臺灣博碩士論文加值系統

　　本論文以模糊邏輯為基礎建構出三種類型模糊估測器。主要目的係藉由模糊邏輯之補償特性，來強化傳統估測器之能力。首先，我們提出一個以李亞普諾夫函數為基礎之模糊狀態估測器，此一估測器之推論法係以李亞普諾夫函數及其衍生之靈敏度函數所推導而成，此方法除能提供一不錯之追蹤能力，同時模糊估測器之穩定性亦可確保。其次，藉由卡爾曼濾波器演算法之狀態估測能力，再給予一適當模糊修正量，建構出一整合型模糊估測器，期能改善卡爾曼濾波器之暫態響應，此方法在3D移動目標物追蹤上有良好之表現。接著針對一個更直接且不需耗時計算之α-β追蹤器，藉由模糊決策邏輯之植入，有效的改善α-β追蹤器為人詬病的準確性問題；原本僅靠α-β追蹤器難以追蹤之機動性逃逸目標物，可經由本文所提出之模糊α-β追蹤器，達成既快又準確之估測。最後，我們再以整合模糊卡爾曼濾波器之架構，應用於雙質量驅動系統上以觀察系統狀態，並分別藉由線性二次控制器和整合型模糊比例微分/比例積分控制器對雙質量驅動系統做速度控制。本文所提三種模糊估測器，李亞普諾夫型態模糊估測器、整合型模糊卡爾曼濾波器及模糊α-β追蹤器，由電腦模擬結果顯示本文所提模糊估測器，均比傳統估測器可獲得較良好之補償效益及更佳的估測效果。

　　Three types of fuzzy logic based estimators are investigated in this dissertation. The main motivation of these schemes is that the compensation characteristics of fuzzy logic can be used to improve the estimation capability of conventional estimators. Firstly, the Lyapunov function based fuzzy state estimator is proposed. The inference scheme of this type of estimator is derived from the Lyapunov function and its associated sensitivity function, and it can provide very good ability of tracking and guarantee the stability of the estimator at the same time. Second, we utilize the Kalman filter and the suitable fuzzy correction gain vector to establish an integrated fuzzy Kalman filter such that the developed fuzzy estimator can improve the transient responses of conventional Kalman filter and have good performance for 3D maneuver target tracking. Next, we develop a novel α-β tracker embedded with the fuzzy logic, in which the scheme has the characteristic of less computation time and can effectively improve the accurate estimation of the conventional α-β tracker. By the proposed α-β fuzzy tracker, we can estimate the dynamics of the maneuvering target that the conventional α-β tracker can not track well. Finally, we apply the integrated fuzzy Kalman filter as the observer of a two-mass drive system, and utilize the Linear Quadratic Controller (LQC) and the hybrid fuzzy PD/PI controller to accomplish the speed control of the two-mass drive system, respectively. From all the computer simulations, we can find that the proposed three types of fuzzy estimators, Lyapunov-type fuzzy filter, integrated fuzzy Kalman filter and fuzzy α-β tracker, possess better estimation than those of the conventional estimators.

中文摘要 I
Abstract II
Acknowledgment (Chinese) III
Contents IV
List of Figures VII
List of Tables Ⅸ

Chapter 1. Introduction
　　1.1 Preliminary 1
　　1.2 Maneuvering Target Tracking 3
　　1.3 2-Mass Drive System 5
　　1.4 Outline of the Dissertation 6

Chapter 2. Estimation Theory
　　2.1 Introduction 8
　　2.2 State Estimation 9
　　2.3 The Kalman Filter 10
　　　　2.3.1 System Model 10
　　　　2.3.2 Discrete-Time Kalman Filter 11
　　2.4 The Tracker 14

Chapter 3. Lyapunov-Type Fuzzy Filter
　　3.1 Introduction 17
　　3.2 Discrete-Time Kalman Filter for a Linear Continuous System 18
　　3.3 Lyapunov-Type Fuzzy Estimator Design Scheme 19
　　3.4 Simulation Results 22
　　3.5 Summary 23

