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研究生:林文彬
研究生(外文):W.B.Lin
論文名稱:基於粒子群最佳化演算法之奈米定位控制系統設計
論文名稱(外文):Design of Nano-Positioning Control Systems Based on Particle Swarm Optimization
指導教授:余國瑞余國瑞引用關係
指導教授(外文):Gwo-Ruey Yu
學位類別:碩士
校院名稱:國立宜蘭大學
系所名稱:電機工程學系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:80
中文關鍵詞:粒子群最佳化演算法平行分佈補償法線性矩陣不等式
外文關鍵詞: PSO (Particle Swarm Optimization) PDC (Parallel Distributed Compensation) LMI(Linear Matrix Inequality)
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  • 被引用被引用:1
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本篇論文使用粒子群最佳化演算法,設計奈米定位系統的最佳模糊控制器。首先,利用Bouc-Wen模型去描述壓電致動器的非線性磁滯曲線;然後應用Takagi-Sugeno模糊模型去近似整個非線性的奈米定位系統;並以平行分佈補償法去設計壓電致動系統的控制器,再使用線性矩陣不等式去驗證模糊控制器的穩定性。最後,應用粒子群最佳化演算法,搜尋模糊控制器的最佳狀態迴授增益。
This paper presents the particle swarm optimization (PSO) based approach to design fuzzy control system is proposed for nano-positioning system. First, the Bouc-Wen model describes the non-linear hysteresis curve of a piezoelectric actuator. Then, the Takagi-Sugeno (T-S) fuzzy model is applied to approximate the non-linear nano-positioning system. Last, the parallel distributed compensation (PDC) is designed to control the piezoelectric actuator, and use linear matrix inequality (LMI) approach to check the stability of fuzzy controller. The parameters of the fuzzy control system are determined by the particle swarm optimization approach to find the best state feedback gain.
目錄

中文摘要 i
英文摘要 ii
目錄 iii
圖目錄 v
表目錄 vii
第一章 緒論
1.1 前言 1
1.2 文獻回顧 3
1.3 內容章節 5
第二章 適應性PI模糊邏輯控制
2.1 壓電模型 6
2.2.1 PID控制器 9
2.2.2 模糊理論 10
2.3.1 PI控制器 15
2.3.2 PI-Like模糊邏輯控制器 17
2.3.3 適應性PI模糊邏輯控制器 21
第三章 應用於基因演算法與粒子群最佳化演算法的PID控制器
3.1.1 基因演算法理論 29
3.1.2 基因演算流程 29
3.2.1 粒子群最佳化演算法(Particle Swarm Optimization, PSO) 34
3.2.2 PSO運算流程 36
3.2.3 基因演算法與粒子群最佳化演算法的比較 37
3.3.1 RGAs PID Controller 39
3.3.2 PSO-Based PID Controller 43
第四章 基於粒子群最佳化演算法之奈米定位控制器
4.1 T-S模糊模型 48
4.2 平行分佈補償(Parallel Distributed Compensation, PDC) 52
4.3 PSO Controller 53
第五章 結論 62
附錄 實驗設備 63
高速功率放大器 63
奈米定位平台 68
資料擷取卡 71
端子平台CB-68LP 73
實驗步驟 75
參考文獻 78
圖目錄

2.1 Bouc-Wen model的磁滯曲線圖 7
2.2 改變alpha參數的磁滯外型 7
2.3 改變beta參數的磁滯外型 8
2.4 改變gamma參數的磁滯外型 8
2.5 PID控制的基本架構 10
2.6 Step response of PI controller 16
2.7 Position of PI controller in step reference with adding disturbance 16
2.8 模糊PID控制器的簡單結構圖 17
2.9 Block diagram of PI-like FLC 18
2.10 Membership function of and 18
2.11 Membership function of 19
2.12 Step response of PI-like FLC 20
2.13 Position of PI-like FLC in step reference with adding disturbance 20
2.14 Block diagram of adaptive PI-FLC 22
2.15 Input membership functions of adaptive PI-FLC 23
2.16 Output membership function of adaptive PI-FLC 23
2.17 Step response of adaptive PI-FLC 24
2.18 The value of adaptive PI-FLC 24
2.19 The diagrams of adaptive PI-FLC 25
2.20 Position of adaptive PI-FLC in step reference with adding disturbance 25
2.21 The diagram of adaptive PI-FLC 26
2.22 The diagrams of adaptive PI-FLC 26
3.1 基因演算法流程圖 33
3.2 PSO個體最佳值與群體最佳值的向量示意圖 35
3.3 粒子群最佳化PSO流程圖 38
3.4 RGAs PID Controller架構圖 40
3.5 The generation of the RGAs PID Controller 40
3.6 Step response of RGAs PID Controller 41
3.7 Control Voltage of RGAs PID Controller 41
3.8 RGAs PID控制器加干擾時的性能圖 42
3.9 RGAs PID控制器加干擾時的控制電壓 42
3.10 PSO Based PID Controller架構圖 44
3.11 Step reponse of PSO Based PID Controller 44
3.12 Control Voltage of PSO-Based PID Controller 45
3.13 PSO-Based PID控制器加干擾的性能圖 45
3.14 PSO-Based PID控制器加干擾時的控制電壓 46
4.1 (a) 響應圖、(b) 的響應圖 51
4.2 T-S模糊模型的歸屬函數 51
4.3 實驗磁滯曲線與T-S 模糊模型近似磁滯曲線圖 52
4.4 PSO-IW與PSO-CF追基本步階 的性能比較 57
4.5 PSO-CF控制器追基本步階 性能 58
4.6 PSO-CF控制器追基本步階 的控制電壓 58
4.7 PSO-CF控制器加干擾的性能圖 59
4.8 PSO-CF控制器加干擾時的控制電壓 59
4.9 PSO-CF控制器追階梯波的性能 60
4.10 PSO-CF控制器追階梯波的控制電壓 60


表目錄

2.1 Bouc-Wen 模型的參數 9
2.2 PI-Like 模糊控制器的模糊規則表 19
2.3 適應性PI模糊控制器的尺度因子 27
2.4 控制器追基本步階的性能表 28
2.5 加干擾時的性能指標 28
3.1 PSO參數及經驗的設定說明表 36
3.2 RGAs PID 控制器的參數 43
3.3 PSO-Based PID 控制器的參數 46
3.4 控制器追基本步階的性能表 47
3.5 加干擾時的性能指標 47
4.1 PSO-IW控制器的參數 55
4.2 PSO-CF控制器的參數 56
4.3 PSO-IW與PSO-CF的基本性能比較 61
4.4 PSO-IW與PSO-CF追基本步階 的性能比較 61
4.5 PSO-IW與PSO-CF在加干擾時的性能比較 61
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