Stable adaptive systems
Automatica (Journal of IFAC)
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Control of Robot Manipulators
Multilayer discrete-time neural-net controller with guaranteed performance
IEEE Transactions on Neural Networks
Error-minimizing dead zone for basis function networks
IEEE Transactions on Neural Networks
Neural-network-based robust fault diagnosis in robotic systems
IEEE Transactions on Neural Networks
Robust backpropagation training algorithm for multilayered neural tracking controller
IEEE Transactions on Neural Networks
Robust Recurrent Neural Network Control of Biped Robot
Journal of Intelligent and Robotic Systems
Fault diagnosis of underwater robots based on recurrent neural network
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
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In this paper, a multi-layered feed-forward neural network is trained on-line by robust adaptive dead zone scheme to identify simulated faults occurring in the robot system and reconfigure the control law to prevent the tracking performance from deteriorating in the presence of system uncertainty. Consider the fact that system uncertainty can not be known a priori, the proposed robust adaptive dead zone scheme can estimate the upper bound of system uncertainty on line to ensure convergence of the training algorithm, in turn the stability of the control system. A discrete-time robust weight-tuning algorithm using the adaptive dead zone scheme is presented with a complete convergence proof. The effectiveness of the proposed methodology has been shown by simulations for a two-link robot manipulator.