控制理论(社会学)
观察员(物理)
补偿(心理学)
李雅普诺夫函数
计算机科学
人工神经网络
国家观察员
机器人
理论(学习稳定性)
控制(管理)
数学
人工智能
物理
非线性系统
机器学习
心理学
量子力学
精神分析
出处
期刊:International Journal of Robotics & Automation
[ACTA Press]
日期:2006-01-01
卷期号:21 (1)
被引量:15
标识
DOI:10.2316/journal.206.2006.1.206-2726
摘要
Normal industrial PD control of Robot has two drawbacks: it needs joint velocity sensors, and it cannot guarantee zero steady-state error. In this paper we make two modifications to overcome these problems. High-gain observer is applied to estimate the joint velocities, and an RBF neural network is used to compensate gravity and friction. We give a new proof for high-gain observer, which explains a direct relation between observer gain and observer error. Based on Lyapunov-like analysis, we also prove the stability of the closed-loop system if the weights of RBF neural networks have certain learning rules and the observer is fast enough.
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