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研究生:唐世謙
研究生(外文):Shih-Chien Tang
論文名稱:應用分解加速度法於平面並聯式機械臂之運動控制研究
論文名稱(外文):Study on Motion Control of Planar Parallel Manipulator Using Resolved Acceleration Method
指導教授:郭永麟
指導教授(外文):Yong-Lin Kuo
口試委員:郭永麟楊振雄郭鴻飛陳亮光
口試委員(外文):Yong-Lin KuoCheng-Hsiung YangHung-Fei KuoLiang-kuang Chen
口試日期:2019-07-11
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:自動化及控制研究所
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:2019
畢業學年度:107
語文別:中文
論文頁數:145
中文關鍵詞:RRR平面並聯式機構PID控制計算力矩控制分解加速度控制影像伺服控制卷積神經網路
外文關鍵詞:3-RRR parallel manipulatorPIDcomputed torqued controlresolved acceleration controlvisual servoing controlconvolutional neural network
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本論文的研究主旨為運用五種不同的控制方法,針對三自由度平面並聯式機械手臂(3-RRR PPM)做運動控制,根據其比較結果找出較佳的控制方案,並探討未來的可行性與改進目標。
本論文首先介紹三自由度平面並聯式機械手臂的模型,接著推導其運動學與動力學方程式,並針對此種機械手臂的工作空間與奇異點做分析,防止機械手臂超出工作空間造成硬體的損壞。在控制理論部分,本論文使用五種不同的控制器做位置控制,分別為比例-積分-微分控制(PID)、計算力矩控制(CTC)、分解加速度控制(RAC) 、影像伺服分解加速度控制(VSRAC)、卷積神經網路分解加速度控制(CNN-RAC),其中後兩者為新型控制方法。VSRAC是RAC的變形,其目的是要降低RAC的運算量與提升追蹤軌跡的精準度,因而加入影像資訊做修正,但是影像處理仍需一定的運算時間。為了解決此問題,本文提出CNN-RAC的控制方法,藉由卷積神經網路學習影像與終端點之間的關係,該方法除了可以有效降低影像處理所需時間,亦改善影像處理中雜訊影像無法抓取問題。
硬體與實驗部分,本論文使用無刷直流馬達(BLDC)作為機械手臂的致動器,藉由德州儀器(TI)的數位訊號處理器(DSP)做馬達控制並搭配開發軟體CCS做程式的撰寫,採用RS485做為電腦與DSP之間的通訊架構,電腦端則是使用C#開發工具運算控制器的輸出命令。而模擬部分則是使用MATLAB數學軟體,由推得的動力學做為系統模型,帶入實際硬體之數據,根據模擬結果找出可收斂的Kp與Kd參數以此做為實驗架構的基礎。
最後根據比例-積分-微分控制、計算力矩控制、分解加速度控制、影像伺服分解加速度控制、卷積神經網路分解加速度控制五種控制理論的模擬與實驗結果做比較與分析,並根據其結果提出較適合的三自由度的平面並聯式機械手臂的控制方法以及未來可深入研究之方向。
The main purposes of this thesis are to use five different control methods to position the three-degree-of-freedom planar parallel manipulator (3-RRR PPM),to find out the better control scheme based on the comparison results, and to explore the feasibility of the future and improve the goal.
This thesis first introduces the model of three-degree-of-freedom planar parallel manipulator, derives its kinematics and dynamics equations, and analyzes the working space and singular points of the robot arm to prevent the manipulator from exceeding the working space and causing the damage of hardware. In the control theory, this thesis uses five different controllers for position control, proportional-integral-derivative (PID) control, computed torque control (CTC), resolved acceleration control (RAC), visual servoing resolved acceleration control (VSRAC), and convolutional neural network resolved acceleration control (CNN-RAC), where the latter two controllers are proposed. The VSRAC is a variant of the RAC and the VSRAC reduces the computational complexity of the RAC and improves the accuracy of the tracking trajectory, where the image information is added for correction. However the image processing still requires much computing time. In order to solve this problem, this thesis proposes the control method of CNN-RAC, which learns the relationship between an image and the end-effector by convolutional neural network. This method can effectively reduce the time required for image processing and reduce the noise in image processing. The image could not be crawled.
