Adaptive systems manage and regulate the behavior of devices or other systems using control loops to automatically adjust the value of some measured variables to equal the value of a desired set-point. These systems normally interact with physical parts or operate in physical environments, where uncertainty is unavoidable. Traditional approaches to manage that uncertainty use either robust control algorithms that consider bounded variations of the uncertain variables and worst-case scenarios or adaptive control methods that estimate the parameters and change the control laws accordingly. In this article, we propose to include the sources of uncertainty in the system models as first-class entities using random variables to simulate adaptive and control systems more faithfully, including not only the use of random variables to represent and operate with uncertain values but also to represent decisions based on their comparisons. Two exemplar systems are used to illustrate and validate our proposal.

自适应系统使用控制回路来管理和调节设备或其他系统的行为，以自动调整某些测量变量的值以等于所需设定点的值。这些系统通常与物理部件交互或在物理环境中运行，其中不确定性是不可避免的。管理这种不确定性的传统方法要么使用考虑不确定变量和最坏情况的有限变化的鲁棒控制算法，要么使用估计参数并相应地改变控制法则的自适应控制方法。在本文中，我们建议将系统模型中的不确定性来源作为一流实体使用随机变量来更忠实地模拟自适应和控制系统，不仅包括使用随机变量来表示和操作不确定值，还包括表示基于它们的比较的决策。两个示例系统用于说明和验证我们的建议。