title: Learning rules for neuro-controller via simultaneous perturbation.
abstract: 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.
authors.affiliation: Dept. of Electr. Eng., Kansai Univ., Osaka.
authors.forename: Y
authors.initials: Y
authors.lastname: Maeda
authors.forename: R P
authors.initials: RP
authors.lastname: De Figueiredo