In the hardware and experimental part, this thesis uses a brushless DC motor (BLDC) as the actuator of the robot arm, which is controlled by Texas Instruments' digital signal processor (DSP) for motor control and programmed with the development software CCS. RS485 is used as the communication architecture between the computer and the DSP, and the computer is the output command of the C# development tool computing controller. The simulation part for manipulator uses the MATLAB mathematics software, and the derived dynamics is used as the system model, which brings the data of the actual hardware. Finding the convergence and parameters based on the simulation results as the basis of the experimental architecture.
Finally, the simulation and experimental results of five control theories of proportional-integral-derivative control, computed torque control, resolved acceleration control and visual servoing resolved acceleration control, convolutional neural network resolved acceleration control are compared and analyzed. According to the results, a suitable three-degree-of-freedom planar parallel manipulator control method is proposed and the future can be further studied.
摘要 I
Abstract III
致謝 V
目錄 VI
圖目錄 IX
表目錄 XVI
第1章 緒論 1
1.1 研究背景 1
1.2 文獻回顧 2
1.2.1 並聯式機械手臂 2
1.2.2 控制理論 2
1.3 研究動機 4
1.4 研究方法 5
1.5 論文架構 6
第2章 3-RRR PPM運動學與動力學模型 7
2.1 3-RRR PPM模型分析 7
2.2 3-RRR PPM運動學分析 9
2.2.1 逆向運動學 9
2.2.2 正向運動學 12
2.2.3 工作空間分析 14
2.2.4 奇異點分析 15
2.2.5 速度轉換 17
2.2.6 加速度轉換 18
2.3 3-RRR PPM動力學分析 18
2.3.1 逆向動力學 18
2.3.2 正向動力學 20
第3章 控制架構與理論 21
3.1 比例-積分-微分控制 21
3.2 計算力矩控制 22
3.3 分解加速度控制 24
3.4 影像伺服分解加速度控制 25
3.4.1 相機模型 26
3.4.2 影像處理 28
3.5 卷積神經網路分解加速度控制 32
3.5.1 卷積神經網路理論 32
第4章 實驗規劃 37
4.1 硬體架構說明 37
4.1.1 3-RRR PPM平台介紹 37
4.1.2 控制器規格 41
4.1.3 馬達規格 45
4.1.4 電源供應器規格 47
4.1.5 通訊架構 48
4.1.6 相機規格與參數 50
4.2 系統流程說明 52
4.2.1 系統硬體流程 52
4.2.2 系統軟體流程 53
4.3 理論驗證 54
4.3.1 運動學驗證 54
4.3.2 工作空間驗證 57
4.3.3 速度、加速度轉換驗證 57
4.3.4 動力學驗證 67
4.4 馬達控制 73
4.4.1 電流控制 73
4.4.2 位置控制 76
4.4.3 速度控制 77
4.5 機械手臂影像處理方法 78
4.6 卷積神經網路設計 80
4.6.1 生成訓練資料 81
4.6.2 卷積神經網路架構 82
4.6.3 訓練結果 83
4.7 人機介面 86
第5章 模擬與實驗 87
5.1 實驗步驟 87
5.2 PID控制實驗架構 88
5.2.1 PID收斂情況 89
5.2.2 PID模擬與實驗結果 90
5.3 CTC控制實驗架構 95
5.3.1 CTC收斂情況 95
5.3.2 CTC模擬與實驗結果 97
5.4 RAC控制實驗架構 101
5.4.1 RAC收斂情況 101
5.4.2 RAC模擬與實驗結果 103
5.5 VSRAC控制實驗架構 107
5.5.1 VSRAC模擬與實驗結果 108
5.6 CNN-RAC控制實驗架構 112
5.6.1 CNN-RAC模擬與實驗結果 113
5.7 控制器比較 117
第6章 結論與未來展望 121
6.1 結論 121
6.2 未來展望 122
參考文獻 123
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