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研究生:朱明輝
研究生(外文):Ming-Huei Chu
論文名稱:直接類神經網路之適應性控制器在直流馬達及液壓伺服系統之應用
論文名稱(外文):Direct Neural Controller Applied to DC Motor and Electro-Hydraulic Servo System
指導教授:康淵康淵引用關係張義鋒
指導教授(外文):Yuan KangYih-Fong Chang
學位類別:博士
校院名稱:中原大學
系所名稱:機械工程學系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:130
中文關鍵詞:直接類神經網路直流馬達液壓伺服系統
外文關鍵詞:direct neural networkdc motorhydraulic servo systems
相關次數:
  • 被引用被引用:2
  • 點閱點閱:153
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:3
本文研究特定學習架構之直接適應性類神經網路控制器之原理及應用,並應用 適應法則於特定學習架構近似輸出層之倒傳遞誤差項,以訓練類神經網路,並且使用雙曲正切函數做為類神經網路活化函數,使類神經網路控制器具有正及負之輸出,將其應用於具有參考模型之控制系統構成直接類神經網路模型追隨適應控制器,若應用於無參考模型之控制系統則構成直接類神經網路自調節適應控制器。
本文提出之直接類神經網路自調節適應控制器應用於高精度之直流馬達速度,位置控制系統及具有外力負載之電液伺服閥控液壓位置控制系統,數學模擬及實驗證明可以經預先訓練得到較佳之神經鍵初使值,再經由線上學習得到更好的調整,可使運動控制系統獲得精確且快速穩定之反應。同時也將線上訓練類神經網路模型追隨控制器應用於電液比例閥控可變排量軸向型柱塞泵斜板偏轉角控制,系統具有快速的學習能力,能強化系統適應性及強健性,在負載變動及不同偏轉角命令下,使斜板偏轉角反應對參考模型有快速之追隨能力。
A direct adaptive neural network controller with specialized learning architecture and its applications are studied in this research.
The adaptation law is applied to the direct neural controller for approximating the term of the output layer so that the back propagation iteration can be executed. An arctangent function is applied to be the activation function so that the neural network controller output has negative or positive value.
The proposed direct adaptive neural network controllers without reference model are applied to speed and position control of DC motors and position control of Electro-hydraulic servo systems. Simulation shows that a previous training of the neural controller can learn the approximate behavior of the plant and create better initial weights, then followed by on-line trained to fine-tune the network in the operating process. Experiment shows stable and fast responses can be achieved.
