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研究生:蔡志瑋
研究生(外文):Chih-Wei Tsai
論文名稱:以實數結構型基因演算法實現微型直流無刷馬達控制器
論文名稱(外文):Implementation of Micro DC Brushless Motor Controller based on Real Structured Genetic Algorithm
指導教授:林俊良林俊良引用關係
學位類別:碩士
校院名稱:國立中興大學
系所名稱:電機工程學系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:96
語文別:英文
論文頁數:38
中文關鍵詞:基因演算法微型直流無刷馬達數位訊號處理器光學弦波編
外文關鍵詞:Genetic algorithmsMicro brushless DC motor feedback systemDSPSinCos encoder
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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.
誌謝 (i)
中文摘要 (ii)
Abstract (iii)
Contents (iv)
List of Figures (vi)
List of Tables (viii)
Chapter 1 Introduction (1)
Chapter 2 Genetic Algorithms (4)
2.1 SGA (4)
2.2 RSGA (4)
2.3 Structured Genetic Mapping (5)
2.4 Genetic Operations (7)
2.5 Dynamic Mutation and Crossover Probability Adjustment based on Butterworth Filter (11)
Chapter 3 Motor Feedback Controller Design (15)
3.1 Control Design (15)
3.2 Frequency domain specifications (15)
3.3 Weighting functions (17)
3.4 Time domain specifications (18)
3.5 Multiple-objective optimization (18)
Chapter 4 Interpolation of Motor Position Signal (20)
Chapter 5 Hardware Implementation (23)
5.1 DSP Chipset (23)
5.2 Hall Signal Receiver (25)
5.3 MBDCM Driver (26)
5.4 MBDCM (26)
5.5 Sine/Cosine Encoder and Sinusoidal Signal Receiver (27)
Chapter 6 Experimental Results (28)
6.1 Simulation Results (28)
6.2 Hardware Implementation (34)
Chapter 7 Conclusions (36)
References (37)
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