16 September 1992 New connectionist control structure for fast robot dynamic learning
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Abstract
A major objective in this paper is the application of connectionist architectures for fast and robust on-line learning of dynamic relations used in robot control at the executive hierarchical level. The proposed new connectionist robot controllers as a new feature use decomposition of robot dynamics. In this way, this method enables the training of neural networks on the simpler input/output relations with significant reduction of learning time. The proposed controller structure comprises a form of intelligent feedforward control in the frame of decentralized control algorithm with feedback-error learning method. The other important features of these new algorithms are fast and robust convergence properties, because the problem of adjusting the weights of internal hidden units is considered as a problem of estimating parameters by recursive least squares method. From simulation examples of robot trajectory tracking it is shown that when a sufficiently trained network is desired the learning speed of the proposed algorithms is faster than that of the traditional back propagation algorithm.
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Dusko Katic, Dusko Katic, Miomir Vukobratovic, Miomir Vukobratovic, } "New connectionist control structure for fast robot dynamic learning", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140039; https://doi.org/10.1117/12.140039
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