Suggestions for Future Research

In this thesis, we only discuss with the affine nonlinear systems. The future research is recommended to work toward more general nonlinear systems.

References

[1] K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators, ” Neural Networks, no. 2, pp.

359-366, 1989.

[2] L. X. Wang, Adaptive Fuzzy Systems and Control, Prentice Hall, 1994.

[3] C. H. Wang, W. Y. Wang, T. T. Lee, and P. S. Tseng, “Fuzzy B-spline membership function (BMF) and its applications in fuzzy-neural control, ” IEEE Transactions on Systems Man and Cybernetics, Vol. 25, No. 5, pp. 841-851, May 1995.

[4] W. Y. Wang, Y.H. Chien, and I.H. Li, “ An On-Line Robust and Adaptive T-S Fuzzy-Neural Controller for More General Unknown Systems,”

International Journal of Fuzzy Systems, vol. 10, no. 1, pp. 33-43, 2008.

[5] C. T. Lin, and L. Siana, “An Efficient Human Detection System Using Adaptive Neural Fuzzy Networks,” International Journal of Fuzzy Systems, vol. 10, no. 3, pp. 150-160, 2008.

[6] S. Wu, M. J. Er, and Y. Gao, “A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks,” IEEE Transactions on Fuzzy Systems, Vol. 9, pp.578-594, 2001.

[7] Y. G. Leu, W. Y. Wang, and T. T. Lee, “Robust Adaptive Fuzzy-Neural Controllers for Uncertain Nonlinear Systems,” IEEE Transactions On Robotics and Automation, Vol. 15, No. 5, pp. 805-817, October 1999.

[8] Y. G. Leu, T. T. Lee, and W. Y. Wang, “On-line tuning of fuzzy neural network for adaptive control of nonlinear dynamic systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 27, pp.

1034–1043, Dec. 1997.

[9] T. Y. Kim and J. H. Han, “Edge representation with fuzzy sets in blurred images,” Fuzzy Sets Syst., vol. 100, pp. 77–87, 1998.

[10] Z. Yang, T. Hachino, and T. Tsuji, “Model reduction with time delay combining the least-squares method with the genetic algorithm, ” IEE Proc. Control Theorem Appl., vol. 143, no. 3, 1996.

[11] W.Y. Wang, Y.G. Leu, and T.T. Lee, "Output-feedback control of nonlinear systems using direct adaptive fuzzy-neural controller,” Fuzzy Sets and Systems 140, pp. 341-358, 2003.

[12] W. Y. Wang, M. L. Chan, T. T. Lee, and C. H. Liu, “Recursive Back-stepping Design of Adaptive Fuzzy Controller for Strict Output Feedback Nonlinear Systems,” Asian Journal of Control, Vol. 4, No.3, Sept. 2002.

[13] W. Y. Wang, M. L. Chan, C. C. Hsu, and T. T. Lee, “H∞ Tracking-Based Sliding Mode Control for Uncertain Nonlinear Systems via an Adaptive Fuzzy-Neural Approach,” IEEE Trans. on System Man and Cybernetics-Part B, Vol. 32, No.4, pp.483-492. 2002.

[14] L. X. Wang, “A Supervisory Controller for Fuzzy Control Systems that Guarantees Stability,” IEEE Trans. On Automatic Control, Vol. 39, No. 9, pp.1845-1847, 1994.

[15] Y.G. Leu, T.T. Lee, and W.Y. Wang, "Observer-based Adaptive Fuzzy-Neural Control for Unknown Nonlinear Dynamical Systems,"

IEEE Trans. Syst. Man, Cyber. Part B: Cybernetics, vol. 29, no. 5, pp.583-591, Oct., 1999.

[16] C.H. Wang, H.L. Liu, 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.

[17] K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Networks, no. 2, pp.

359-366, 1989.

[18] Li-Xin Wang, Adaptive Fuzzy Systems and Control, Prentice Hall, 1994.

[19] 感應機向量控制驅動器之PID控制器調適 王進力 淡江大學 電機工 程學系

[20] S Kirkpatrick, CD Gelatt Jr, MP Vecchi, “Optimization by Simulated Annealing,” Science Vol. 220. no. 4598, pp. 671 – 680, 1983

[21] J. Sheild, ‘”Partitioning concurrent VLSI simulation programs onto a multiprocessor by simulated annealing,”IEE PROCEEDINGS, vol.134, Pt.E,NO.1,JANURAY 1987.

[22] K. Kurbel, B. Schneider, and K. Singh, “Solving Optimization Problems by Parallel Recombinative Simulated Annealing on a Parallel Computer-An Application to Standard Cell Placement in VLSI Design, ” IEEE Transactions on Systems, MAN, AND CYBERNETICS-PART B:

CYBERNETICS, vol. 28, on,3, pp. 454-461, JUNE 1998.

[23] M. Gao, and J. Tian, “Path Planning for Mobile Robot Based on Improved Simulatded Annealing Artificial Neural Network,” Third International Conference on Natural Computation, 2007.

