• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Frontiers in Neurorobotics >An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots
【2h】

An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots

机译：改进的类人机器人模糊脑情感学习模型网络控制器

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The brain emotional learning (BEL) system was inspired by the biological amygdala-orbitofrontal model to mimic the high speed of the emotional learning mechanism in the mammalian brain, which has been successfully applied in many real-world applications. Despite of its success, such system often suffers from slow convergence for online humanoid robotic control. This paper presents an improved fuzzy BEL model (iFBEL) neural network by integrating a fuzzy neural network (FNN) to a conventional BEL, in an effort to better support humanoid robots. In particular, the system inputs are passed into a sensory and emotional channels that jointly produce the final outputs of the network. The non-linear approximation ability of the iFBEL is achieved by taking the BEL network as the emotional channel. The proposed iFBEL works with a robust controller in generating the hand and gait motion of a humanoid robot. The updating rules of the iFBEL-based controller are composed of two parts, including a sensory channel followed by the updating rules of the conventional BEL model, and the updating rules of the FNN and the robust controller which are derived from the “Lyapunov” function. The experiments on a three-joint robot manipulator and a six-joint biped robot demonstrated the superiority of the proposed system in reference to a conventional proportional-integral-derivative controller and a fuzzy cerebellar model articulation controller, based on the more accurate and faster control performance of the proposed iFBEL.
机译：大脑情感学习（BEL）系统受生物杏仁核-眶额叶模型的启发，模仿了哺乳动物大脑中情感学习机制的高速运行，该机制已成功应用于许多实际应用中。尽管取得了成功，但这种系统经常因在线人形机器人控制而收敛缓慢。通过将模糊神经网络（FNN）与常规BEL集成，本文提出了一种改进的模糊BEL模型（iFBEL）神经网络，以更好地支持类人机器人。特别地，系统输入被传递到共同产生网络最终输出的感官和情感渠道。 iFBEL的非线性近似能力是通过将BEL网络作为情感渠道来实现的。拟议的iFBEL与鲁棒控制器配合使用，可以产生人形机器人的手和步态运动。基于iFBEL的控制器的更新规则由两部分组成，包括感觉通道，后跟常规BEL模型的更新规则，以及从“ Lyapunov”函数派生的FNN和鲁棒控制器的更新规则。在三关节机器人操纵器和六关节Biped机器人上进行的实验证明了该系统相对于常规比例-积分-微分控制器和模糊小脑模型关节控制器的优越性，它基于更精确，更快速的控制建议的iFBEL的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号