Neurocontroller for unknown systems using simultaneous perturbation

Describes a neurocontroller via the simultaneous perturbation learning rule to control an unknown plant. When we apply a direct inverse control scheme by a neural network, the neural network must learn an inverse system of the objective unknown plant. When we use a kind of gradient method as a learning rule of the neural network, the Jacobian of the plant is required. On the other hand, our control scheme described here does not require information about the plant Jacobian, because the simultaneous perturbation method estimates the gradient using only values of the error defined by output of the plant and its desired one. A regulation problem for a flexible beam is handled to confirm the feasibility of the method