Chapter 4. Integrated Fuzzy Kalman Filter for 3D Moving Target Tracking System
　　4.1 Introduction 26
　　4.2 Modeling of the 3D Moving Target 28
　　　　4.2.1 The Target Model without Maneuver 28
　　　　4.2.2 The Target Model with Maneuver 30
　　4.3 Integrated Fuzzy Kalman Filter 30
　　　　4.3.1 Integrated Fuzzy Kalman Filter Design Scheme 31
　　　　4.3.2 Fuzzy Decision-Making Mechanism of 33
　　　　4.3.3 Fuzzy Decision-Making Mechanism of 35
　　4.4 Computer Simulation and Results 36
　　　　4.4.1 Simulations of Rectilinear Motion 36
　　　　4.4.2 Simulation of Manned Maneuvering Motion 40
　　4.5 Summary 41

Chapter 5. Fuzzy Tracker for Maneuvering Target Tracking System
　　5.1 Introduction 51
　　5.2 The Optimal Steady-State Tracker 53
　　5.3 Fuzzy Tracker Design Scheme 56
　　5.4 Simulations and Performance Evaluation 59
　　5.5 Summary 61

Chapter 6. Speed Control Design of a 2-Mass Drive System by Using Observer-Based Controllers
　　6.1 Introduction 68
　　6.2 Structure of 2-Mass Drive System Model 70
　　6.3 Integrated Fuzzy Observer 72
　　6.4 Hybrid Fuzzy PD/PI Controller 75
　　　　6.4.1 Static HF PD/PI controller 75
　　　　6.4.2 Dynamic HF PD/PI controller 76
　　6.5 Linear Quadratic Controller 77
　　6.6 Simulations 80
　　　　6.6.1 Simulations by Using Hybrid Fuzzy PD/PI Controller 80
　　　　6.6.2 Simulations by Using Linear Quadratic Controller 81
　　6.7 Summary 82

Chapter 7. Conclusions 95

Reference 98

[1]Astrom, K. J., Introduction to Stochastic Control Theory, Academic Press, New York, 1970.

[2]Averbuch, A., Itzikowitz, S. and Kapon, T., “Rader target tracking – Viterbi versus IMM,” IEEE Trans. Aerospace and Electronic Systems, vol. 27, no. 3, pp. 550-563, 1991.

[3]Banfield, D., Ingersoll, A. P. and Keppenne, C. L., “A steady-state Kalman filter for assimilating data from a single polar orbiting satellite,” Oceanographic Literature Review, vol. 43, no. 1, pp. 86, 1996.

[4]Bar-Shalom, Y. and Fortmann, T. E., Tracking and Data Association, Academic Press, San Diego, CA, 1988.

[5]Bar-shalom, Y. and Birmiwal, K., “Variable dimension filter for maneuvering target tracking,” IEEE Trans. Aerospace and Electronic Systems, vol.18, no.5, pp. 621-629, 1982.

[6]Bar-Shalom, Y. and Chang, K. C., “Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm,” IEEE Trans. Aerospace and Electronic Systems, vol. 25, no. 2, pp. 296-300, 1989.

[7]Benedict, T. and Bordner, G., “Synthesis of an optimal set of radar track-while-scan smoothing equations,” IRE Trans. On Automated Control, vol. 7, pp. 27-32, 1962.

[8]Blackman, S. S., “Multiple target tracking with radar applications,” Artech House, Inc., Dedham, MA, 1986.

[9]Blair, W. D., “Fixed-gain two-stage estimators for tracking maneuvering targets,” IEEE Trans. Aerospace and Electronic Systems, vol. 29, pp. 1004-1014, 1970.

[10]Blom, H. A. P. and Bar-Shalom, Y., “The interacting multiple model algorithm with Markovian switching coefficients,” IEEE Tran. On Automatic Control, vol. 33, no. 8, pp. 780-783, 1988.

[11]Bogler, P. L., “Tracking a maneuvering target using input estimation,” IEEE Trans. Aerospace and Electronic Systems, vol. 23, no. 3, pp. 298-310, 1987.

