Control Engineering Methods for the Design of Robust Behavioral Treatments.
Control Engineering Methods for the Design of Robust Behavioral Treatments.
复制标题
DOI：
10.1109/tcst.2016.2580661
复制
发表时间：
2017-05
期刊：
影响因子：
--
通讯作者：
Lanza ST
中科院分区：
文献类型：
--
作者：
Bekiroglu K;Lagoa C;Murphy SA;Lanza ST
本文采用鲁棒控制方法来解决自适应行为治疗设计问题。人类行为（如吸烟和运动）和对治疗的反应是复杂的，依赖于许多无法测量的外部刺激，其中一些是未知的。因此，对许多主体反应的人类行为进行建模是至关重要的。我们提出了一个简单的（低阶）不确定仿射模型，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：动力学的不确定性和患者在日常生活中面临的外部扰动。一旦不确定模型被定义，我们展示了最小绝对收缩和选择算子（套索）如何被用作识别工具。lasso算法提供了一种直接估计受稀疏扰动影响的模型的方法。利用这一估计模型，提出了一种鲁棒控制算法，其中依赖于不确定性的特殊结构来开发有效的优化算法。最后，本文将该算法应用于模拟吸烟冲动治疗的数值实验。
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.