Trajectory control of robotic manipulators using neural networks

The authors present a nonlinear compensator using neural networks for trajectory control of robotic manipulators. The neural networks are not used to learn inverse-dynamics but to compensate nonlinearities of robotic manipulators. The performance of the proposed neural network controller is compared with that of the adaptive controller proposed by J.J. Craig (1988), and the effectiveness of the proposed neural network controller in compensating the unstructured uncertainties is clarified. A learning scheme using a model of known dynamics of manipulators is also proposed. The model learning can be done offline and needs no data recording of actual manipulator operation