[12]Boussalis, D. and Wang, S. J., “Multivariable neural network vibration control based on output feedback,” Second IEEE Conference on Control Application, vol.1, pp. 345-350, 1993.

[13]Boverie, S., Demaya, B. and Titli, A., “Fuzzy logic control compared with other automatic control approaches,” Proc. of the 30th Conf. on Decision and Control, Brighton, England, pp. 1212-1216, 1991.

[14]Buse, R., Liu, Z. Q. and Bezdek, J., “Word recognition using fuzzy logic,” IEEE Transaction on Fuzzy Systems, vol. 10, no. 1, pp. 65-76, 2002.

[15]Caccia, M., Bruzzone, G. and Veruggio, G., “Active sonar-based bottom-following for unmanned underwater vehicles,” Control Engineering Practice, vol. 7, no. 4, pp. 459-468, 1999.

[16]Chan, K. C. C., Lee, V. and Leung, H., “Radar tracking for air surveillance in a stressful environment using a fuzzy-gain filter,” IEEE Trans. Aerospace and Electronic Systems, vol.5, no.1, pp. 80-89, 1997.

[17]Chan, Y. T. and Couture, F., “Maneuver detection and track correction by input estimation,” IEE Proceedings-F, Vol. 140, No. 1, pp. 21-28, Feb. 1993.

[18]Chan, Y. T., Hu, A. G. C., and Plant, J. B., “A Kalman filter based tracking scheme with input estimation,” IEEE Trans. Aerospace and Electronic Systems, vol. 15, no. 2, pp. 237-244, 1979.

[19]Chan, Y. T., Plant, J. B., and Bottomley, J. R. T., “A Kalman tracker with a simple input estimator,” IEEE Trans. Aerospace and Electronic Systems, vol. AES-18, no. 2, pp. 235-240, March 1982.

[20]Chang, C. B. and Tabaczynski, J. A., “Application of state estimation to target tracking,” IEEE Trans. Automat. Contr., vol.29, pp. 98-109, 1984.

[21]Chen, B. S., Wu, C. S. and Jan, Y. W., “Adaptive fuzzy mixed attitude control of spacecraft,” IEEE Trans. Aerospace and Electronic Systems, vol. 36, no.4, pp. 1343-1359, 2000.

[22]Chiaradia, A. P. M., “Application of the GPS system for preliminary satellite orbit determination,” Advances in Space Research, vol. 19, no. 11, pp. 1671-1675, 1997.

[23]Cho, Y. W., Park, C. W. and Park, M., “An indirect model reference adaptive fuzzy control for SISO Takagi-Sugeno model,” Fuzzy Sets and Systems, vol. 131, pp. 197-215, 2002.

[24]Cloutier, J. R., Lin, C. F. and Yang, C., “Maneuvering target tracking via smoothing and filtering through measurement concatenation,” Proc. of the AIAA Guidance, Navigation and Control Conference, pp. 827-836, 1991.

[25]Cloutier, J. R., Lin, C.F. and Yang, C., “Enhanced variable dimension filter for maneuvering target tracking,” IEEE Trans. Aerospace and Electronic Systems, vol. 29, no. 3, pp. 786-796, 1993.

[26]Del, V. R. G. and Thibault, J., “Development of a softsensor for particle size monitoring,” Minerals Engineering, vol. 9, pp.55-72, 1996.

[27]Desoer, C. A. and Lin, C. A., “Tracking and disturbance rejection of MIMO nonlinear systems with PI controller,” IEEE Trans. Automat. Contr., vol.30, pp. 861-867, 1985.

[28]Dhaouadi, R., Kubo, K. and Tobise, M., “Robust speed control of rolling mill drive systems using the loop transfer recovery design methodology,” Proceedings of the IECON International Conference on Industrial Electronics, Control, and Instrumentation, vol. 1, pp. 555-560, 1991.

[29]Dhaouadi, R., Kubo, K. and Tobise, M., “Two-degree-of-freedom robust speed controller for high-performance rolling mill drives,” IEEE Trans. Industry Applications, vol.29, pp. 919-926, 1993.

