Adaptive neural network control for a class of stochastic nonholonomic systems with both full-state constraints and full-input saturation

This article presents an adaptive control algorithm for stochastic nonholonomic systems subject to state constraints and input saturation simultaneously. We give a state-input scaling transformation and a novel auxiliary variable to transform the stochastic nonholonomic system into a new one to make it easier to design the controller, and adopt a switching strategy to eliminate the phenomenon of uncontrollability. By introducing a barrier Lyapunov function and defining a suitable adaptive parameter, we construct a novel adaptive neural network controller with only one adaptive law, which can alleviate computation burden. The presented controller not only overcomes the effects of both full-state constraints and full-input saturation on system performance, but also ensures that all states of the closed-loop are semi-globally uniformly ultimately bounded in probability. Additionally, all state variables are restricted to the predefined compact sets. Finally, an example is used to validate the efficacy of the established controller.