Event-triggered low-computation adaptive output-feedback fuzzy tracking control of uncertain nonlinear systems

A fuzzy adaptive tracking control scheme is studied for a family of uncertain systems with immeasurable system states. The controller takes up few computation and transmission resources to achieve prescribed boundaries of the dynamic and steady-state performance indicators. Compared with the existing schemes, the low computational complexity is reflected in the following two points: (1) a fuzzy state observer is introduced, where only the estimation of states are incorporated into the input space of fuzzy logic systems (FLSs). (2) The problem of complexity explosion can be avoided without utilizing additional command filters or auxiliary dynamic surface control techniques. In addition, using the event-triggered control scheme, the data in the transmission is significantly reduced. Finally, the effectiveness of the scheme is fully verified by simulation.