A Novel Self-Organizing Emotional CMAC Network for Robotic Control

Authors Organisations
Type Conference Proceeding (Non-Journal item)
Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PublisherIEEE Press
ISBN (Electronic)9781728169262
DOI
Publication statusPublished - 28 Sep 2020
Event2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom of Great Britain and Northern Ireland
Duration: 19 Jul 202024 Jul 2020

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2020 International Joint Conference on Neural Networks, IJCNN 2020
CountryUnited Kingdom of Great Britain and Northern Ireland
CityVirtual, Glasgow
Period19 Jul 202024 Jul 2020
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Abstract

This paper proposes a self-organizing control system for uncertain nonlinear systems. The proposed neural network is composed of a conventional brain emotional learning network (BEL) and a cerebellar model articulation controller network (CMAC). The input value of the network is feed to a BEL channel and a CMAC channel. The output of the network is generated by the comprehensive action of the two channels. The structure of the network is dynamic, using a self-organizing algorithm allows increasing or decreasing weight layers. The parameters of the proposed network are on-line tuned by the brain emotional learning rules; the updating rules of CMAC and the robust controller are derived from the Lyapunov function; in addition, stability analysis theory is used to guaranty the proposed controller's convergence. A simulated mobile robot is applied to prove the effectiveness of the proposed control system. By comparing with the performance of other neural-network-based control systems, the proposed network produces better performance.