Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/8398
標題: 以實數結構型基因演算法實現微型直流無刷馬達控制器
Implementation of Micro DC Brushless Motor Controller based on Real Structured Genetic Algorithm
作者: 蔡志瑋
Tsai, Chih-Wei
關鍵字: Genetic algorithms;基因演算法;Micro brushless DC motor feedback system;DSP;SinCos encoder;微型直流無刷馬達;數位訊號處理器;光學弦波編
出版社: 電機工程學系所
引用: 1. Liaw C.M., Shue R.Y., Chen H.C., and Chen S.C., “Development of a Linear Brushless DC Motor Drive with Robust Position Control,” Proc. Inst. Elect. Eng. Elect. Power Applicat., vol. 148, pp.111-118, 2001. 2. Fidel F.B., Aurelio G.C., and Roberto F., “Model-Based Loss Minimization for DC and AC Vector-Controlled Motors Including Core Saturation,” IEEE Trans. Ind. Elect., vol. 36, pp. 755-763, 2000. 3. Butler H., Honderd G., and Amerongen J. van, “Model Reference Adaptive Control of a Direct-Drive DC Motor,” IEEE Contr. Systems Magazine, vol. 9, pp. 80-84, 1989. 4. Kazmierkowski M.P., and Malesani L., “Current Control Techniques for Three-Phase Voltage-Source PWM Converter: A Survey,” IEEE Trans. Ind. Elect, vol. 45, pp. 691-703, 1998. 5. Jouve D., Rognon J.P., and Roye D., “Effective Current and Speed Controllers for Permanent Magnet Machines: A Survey,” in Conf. Applied Power Electronics and Exposition, pp. 384-393, 1990. 6. Mammano R.A., and Galvin J.J., “Driving Three-Phase Brushless DC Motors-a New Low Loss Linear Solution,” in Conf. Proc. Applied Power Elect. Conf. and Exposition, pp. 75-80, 1989. 7. Iwasaki M., Matusi N., “Robust Speed Control of IM with Torque Feedforward Control,” IEEE Trans. Ind. Elect., vol. 40, pp. 553-560, 1993. 8. Baik I.C., Kim K.H., and Youn M.J., “Robust Nonlinear Speed Control of PM Synchronous Motor Using Boundary Layer Integral Sliding Mode Control Technique,” IEEE Trans. Contr. Systems Technology, vol. 8, pp. 47-54, 2000. 9. Low K.S., and Hualiang Z., “Robust Model Predictive Control and Observer for Direct Drive Application,” IEEE Trans. Power Elect., vol. 15, pp. 1018-1028, 2000. 10. Holland J.H.: ‘Adaptation in Natural and Artificial Systems' (Cambridge, MIT Press, MA, 1975). 11. Gen M. and Cheng R.: ‘Genetic Algorithms and Engineering Optimization' (Wiley, New York, 2000). 12. Goldberg D.E., “Real-Coded Genetic Algorithms, Virtual Alphabets and Blocking,” Complex Systems, vol. 5, pp. 139-167, 1991. 13. Dasgupta D. and McGregor D. R., “A Structured Genetic Algorithm: the Model and the First Results,” Univ. strathclyde, U.K., Res, Rep. IKBS-2-91, 1991. 14. Lai C.C., and Chang C.Y., “A Hierarchical Genetic Algorithm Based Approach for Image Segmentation,” in Proc. IEEE Int. Conf. on Networking, Sensing and Control, Taipei, Taiwan, pp. 1284-1288, 2004. 15. Chen B.S., and Cheng Y.M., “A Structure-Specified Optimal Control Design for Practical Applications: A Genetic Approach,” IEEE Trans. Control System Technology, 6, (6), pp. 707-718, 1998. 16. Kang K.S., Man K.F., and Gu D.W., “Structured genetic algorithm for robust control system design”, IEEE Trans. Ind. Electronics, vol. 43, no. 5, pp. 575-582, 1996. 17. Cheung N. C. “An Innovative Method to Increase the Resolution of Optical Encoders in Motion Servo Systems,” IEEE 1999 International Conference on Power Electronics and Drive Systems, PEDS''99, vol. 2, pp. 797-802, Hong Kong, 1999. 18. Lin C. L. and Jan H. Y. “Mixed/Multiobjective PID Control for a Linear Brushless DC Motor: An Evolutionary Approach,” Control and Intelligent Systems, vol. 33, no. 2, 2005. 19. Jan H. Y, Wu M. T., Chen C. N., Lin, T. W. and Lin C. L. “Implement of Intelligent Servo Controllers for Micro Brushless DC Motors” Proceedings of Automation 2005 The Eighth International Conference on Automation Technology Conference Taichung, Taiwan, May 5-6, 2005.
摘要: 
本論文提出以實數結構型基因演算法(RSGA)實現的微型直流無刷馬達控制器。此演算法同時結合了實數型基因演算法(RGA)與結構型基因演算法(SGA)的優點,並且加入了一區間變化調整法與以Butterworth濾波器為基礎的動態交配及突變機率調整機制來強化RSGA的整體性能。在實驗平台上,本論文將RSGA設計出的最佳控制器嵌入TMS320F240的數位訊號處理器(DSP)。其中,控制器的性能需求需滿足強健穩定,低功率消耗,及良好的位置追蹤性能。硬體上加入了超高解析度的光學弦波編碼器配合角度內插來達到精密位置控制。本論文使用基本邏輯元件所組成的內插電路來有效地達到內插的目的。
本論文以數值模擬和硬體實作併行來驗證方法的可行性,實作的平台包含自行設計的馬達驅動電路、微型DC無刷馬達、及光學弦波編碼器。模擬與實作的結果都證明本論文提出的演算法相較於傳統演算法有較佳的效能及更快的收斂速度。

This thesis presents the realization of a micro brushless DC motor (MBDCM) feedback system based on a real ordered genetic algorithm (RSGA) approach, which combines the advantages of conventional real genetic algorithms (RSGA) and structured genetic algorithms (SGA), for determining an optimal controller. A dynamic crossover and mutation probability adjusting method based on Butterworth filters was proposed to improve the overall searching performance of the RSGA. The optimal MBDCM controller is realized on the TMS320F240 digital signal processing (DSP) development board to achieve hardware compactness. Control design requirements include robust stability, control consumption and tracking performance. A SinCos encoder with a line drive of 128 sin/cos signals per revolution was implemented to achieve and position control. SinCos encoders have the inherent advantage of providing high resolutions via signal interpolation. The interpolation method inherited is simple yet effective, based on logic devices.
To verify the effectiveness of the proposed methodology, simulations are performed on MATLAB and an experimental platform involving a motor driver, a micro brushless DC motor, and a SinCos encoder was built to verify the applicability of the proposed method in practical situations.
Simulation studies and experimental results show that the proposed algorithm converges faster, excels in performance, and yields better performance for a motor feedback controller in comparison to traditional approaches.
URI: http://hdl.handle.net/11455/8398
其他識別: U0005-3007200816001900
Appears in Collections:電機工程學系所

Show full item record
 

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.