Low-Computation Adaptive Saturated Self-Triggered Tracking Control of Uncertain Networked Systems

In this paper, a low-computation adaptive self-triggered tracking control scheme is proposed for class of strict-feedback nonlinear systems with input saturation. By introducing two novel error transformation functions, the designed can overcome problem complexity explosion in absence any filters, such that developed more applicable. addition, to save communication resources networked systems, strategy which predict next trigger point based on current information. Compared traditional event-triggered mechanisms, computational burden arising from continuous monitoring threshold conditions was successfully avoided. Furthermore, saturation considered paper prevents overload phenomenon caused by signal jumps, and adverse effects are compensated an auxiliary system. The effectiveness verified through simulation example.