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ROBUST NEURAL NETWORK CONTROL OF RIGID LINK FLEXIBLE‐JOINT ROBOTS
Author(s) -
Kwan C.M.,
Lewis F.L.,
Kim Y.H.
Publication year - 1999
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1111/j.1934-6093.1999.tb00019.x
Subject(s) - revolute joint , control theory (sociology) , robot , controller (irrigation) , artificial neural network , link (geometry) , nonlinear system , computer science , bounded function , mathematics , artificial intelligence , control (management) , computer network , mathematical analysis , physics , quantum mechanics , agronomy , biology
A robust Neural Network (NN) controller is proposed for the motion control of rigid‐link flexible‐joint (RLFJ) robots. No weak joint elasticity assumption is needed. The NNs are used to approximate three very complicated nonlinear functions. Our NN approach requires no off‐line learning phase, and no lengthy and tedious preliminary analysis to find the regression matrices. Most importantly, we can guarantee the uniformly ultimately bounded (UUB) stability of tracking errors and NN weights. The controller can be regarded as a universal reusable controller because the same controller can be directly applied to different RLFJ robots with different masses and lengths within the same class, for instance, of two‐link revolute RLFJ robots.