applications of Fuzzy Control, North-Holland, 1985.
109. Takagi, T. and Sugeno, M., “Fuzzy identification of systems and its applications to modeling and control,” IEEE Trans. Syst., Man, Cybern., vol. 15, pp. 116-132, 1985.
110. Tamura, H. and Yoshikawa, T., Large Scale Systems Control and Decision Making, Marcel Dakker, 1990.
111. Tanaka, K., Hori, T., Wang, H.O., “A multiple Lyapunov function approach to stabilization of fuzzy control systems,” IEEE Trans. Fuzzy Syst., vol. 11, no. 4, pp. 582 - 589, 2003.
112. Tanaka, K. and Sugeno, M., “Stability analysis and design of fuzzy control systems,” Fuzzy Sets Syst., vol. 45, pp. 135-156, 1992.
113. Tanaka, K. and Sugeno, M., “Stability analysis of fuzzy systems using Lyapunov’s direct method,” in Proc. NAFIPS 90, pp. 133-136.
114. Tanaka, K., “An approach to stability criteria of neural-network control systems,” IEEE Trans. Neural Networks, vol. 7, pp. 629-643, 1996.
115. Tanaka, K., “Stability and stabilization of fuzzy-neural-linear control systems,” IEEE Trans. Fuzzy Syst., vol. 3, pp. 438-447, 1995.
116. Tanaka, K., and Sano, M., “A robust stabilization problem of　fuzzy control systems and its application to backing up control of a truck-trailer,” IEEE Trans. Fuzzy Syst., vol. 2, pp. 119-134, 1994
117. Tanaka, K., Ikeda, T., and Wang, H. O., “Fuzzy regulators and fuzzy observers: Relaxed stability conditions and LMI-based designs,” IEEE Trans. Fuzzy Systems, vol. 6, no. 2, pp. 250-265, 1998.
118. Tanaka, K., Ikeda, T., and Wang, H. O., “Robust stabilization of a class of uncertain nonlinear systems via fuzzy control: Quadratic stability, control theory, and linear matrix　inequalities,” IEEE Trans. Fuzzy Systems, vol. 4, no. 1, pp. 1-13, 1996.
119. Teixeira, M. C. M. and Zak, S. H. “Stabilizing controller design for uncertain nonlinear systems using fuzzy models,” IEEE Trans. Fuzzy Syst., vol. 7, no. 2, pp.133-142, 1999.
120. Thompson, W. E., “Exponential stability of interconnected systems,” IEEE Trans. Automatic Control, Vol. AC, vol. 15, pp. 504-506, 1970
121. Tong, R. M., “The construction and evaluation of fuzzy models,” In: Advances in Fuzzy Set Theory and Applications, Gupta M. M., Ragadc R. K. and Yager R. R. (eds), North Holland, 1979.
122. Trinh, H. and Aldeen, M., “A comment on decentralized stabilization of large scale interconnected systems with delays,” IEEE Trans. Automatic Control, vol. 40, pp. 914-916, 1995.
123. Tzafestas, S. G., and Watanabe, K., Stochastic large-scale engineering systems (New York: Marcel Dekker), 1992.
124. Utkin, V. I., Sliding models in control and optimization, New York: Springer-Verlag, 1992.
125. Vidyasagar, M., Nonlinear Systems Analysis, second edition, Prentice-Hall, Inc., 1993.
126. Wang, H. O., Tanaka, K., and Griffin, M. F., “An approach to fuzzy control of nonlinear systems: stability and design issues,” IEEE Trans. Fuzzy Systems, vol. 4, pp. 14-23, 1996a.
127. Wang, H. O., Tanaka, K., and Griffin, M. F., “Parallel distributed compensation of nonlinear systems by Takagi-Sugeno fuzzy model,” in Proc. Fuzz-IEEE/IFES’95, Yokohama, Japan, Mar. 1995, pp. 531-538.
128. Wang, H. O., Tanaka, K., and Ikeda, T., “Fuzzy modeling and control of chaotic systems,” in IEEE Symp. Circuits Syst., Atlanta, GA, pp. 209-212, 1996b.
129. Wang, L. X., A Course in Fuzzy Systems and Control, Prentice-Hall PTR, 1997.
130. Wang, W. J, and Lin, H. R., “Fuzzy control design for the trajectory tracking on uncertain nonlinear systems,” IEEE Trans. Fuzzy Syst., vol. 7, pp. 53-62, 1999.
131. Wang, W. J. and Cheng, C. F., "Robustness of perturbed large-scale systems with local constant state feedback," Int. J. Control, vol. 50, no. 1, pp. 373-384, 1989.
132. Wang, W. J. and Luoh, L., "Alternative stabilization design for fuzzy systems," Electronics Letter, vol. 37, no. 9, pp. 601-603, 2001.
133. Wang, W. J., and Cheng, C. F., “Stabilizing controller and observer synthesis for uncertain large-scale systems by the Riccati equation approach,” IEE Proceeding – D, vol. 139, pp. 72-78, 1992.
134. Wang, W. J., and Mau, L. G., “Stabilization and estimation for perturbed discrete time-delay large-scale systems,” IEEE Trans. Automatic Control, vol. 42, pp. 1277-1282, 1997.
135. Yan, X. G., and Dai, G. Z., “Decentralized output feedback robust control for nonlinear large-scale systems,” Automatica, vol. 34, pp. 1469-1472, 1998.
136. Yang, G. H., and Zhang, S. Y., “Decentralized control of a class of large-scale systems with symmetrically interconnected subsystems,” IEEE Trans. Automatic Control, vol. 41, pp. 710-713, 1996.
137. Yang, J. J., “Robust stability analysis of uncertain time delay systems with delay-dependence,” Electronics Letters, vol. 37, pp135-137, 2001.
138. Yeh, K., Chiang, W. L., and Juang, D. S., “Application of fuzzy control theory in active control of structures,” Int. J. Uncertainty Fuzziness and Knowledge-based Syst., vol. 3, pp. 255-274, 1996.
139. Yeh, Z. M., “A systematic method for design of multivariable fuzzy logic control systems,” IEEE Trans. Fuzzy Syst., vol. 7, no. 5, pp. 741-752.
140. Yen, J., and Wang, L., “Simplifying fuzzy rule-based models using orthogonal transformation methods,” IEEE Trans. Syst., Man, Cybern., part B, vol. 29, pp. 13-24, 1999.
141. Ying, H., “An analytical study on structure, stability and　design of general nonlinear Takagi-Sugeno fuzzy control　systems,” Automatica, vol. 34, No. 12, pp. 1617-1623, 1998.
142. Zadeh, L., “Outline of a new approach to the analysis of complex systems and decision processes,” IEEE Trans. Syst., Man, Cybern., vol. 3, pp. 28-44, 1973.
143. Zak, S. H., “Stabilizing fuzzy system models using linear controllers,” IEEE Trans. Fuzzy Syst., vol. 7, no. 2, pp. 236-240, 1999.
144. Zhou, K., and Khargonedkar, P. P., “Robust stabilization of linear systems with norm-bounded time-varying uncertainty,” Syst. Control Letters, vol. 10, pp.17-20, 1988.

指導教授

蕭鳳翔、蔣偉寧
(Feng-Hsiag Hsiao、Wei-Ling Chiang)

審核日期

2004-6-10

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