An error-learning neural network for the tuning of robot dynamic models

DOI10.1080/00207729508929021zbMath0825.93500OpenAlexW2072003544MaRDI QIDQ4698729

Albert Y. Zomaya

Publication date: 28 November 1995

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207729508929021

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Control/observation systems with incomplete information (93C41) Adaptive control/observation systems (93C40) Automated systems (robots, etc.) in control theory (93C85)

Cites Work

• Unnamed Item
• Multilayer feedforward networks are universal approximators
• An Adaptive Control Scheme for Mechanical Manipulators—Compensation of Nonlinearity and Decoupling Control
• Principles and design of model-based robot controllers
• Adaptive controller designs for robot manipulator systems yielding reduced Cartesian error
• Efficient Dynamic Computer Simulation of Robotic Mechanisms
• Approximation by superpositions of a sigmoidal function

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