電腦數控機床在兩軸同動時所達到的循圓精度是運動控制上的一個重要課題。本論文之第一部分著重在此課題，藉由改善桌上型電腦數控銑床的循圓誤差及最小化定位時間差以提升其性能。一種可解決這些問題的控制方法在本研究中被加以提出。此方法結合了田口法、灰色系統與PID控制器，並著重在透過控制器的強健性，諸如以較快的初始化增益做為適當的局部極小值以及高響應，以改進系統性能。其目的在於強化多重性能特徵，亦即實際半徑和定位時間。此控制方法提供了一個基於實驗的簡易處理。此方法被成功地應用在多軸線性運動系統的性能最佳化，以透過多個控制參數的最佳化改善系統的性能。本研究所提出的控制法則，透過電腦數控機床的循圓控制以進行演示。
在奈米技術中，壓電致動器被廣泛地使用。由於壓電材料本身的性質取決於居里溫度及其可逆效應，採用線性壓電陶瓷馬達之精密定位為本論文之第二部分所著重的課題。一種結合了順滑模態控制與模糊推理系統的控制器，即所謂的滑態—模糊控制器，被加以提出。在此控制演算法則中，滑態部分用以抵消干擾，而模糊邏輯則用以消除不確定性。結果顯示此控制器具備優異的性能以減少系統的穩態誤差。然後，此控制法則透過循圓控制以進行驗證。本研究所提出的滑態—模糊控制器具有良好的追蹤速度（適應性）和強健性。
本研究之其餘貢獻在於所發展的電腦數控機床及線性壓電陶瓷馬達可以作為在控制工程相關領域的教學工具。

An important problem in the control of circular motion of CNC machine is making X and Y axes move simultaneously. First part of this work addresses this problem for the performance improvement of desktop-scale CNC milling machine for reducing roundness error (REB), and minimizing position time difference (Tt). An approach that can solve those problems will be introduced. The approach uses a Taguchi, Grey System and Proportional-Integral-Derivative (TGPID). This method emphasizes an improvement of system performance through this controller’s robustness, such as a faster initialization in gaining as appropriate local minima and also high responsive. It aims to enhance on multi-performance characteristics, namely actual radius (R_act) and position time (Tt). This proposed method also offers a simple experimental-based approach. An improvement of the performance indicated that this proposed approach is applied successfully to multi linear motion performance optimization which is determined by many parameters at multi quality performances. Performances of the proposed controller scheme, as well as some practical design aspects, are demonstrated by the control of a circular motion of CNC machine.
In nanotechnologies, usages of piezoelectric actuators are widely used. Since the properties of piezo-materials itself are dependent on Curie temperature and they have a reversible effect, a precise positioning using linear piezoelectric ceramic motor (LPCM) is a subject to achieve in second part of this work. A proposed controller mode by combining sliding mode (SM) and fuzzy inference system, so called Fuzzy-Sliding Mode (FSM) controller, is hereafter introduced. In this controller scheme, a sliding part obtains to counter disturbances, while the fuzzy eliminates uncertainties. The results show Excellencies in this work that emphasizes performance improvement of minimizing errors with steady-state errors. The circular motion is then conducted to verify the controller’s performance. The controller (FSM) presented in this research possessed excellent tracking speed (adaptive) and robustness properties.
Other contribution of this work is the developed CNC machine and the LPCM can be used as educators or teaching aids in field of science education of control engineering.
In summary, the works are valuable for all purposes in practical- or education-applications.

[1] Adriaens H.J.M.T.A., de Koning W.L., Banning R. (2000) Modeling piezoelectric actuators, IEEE/ASME Trans. Mechatronics, 5(4), pp. 331-341.
[2] Alli H., Yakut O. (2005) Fuzzy sliding-mode control of structures, Engineering Structures, 27, pp.277-284.
[3] Arvidsson M., Gremyr I. (2008) Principles of robust design methodology, Quality and Reliability Eng. Int., Volume 24, Issue 1: pp. 23-35.
[4] Astrom K.J., Wittenmark B. (2008) Adaptive Control: Second Edition, Dover Publications, US.
