Artificial Neural Networks: A Tutorial
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Control of Robot Manipulators
Self-Organizing Maps for Robot Control
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
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An adaptive neural system for positioning control of a PUMA 560 manipulator is presented". The computed torque method was implemented with a Multi-Layer Perceptron with on-line learning. The control scheme is implemented into two phases. The first one is the off-line phase in which the neural network is trained with previously known control actions. The second one is the on-line phase in which the neural network parameters are adapted while controlling the manipulator. The control system is able to respond to changes in the manipulator model and to load disturbances. As will be shown, control system performance is improved with the on-line learning strategy presented in this paper.