In this paper, a robust control approach is used to address the problem of adaptive behavioral treatment design. Human behavior (e.g., smoking and exercise) and reactions to treatment are complex and depend on many unmeasurable external stimuli, some of which are unknown. Thus, it is crucial to model human behavior over many subject responses. We propose a simple (low order) uncertain affine model subject to uncertainties whose response covers the most probable behavioral responses. The proposed model contains two different types of uncertainties: uncertainty of the dynamics and external perturbations that patients face in their daily life. Once the uncertain model is defined, we demonstrate how least absolute shrinkage and selection operator (lasso) can be used as an identification tool. The lasso algorithm provides a way to directly estimate a model subject to sparse perturbations. With this estimated model, a robust control algorithm is developed, where one relies on the special structure of the uncertainty to develop efficient optimization algorithms. This paper concludes by using the proposed algorithm in a numerical experiment that simulates treatment for the urge to smoke.

中文翻译：

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用于设计稳健行为治疗的控制工程方法


在本文中，采用鲁棒控制方法来解决适应性行为治疗设计问题。人类行为（例如吸烟和运动）和对治疗的反应是复杂的，并且取决于许多无法测量的外部刺激，其中一些是未知的。因此，根据许多受试者反应对人类行为进行建模至关重要。我们提出了一个简单的（低阶）不确定仿射模型，该模型受到不确定性的影响，其响应涵盖了最可能的行为响应。所提出的模型包含两种不同类型的不确定性：动态的不确定性和患者在日常生活中面临的外部扰动。一旦定义了不确定模型，我们将演示如何使用最小绝对收缩和选择算子（套索）作​​为识别工具。套索算法提供了一种直接估计受到稀疏扰动的模型的方法。利用这个估计模型，开发了一种鲁棒控制算法，依靠不确定性的特殊结构来开发有效的优化算法。本文通过在模拟吸烟冲动治疗的数值实验中使用所提出的算法得出结论。