Dynamic structure fuzzy neural networks for robust adaptive control of robot manipulators 謝昇峰、陳昭雄
E-mail: [email protected]
ABSTRACT
Robotic manipulators are systems with high nonlinearities that are often unknown and time varying, and they also have to suffer from various uncertainties in their dynamics. This thesis proposes a dynamic structure neural-fuzzy network (DSNFN) to address trajectory-tracking Control of robot manipulators. The DSNFN is used to model complex processes. Based on this DSNFN, a robust controller is designed to compensate for structured and unstructured uncertainties. In the DSNFN, the number of rule nodes can be either increased or descreased over time, and the adaptation laws is the sense of a projection algorithm are derived for tuning all network parameters. A hybrid controller that integrates an adaptive control and a sliding control through a modulation function is developed to guarantee the convergence and stability of the control system. The experimental setup consists of a host computer, an encoder card, a D/A card and a two-axises robot. Simulations and experiments are performed to demonstrate the effectiveness of the proposed scheme.
Keywords : neural fuzzy network、robot control、nonlinear systems、adaptive control.
Table of Contents
中文摘要...iv 英文摘要...v 致謝...vi 目 錄...vii 圖目錄...ix 表目錄...xii 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...3 1.4 論文 架構...5 第二章 系統架構介紹...6 2.1兩軸機械臂系統硬體架構...6 2.2兩軸機械手臂系統 數學模型...14 2.2.1拉格朗日運動方程式...16 2.2.2兩軸機械手臂數學模型...16 第三章 軌跡跟隨與控制器設 計...22 3.1機械手臂控制問題描述...22 3.2類神經模糊網路系統...25 3.3自適應性類神經模糊網路 控制器...39 3.4動態結構類神經模糊網路控制系統...39 第四章 控制系統模擬與實驗...51 4.1兩軸機械臂模 擬系統...51 4.2控制器介紹...53 4.3模擬結果...55 4.3.1 CASE1的模擬結果...55 4.3.2 CASE2的模擬結果...65 4.4實驗結果...75 4.4.1 CASE1的實驗結果...75 4.4.2 CASE2的實驗結果...84 第五章 結論...93 參考文獻...94
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