[30]Dhaouadi, R., Kubo, K. and Tobise, M., “Vibration suppression and high performance speed control of rolling mill drive,” Proceedings of the 2nd IEEE International Workshop on Advanced Motion Control, pp. 432-438, 1992.

[31]Didriksen, H., “Model based predictive control of a rotary dryer,” Chemical Engineering Journal, vol. 86, pp. 53-60, 2002.

[32]Droesen, W. J., “Formalisation of ecohydrological expert knowledge appling fuzzy techniques,” Ecological Modelling, vol. 85, pp. 75-81, 1996.

[33]Farooq, M., and Bruder, S., “Information type filters for tracking a maneuvering target,” IEEE Trans. Aerospace and Electronic Systems, vol. 26, no. 3, pp. 441-453, 1990.

[34]Feijoo, C., Asensio, A. and Perez, F., “New practical method for measurement error determination in radar systems under real traffic conditions,” IEE Proceedings-F, vol. 138, no. 6, pp.525-530, 1991.

[35]Filatov, N. M., “Application of adaptive dual controllers to a DC motor,” IEE Proc. Control Theory and Application, vol. 145, pp. 299-305, 1998.

[36]Filev, D. P. and Yager, R. R., “On the analysis of fuzzy logic controllers,” Fuzzy Sets and Systems vol. 68, pp.39-66, 1994.

[37]Fitzgerald, R. J., “Target Tracking Filters,” Electronic Progress, vol. 17, no. 1, pp. 31-38, 1975.

[38]Frank, H., Frank, K. and Thomas, R., Fuzzy Cluster Analysis, John Wily & Sons, New York, 1999.

[39]Franklin, G. F., Powell, J. D. and Workman, M. L., Digital of Dynamic Systems- 3th edition, Addison-Wesley, Canada, 1998.

[40]Fujikawa, K., Yan, Z. Q., Kobayashi, H. and Koga, T., “Robust and fast speed control for torsional system based on state-space method,” Proceedings of the IECON International Conference on Industrial Electronics, Control, and Instrumentation, vol. 1, pp. 687-692, 1991.

[41]Fung, R. F., Chen, K.W. and Yen, J. Y., “Fuzzy sliding mode controlled slide-crank mechanism using a PM synchronous servo motor drive,” Internal Journal of Mechanical Science, vol. 41, pp. 337-355, 1999.

[42]Gholson, N. H. and Moose, R. L., “Maneuvering target tracking using adaptive state estimation,” IEEE Trans. Aerospace and Electronic Systems, vol. 13, no. 3, pp. 310-317, 1977.

[43]Grewal, M. S. and Andrews, A. P., Kalman Filtering Theory and Practice, Prentice-Hall, Englewood Cliff. NJ, 1993.

[44]Hamamoto, K. and Sugie, T., “Controller design of 2 mass-spring system via LMI,” Proceedings of the 35th IEEE Conference on Decision and Control, pp. 745-746, 1996.

[45]Harris, C. J., Moore, C. G. and Brown, M., Intelligent Control: Aspects of Fuzzy Logic and Neural Nets, World Scientific Pub. Co. Pte. Ltd., Singopore, 1993.

[46]Hori, Y., “Comparison of vibration suppression control strategies in 2-mass systems including a novel two degree-of freedom controller,” Proceedings of the 2nd IEEE International Workshop on Advanced Motion Control, pp. 409-416, 1992.

[47]Houles, A. and Bar-shalom, Y., “Multisensor tracking of a maneuvering target in clutter,” IEEE Trans. Aerospace and Electronic Systems, vol.25, no.2, pp. 176-189, 1989.

[48]Hsiao, C. Y., “Modified gain fuzzy Kalman filtering algorithm,” Trans. Jpn. Soc. Mech. Eng., vol. 42, no. 2, pp. 363-368, 1999.

[49]Hu, B. G., Mann, G. K. I. and Gosine, R. G., “A systematic study of fuzzy PID controllers-function-based evaluation approach,” IEEE Transaction on Fuzzy Systems, vol. 9, no. 5, pp. 699-711, 2001.

