Learning rules for neuro-controller via simultaneous perturbation.
This paper describes learning rules using simultaneous perturbation for a neurocontroller that controls an unknown plant. When we apply a direct control scheme by a neural network, the neural network must learn an inverse system of the unknown plant. In this case, we must know the sensitivity function of the plant using a kind of the gradient method as a learning rule of the neural network. On the other hand, the learning rules described here do not require information about the sensitivity function. Some numerical simulations of a two-link planar arm and a tracking problem for a nonlinear dynamic plant are shown.
Maeda
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Dept. of Electr. Eng., Kansai Univ., Osaka.
De Figueiredo
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RP