The Robust Neural Network Control Theorem Apply to Manipulator Tracking 黃睿祥、陳昭雄
E-mail: 9419866@mail.dyu.edu.tw
ABSTRACT
The two-link robot manipulators are typical nonlinear and MIMO systems. They are usually used as experimentation equipment to verify the effectiveness of the proposed control theory. This paper presents a new robust neural network controller for robot manipulators, whose dynamic models are poorly understood. A neural network system is used to model the unknown nonlinearities of the robot dynamic systems. Then, a control law is constructed based on this neural-network model for the robot tracking
problems. By using a robust adaptive control technique, an adaptive law is presented for tuning all parameters of the neural network system, including the output weights, the widths and the centers, thereby reducing the approximation error. Global stability of the overall control scheme is guaranteed in the sense of Lyapunov, and the tracking errors converge to the required precision. Finally, Simulations and experiments performed on a practical two-link robot manipulator demonstrate the effectiveness of our scheme.
Keywords : adaptation control ; nonlinear system ; neural network
Table of Contents
中文摘要 v ABSTRACT vi 誌謝 vii 目錄 viii 表目錄 xiv 第一章 緒論 1 1.1 研究動機 1 1.2 研究目的 2 1.3 文獻回顧 2 1.4 文章 內容簡介 4 第二章 機械臂硬體架構 6 2.1兩軸機械臂系統硬體架構 6 2.2 Lagrange運動方程式 13 2.3 兩軸機械臂數學模型 15 第三章 路徑規劃 18 3.1 運動學 18 3.2 順向運動學 19 3.3 逆向運動學 22 3.4 機械手臂工作空間 24 3.5 程式的撰寫 25 3.6 機械 臂路徑之規劃 26 第四章 強健類神經控制器之設計 34 4.1 類神經網路理論 34 4.2 人工神經元 35 4.3 類神經網路架構 38 4.4 機械臂控制問題描述 40 4.6 多連桿機器手臂的適應性RBFN控制器 45 第五章 控制系統模擬 53 5.1 PD控制器 53 5.2強健類 神經網路控制器 61 第六章 實驗 70 第七章 結論 81 7.1 結論 81 7.2 未來展望 81 參考文獻 82
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