[5] Bagheri A., Moghaddam J.J. (2009) Decoupled adaptive neuro-fuzzy (DANF) sliding mode control system for a Lorenz chaotic problem, Expert Systems with Applications, 36, pp.6062-6068.
[6] Bang Y.B., Lee K.M. (2004) Linear motor for ejector mechanism, Int J Adv Manuf Technol, Vol. 24, pp. 582-589.
[7] Bartolini G., Ferrara A., Stotsky A.A. (1995) Stability and Exponential Stability of an Adaptive Control Scheme for Plants of any Relative Degree, IEEE Tr on Automatic Control, Vol. 40(1), pp.100-104.
[8] Bartolini G., Fridman L., Pisano A., Usai E. (2008) Modern Sliding Mode Control Theory: New Perspectives and Applications, Springer Verlag, Berlin.
[9] Baruch I.S., Lopez R.B.,Guzman J.L.O., Flores J.M. (2008) A fuzzy-neural multi-model for nonlinear systems identification and control, Fuzzy Sets and Systems, 159, pp.2650-2667.
[10] Botta A., Lazzerini B., Marcelloni F. (2008) Context adaptation of mamdani fuzzy rule based systems, Int. J. Intell. Syst., 23 (4), pp.397-418.
[11] Boubakir A., Fares Boudjema F., Labiod S. (2009) A Neuro-fuzzy-sliding Mode Controller Using Nonlinear Sliding Surface Applied to the Coupled Tanks System, International Journal of Automation and Computing, 6(1), pp.72-80.
[12] Buckley J.J., Eslami E. (2002) An Introduction to Fuzzy Logic and Fuzzy Sets (Advances in Intelligent and Soft Computing), Physica-Verlag, Heidelberg.
[13] Chao P.C.P., Lai C.L. (2003) Boundary control of an axially moving string via fuzzy sliding mode control and fuzzy neural network methods, Journal of Sound and Vibration, 262, pp.795-813.
[14] Chen C.S. (2009) Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems, Information Sciences, 179, pp.2676-2688.
[15] Chen C.Y., Li T.H.S., Yeh Y.C, Chang C.C. (2009) Design and implementation of an adaptive sliding-mode dynamic controller for wheeled mobile robots, Mechatronics, 19, pp.156-166.
[16] Chen H.Y., Huang S.J. (2004) Adaptive fuzzy sliding-mode control for the Ti6Al4V laser alloying process, Int J Adv Manuf Technol, 24, pp.667-674.
[17] Chen P.C., Chen C.W., Chiang W.L. (2009) GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems, Expert Systems with Applications, 36, pp.5872-5879.
[18] Chen S.H., Zheng L.A., Chou J.H. (2007) A Mixed Robust/Optimal Active Vibration Control for Uncertain Flexible Structural Systems with Nonlinear Actuators Using Genetic Algorithm, J. of Vibration and Control, Vol. 13: pp. 185-201.
[19] Chen Y..P, Li K.T. (2003) A novel modified grey model, Proceed. of IEEE SICE Annual Conf., Fukui, Japan, Vol. 1, pp. 890-895.
[20] Da F. (2008) Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction, Neural Comput & Applic, 17, pp.531-539.
[21] Dandil B., Gokbulut M., Ata F. (2005) A PI type fuzzy-neural network controller for induction motor drives, Journal of Applied Sciences, 5(7), pp.1286-1291.
[22] Deng J.L. (1982) Control Problems of Grey Systems, System and Control Letters, Vo. 1, Number 5: pp.288-294.
[23] Deng J.L. (1989). Introduction to grey system theory, The Journal of Grey System, Vol. 1: pp.1-24.
[24] Deng J.L. (1990) A Course on Grey System Theory, HUST Press, Wuhan, China: pp.33-84.
[25] Dorf R.C., Bishop R.H. (2007) Modern Control Systems, Prentice Hall, NJ.
[26] Duelger L.C., Kirecci A. (2007) Motion Control and Implementation for an AC Servomotor System, Modelling and Simulation in Engineering, Hindawi Publishing Corporation, Article ID 50586, doi: 10.1155/2007/50586.
