Adaptive backstepping neural control for nonlinear systems with quantized input signals

The adaptive neural backstepping control for a class of multiple-input and multiple-output nonlinear systems with quantized input signals is studied in this paper. We design an output feedback adaptive control scheme using backstepping method based on a high-gain state observer, and use neural networks to approximate the unknown nonlinear functions. A new output feedback neural controller is proposed to ensure that the state trajectories in the quantized nonlinear systems are ultimately bounded. The control signals are quantized by the hysteretic quantizer that can avoid chattering. A simulation example is given to illustrate the usefulness of the new designed adaptive method.