Gary Flake, Guo-Zhen Sun, Yee-Chun Lee
Recently, Ott, Grebogi and Yorke (OGY)  found an effective method to control chaotic systems to unstable fixed points by us(cid:173) ing only small control forces; however, OGY's method is based on and limited to a linear theory and requires considerable knowledge of the dynamics of the system to be controlled. In this paper we use two radial basis function networks: one as a model of an unknown plant and the other as the controller. The controller is trained with a recurrent learning algorithm to minimize a novel objective function such that the controller can locate an unstable fixed point and drive the system into the fixed point with no a priori knowl(cid:173) edge of the system dynamics. Our results indicate that the neural controller offers many advantages over OGY's technique.