[50]Huang, Z. and Chalabi, Z. S., “Use of time-series analysis to model and forecast wind speed,” Journal of Wind Engineering And Industrial Aerodynamics, vol.56, pp. 311-322, 1995.

[51]Inoue, K. and Nakaoka, M., ”Theoretical considerations of AC servo motion drive control system with 2-mass mechanically resonant load,” Proceedings of the IEEE IECON 22nd International Conference on Industrial Electronics, Control, and Instrumentation, vol. 2, pp. 1143-1148, 1996.

[52]Ji, J.-K. and Sul, S.-K., “Kalman filter and LQ based speed controller for torsional vibration suppression in a 2-mass motor drive system,” IEEE Trans. Industrial Electronics, vol.42, pp. 564-571, 1995.

[53]Kailath, T., Sayed, A. H. and Hassibi, B., Linear Estimation, Prentice Hall, N. J., 2000.

[54]Kalata, P. R. and Chmielewski, T.A., “Smoothing improvement ratio for and tracking systems,” in Proc. Amer. Cont. Conf., pp. 862-866, 1992.

[55]Kalata, P. R., “ target tracking system: A survey,” in Proc. Amer. Cont. Conf., pp.832-836, 1992.

[56]Kalata, P. R., “The tracking index: a generalized parameter for and target trackers,” IEEE Trans. Aerospace and Electronic Systems, vol.20, no.2, pp. 174-182, 1984.

[57]Kim, J.-S., Yang, I.-W., Kim, Y.-S. and Kim, Y.-J., “The vibration suppression control of the two-mass resonant system using the filter,” Proceedings of the 24th IEEE Annual Conference on Industrial Electronics Society, vol.3, pp. 1465-1470, 1998.

[58]Kim, K. and Hansen, J., “Development of fuzzy algorithm for tracking of maneuvering targets,” Proceedings of the IEEE Conf. on Decision and Control, pp. 803-808, 1992.

[59]Kobayashi, K., Cheok, K. C., Watanabe K. and Munekata F., “Accurate differential global positioning system via fuzzy logic Kalman filter sensor fusion technique,” IEEE Trans. on Industrial Electronics, vol. 45, no.3, pp. 510-518, 1998.

[60]Korn, J., “Application of the generalized likelihood ratio algorithm to maneuver detection and estimation,” Proc. of the ACC, Arlington, Virginia, pp. 792-798, 1982.

[61]Korner, R. and Nather, W., “Linear regression with random fuzzy variables: extended classical estimates, best linear estimates, least squares estimates,” Information Sciences, vol. 109, pp. 95-118, 1998.

[62]Kosko, B., ‘Fuzzy systems as universal approximators,” IEEE Trans. on Computers, vol. 43, no. 11, pp. 1329-1333, 1994.

[63]Kuo, H. C. and Wu, L. J., “An image tracking system for welded seams using fuzzy logic,” Journal of Materials Processing Technology, vol. 120, pp. 169-185, 2002.

[64]Kuo, R. J., Wu, P. and Wang, C. P., “An intelligent sales forecasting system through integration of artificial neural networks an neural networks with fuzzy weight elimination,” Neural Networks, vol. 15, pp. 909-925, 2002.

[65]Kuo, Yi-Pin and Li, Tzuu-Hseng S., “A Composite EP-based Fuzzy Controller for Active Suspension System,” International Journal of Fuzzy Systems, vol. 2, no. 3, pp.182-190, 2000.

[66]Kuo, Yi-Pin and Li, Tzuu-Hseng S., “GA-based Fuzzy PI/PD Controller for Automotive Active Suspension System,” IEEE Trans. Industrial Electronic. vol. 46, no. 6, pp. 1051-1056, 1999.

[67]Langari, R. and Wang, L., “Fuzzy models, modular networks, and hybrid learning,” Fuzzy Sets Syst., vol. 79, pp. 141-150, 1996.

