Event-triggered adaptive prescribed performance control for a class of pure-feedback stochastic nonlinear systems with input saturation constraints

Aiming at the tracking control problem of non-affine stochastic nonlinear systems with input saturation constraints, the event trigger control (ETC) based on Radial basic function neural networks (RBFNNs) and prescribed performance control (PPC) is considered. First, the mean value theorem is used to decouple the non-affine terms existing in the system. Second, the design process of PPC is reconstructed for a class of stochastic nonlinear systems, because the existence of stochastic disturbances has not been fully considered in previous literature on PPC, so that the system output is not to violate the set constraint bound by preset function. Finally, unlike the existing event triggering results, a special event triggering strategy is designed, which further takes into account the error between the preset function and the system output and the errors in all states of stochastic nonlinear systems, so it is expected that the amount of communications will be further reduced. Also, the proposed adaptive PPC scheme with event triggering mechanism can guarantee that all closed-loop signals are uniformly ultimately bounded (UUB) in probability within the appropriate compact sets. Finally, the effectiveness of the proposed method is verified by a practical example.