[27] Efe M.O. (2008) Fractional Fuzzy Adaptive Sliding-Mode Control of a 2-DOF Direct-Drive Robot Arm, IEEE Tr. On Systems, Man and Cybernetics-Part B: Cybernetics, Vol. 38(6), pp.1561-1570.
[28] Efe M.O., Kaynak O., Wilamowski B.M. (2000) Creating a sliding mode in a motion control system by adopting a dynamic defuzzification strategy in an adaptive neuro fuzzy inference system, Industrial Electronics Society, IECON 26th Annual Conference of the IEEE, Vol.2, pp.894-899.
[29] Fallahi M., Azadi S. (2009) Fuzzy PID Sliding Mode Controller Design for the position control of a DC Motor, ICETC '09, International Conference on Education Technology and Computer, pp.73-77.
[30] Fan K.C., Wang W.L., Chiou H.S. (2008) Fabrication optimization of a micro-spherical fiber probe with the Taguchi method, J. Micromech. Microeng. 18 No 1: 015011 (8pp).
[31] Fellani M.A., Abaid D.E. (2008) Sliding-Mode Control of Synchronous Reluctance Motor, World Academy of Science, Engineering and Technology, 46, pp.401-405.
[32] Fischer M. and Tomizuka M. (1996) Application and comparison of alternative position sensors in high-accuracy control of an X-Y table, Proceed. of IEEE Int. Workshop on Advanced Motion Control, 2, Mie, Japan, pp.494-499.
[33] Gao X.K., Liu Z.J., Chen S.X., Low T.S. (2002) Grey relational analysis for robust design of BLDC spindle motor, Magnetic Recording Conf., Digest of the Asia-Pacific 27-29 Aug. 2002: pp. WE-P-28-01 - WE-P-28-02.
[34] Gao Y., Tse S. (2005) Modelling of Piezoelectric Actuator Based Nano-Positioning System under Sinusoidal Excitation Using Multi-Polynomial Regression, 7th International Symposium on Measurement Technology and Intelligent Instruments, Journal of Physics: Conference Series, 13, pp.98-101.
[35] Ghoshal S.P. (2004) Optimizations of PID gains by particle swarm optimizations in fuzzy based automatic generation control, Electric Power Systems Research, 72, pp.203-212.
[36] Gordon S., Hillery M.T. (2005) Development of a high-speed CNC cutting machine using linear motors, Journal of Materials Processing Technology, Vol. 166 (3), pp. 321-329.
[37] Hace A., Jezernik K., Curk B., Terbuc M. (1998) Robust motion control of XY table for laser cutting machine, Proceed. of IEEE 24th Annual Conf. IECON, 2, Aachen, Germany, pp.1097-1102.
[38] Hissel D., Maussion P., Faucher J. (1998) On Evaluating Robustness of Fuzzy Logic Controllers through Taguchi Methodology, Industrial Electronics Society, IECON '98, Proceedings of the 24th Annual Conference of the IEEE, Volume 1, pp.17-22.
[39] Hsiao Y.F., Tarng Y.S., Kung K.Y. (2007) Application of Grey-based Taguchi methods on Determining Process Parameter of Linear Motion Guide with Multiple Performance Characteristics, Proceedings of the 11th WSEAS Int. Conf. on Applied Mathematics: pp.72-79.
[40] Hsu C.F., Lee B.K., Lee T.T. (2008) Robust Intelligent Motion Control for Linear Piezoelectric Ceramic Motor System Using Self-constructing Neural Network, Lecture Notes in Electrical Engineering, Volume 5, Book of Advances in Industrial Engineering and Operations Research, Chapter 27, Springer, US, pp 375-390.
[41] Hsu L. (1988) Variable structure model reference adaptive control using only input and output measurements: Part II, Proceedings of the 27th Conf on Decision and Control, Austin, Texas, U.S.A., pp.2396-2401.
[42] Hsu L. (1990) Variable structure model-reference adaptive control (VS-MRAC) using only input and output measurements: The general case, IEEE Tr on Automatic Control, Vol.35(11), pp.1238-1243.
