Learning rules for neuro-controller via simultaneous perturbation

This paper describes learning rules using simultaneous perturbation for a neuro-controller 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 the case, we must know the sensitivity function of the plant to use 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.