[68]Lee, C. C., “Fuzzy logic in control systems: fuzzy logic controller-Part II,” IEEE Trans. Systems Man Cybernet. vol. 20, no. 2, pp. 404-435, 1990.

[69]Lee, J., “On methods for improving performance of PI-type fuzzy logic controllers,” IEEE Trans. Fuzzy System, vol.1, pp.298-301, 1993.

[70]Lee, J., Liu, K. F. R. and Chiang, W., “A possibilistic-logic-based approach to integrating imprecise and uncertain information,” Fuzzy Sets and Systems, vol. 113, pp. 309-322, 2000.

[71]Lee, C. C., “Fuzzy logic in control system: fuzzy logic controller parts I,” IEEE Trans. System, Man and Cyber., vol. 20, no. 2, pp. 404-435, 1990.

[72]Lefferts, R. E., “Alpha-Beta filters in polar coordinates with acceleration corrections,” IEEE Trans. Aerospace and Electronic Systems, vol. 24, pp. 693-699, 1988.

[73]Li, T.-H. S. and Lai, C.-C., “Lyapunov function based fuzzy estimator,” IEEE ISCAS’95, SEATTLE, WA, May, pp. 497-500, 1995.

[74]Li, T.-H. S., “Estimation of one dimensional radar tracking via fuzzy-Kalman filter,” Proc. of the IECON., Maui, HW, USA, pp.2384-2388, 1993.

[75]Lin, C. F., and Shafroth, M. W., “Modern tracking, estimation and identification in tactical missile control,” Proc. of the IEEE Conf. on Decision and Control, San Antonio, Texas, pp. 1310-1315, 1983.

[76]Lin, F. J. and Wai, R. J., “Hybrid computed torque controlled motor-toggle servomechanism using fuzzy neural network uncertainty observer,” Neurocomputing, vol. 48, pp. 403-422, 2002.

[77]Lin, S. F., Liao, S. S. and Chen, J. Y., “Design of fuzzy predictor in the moving target system,” International Journal of Fuzzy System, vol. 3, no. 1, pp. 323-330, 2001.

[78]Liu, W., “An extended Kalman filter and neural network cascade fault diagnosis strategy for the glutamic acid fermentation process,” Artificial Intelligence in Engineering, vol. 13, no. 2, pp. 131-140, 1999.

[79]Mazor, E., Averbuch, A., Bar-Shalom, Y. and Dayan, J., “Interacting multiple model methods in target tracking: a Survey,” IEEE Trans. Aerospace and Electronic Systems, vol. 34, pp. 103-123, 1998.

[80]Moose, R. L., “Modeling and estimation for tracking maneuvering targets,” IEEE Trans. Aerospace and Electronic Systems, vol. 15, no. 3, pp. 448-455, 1979.

[81]Mudi, R. and Pal, N. R., “A self-tuning fuzzy PI controller,” Fuzzy Sets and Systems, vol. 115, pp. 327-338, 2000.

[82]Mulgund, S. S. and Stengel, R. F., “Optimal nonlinear estimation for aircraft flight control in wind shear,” Automatica, vol. 32, no. 1, pp. 3-13, 1996.

[83]Munir, A. and Atherton, D. P., “Adaptive interacting multiple model algorithm for tracking a maneuvering target,” IEE Proc.-Rader, Sonar Navig., vol. 142, no. 1, pp.11-17, 1995.

[84]Munu, M., Harrison, I., Wilkin, D. and Woolfson, M. S., “Comparison of adaptive target-tracking algorithms for phase-array radar,” IEE Proc., Pt. F., vol. 139, no. 5, pp.336-342, 1992.

[85]Novakovic, B., Scap, D. and Novakovic, D., “An analytic approach to fuzzy robot control synthesis,” Engineering Applications of Artificial Intelligence, vol. 13, no. 1, pp. 71-83, 2000.

[86]Ohmae, T., Matsuda, T., Kanno, M., Saito, K. and Sukegawa, T., “A microprocessor-based motor speed regulator using fast response state observer for reduction of torsional vibrations,” IEEE Trans. Industry Applications, vol.23, pp. 863-871, 1987.