[43] Huang A.C., Chen Y.C. (2004) Adaptive multiple-surface sliding control for non-autonomous systems with mismatched uncertainties, Automatica, 40, pp.1939-1945.
[44] Huang A.C., Chen Y.C. (2004) Adaptive Sliding Control for Single-Link Flexible-Joint Robot With Mismatched Uncertainties, IEEE Tr. on Control System Technology, Vol. 12(5), pp.770-775.
[45] Huang S.J., Huang C.L. (2000) Control of an Inverted Pendulum Using Grey Prediction Model, IEEE Tr on Industry Applications, Vol. 36 (2), pp. 452-458.
[46] Hwang C.L., Jan C., and Chen Y.H. (2001) Piezomechanics using intelligent variable-structure control, IEEE Trans. Ind. Electron., 48(1), pp. 47-59.
[47] Ioannou P.A., Sun J. (1995) Robust Adaptive Control, Prentice Hall, NJ.
[48] Isidori A. (1989) Nonlinear cotrol systems: An introduction, Springer Verlag, Berlin.
[49] Jang M.J., Chen C.L., Lee J.R. (2009) Modeling and control of a piezoelectric actuator driven system with asymmetric hysteresis, Journal of the Franklin Institute, 346, pp.17-32.
[50] Johnson D., Johnson R., Holubec E. (1998) Cooperation in the classroom, 6th ed. Allyn and Bacon, Boston MA.
[51] Kang J.H., Yim C.H., Kim D.I. (1995) Robust Position Control of AC Servo Motors, IECON Proceedings (Industrial Electronics Conf) 1: pp.621-626.
[52] Khalil H.K.(2001) Nonlinear Systems, 3rd Eds., Prentice-Hall, NJ.
[53] Khiar D., Lauber J., Floquet T., Colin G., Guerra T.M., Chamaillar Y. (2007) Robust Takagi–Sugeno fuzzy control of a spark ignition engine, Control Engineering Practice, 15, pp.1446-1456.
[54] Kim J., Kasabov N. (1999) HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems, Neural Networks, 12, pp.1301-1319.
[55] Kim T.W., Yuh J. (2002) Application of on-line neuro-fuzzy controller to AUVs, Information Sciences, 145, pp.169-182.
[56] Kim Y.J., Dohmeki H. (2007) Driving characteristics analysis of stationary discontinuous armature permanent magnet linear synchronous motor for factory automation systems, Electr Eng, Vol. 89, pp. 617-627.
[57] Kokotovic P.V. (1992) The joy of feedback: nonlinear and adaptive, IEEE Ctrl Syst Magazine, 12(3), pp.7-17.
[58] Kopac J., Krajnik P. (2007) Robust design of flank milling parameters based on grey-Taguchi method, J. of Materials Processing Tech., Volume 191, Issues 1-3: pp. 400-403.
[59] Kucukdemiral I.B., Engin S.N., Omurlu V.E., Cansever G. (2005) A Robust Single Input Adaptive Sliding Mode Fuzzy Logic Controller for Automotive Active Suspension System, L. Wang and Y. Jin (Eds.): FSKD 2005, LNAI 3613, pp. 981-986.
[60] Kurnaz S., Cetin O., Kaynak O. (2009) Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles. Expert Systems with Applications, doi:10.1016/j.eswa.2009.06.009.
[61] Lee J.H., Lee S.K. (2004) Chucking compliance compensation with a linear motor-driven tool system, Int J Adv Manuf Technol, Vol. 23, pp. 102-109.
[62] Li T.H.S., Guo N.R., Kuo C.L. (2005) Design of adaptive fuzzy model for classification problem, Engineering Applications of Artificial Intelligence, 18, pp.297-306.
[63] Liang Y., Cong S., Shang W. (2008) Function approximation-based sliding mode adaptive control, Nonlinear Dyn 54: 223-230.
[64] Lin C.T., Lee C.S.G. (1996) Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice Hall, NJ.