The same controller with reference model is applied to control the swash plate angle of a variable displacement axial piston pump, which is nonlinear, time variant and with load disturbance. Mathematic simulation and experiment show that the direct adaptive neural network controllers enhance adaptability and robustness of the system and improve the pump performance.
目 錄
第一章導論 1
1-1 研究背景 1
1-2 文獻回顧 3
1-3 研究內容 10
第二章類神經網路控制器與 適應法則 13
2-1類神經網路控制器 15
2-2 類神經網路控制器與 適應法則 16
第三章直接類神經網路自調節適應控制器應用於直流伺服馬達速度調節 20
3-1控制系統描述 20
3-2動態模擬 21
3-3實驗結果 23
3-4結論 25
第四章比例微分與類神經網路混合控制器與直接類神經網路自調節適應控制器應用於直流伺服馬達位置控制之比較 26
4-1控制系統描述 27
4-2動態模擬 29
4-3實驗結果 31
4-4結論 33
第五章直接類神經網路自調節適應控制器應用於電液伺服閥控液壓系統位置控制 35
5-1控制系統描述 35
5-2動態模擬 38
5-3實驗結果 42
5-4結論 43
第六章直接類神經網路模型追隨適應控制器應用於可變排量軸向型柱塞泵 44
6-1導論 44
6-2電液比例閥可變排量泵之建模 46
6-3電液比例閥建模 49
6-4類神精網路控制器設計 50
6-5動態模擬 51
6-6實驗結果 53
6-7本章結論 55
第七章結論與未來展望 56
7-1研究結論 56
7-2 研究貢獻 57
7-3 未來展望 58
參考文獻 60
表 目 錄
表6-1 電液比例閥控變量泵系統物理參數定義 66
表6-2 電液比例閥控變量泵系統動態模擬參數值 67
圖 目 錄
圖2-1類神經網路控制器之學習架構 68
圖2-2類神經網路控制器之學習架構 69
圖2-3線上訓練類神經網路適應性控制器 70
圖2-4比例積分微分(PID)與類神經網路混合控制器 70
圖2-5直接類神經網路適應性控制 70
圖2-6三層類神經網路控制器結構 71
圖3-1線上訓練自調節類神經網路控制器應用於直流馬達轉速調節 72
圖3-2 類神經網路三層結構 72
圖3-3 顯示馬達速度反應及控制器輸出量為穩定 74
圖3-4馬達速度反應及控制器輸出量(取樣時間為0.01s, = =0.003, = =0.00003, 0.3) 75
圖3-5馬達速度反應及控制器輸出量(取樣時間為0.01s, = = 0.003, 0.5, = = 0.00003:¾, = =0.003, = = 0.00004:----) 76
圖3-6馬達速度反應及控制器輸出量(取樣時間為0.001s, = =0.03, = = 0.00003, 0.3) 77
圖3-7馬達速度反應及控制器輸出量(取樣時間為0.001s, = =0.03, = = 0.00003, 0.5) 78
圖3-8馬達速度反應及控制器輸出量(取樣時間為0.001s, = =0.033, = = 0.00003, 0.5) 79
圖4-1 比例微分與類神經網路混合型控制器應用於直流馬達位置控制 80
圖4-2 類神經網路三層結構 81
圖4-3直接類神經網路適應控制器應用於直流馬達位置控制 81
圖4-4混合控制器選擇取樣時間為0.0001s, =0.1, =0.6, = 0.1 , = 0.8, =0.2,以單位步階命令輸入時,馬達轉角反應及控制器輸出量 84
圖4-5混合控制器輸入週期方波命令時因反復訓練而改善伺服馬達轉角單位步階反應 86
圖4-6 應用線上訓練自調節類神經網路控制器選擇取樣時間為0.0001s, = 0.1, =0.6, =0.1, =0.8, =0.2,以單位步階命令輸入時,馬達轉角反應及控制器輸出量 87
圖4-7 應用線上訓練自調節類神經網路控制器選擇取樣時間為0.0001s, = 0.1, =0.6, =0.1, =0.8, =0.2,輸入週期方波命令因反復訓練而改善伺服馬達轉角反應 89
圖4-8 應用線上訓練自調節類神經網路控制器選擇取樣時間為0.0001s, =0.1, =0.6, =0.1, =0.8, =0.0001時,顯示馬達轉角反應無法因反復訓練而改善 90
圖4-9比例微分控制器之比例參數 =0.5,微分參數 =0.01伺服馬達角度輸出反應 91
圖4-10 應用混合控制器選擇 =0.5, = =0.01, = = 0.0002,比例參數 =0.5,微分參數 =0.01以50 pulses步階命令輸入時,馬達轉角反應及控制器輸出量 92
