Event-triggered-based fuzzy adaptive tracking control for stochastic nonlinear systems against multiple constraints

Abstract

In this article, we propose an adaptive event-triggered fuzzy dynamic surface controller for non-strict feedback stochastic nonlinear systems, where the nonlinear stochastic system exhibits input dead-zone characteristics and state constraints. Firstly, in contrast to many existing adaptive backstepping control results, we consider designing a dynamic surface control strategy to simplify complexity and enhance performance in stochastic systems. Then, the unknown nonlinearities within the system are addressed with the help of the approximation capability of the fuzzy logic system. It is worth noting that the design of the event-triggered mechanism effectively reduces resource waste in the data channel and improves communication efficiency. In addition, the adaptive backstepping control is combined with the barrier Lyapunov function in a unified framework to handle the state constraints. Resorting to a novel auxiliary system, the effect of the input dead-zone nonlinearity is countered. The presented fuzzy dynamic surface control scheme not only ensures the semi-globally uniformly ultimately bounded of the controlled system but also constrains the state within a fixed range and gets rid of the Zeno phenomenon. Finally, numerical simulation results validate the effectiveness of the control strategy and illustrate the feasibility of the controller through a single-link manipulator system.