[65] Lin J., Lian R.J., Huang C.N., Sie W.T. (2009) Enhanced fuzzy sliding mode controller for active suspension systems, Mechatronics, doi:10.1016/j.mechatronics.2009.03.009.
[66] Lin Y. and Liu S. (2004) A Historical Introduction to Grey System Theory, IEEE Int Conf on Systems, Man and Cybernetics: pp.2403-2408.
[67] Liu D., Yi J., Zhao D., Wang W. (2005) Adaptive sliding mode fuzzy control for a two-dimensional overhead crane, Mechatronics 15, pp.505-522.
[68] Liu Y.J., Tong S.C., Wang W. (2009) Adaptive fuzzy output tracking control for a class of uncertain nonlinear systems, Fuzzy Sets and Systems,160, pp.2727-2754.
[69] Liu Z.Z., Luo F.L., Rashid M.H. (2004) Robust high speed and high precision linear motor direct-drive XY-table motion system, Proceed. of IEE Control Theory and Applications, 151(2), pp.166-173.
[70] Lyapunov A.M. (1949) The general problem of motion stability, in Russian (1892). Translated in French, Ann. Fac. Sci. Toulose 9, pp.203-474 (1907). Reprinted in Ann. Math. Study, No.17, Princeton Univ. Press.
[71] Mamdani E., Assilian S. (1999) An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Human-Computer Studies, 51(2), pp. 135-147.
[72] Mayer D., Atzrodt H., Herold S., Thomaier M. (2005) An approach for the model based monitoring of piezoelectric actuators, II ECCOMAS Thematic Conf. on Smart Structures and Materials, C.A. Mota Soares et al. (Eds.), Lisbon, Portugal, pp.1-13.
[73] Mitsuishi T. and Shidama Y. (2009) Height defuzzification method on L∞ space, C. Alippi et al. (Eds.): ICANN 2009, Part I, LNCS 5768, pp.598-607.
[74] Mizumoto M. (1990) Improvement of fuzzy control (IV) - Case by product-sum-gravity method, In: Proc. 6th Fuzzy System Symposium, pp.9-13.
[75] Moghaddam J.J., Bagheri A. (2010) An adaptive neuro-fuzzy sliding mode based genetic algorithm control system for under water remotely operated vehicle, Expert Systems with Applications 37, pp.647-660.
[76] Mrozek Z., Tarasiewicz S. (2001) Attempting sliding mode controller to mobile robot arc welding process, III Krajowa Konf. Metody i Systemy Komputerowe, ed R.Tadeusiewicz, A.Lig za, M.Szymkat, Kraków, pp.369-373.
[77] Narendra K.S., Annaswamy A.M. (2005) Stable Adaptive Systems, Dover Publications, US.
[78] Narendra K.S., Lin Y.H., Valavani L.S. (1980) Stable Adaptive Controller Design, Part II: Proof of Stability, IEEE Tr on Automatic Control, Vol.AC-25, No.3, pp.440-448.
[79] Narendra K.S., Valavani L.S. (1978) Stable Adaptive Controller Design - Direct Control, IEEE Tr on Automatic Control, Vol.AC-23, No.4, pp.570-583.
[80] Ng D.K.W (1994) Grey system and grey relational model, ACM SIGICE Bulletin archive Volume 20, Issue 2: pp.2-9.
[81] Noaman N.M. (2008) Speed Control for IFOC Induction Machine with Robust Sliding Mode Controller, Asian Journal of Scientific Research, 1(4), pp.324-337.
[82] Nurhadi H., Tarng Y.S. (2007a) Straightness Errors Measurement of a Friction Linear Guideways using Position Sensing Detectors (PSD), Proceed. of Int. Conf. on Instrumentation, Communication, and Information Technology (ICICI); Bandung, Indonesia.
[83] Nurhadi H., Tarng Y.S. (2007b) A Linear Table Motion Performance Optimization; Proceed. of Int. Conf. of The 8th Production System Conference and the 1st Asia-Pacific Conf. on Manufacturing Systems (The 1st APCOMS), Kuta-Bali, Indonesia.