[87]Pai, N. S. and Li, T.-H. S., “Speed control design of 2-mass drive system by using integrated fuzzy observer and linear quadratic control,” Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, pp. 58-63, 2002.

[88]Pai, N. S. and Li, T.-H. S., “State Estimation via Lyapunov-type fuzzy filter,” Electronics Letters, vol. 35, pp. 1028-1030, 1999.

[89]Park, Y. H., Seo, J. H., and Lee, J. G., “Tracking using the variable-dimension filter with input estimation,” IEEE Trans. Aerospace and Electronic Systems, vol. AES-31, no. 1, pp. 399-408, Jan 1995.

[90]Philips, C. L. and Harbor, R. D., Feedback Control Systems, Prentice-Hall, Englewood Cliff. NJ, 1996.

[91]Phillips, C. L. and Nagle, H. T., Digital Control System Analysis and Design, Prentice Hall, Englewood Cliffs, N.J., 1995.

[92]Pivonka, P., “Physical background of fuzzy PI and PD controller,” IFSA Eighth International Fuzzy Systems Association World Congress, Taipei, Taiwan, pp. 635-639, 1999.

[93]Ramachandra, K. V., Kalman Filtering Techniques for Radar Tracking, Marcel Dekker, New York, 2000.

[94]Rawicz, P. L. and Kalata, P. R., Chmielewski, T.A. and Murphy, K. M., “On target tracking initiation,” in Proc. Amer. Cont. Conf., pp. 2434-2438, 1998.

[95]Richard, A. H. and Thomas, W. P., Digital Signal Processing, W. H. Freeman and Company, New York, 1991.

[96]Ricker, G. C., and Williams, J. R., “Adaptive tracking filter for maneuvering target,” IEEE Trans. Aerospace and Electronic Systems, vol. 14, no. 1, pp. 185-193, 1978.

[97]Saridis, G. N., Stochastic Process, Estimation, and Control: the Entropy Approach, John Wiely & Sons, New York, 1995.

[98]Shieh, M. Y. and Li, T.-H. S., “Integrated fuzzy logic controller design,” Proceedings of the IECON International Conference on Industrial Electronics, Control, and Instrumentation, Maui, HW, USA , pp. 279-284, 1993.

[99]Shieh, M.-Y. and Li, T.-H. S., “Switching-type fuzzy sliding-mode control,” Mechatronics, vol. 10, no. 1-2, pp. 91-109, 2000.

[100]Shieh, M. Y. and Li, T.-H. S., “Implementation of integrated fuzzy logic controller,” Proc. of FUZZ-IEEE/IFES’95 IV, Yokohama, Japan, pp. 1755-1762, 1995.

[101]Shinners, S. M., Modern Control System Theory and Application, Addison-Wesley, Reading, MA, 1978.

[102]Simon, D. and El-Sherief, H., “Hybrid Kalman/minimax filtering in phase-locked loops,” Control Engineering Practice, vol. 4, no. 5, pp. 615-623, 1996.

[103]Singer, R. A., “Estimating optimal tracking filter performance for manned maneuvering targets,” IEEE Trans. Aerospace and Electronic Systems, vol. 6, no. 4, pp. 473-483, 1970.

[104]Song, J.-H., Lee, K.-B., Choy, I., Kim, K.-B. , Choi, J.-Y. and Lee, K.-W., “ Vibration control of 2-mass system using a neural network torsional torque estimator,” Proceedings of the 24th IEEE Annual Conference on Industrial Electronics Society, vol.3, pp. 1785-1788, 1998.

[105]Sudano, J. J. and Marietta, M., “Maneuver-Driven and tracking filters,” IEEE Trans. Aerospace and Electronic Systems, vol. 31, pp. 341-357, 1995.

[106]Sugeno, M. and Kang, G. T., “Structure identification of fuzzy model,” Fuzzy Sets Syst., vol. 28, pp. 15-33, 1988.

[107]Sugeno, M., Industrial Applications of Fuzzy Control, North-Holland, Amsterdam, 1985.

