Adaptive Neural Stabilization Based on Input‐Output Quantization for Uncertain Nonlinear Systems With a High‐Order Output Filter

In this paper, we study the control scheme of robust adaptive neural output feedback stabilization for uncertain input‐output‐quantization nonlinear systems with a high‐order output filter. Neural network technology is employed to approximate nonlinear functions, and disturbance estimators are used to estimate unknown noise and disturbances in the system. An adaptive neural observer is well‐designed to estimate unmeasured states while preventing quantization errors from affecting the observer's state equations. To stabilize the system, an adaptive backstepping quantization controller is obtained by using two kinds of filters. The first kind of filters (first‐order filters) is employed to address the problem of the virtual control laws of differential explosion. The other kind of filter (high‐order output filter) is used to make quantized sensor output signals differentiable and solve the problem of noncontinuity of quantized sensor outputs in the traditional backstepping method. The stability analysis illustrates that all signals in the closed‐loop system are ultimately uniformly bounded and adjustable. In the simulation results, a fan speed model is constructed to verify the accuracy and effectiveness of the proposed scheme.