[84] Nurhadi H., Tarng Y.S. (2009a) Experimental approached optimization of a linear motion performance with grey hazy set and taguchi analysis methods (GHST) for ball-screw table type, Int J Adv Manuf Tech, 44, pp.149-160.
[85] Nurhadi H., Tarng Y.S. (2009b) Basic Control Educator Development in Science and Engineering, Proceed. of 2009 Int. Conf. of East Asian Science Education (EASE2009); Taipei, Taiwan R.O.C.
[86] Nurhadi H., Tarng Y.S. (2009c) Open- and Closed-loop System of Computer Integrated Desktop-scale CNC Machine, Proceed. of 2009 CACS International Automatic Control Conference, Taipei, Taiwan R.O.C.
[87] Ogata K. (1970) Modern Control Engineering, Prentice-Hall, NJ.
[88] Ohle F. (1995) No-feedback control and slaving principles in intermittent wake flows, Chaos, Solitons & Fractals, 5(10), pp.1871-1880.
[89] Omurlu V.E., Engin S.N., Yuksek I. (2008) Application of fuzzy PID control to cluster control of viaduct road vibrations, J. Vibration and Control, 14(8), pp.1201-1215.
[90] Osorio R., Ponce M., Oliver M.A., Olivares V.H., Juárez M. (2005) Analysis and design of discrete sliding mode control for a nonresonant electronic ballast, HAIT Journal of Science and Engineering B, Volume 2, Issues 5-6, pp.625-637.
[91] Park B.S., Yoo S.J., Park J.B., Choi Y.H. (2009) Adaptive Neural Sliding Mode Control of Nonholonomic Wheeled Mobile Robots With Model Uncertainty, IEEE Tr. on Control System Technology, Vol. 17(1), pp.2-7-214.
[92] Peng H. (2006) A Data Mining Approach Based on Grey Prediction Model in Web Environment, Proceed. of the 2nd Int. Conf. on Semantics, Knowledge, and Grid (SKG'06), Guilin, Guanxi, China, pp. 76-76.
[93] Peng Y.F., Wai R.J. and Lin C.M. (2003) Adaptive CMAC model reference control system for linear piezoelectric ceramic motor, Proceed. IEEE Int. Symp. on Computational Intell. in Robotics and Automation, Kobe, Japan, pp. 30-35.
[94] Perruquetti W., Barbot J.P. (2002) Sliding Mode Control in Engineering, CRC Press, NY.
[95] Ping X.L., Liu S.L., Li J.L., Zhou R.R. (2007) Application of grey prediction for predicting the measuring point for a CMM, Int J Adv Manuf Tech, Vol. 32, pp. 288-292.
[96] Popov V.M. (1962) Absolute Stability of Nonlinear Control Systems of Automatic Control, Automation and Remote Control, 22.
[97] Pourboghrat F., Vlastos G. (2002) Model reference adaptive sliding control for linear systems, Computers and Electrical Engineering, 28, pp.361-374.
[98] Rake H. (2001) Teaching control engineering to mechanical engineering students by a combination of traditional and modern methods, Weichert, D., Rauhut, B., Schmidt, R. (ed.), Educating the Engineer for the Century, pp.189-190. Kluwer Academic Publishers, Printed in the Netherlands.
[99] Rashidi F., Moshiri B. (2004) Design of a Robust Sliding Mode Fuzzy Controller for Nonlinear HVAC Systems, L. Rutkowski et al. (Eds.): ICAISC 2004, LNAI 3070, pp.984-989.
[100] Ross P.J. (1996) Taguchi techniques for Quality Engineering. McGraw-ill Inc., NY.
[101] Salahshoor K., Hamzehnejad M. (2009) A novel online affine model identification ofmultivariable processes using adaptive neuro-fuzzy networks, Chem Eng Res Des, doi:10.1016/j.cherd.2009.07.009
[102] Salinas R., Barbieri E. (2006) Quasioptimal Sliding Mode Controller for a Flexible Structure, Proceedings of the 2006 American Control Conference, Minneapolis, Minnesota, USA, pp.4718-4723.
