A decentralized adaptive joint neurocontroller

Cox, C.; Lothers, M.; Pap, Robert M.; Thomas, Charles R.

doi:10.1109/ijcnn.1992.226878

Cited by 3 publications

(1 citation statement)

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“…For example, the FNN proposed in [l] differs from conventional systems by comprising different types of neural networks, as well as fuzzy expert systems which model the knowledge of neural network experts. Meanwhile, in the field of robotic control, conventional neural network paradigms have been applied, as exemplified by the computer simulations of a 2-joint robot arm [4], and in dynamic control of an industrial manipulator [5]. In addition, certain aspects of robot dynamics have been modeled via a fuzzy neural network [6].…”

Section: Introductionmentioning

confidence: 99%

Robotic modeling and control using a fuzzy neural network

Vukovich

IEEE International Conference on Neural Networks

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In this paper, a fuzzy neural network (FNN) is applied to modeling and control of a robot. Comparisons are made between the FNN and standard back propagation neural networks as well as a commercially available neural network software package for modeling the robot, and observations on robustness of these networks made. A number of experiments demonstrate that the F" can learn faster and more accurately than the back propagation and commercial neural networks for modeling and control of a real robot. The FNN will be used as a prototype dynamic model and a control component in the control system of our 7 degree of freedom robot.

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Section: Introductionmentioning

confidence: 99%