[24] P. Lucidarme, and A. Liegeois, “Learning Reactive Neuroconrtollers using Simulated Annealing for Mobile Robots,” Intf. Conference on Intelligent Robots and Systems, Oct. 2003.

[25] Y. Wang, W. Yan, and G. Zhang, “Adaptive Simulated Annealing for the optimal of Electromagnetic Devices,” IEEE Transactions on MAGNETICS, vol. 32, no.3, pp.1214–1217, May 1996.

[26] A. F. Atiya, A. G. Parlos, and L. Ingber, ”A Reinforcement Learning Method Based on Adaptive Simulated Annealing,” IEEE Circuits and Systems , vol. 1, pp.121–124, Dec. 2003.

[27] L. Ingber “Very fast simulated re-annealing,” Mathematical Computer Modelling, vol.12, no.8, pp. 967-973, 1989.

[28] S. J. Ho, L. S. Shu, and S. Y. Ho, “Optimizing Fuzzy Neural Networks for Tuning PID Controllers Using an Orthogonal Simulated Annealing Algorithm OSA,” IEEE Tranactions on Fuzzy Systems,vol.14,no.3,JUNE 2006.

[29] W.K. Ho, C.C.Hang,and J. Zhou,“Self-tuning PID control of a plant with under-dampd response with specifications on gain and phase margins, ”IEEE Trans. Control Syst. Technol .,vol.5, no.4, pp.446-452, Jul.1997.

[30] S. J.Ho, S. Y. Ho, and L.S. Shu, “OSA:orthogonal simulated annealing algorithm and its application to designing mixed H2/ ^H∞ optimal controllers,” IEEE Trans.Syst.,Man,Cybern.A,Syst. Humans, vol. 34, no.

5, pp.588-600,Sep.2004.

[31] S. Y. Ho, S.J.Ho,Y.k.Lin and C.C.W.Chu, “An orthogonal simulated annealing algorithm for large floorplanning problems,” IEEE Trans. Very Large Scale(VLSI)Syst., vol. 12, no. 8, pp.874-876,Aug. 2004.

[32] W. Y. Wnag, and Y. H. Li, “Evolutionary learning of BMF fuzzy-neural networks using a reduced-form genetic algorithm,” IEEE Trans. on Systems, Man and Cybernetics ,part B vol. 33, pp.966-976, 2003.

[33] R.A. Krohling, and J.P. Ray, “Design of optimal disturbance rejection PID controllers using genetic algorithms,” IEEE Trans. Evol.Comput., vol. 5, no. 1, pp.78-82,Feb. 2001.

[34] N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth,A. H. Teller,and E.

Teller,“Equation of state calculations by fast computing machines,”

J.Chem.Phys., vol. 21, no. 6, pp.1087-1092,1953.

[35] T. Renyuan, S.Jianzhong, and L.Yan,”Optimization of electromagnetic devices by using intelligent simulated annealing algorithm,” IEEE Trans.

Magn.,vol.34,no.5,pp.2992-2995,Sep.1998.

[36] H. Szu and R. Hartley, “Fast simulated annealing,”Phys.Lett.,vol.122,pp.

157-162,1987.

[37] S.Yang, J.M.Machado,G.Ni,S.L.Ho,and P.Zhou,”A self-learning simulated annealing algorithm for global optimizations of electromagnetic devices,”IEEE Trans. Magn.,vol.36,no.7,pp.1004-1008,Jul.2000.

[38] M. Zhaung and D. P. Atherton, “Automatic tuning of optimal PID controllers,” Proc. Inst. Elect. Eng.,D, vol. 140, pp. 216-224, 1993.

[39] Y. S. Ahmed, S. Tomonobu, M. Tsukasa, U. Naomitsu, and F.

Toshihisa, ”Fuzzy Unit Commitment Scheduling Using Absolutely Stochastic Simulated Annealing,” IEEE Transactions on power systems, vol. 21, no. 2, May 2006.

[40] T. Satoh, and K. Nara, “Maintenance scheduling by using simulated annealing method [for power plants ,” IEEE Trans. Power Syst., vol. 6, no.2, pp. 850-857, May 1991.

[41] S. Y. W. Wang, “An enhanced simulated annealing approach to unit commitment,” Elect. Power Energy Syst., vol. 20, pp. 359-368, May 1998.

[42] F. Zhuang and F. D. Galiana, ”Unit commitment by simulated annealing,”

IEEE Trans. Power Syst., vol. 5, no.1, pp. 311-318, Feb. 1990.

[43] A. H. Mantawy, Y. L. Abel-Mogid, and S.Z. Selim, “A simulated annealing algorithm for unit commitment,” IEEE Trans. Power Syst., vol.

13, no.1, pp. 197-204, Feb. 1998.

[44] C. P. Cheng, C. W. Liu and C.C. Liu,” Unit commitment by simulated annealing algorithms,” Elect. Power Energy Syst., Vol. 24, PP. 149 –158, 2000.

[45] M. M. El-Saadawi, M. A. Tantawi, and E. Tawfik, “A fuzzy optimization based approach to large scale thermal unit commitment,” Elect. Power Syst. Res.,Vol. 72, pp.245–252, 2004.

