Observer-based adaptive neural control for switched stochastic pure-feedback systems with input saturation

Globally Adaptive Neural Network Tracking for Uncertain Output-Feedback Systems

This article investigates the problem of global neural network (NN) tracking control for uncertain nonlinear systems in output feedback form under disturbances with unknown bounds. Compared with the existing NN control method, the differences of the proposed scheme are as follows. The designed actual controller consists of an NN controller working in the approximate domain and a robust controller working outside the approximate domain, in addition, a new smooth switching function is designed to achieve the smooth switching between the two controllers, in order to ensure the globally uniformly ultimately bounded of all closed-loop signals. The Lyapunov analysis method is used to strictly prove the global stability under the combined action of unmeasured states and system uncertainties, and the output tracking error is guaranteed to converge to an arbitrarily small neighborhood through a reasonable selection of design parameters. A numerical example and a practical example were put forward to verify the effectiveness of the control strategy.

Output Feedback Control of Micromechanical Gyroscopes Using Neural Networks and Disturbance Observer

This article addresses the output feedback control of micromechanical (MEMS) gyroscopes using neural networks (NNs) and disturbance observer (DOB). For the unmeasured system states, the state observer and the high gain observer are constructed. The adaptive NNs are investigated to approximate the nonlinear dynamics, including the known nominal terms and the system uncertainties caused by environmental fluctuations. For the time-varying disturbances, the DOB is utilized. The sliding mode control is employed to enhance the robustness. Through simulation verification, the output feedback control using NNs and DOB can adapt to the dynamics of MEMS gyroscope with unmeasured system speed, while an expected effective tracking performance is obtained in the presence of unknown system nonlinearities and external disturbances.