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標題: 實數型結構化基因演算法應用於最佳化濾波和控制設計
Optimal Filtering and Control Designs Using Real Structured Genetic Algorithms
作者: 林久富
Lin, Jiou-Fu
關鍵字: Genetic algorithm;基因演算法;Structured genetic algorithm;Muliobjective optimization;Filter design;Digital filter;H∞ control;結構化基因演算法;多目標最佳化;數位濾波器;濾波器設計;H∞控制
出版社: 電機工程學系所
引用: References [1] J. H. Holland, adaptation in natural and artificial systems. Cambridge, MA: MIT Press, 1975. [2] D. E. Goldberg, Genetic Algorithms in Search Optimization and Machine Learning, Addisions-Wesley, Boston, 1989. [3] K. F. Man, K. S. Tang, and S Kwong, “Genetic algorithm: concepts and applications,” IEEE Transactions on Industrial Electronics, vol. 43, no. 5, pp. 519-534, 1996. [4] M. Gen and R. Cheng, Genetic Algorithms and Engineering Optimization, Wiley, New York, 2000. [5] D. E. Goldberg, “Real-coded genetic algorithms, virtual alphabets and blocking,” Complex Systems, vol. 5, pp. 139-167,1991 [6] D. Dasgupta and D. R. McGregor, “A structured genetic algorithm: the model and the first results,” Univ. strathclyde, U.K., Res, Rep. IKBS-2-91, 1991. [7] D. Dasgupta and D. R. Mcgregor, “Designing Application-Specific Neural Networks using the Structured GeneticAlgorithm,” in International Workshop on Combinations of Genetic Algorithms and Neural Networks, 1992. COGANN-92, 1992, pp. 87-96. [8] D. Dasgupta and D. R. McGregor,” Nonstationary function optimization using the structured genetic algorithm,” Parallel Problem Solving from Nature, vol. 2, pp.145-154, 1992. [9] K. S. Tang, K. F. Man and D. W. Gu, “Structured genetic algorithm for robust control system design,” IEEE Transactions on Industrial Electronics, vol. 43, no. 5, pp. 575-582, 1996. [10] C. C. Lai and C. Y. Chang, “A hierarchical genetic algorithm based approach for image segmentation,” in Proceeding of IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan pp. 1284-1288, 2004. [11] K. S. Tang, K. F. Man, S. Kwong, and Z. F. Liu, “Minimal fuzzy memberships and rules using hierarchical genetic algorithms,” IEEE Transactions on Industrial Electronics, vol. 45, no. 1, pp. 162-169, 1998. [12] A. V. Oppenheim, R. W. Schafer, and J. R. Buck, Discrete-Time Signal Processing, Prentice Hall, New Jersey,1989. [13] MATLAB User's Guide, The MathWorks, Inc. Natick, MA, 1991. [14] D. M. Etter, M. J. Hicks, and K. H. Cho, “Recursive adaptive filter design using an adaptive genetic algorithm,” in Proceeding of IEEE International Conference on Acoustics, Speech, and Signal Processing, Paris, France, pp. 635-638, 1982. [15] S. C. Ng, C. Y. Chung, S. H. Leung, and A. Luk, “Fast convergent genetic search for adaptive IIR filtering,” in Proceeding of IEEE International Conference on Acoustics, Speech, and Signal Processing, Adelaide, SA, pp. 105-108, 1994. [16] K. S. Tang, K. F. Man, S. Kwong, and Z. F. Liu, “Design and optimization of IIR filter structure using hierarchical genetic algorithms,” IEEE Transactions on Industrial Electronics, vol. 45, no. 3, pp. 481-487, 1998. [17] K. Zhou, J. C. Doyle, and K. Glover, Robust and Optimal Control, Prentice-Hall, New Jersey, 1996. [18] M. Jamshidi, R. A. Krohling, L. S. Coelho and P. J. Fleming, Robust Control Systems with Genetic Algorithms, CRC, New York, 2003 [19] B. S. Chen and Y. M. Cheng, “ A structure-specified optimal control design for practical applications: a genetic approach,” IEEE Transactions on Control Systems Technology, vol. 6, no. 6, pp. 707-718, 1998. [20] H. Panagopulos and K. J. Astrom, “PID control design and loop shaping,” Proceeding of IEEE International Conference on Control Applications, pp. 103-108, 1999. [21] C. L. Lin and H. Y. Jan, “Mixed/multiobjective PID control for a linear brushless DC Motor: An evolutionary approach,” Control and Intelligent Systems, vol. 33, no. 2, 2005. [22] K. Ogate, Modern Control Enginering, 3rd ed. Prentice Hall, Englewood Cliffs, 1997. [23] L. L. William, “Tuning proportional-integral-derivative controller for integrator /deadtime processes,” Industrial Engineering Chemical Research, vol. 35, no. 10, pp. 3480-3483, 1996. [24] L. L. Michael and L. L. William, Essentials of Process Control, McGraw-Hill, New York, 1996. [25] A. Kosir and J. F. Tasic, “Genetic algorithms and filtering” in Proc. Int. Conf. GALESIA, Sheffield, UK, pp 343-348, 1995. [26] J. J. Shynk, “Adaptive IIR filtering,” IEEE ASSP Magazine., vol. 6, no. 2, pp. 4-21, 1989. [27] M. Green and D. J. N. Limebeer, Linear Robust Control, Prentic-Hall, New York, 1995.
本篇論文主要發展新的實數型結構化基因演算法(real structured genetic algorithm; RSGA)針對結構和參數最佳化,此演算法結合了實數型基因演算法和結構化基因演算法兩種的優點,並且應用多目標最佳化方法設計數位濾波器及直流無刷線性馬達控制器。
針對無限脈衝響應(infinite impulse response; IIR)數位濾波器的設計,本論文利用濾波器的最小階數及最小帶通、帶止的誤差為目標獲得各種類型的最佳化無限脈衝響應數位濾波器。藉由提出演算法的染色體架構,同時獲得系統結構及參數最佳化。
針對含有模式不確定性之直流無刷線性馬達控制器設計,本論文應用所提出的方法探討 控制設計問題,並且利用時域、頻域的性能規格及控制器結構作為多目標函數以求得滿足強健穩定性和最佳性能的控制器。

This thesis develops a real structured genetic algorithm (RSGA) which combines the advantages of traditional real genetic algorithm (RGA) with the structured genetic algorithm (SGA) and applies it as an optimization strategy for digital filters and controller designs.
For infinite impulse response (IIR) filter design, the approach fulfills all types of filters by minimizing the order of filter and the absolute error of both passband and stopband. With the proposed chromosome scheme, both of the system structures and parametric variables are simultaneously optimized.
As an application, the proposed approach has also been extended to deal with control design problems. For a linear brushless DC motor with modeling uncertainty, we utilize the multiobjective performance specifications that directly related to time, frequency domains and structure as the design objective. A genetic evolutionary approach offers a simple way to design an optimal controller that achieves satisfactory stability robustness.
Simulation studies show that the proposed algorithm converges faster, has better performance and generates a digital filter or a controller which is more effective than those obtained by the traditional approaches.
其他識別: U0005-2108200715124300
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