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

Wubing Fang, Fei Chao, Chih-Min Lin, Longzhi Yang, Changjing Shang, Zhou Changle

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

22 Dyfyniadau(SciVal)
154 Wedi eu Llwytho i Lawr (Pure)


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.

Iaith wreiddiolSaesneg
Rhif yr erthygl2
CyfnodolynFrontiers in Neurorobotics
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 04 Chwef 2019

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