<p>Existing adaptive sampling mechanisms often face a rigid trade-off between communication reduction and control performance, and may suffer from continuous sampling (Zeno behavior) near equilibrium points. To overcome these limitations, this paper proposes an improved event-triggered control mechanism for nonlinear systems. Distinct from traditional approaches, the proposed framework explicitly incorporates the sampling error as an auxiliary variable within an augmented system model. This design allows for the rigorous exclusion of Zeno behavior while guaranteeing semi-global asymptotic stability. A key innovation of this work is the introduction of a novel tuning term within the triggering condition. This term enables flexible adjustment of the sampling frequency without compromising the established control performance—a feature rarely achieved in conventional adaptive sampling designs. Furthermore, the proposed strategy is extended to solve the adaptive asymptotic tracking control problem. Comparative simulations verify that the proposed mechanism significantly outperforms existing methods in balancing sampling efficiency and system stability.</p>