Stabilizing control of a class of unknown nonlinear systems using dynamic neural networks

This paper presents an online modeling and control strategy for a class of linearizable nonlinear systems with unknown parameters and nonlinearities. A dynamic neural network (DNN) is utilized for modeling of nonlinear systems in their equivalent feedback linearized form. Lyapunov techniques are used to derive the adaptation rules for training the DNN´s weight matrices. An adaptive state feedback stabilizing control is developed based on the equivalent DNN model of the systems. An observer is also designed to estimate the states of the DNN model of the system. Subsequently, an adaptive output feedback stabilizing control law is derived for unknown nonlinear systems, using their equivalent DNN model, with guaranteed closed-loop stability. Simulation results show the effectiveness of the proposed technique.