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研究生:林旻局
研究生(外文):Min-Chu Lin
論文名稱:切換式磁阻馬達驅動系統之適應性滑動自組織遞迴小腦模型控制器設計
論文名稱(外文):Design of Adaptive Sliding Self-organizing Recurrent Cerebellar Model Articulation Controller for Switched Reluctance Motor Drive Systems
指導教授:王順源王順源引用關係
口試委員:周仁祥曾傳蘆黃仲欽宋文財
口試日期:2016-07-22
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電機工程研究所
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:104
中文關鍵詞:切換式磁阻馬達、小腦模型控制器、自組織、遞迴、直接轉矩控制
外文關鍵詞:Switched reluctance motorCerebellar model articulation controllerSelf-organizingRecurrentDirect torque control
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本研究結合小腦模型控制器、自組織類神經網路及遞迴類神經網路架構,來設計適應性滑動自組織遞迴小腦模型控制器(adaptive sliding self-organizing recurrent cerebellar model articulation controller, ASSORC)。新穎的設計觀點是採用自組織類神經網路、遞迴類神經網路架構與適應性法則,使原本靜態且聯想記憶體層數固定的小腦模型控制器具有動態記憶性能與參數修正能力。並且聯想記憶體層數依據層數決策機制進行增加或減少,以得到更佳的控制性能。所提出的控制器內含自組織遞迴小腦模型控制器(self-organizing recurrent cerebellar model articulation controller, SORCMAC)及改良型補償控制器。ASSORC以滑動面之信號作為輸入,再將其引入自組織遞迴小腦模型控制器中,且以改良型補償控制器來補償理想控制器和自組織遞迴小腦模型控制器間的誤差。另外,本研究以Lyapunov定理推導小腦模型控制器權重值、遞迴權重值、高斯函數中心點及高斯函數標準差之適應性法則,以確保系統的穩定度。
為了驗證所設計控制器之性能及可行性,本研究將所設計的適應性滑動自組織遞迴小腦模型控制器植入切換式磁阻馬達直接轉矩驅動系統中作為速度控制器。經由模擬及實驗結果顯示,馬達運轉在負載轉矩為1 Nm,轉速命令為100 rpm、800 rpm、1600 rpm及±800 rpm下,其暫態最大誤差皆在20 rpm以下,穩態誤差皆在±3 rpm以內,且聯想記憶體層數會根據判斷式而調整。轉速命令為800 rpm時,傳統小腦模型控制器、高斯小腦模型控制器與ASSORC三者比較,均方根誤差值分別為2.05 rpm、1.65 rpm及0.97 rpm。無載轉速命令800 rpm且在3秒時加載1 Nm,傳統小腦模型控制器、高斯小腦模型控制器與ASSORC,均方根誤差值則分別為3.67 rpm、2.36 rpm及1.28 rpm;且所提出的ASSORC較快回復至轉速命令。從上述結果得知,所提出之ASSORC於各種轉速運轉下的均方根誤差值均較小,能提供更佳的速度響應,且具有較佳的強健性。
This study used a cerebellar model articulation controller (CMAC), self-organizing neural networks, and recurrent network neural architecture to design an adaptive sliding self-organizing recurrent cerebellar model articulation controller (ASSORC). A novel design approach was employed to implement this controller, using a self-organizing neural network, recurrent neural network structures, and adaptive laws, enabling the resulting static CMAC with fixed association memory layers to achieve dynamic memory capability and parameter tuning functions. Furthermore, association memory layers were generated or eliminated on the basis of the decision-making mechanism of the layers, consequently achieving high control performance. The proposed controller comprises a self-organizing recurrent cerebellar model articulation controller (SORCMAC) and an improved compensating controller. A sliding surface signal is used as the input to the ASSORC. In addition, the improved compensating controller was designed to dispel the errors between an ideal controller and the SORCMAC. Moreover, the adaptive laws of CMAC weights, recurrent weights, the Gaussian function mean parameter, and Gaussian function standard were determined using an analytical method on the basis of a Lyapunov function to ensure the stability of the control system.
To verify the performance and effectiveness of the proposed ASSORC system, we used the proposed ASSORC scheme for controlling the direct torque control drive system of a switched reluctance motor (SRM). The simulation and experimental results showed that when the drive system was operated at 100, 800, 1600, and ±800 rpm, the load torque of the motor was 1 Nm. The maximum speed error in the transient response was lower than 20 rpm (in the steady state, it was within ±3 rpm), and the SRM exhibited a desirable torque response in a wide speed range. Furthermore, associative memory layers were adjusted on the basis of the decision-making mechanism of the layers. When the speed command was set to 800 rpm, the root mean square error (RMSE) was used for a performance comparison of the speed responses among the conventional CMAC, GCMAC, and ASSORC; the RMSE values were 2.05, 16.5, and 0.97 rpm, respectively. When the speed command was set to 800 rpm, which was not accompanied by a starting-load operation or an additional 1-Nm load at the third second, the RMSE values were 3.67, 2.36, and 1.28 rpm, respectively. In addition, the ASSORC recovered quickly to the speed command. These results demonstrated that the RMSE value of ASSORC was lower than that of either CMAC or GCMAC at each speed. Furthermore, the proposed controller exhibited enhanced robustness to external disturbances.
摘 要 i
ABSTRACT iii
誌 謝 v
目 錄 vi
表目錄 ix
圖目錄 x
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 文獻探討 3
1.4 大綱 7
第二章 切換式磁阻馬達 8
2.1 前言 8
2.2 切換式磁阻馬達基本構造與特性 8
2.3 切換式磁阻馬達數學模型 12
2.3.1 電壓與電流方程式 12
2.3.2 轉矩方程式 14
2.4 切換式磁阻馬達驅動原理 16
2.5 轉換器電路分析 22
2.6 切換式磁阻馬達參數測量 24
2.7 本章結論 28
第三章 小腦模型控制器理論 29
3.1 前言 29
3.2 小腦模型控制器之架構 30
3.3 小腦模型控制器之工作原理 31
3.3.1 小腦模型控制器之回想階段 32
3.3.2 小腦模型控制器之學習階段 36
3.4 高斯小腦模型控制器 38
3.5 自組織遞迴小腦模型控制器 39
3.6 函數學習比較 42
3.7 結論 45
第四章 適應性滑動自組織遞迴小腦模型控制器設計 46
4.1 前言 46
4.2 適應性滑動自組織遞迴小腦模型控制器之架構 47
4.3 自組織遞迴小腦模型控制器設計 48
4.4 補償控制器之設計 51
4.5 改良型補償控制器設計 62
4.6 切換式磁阻馬達直接轉矩控制系統 63
4.6.1 轉矩分配函數 64
4.6.2 實際轉矩計算 67
4.6.3 換相機制與轉矩控制器 68
4.6.4 電壓脈波寬度調變與轉換器 69
4.7 模擬結果與分析 70
4.8 本章結論 85
第五章 切換式磁阻馬達控制系統實驗 86
5.1 前言 86
5.2 功率級轉換器與驅動電路 88
5.3 電流量測與過電流保護電路 90
5.4 實驗內容 91
5.5 實驗結果與分析 92
5.6 本章結論 126
第六章 結論與未來研究方向 127
6.1 結論 127
6.2 未來研究方向 128
參考文獻 129
附錄A 134
附錄B 135
附錄C 136
符號彙編 137
作者簡介 143
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