[103] Sashida T. and Kenjo T. (1993) An Introduction to Ultrasonic Motors, Oxford, U.K.: Clarendon.
[104] Sastry S., Bodson M. (1989) Adaptive control: Stability, Convergence, and Robustness, Prentice-Hall, NJ.
[105] Shao B., Rong W., Guo B., Ru C., Sun L. (2008) Modeling and control of a novel piezoelectric actuated precision fast positioning system, Smart Mater. Struct. 17 (2008) 025032 (7pp) doi:10.1088/0964-1726/17/2/025032.
[106] Sivrioglu S., Nonami K. (1998) Sliding mode control with time-varying hyperplane for AMB systems, IEEE/ASME Transactions on Mechatronics, Vol. 3(1), pp.51-59.
[107] Slotine J.J.E. and Li W. (1991) Applied nonlinear control, Prentice Hall, NJ.
[108] Soltani J., Szabados B., Hoolboom G. (2002) A new synthetic loading for large induction machines with no feedback into the power system, IEEE Tr on Energy Conversion, 17(3), pp.319-324.
[109] Sorensen K.L., Singhose W., Dickerson S. (2007) A controller enabling precise positioning and sway reduction in bridge and gantry cranes, Control Engineering Practice, 15(7), pp.825-837.
[110] Soyguder S., Alli H. (2009) Fuzzy adaptive control for the actuators position control and modeling of an expert system, Expert Systems with Applications, doi:10.1016/j.eswa.2009.06.071.
[111] Stevens A.J. and Liang S.Y. (1995) Runout rejection in end milling through two-dimensional repetitive force control, Mechatronics, 5(1), pp.1-13.
[112] Sun D., Shan J., Su Y., Liu H.H.T. and Lam C. (2005) Hybrid control of a rotational flexible beam using enhanced PD feedback with a nonlinear differentiator and PZT actuators, Smart Mater. Struct. 14, pp.69-78.
[113] Sun F., Sun Z., Li L., Li H.X. (2003) Neuro-fuzzy adaptive control based on dynamic inversion for robotic manipulators, Fuzzy Sets and Systems, 134, pp.117-133.
[114] Sung M.Y. and Huang S.J. (2004) Fuzzy logic motion control of a piezoelectrically actuated table, Proc. Isntn Mech. Engrs, Vol. 218, Part I: J. Systems and Control Engineering, pp.381-397.
[115] Szabat K., Kaminski M., Orlowska-Kowalska T. (2007) Robust Control of an Electrical Drive using Adaptive Fuzzy Logic Control Structure with Sliding-Mode Compensator, EUROCON 2007, The International Conference on Computer as a Tool, Warsaw, September 9-12, pp.1706-1711.
[116] Tahour A., Meroufel A., Abid H., Aissaoui A.G. (2008) Sliding Controller of Switched Reluctance Motor, Leonardo Electronic Journal of Practices and Technologies, Issue 12, pp.151-162.
[117] Tarng Y.S., Chuang H.Y., Hsu W.T. (1999) Intelligent cross-coupled fuzzy feedrate controller design for CNC machine tools based on genetic algorithms, International Journal of Machine Tools & Manufacture, 39, pp.1673-1692.
[118] Ting Y., Huang J.S., Chuang F.K., and Li C.C. (2003) Dynamic analysis and optimal design of a piezoelectric motor, IEEE Trans. Ultrason., Ferroelectr., Freq. Control, 50(6), pp. 601-613.
[119] Topalov A.V, Kayacan E., Oniz Y., Kaynak O. (2009) Adaptive Neuro-Fuzzy Control with Sliding Mode Learning Algorithm: Application to Antilock Braking System, Proceedings of the 7th Asian Control Conference, Hong Kong, China, pp.784-789.
[120] Tosun N. (2006) Determination of Optimum Parameters for Multi-Performance Characteristics in Drilling by using Grey Relational Analysis, Int. J. of Adv. Manufacturing Tech. 28: pp.450-455.
[121] Truong D.Q., Ahn K.K (2008) Force control for hydraulic load simulator using self-tuning grey predictor – fuzzy PID, Mechatronics, doi:10.1016/j.mechatronics.2008.07.007.
