Stabilizing unstable equilibria using observer-based neural networks with applications in chaos suppression

In this paper, the observer-based stabilization of unstable equilibrium points of a class of unknown nonlinear systems is proposed. The controller is based on feedback linearization where the observer system and control signal are directly estimated by a nonlinear in parameter neural network (NLPNN). A modified Back Propagation (BP) algorithm with e-modification was used to update the weights of the network. Globally uniformly ultimately boundedness of overall closed-loop system is ensured using Lyapunov's direct method. To verify the effectiveness of the proposed observer-based controller, a set of simulations was performed on a Rossler and Lorenz chaotic systems.