Intelligent Fuzzy Q-Learning control of humanoid robots

被引：0

作者：

Er, MJ ^{[1
]}

Zhou, Y ^{[1
]}

机构：

[1] Intelligent Syst Ctr, Singapore 637533, Singapore

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 3, PROCEEDINGS | 2005年 / 3498卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a design methodology for enhancing the stability of humanoid robots is presented. Fuzzy Q-Learning (FQL) is applied to improve the Zero Moment Point (ZMP) performance by intelligent control of the trunk of a humanoid robot. With the fuzzy evaluation signal and the neural networks of FQL, biped robots are dynamically balanced in situations of uneven terrains. At the mean time, expert knowledge can be embedded to reduce the training time. Simulation studies show that the FQL controller is able to improve the stability as the actual ZMP trajectories become close to the ideal case.

引用

页码：216 / 221

页数：6

共 9 条

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