[122] Tung E.D., Tomizuka M., Urushisaki Y. (1994) High-speed end mill boring and rounded corner cutting, Proceed. of IEEE the American Control Conf., 3, Baltimore, Maryland, pp.2894-2898.
[123] Turkmen I., Guney K. (2008) Genetic tracker with adaptive neuro-fuzzy inference system for multiple target tracking, Expert Systems with Applications, 35, pp.1657-1667.
[124] Tzafestas S.G., Rigatos G.G. (1999) A Simple Robust Sliding-Mode Fuzzy-Logic Controller of the Diagonal Type, Journal of Intelligent and Robotic Systems 26: 353-388.
[125] Utkin V.I. (1992) Sliding mode in control and optimization, Springer Verlag, Berlin Heidelberg.
[126] Utkin V.I., Guldner J. and Shi J. (1999) Sliding mode control in electromechanical systems, CRC press, NY.
[127] Vidyasagar M. (1978) Nonlinear Systems Analysis, Prentice-Hall, Englewood Cliffs, NJ.
[128] Wai R.J. and Su K.H. (2006) Supervisory control for linear piezoelectric ceramic motor drive using genetic algorithm, IEEE Trans. Ind. Electron., 53(2), pp.657-673.
[129] Wang L.X. (1997) A course in fuzzy systems and control, Prentice-Hall international, NJ.
[130] Wei L.S., Fei M.R. (2007) A Real-Time Optimization Grey Prediction Method for Delay Estimation in NCS, Proced. of IEEE Int. Conf. on Control and Automation, Guangzhou, China, pp. 514-517.
[131] Wu W.Y., Chen S.P. (2005) A prediction method using the grey model GMC(1,n) combined with the grey relational analysis: a case study on Internet access population forecast, Applied Mathematics and Computation, Vol. 169, pp. 198-217.
[132] Yamaguchi D., Li G.D., Nagai M. (2005) New Grey Relational Analysis for Finding the Invariable Structure and its Applications, Journal of Grey System, Vol. 8 (2), pp. 167-178.
[133] Yamaguchi D., Li G.D., Nagai M. (2007) Verification of Effectiveness for Grey Relational Analysis Models, Journal of Grey System, Vol. 10 (3), pp. 169-182.
[134] Yan M.T., Cheng T.H. (2009) High accuracy motion control of linear motor drive wire-EDM machines, Int J Adv Manuf Technol, Vol. 40, pp. 918-928.
[135] Yang L., Zhou Y., Yuan J. (2002a) A non-feedback multiphase clock generator, IEEE Intl Symposium on Circuits and Systems ISCAS, 4, pp.IV-389 – IV-392.
[136] Yang L., Zhou Y., Yuan J. (2002b) A non-feedback multiphase clock generator using direct interpolation, 45th Midwest Symposium on Circuits and Systems MWSCAS, 1, pp.I-340 – I-343.
[137] Yang P., Li Q. (2007) Design and Realization of Grey PID Control System for High-Precision Simulated Turntable, Proceed. of IEEE Int. Conf. on Grey Systems and Intelligent Services, Nanjing, China, pp. 881-886.
[138] Zhang D., Chen Y., Ai W., Zhou Z. (2007) Precision motion control of permanent magnet linear motors, Int J Adv Manuf Technol, Vol. 35, pp. 301-308.
[139] Zhang S.H., Chen M.Y., Song Y.X. (2001) The application of grey system theory combined with fuzzy reasoning in high order nonlinear control system, Info-tech and Info-net, Proceedings ICII Volume 4: pp.291-296.
[140] Zhang X., Zhang Q., Xiao L., Su H. (2008) Adaptive Sliding Mode-Like Fuzzy Logic Control for Nonlinear Systems, IEEE The 3rd Int. Conference on Innovative Computing Information and Control (ICICIC'08), doi: 10.1109/ICICIC.2008.119.
[141] Zheng Y., Lewis R.W. (1993) On the optimization concept of grey systems, Appl. Math. Modelling, Vol.17, pp.388-392.