A robust training algorithm of discrete-time MIMO RNN and application in fault tolerant control of robotic system

DOI10.1007/S00521-010-0343-2zbMath1327.93288OpenAlexW2106878071MaRDI QIDQ710447

Jinchuan Zheng, Qing Song, Fuchun Sun, Yilei Wu

Publication date: 19 October 2010

Published in: Neural Computing and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00521-010-0343-2

zbMATH Keywords

Lyapunov functionfault tolerant controlweight convergence\(L_2\) stabilityadaptive trainingCluett's law

Mathematics Subject Classification ID

Discrete-time control/observation systems (93C55) Automated systems (robots, etc.) in control theory (93C85)

Cites Work

• Unnamed Item
• An RBF neural network-based adaptive control for SISO linearisable nonlinear systems
• A normalised real time recurrent learning algorithm.
• Robust adaptive dead zone technology for fault-tolerant control of robot manipulators using neural networks
• Robust adaptive fault accommodation for a robot system using a radial basis function neural network
• Robustness analysis of discrete-time adaptive control systems using input-output stability theory: a tutorial

This page was built for publication: A robust training algorithm of discrete-time MIMO RNN and application in fault tolerant control of robotic system