Event-triggered computation-reducing fuzzy intelligence control incorporating fixed-time predefined behaviors in discrete-time

Abstract

The focus of this article lies in the computation-reducing fuzzy intelligence control of discrete-time systems with unknown nonlinearities through the event-triggered mechanism, aiming to confine the system output within a prescribed envelope to satisfy a fixed convergence time and achieve a given steady-state value. Firstly, we introduce an innovative performance function that enforces fixed-time prescribed qualities on the discrete-time system output. Subsequently, we transform the boundary constraint into an error term which is further utilized to define an intermediate variable function. In contrast, instead of using the transformed error, we employ the intermediate variable function to design a discrete-time prescribed performance controller, presenting a new framework distinct from existing sliding-mode-control-based structures. Moreover, our approach requires only one fuzzy estimator for nonlinearity approximation while incorporating an event-triggered adaptive law for updating fuzzy weights, resulting in a low-complexity implementation with reduced computational cost. Finally, comparative simulation results validate its superiority.