[108]Takagi, T. and Sugeno, M., “Fuzzy Identification of fuzzy systems and its application to modeling and control,” IEEE Trans. Systems Man Cybernet, vol. 15, no. 1, pp. 116-132, 1985.

[109]Tang, K. S., Man, K. F., Chen, G. and Kwong, S., “An optimal fuzzy PID controller,” IEEE Trans. Industrial Electronics, vol.48, pp. 757-765, 2001.

[110]Thorp, J. S., “Optimal tracking of maneuvering targets,” IEEE Trans. Aerospace and Electronic Systems, vol. 9, no. 4, pp. 512-519, 1973.

[111]Tseng, H. C. and Hwang, V. H., “Servocontroller tuning with fuzzy logic,” IEEE Trans. Control System Technology, vol. 1, pp. 262-269, 1993.

[112]Uhlmann, J. K., “Algorithms for multiple-target tracking,” American Scientist, vol. 80, pp. 128-141, 1992.

[113]Wai, R. J., “Hybrid control for speed sensorless induction motor drive,” IEEE Transaction on Fuzzy Systems, vol. 9, no. 1, pp. 116-138, 2001.

[114]Wang, L. and Langari, R., “Complex systems modeling via fuzzy logic,” IEEE Trans. Syst., Man, Cybern., vol. 26, pp. 100-106, 1996.

[115]Wang, L. X. and Mendel, J. M., “Fuzzy basis functions, universal approximation, and orthogonal least squares learning,” IEEE Trans. Neural Networks, vol. 3, pp. 807-814, 1992.

[116]Wang, L. X., “Universal approximation by hierarchical fuzzy systems,” Fuzzy Sets and Systems, vol. 93, no. 2, pp. 223-230, 1998.

[117]Wang, T. C. and Varshney P. K., “A tracking algorithm for maneuvering targets,” IEEE Trans. Aerospace and Electronic Systems, vol. 29, no. 3, pp. 910-924, 1993.

[118]Whang, I. H., Lee J. G., and Sung T. K., “Modified input estimation technique using pesudoresiduals,” IEEE Trans. Aerospace and Electronic Systems, vol. 30, pp. 220-227, 1994.

[119]Wiegand, M., “Autonomous satellite navigation via Kalman filtering of magnetometer data, ” Acta Astronautica, vol. 38, no. 4-8, pp. 395-403, 1996.

[120]Wu, B. and Tseng, N. F., “A new approach to fuzzy regression models with application to business cycle analysis,” Fuzzy Sets and Systems, vol. 130, pp. 33-42, 2002.

[121]Wu, Q. H. and Hogg, B. W., “On-line evaluation of auto-tuning optimal PID controller on micromachine system,” Int. J. Control, vol. 53, pp. 751-769, 1991.

[122]Wu, W. R., Kundu, A., “Kalman filtering in non-Gaussian environment using efficient source function approximation,” Proceedings of 1989 International Symposium on Circuits and System, pp. 413-416, 1989

[123]Xu, J.-X., Liu, C. and Hang, C.-C., “Modified tuning of a fuzzy logic controller,” Engng. Applic. Artif. Intell, vol. 9, pp. 65-74, 1996.

[124]Yazici, A. and Cibiceli, D., “An access structure for similarity-based fuzzy databases,” Information Sciences, vol. 115, pp. 137-163, 1999.

[125]Yen, J. and Gillespie, W., “Integrating global and local evaluations for fuzzy model identification using genetic algorithms,” Proc. 6th Int. Fuzzy Syst. Assoc. World Congress (IFSA’95), Sao Paolo, Brazil, 1995.

[126]Yen, J. and, Wang, L. and Gillespie, C. W., “Improving the interpretability of TSK fuzzy models by combining global learning and local learning,” IEEE TRANSAT ON FUZZY SYSTEMS, vol. 6, no. 4, pp. 530-537, 1998.

[127]Zadeh, L. A., “Fuzzy sets,” Information Control, vol. 8, pp.338-353, 1965