A Rapid Base Parameter Physical Feasibility Test Algorithm for Industrial Robot Manipulator Identification Using a Recurrent Neural Network

A rapid recurrent neural network (RNN)-based physical feasibility test algorithm for base parameters during the identification process of a robot dynamic model is proposed in this paper. Firstly, related physical constraints such as inertia tensor, drive chain inertia, and friction are combined into...

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Vydané v:	IEEE access Ročník 11; s. 1
Hlavní autori:	Huang, Hao-Lun, Cheng, Ming-Yang, Huang, Tzu-Yuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Chain drives Dynamic models Dynamic parameter identification Evolutionary algorithms Feasibility Feasibility studies Heuristic algorithms Identification Identification methods industrial robot manipulator Industrial robots Inertia Linear matrix inequalities Manipulator dynamics Manipulators Neural networks Object recognition Optimization Parameter identification physical feasibility recurrent neural network Recurrent neural networks Robot arms Robots Semidefinite programming Stability analysis Tensors Torque
ISSN:	2169-3536, 2169-3536
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