Adaptive neural network control for a class of stochastic nonholonomic systems with asymmetric full-state constraints

This paper proposes a novel adaptive neural network control scheme for a class of stochastic nonholonomic systems subject to asymmetric full-state constraints (AFSC), input saturation, and noise. Due to the limitations of physical structure and manufacturing technology, AFSC, input saturation, and noise widely exist in the actual system, which will lead to system performance degradation and even system instability. To facilitate controller design, we introduce a state-input scaling transformation to reconfigure the stochastic nonholonomic system into a more suitable format and adopt a novel barrier Lyapunov function (BLF) that simplifies the computation process by utilizing an adaptive neural network controller with a singular adaptive law. Furthermore, we implement a strategic switching control mechanism to mitigate the influence of uncontrollable phenomena. The proposed adaptive neural network controller, which requires only one adaptive law, can ensure that all states of the closed-loop system are uniformly ultimately bounded, while enforcing compliance with the specified asymmetric constraints on the system states and overcoming the influence of input saturation on the system. The efficacy of our proposed control approach is validated through a simulation example.