分散式控制在大規模的系統理論中是一項非常重要的議題，其中，對於類比系統所做的分散式預先數位控制器在這一篇論文中做了一些推導和研究。此外將線性二次調節器的設計方法考慮進來，透過這個方法使得分散式控制器依然擁有集中式控制所期望的效能，並且對於原先控制器在硬體實現上的複雜度也加以改進。而為了更增進系統的效能，我們可以利用進化演算法來調變我們所要設計的追蹤器，進而達到改善系統的效能。各章節使用相關的例子，並比較系統改善前後的情況。

　　Decentralized control is an important idea in large-scale system’s theory. In this study, the LQR design methodology is considered so that the decentralized controller preserves the desired performance of the centralized controlled system and the complexity of hardware implementation of the controller can be significantly improved. Then, a decentralized prediction -based digital controller for analog systems is formulated and studied. In order to improve the performance, the evolutionary programming (EP) is utilized to tune the decentralized observer-based tracker. Some examples are presented to illustrate the developed design methodology.

[1] Fogel, D. B., “An overview of evolutionary programming,” Evolutionary Algorithms, Davis, L. D., Jong, K. D., Vose, M. D. and Whitley, L. D., Eds, New York: Springer-Verlag, IMA-vol. 11, pp. 89-109, 1999.

[2] Fogel, D. B., Evolutionary Computation: The Fossil Record, New Jersey: IEEE Press, 1998.

[3] Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Reading, MA: Addison-Wesley, pp. 59-88, 1989.

[4] Guo, S. M., Shieh, L. S., Chen, G. and Lin, C. F., “Effective chaotic orbit tracker: a prediction-based digital redesign approach,” IEEE Transaction on Circuits and Systems–I, Fundamental Theory and Applications, vol. 47, no. 11, pp. 1557-1570 , November 2000.

[5] Guo, S. M., “Effective Digital Chaotic Orbit Tracker and Kalman Filtering for Nonlinear Stochastic Hybrid Systems,” Ph. D. Dissertation by University of Houston, May, 2000.

[6] Hammersley, J. M. and Handscomb, D. C., Monte Carlo Methods, London: Methuen, 1964.

[7] Halton, J. H., “On the efficiency of certain quasi-random sequences of points in evaluating multidimensional integrals,” Numerische Mathematik, vol. 2, pp. 84-90, 1960 with Corrigenda on p. 196.

[8] Iftar, A. and Ozguner, U., “Local LQG/LTR controller design for decentralized systems,” IEEE Transactions on Automatic Control, vol. AC-32, pp. 926-930, 1987.

[9] Krishnakumar, K. and Goldberg, D. E., “Control system optimization using genetic algorithm,” Journal of Guidance, Control, and Dynamics, vol. 15, pp. 735-740, 1992.

[10] Kramer, S. C. and Martin, R. C. IV., “Direct optimization of gain scheduled controllers via genetic algorithms,” Journal of Guidance, Control, and Dynamics, vol. 19, pp. 636-642, 1996.

[11] Lewis, F. L. and Syrmos, V. L., Optimal Control, 2nd Edition, New York: Wiley, 1986.

[12] Man, K. F., Tang, K. S. and Kwang, S., Genetic Algorithms: Concepts and Design, London: Springer-Verlag, 1999.

[13] Michalewicz, Z., Genetic Algorithm + Data Structure = Evolution Programs, New York: Springer-Verlag, 1996.

[14] Ozguner, U., Khorrami, F. and Iftar, A., “Two controller design approaches for decentralized systems,” in proceedings of the AIAA Guidance, Navigation, and Control Conference, pp. 337-344, Minneapolis, MN, 1988.

[15] Polak, E. and Wardi, Y. Y., “Nondifferentiable optimization algorithm for designing control systems having singular value inequalities,” Automatica, vol. 18, pp. 267-283, 1982.

[16] Rafee N., Chen T. and Malik, O. P., “A technique for optimal digital redesign of analog controllers,” IEEE Transactions on Control Systems Technology, vol. 5, no. 1, pp. 89-99, 1997.

[17] Shieh, L. S., Wang, W. M. and Appu Panicker, M. K., “Design of PAM and PWM digital controllers for cascaded systems,” ISA Transactions, vol. 37, pp. 201-213, 1998.