圖4-11 應用線上訓練自調節類神經網路控制器選擇 =0.5, = =0.01, = =0.00015,以0.628rad步階命令輸入時,馬達轉角反應及控制器輸出量 93
圖4-12 應用線上訓練自調節類神經網路控制器選擇 =0.5, = =0.01, = =0.00018,以0.628rad步階命令輸入時,馬達轉角反應及控制器輸出量 94
圖4-13 應用線上訓練自調節類神經網路控制器選擇 =0.5, = =0.01, = =0.0002,以振幅25pulses週期方波命令輸入控制系統,馬達轉角反應及控制器輸出量 96
圖5-2 直接類神經網路適應控制器應用於電液伺服閥控液壓系統 96
圖5-3 類神經網路三層結構 97
圖5-4週期性方形波命令輸入,模擬線性比例微分控制器電液伺服閥控液壓缸位移輸出反應(Kp=7,Kd=1) 98
圖5-5週期性方形波命令輸入,系統受週期0.2s振幅1200N之正弦干擾力模擬線性比例微分控制電液伺服閥控液壓缸位移輸出反應(Kp=7,Kd=1) 99
圖5-6無載下以週期性方形波命令輸入模擬類神經網路控制電液伺服閥控液壓缸位移輸出反應( = =3 , =0.2) 100
圖5-7神經鍵加權值反應 101
圖5-8無載下以週期性方形波命令輸入模擬類神經網路控制器電液伺服閥控液壓缸位移輸出反應( = =3, =0.02) 102
圖5-9受週期5s振幅1200N之同向正弦之干擾力以週期性方形波命令輸入,模擬類神經網路控制器電液伺服閥控液壓缸位移輸出反應 103
圖5-10受週期5s振幅1200N之逆向正弦干擾力以週期性方形波命令輸入,模擬類神經網路控制器電液伺服閥控液壓缸位移輸出反應 104
圖5-11受週期5s振幅5000N之同向正弦干擾力以週期性方形波命令輸入,模擬類神經網路控制器電液伺服閥控液壓缸 105
圖5-12受週期5s振幅5000N之逆向正弦干擾力以週期性方形波命令輸入,模擬類神經網路控制器電液伺服閥控液壓缸位移輸出反應 106
圖5-13受5000N之同向固定干擾力以週期性方形波命令輸入,模擬類神經網路控制器電液伺服閥控液壓缸位移輸出反應 107
圖5-14受5000N之逆向固定干擾力以週期性方形波命令輸入,模擬類神經網路控制器電液伺服閥控液壓缸位移輸出反應 108
圖5-15步階命令輸入比例微分控制器電液伺服閥控液壓缸位移輸出反應(Kp=2,Kd=0.07) 109
圖5-16步階命令輸入比例微分控制器電液伺服閥控液壓缸位移輸出反應(Kp=1.5,Kd=0.05) 110
圖5-17週期性方形波命令輸入直接類神經網路適應控制器電液伺服閥控液壓缸位移輸出反應( =0.3, = =0.001, =0.00016) 111
圖5-18週期性方形波命令輸入直接類神經網路適應控制器電液伺服閥控液壓缸位移輸出反應( =0.3, = =0.001, =0.00014) 112
圖5-19阻尼作用下應用PD控制器之電液伺服閥控液壓缸位移輸出反應(Kp=1.5,Kd=0.05) 113
圖5-20負載缸加壓至9 順向負載下應用PD控制器之電液伺服閥控液壓缸位移輸出反應(Kp=1.5,Kd=0.05) 114
圖5-21負載缸加壓至9 逆向負載下應用PD控制器之電液伺服閥控液壓缸位移輸出反應(Kp=1.5,Kd=0.05) 115
圖5-22阻尼作用下應用類神經網路控制器之電液伺服閥控液壓缸位移輸出反應( =0.3, = =0.001, =0.00014) 116
圖5-23負載缸加壓至9 順向負載下應用類神經網路控制器之電液伺服閥液壓缸位移輸出反應( =0.3, = =0.001, =0.00014) 117
圖5-24負載缸加壓至9 逆向負載下電液伺服閥控液壓缸位移輸出反應( =0.3, = =0.001, =0.00014) 118
圖6-1三口比例方向閥控斜板型柱塞可變排量泵 119
圖6-2電液比例閥控可變排量泵控制迴路 120
圖6-3 電液比例閥控可變排量軸向型柱塞泵方塊圖 120
圖6-4電液變量泵控制系統模型方塊圖 120
圖6-5三層類神經網路控制器結構 121
圖6-6 =0Mpa, 20%時電液比例閥控變量泵斜板偏轉角控制模擬結果
123
圖6-7 100%,應用類神經網路控制器電液比例閥控變量泵斜板偏轉角控制模擬結果 124
圖6-8 =4Mpa, 20%:電液比例閥控變量泵斜板偏轉角控制模擬結果
126
圖6-9 =0Mpa,應用比例閥控制器參數調整適當時偏轉角控制實驗結果 128
圖6-10 =1Mpa,應用類神經網路控制器於變量泵斜板偏轉角控制實驗結果 129
圖6-11 =1Mpa,應用類神經網路控制器於變量泵斜板偏轉角控制實驗結果 130
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