Backstepping high-order differential neural network control of a type of nonlinear systems

This paper presents an output feedback control. The control algorithm does not require plant mathematical model. However, the actual plant is assumed to be affine with respect to local inputs. High order differential neural networks are used to identify the unknown plant. Luenberger-type observer provides estimated states. Controller is based on backstepping and Lyapunov direct method. Variable structure control handles uncertainties arising from the estimation processes. Closed loop errors are proved to be bounded. A trajectory tracking example demonstrates the effectiveness of the design.