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.

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