  In this article, we develop a new adaptive event-triggered asymptotic control
scheme for strict-feedback systems with fast time-varying parameters. To deal
with time-varying parameters with unknown variation boundaries in the feedback
path and the input path, we construct three adaptive laws for parameter
estimation, two for the uncertain parameters in the feedback path and one for
the uncertain parameters in the input path. In particular, two sets of tuning
functions are introduced to avoid over-parametrization. Additionally, an
event-triggering mechanism is embedded in this adaptive control framework to
reduce the data transmission from the controller to the actuator. We also
introduce a soft sign function to handle the perturbations caused by sampling
errors to achieve asymptotic stability and avoid the so-called parameter drift.
The stability analysis shows that the closed-loop system is globally uniformly
asymptotically stable and the Zeno behavior can be excluded. Simulation results
verify the effectiveness and performance of the proposed adaptive scheme.
