Memory-type control charts are widely used for monitoring small to moderate shifts in the process parameter(s). In the present article, we present an exponentiated exponentially weighted moving average (Exp-EWMA) control chart that weights the past observations of a process using an exponentiated function. We evaluated the run-length characteristics of the Exp-EWMA chart via Monte Carlo simulations. A comparison study versus the CUSUM, EWMA and extended EWMA (EEWMA) charts under similar in-control (IC) run-length properties demonstrates that the Exp-EWMA chart is more effective for detecting small and, under certain circumstances, moderate shifts for both the zero-state and steady-state cases. Moreover, the Exp-EWMA chart has better zero-state out-of-control (OOC) performance than an EWMA chart with smoothing parameter equal to the limit to the infinity of the exponentiated function, while the two charts perform similarly for the steady-state case. Finally, it is shown that the Exp-EWMA chart is more IC robust than its competitors under several non-normal distributions. Two examples are provided to explain the implementation of the proposed chart

记忆型控制图广泛用于监控过程参数的小到中等变化。在本文中，我们提出了一个指数加权移动平均 (Exp-EWMA) 控制图，该控制图使用指数函数对过程的过去观测值进行加权。我们通过蒙特卡罗模拟评估了 Exp-EWMA 图的游程特征。在类似的受控 (IC) 游程长度属性下与 CUSUM、EWMA 和扩展 EWMA (EEWMA) 图进行的比较研究表明，Exp-EWMA 图对于检测两者的微小变化（在某些情况下为中等变化）更为有效零状态和稳态情况。此外，Exp-EWMA 图比平滑参数等于幂函数无穷大极限的 EWMA 图具有更好的零状态失控 (OOC) 性能，而这两个图对于稳态失控 (OOC) 性能相似。状态案例。最后，结果表明，在几种非正态分布下，Exp-EWMA 图比竞争对手的 IC 更稳健。提供了两个示例来解释拟议图表的实施