[46] D. W. Jeffrey,” A simulated annealing algorithm for optimizing RF power efficiency in coupled-cavity traveling-wave tubes,” IEEE Transactions onelectron devices, Vol. 44, No. 12, Dec. 1997.

[47] S. Kirkpatrick, C. D. Gelatt Jr.,and M. P. Vecchi,”Optimization by simulated annealing,”Science, Vol.220, pp. 671-680, May 1983.

[48] E. Aarts and J. Korst, Simulated Annealing and Boltzmann Machines.New York: Wiley, 1989, pp. 88-91.

[49] A. Isidori, Nonlinear Control System. New York: Springer-Verlag, 1989.

[50] M. Krstic, I. Kanellakopoulos, and P.V.Kokotovic, Nonlinear and Adaptive Control Design. New York: Wiley, 1995.

[51] I. Kanellakopoulos, P. V. Kokotovic, and A. S. Morse, “Systematic design of adaptive controller for feedback linearizable system,” IEEE Transactions Automat. Contr., vol. 36, pp. 1241–1253, 1991.

[52] C. Kwan and F. L. Lewis, “Robust backstepping control of nonlinear systems using neural networks,” IEEE Transactions Syst., Man, Cybern.

A, vol. 30, pp. 753–765, 2000.

[53] T. Knohl and H. Unbehauen, “ANNNAC—extension of adaptive backstepping algorithm with artificial neural networks,” Inst. Elect. Eng.

Proc. Contr. Theory Appl., vol. 147, pp. 177–183, 2000.

[54] C. M. Kwan and F. L. Lewis, “Robust backstepping control of induction motors using neural networks,” IEEE Transactions Neural Networks, vol.

11, pp. 1178–1187, 2000.

[55] J. Y. Choi and J. A. Farrell, “Adaptive observer backstepping control using neural networks,” IEEE Transactions Neural Networks, vol. 12, pp.

1103–1112, 2001.

[56] Y. Zhang, P.Y. Peng, and Z.P. Jiang, “Stable Neural Controller Design for Unknown Nonlinear Systems Using Backstepping,” IEEE Transactions on Neural Networks, vol. 11, no. 6, November 2000.

[57] C.F. Hsu, C.M. Lin, and T.T. Lee, “Wavelet Adaptive Backstepping Control for a Class of Nonlinear Systems,” IEEE Transactions on Neural Networks, vol. 17, no. 5, September 2006.

[58] S. S. Sastry and A. Isidori, “Adaptive control of linearization systems,”

IEEE Transactions Automat. Contr., vol. 34, pp. 1123–1131, 1989.

[59] R. Marino and P. Tomei, “Globally adaptive output-feedback control of nonlinear systems, part I: Linear parameterization,” IEEE Transactions Automat. Contr., vol. 38, pp. 17–32, Jan. 1993.

[60] R. Marino and P. Tomei, “Globally adaptive output-feedback control of nonlinear systems, part II: Nonlinear parameterization,” IEEE Transactions Automat. Contr., vol. 38, pp. 33–48, Jan. 1993.

[61] I. Kanellakopoulos, P. V. Kokotovic, and A. S. Morse, “Systematic design of adaptive controllers for feedback linearizable systems,” IEEE Transactions Automat. Contr., vol. 36, pp. 1241–1253, Nov. 1991.

[62] W. Y. Wang, M. L. Chan, T. T. Lee, and C. H. Liu, “Recursive

Back-stepping Design of Adaptive Fuzzy Controller for Strict Output Feedback Nonlinear Systems,” Asian Journal of Control, Vol. 4, No.3, Sept. 2002.

[63] L. X. Wang, “A Supervisory Controller for Fuzzy Control Systems that Guarantees Stability,” IEEE Trans. On Automatic Control, Vol. 39, No. 9, pp.1845-1847, 1994.

[64] Y.G. Leu, T.T. Lee, and W.Y. Wang, "Observer-based Adaptive Fuzzy-Neural Control for Unknown Nonlinear Dynamical Systems,"

IEEE Transactions on Systems, Man, and Cybernetics. Part B:

Cybernetics, vol. 29, no. 5, pp.583-591, Oct., 1999.

[65] C.H. Wang, H.L. Liu, 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.

[66] W. Y. Wang, C. Y. Cheng, and Y. G. Leu, “An online GA-based output-feedback direct adaptive fuzzy-neural controller for nonlinear systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 34, pp.334-345, February. 2004.

[67] 結合基因演算法和模擬退火法在機組排程決策之應用 張振松 南開技 術學院資管系

[68] J. E. Slotine and W. Li, Applied Nonlinear Control. Englewood Cliffs, NJ:

Prentice-Hall, Inc., 1991.

[69] S. Sastry and M. Bodson, Adaptive Control: Stability, Convergence, and Robustness, Englewood Cliffs, NJ: Prentice-Hall, 1989.

[70] Y. Tang, M. Tomizuka, G. Guerrero, and G. Montemayor, “Decentralized robust control of mechanical systems,” IEEE Trans. Automat.Contr.,vol.

45, pp. 771-776, Apr.2000.