Open AccessNovel Adaptive Forward Neural MIMO NARX Model for the Identification of Industrial 3-DOF Robot Arm Kinematics
Author(s) -
Hồ Phạm Huy Ánh,
Nguyen Thanh Hoai Nam
Publication year - 2012
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/51277
Subject(s) - nonlinear autoregressive exogenous model , kinematics , computer science , robot , control theory (sociology) , robotic arm , forward kinematics , mimo , identification (biology) , nonlinear system , artificial neural network , industrial robot , artificial intelligence , inverse kinematics , control engineering , engineering , channel (broadcasting) , computer network , physics , botany , control (management) , classical mechanics , quantum mechanics , biology
In this paper, a novel forward adaptive neural MIMO NARX model is used for modelling and identifying the forward kinematics of an industrial 3-DOF robot arm system. The nonlinear features of the forward kinematics of the industrial robot arm drive are thoroughly modelled based on the forward adaptive neural NARX model-based identification process using experimental input-output training data. This paper proposes a novel use of a back propagation (BP) algorithm to generate the forward neural MIMO NARX (FNMN) model for the forward kinematics of the industrial 3-DOF robot arm. The results show that the proposed adaptive neural NARX model trained by a Back Propagation learning algorithm yields outstanding performance and perfect accuracy
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