Type-2 Fuzzy Hybrid Controller Network for Robotic Systems

Dynamic control, including robotic control, faces both the theoretical challenge of obtaining accurate system models and the practical difficulty of defining uncertain system bounds. To facilitate such challenges, this paper proposes a control system consisting of a novel type of fuzzy neural networ...

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Veröffentlicht in:	IEEE transactions on cybernetics 2020-08, Vol.50 (8), p.3778-3792
Hauptverfasser:	Chao, Fei, Zhou, Dajun, Lin, Chih-Min, Yang, Longzhi, Zhou, Changle, Shang, Changjing
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive control Adaptive systems Artificial neural networks Biological neural networks Brain brain emotional learning controller (BELC) network Cerebellar model articulation controller Comparative studies Control systems Controllers Dynamic control Fuzzy control Fuzzy logic Fuzzy sets Learning Liapunov functions Mathematical model Mobile robots Neural networks Nonlinear equations Robot arms Robot control robot dynamic control Robot sensing systems Robust control Sliding mode control Type-2 inference system
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