Proceedings of the IFAC World Congress. International Federation of Automatic Control. World Congress最新文献

The growing prevalence of obesity and related health problems warrants immediate need for effective weight control interventions. Quantitative energy balance models serve as powerful tools to assist in these interventions, as a result of their ability to accurately predict individual weight change based on reliable measurements of energy intake and energy expenditure. However, the data collected in most existing weight interventions is self-monitored; these measurements often have significant noise or experience losses resulting from participant non-adherence, which in turn, limits accurate model estimation. To address this issue, we develop a Kalman filter-based estimation algorithm for a practical scenario where on-line state estimation for weight, or energy intake/expenditure is still possible despite correlated partial data losses. To account for non-linearities in the models, an algorithm based on extended Kalman filtering is also developed for sequential state estimation in the presence of missing data. Simulation studies are presented to illustrate the performance of the algorithms and the potential benefits of these techniques in real-life interventions.

肥胖症和相关健康问题日益普遍，迫切需要有效的体重控制干预措施。能量平衡定量模型能够根据可靠的能量摄入和能量消耗测量结果准确预测个人体重变化，是协助这些干预措施的有力工具。然而，现有的大多数体重干预措施所收集的数据都是自我监测的；这些测量结果往往有很大的噪音，或者由于参与者不坚持测量而造成损失，这反过来又限制了模型的准确估算。为了解决这个问题，我们开发了一种基于卡尔曼滤波器的估算算法，在实际应用中，尽管存在相关的部分数据损失，但仍可对体重或能量摄入/消耗进行在线状态估算。为了考虑模型中的非线性因素，还开发了一种基于扩展卡尔曼滤波的算法，用于在数据缺失的情况下进行顺序状态估计。模拟研究说明了这些算法的性能以及这些技术在实际干预中的潜在优势。

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引用次数: 0

Nonlinear Model Predictive Control For Circadian Entrainment Using Small-Molecule Pharmaceuticals. 利用小分子药物进行昼夜节律调节的非线性模型预测控制

Proceedings of the IFAC World Congress. International Federation of Automatic Control. World Congress

Pub Date : 2017-07-01 Epub Date: 2017-10-18 DOI: 10.1016/j.ifacol.2017.08.1596

John H Abel, Ankush Chakrabarty, Francis J Doyle

Recent in vitro studies have identified small-molecule pharmaceuticals effecting dose-dependent changes in the mammalian circadian clock, providing a novel avenue for control. Most studies employ light for clock control, however, pharmaceuticals are advantageous for clock manipulation through reduced invasiveness. In this paper, we employ a mechanistic model to predict the phase dynamics of the mammalian circadian oscillator under the effect of the pharmaceutical under investigation. These predictions are used to inform a constrained model predictive controller (MPC) to compute appropriate dosing for clock re-entrainment. Constraints in the formulation of the MPC problem arise from variation in the phase response curves (PRCs) describing drug effects, and are in many cases non-intuitive owing to the nonlinearity of oscillator phase response effects. We demonstrate through in-silico experiments that it is imperative to tune the MPC parameters based on the drug-specific PRC for optimal phase manipulation.

最近的体外研究发现，小分子药物可影响哺乳动物昼夜节律钟的剂量依赖性变化，为控制提供了一条新途径。大多数研究利用光来控制时钟，然而，药剂因其侵袭性较低而在时钟控制方面具有优势。在本文中，我们采用一个机理模型来预测哺乳动物昼夜节律振荡器在所研究药物作用下的相位动态。这些预测结果将用于为受限模型预测控制器（MPC）提供信息，以计算时钟再诱导的适当剂量。由于振荡器相位响应效应的非线性，在许多情况下是非直观的。我们通过室内实验证明，必须根据特定药物的相位响应曲线调整 MPC 参数，以实现最佳的相位控制。

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引用次数: 0

System Identification Algorithm for Non-Uniformly Sampled Data. 非均匀采样数据的系统识别算法

Proceedings of the IFAC World Congress. International Federation of Automatic Control. World Congress

Pub Date : 2017-07-01 DOI: 10.1016/j.ifacol.2017.08.1460

Korkut Bekiroglu, Constantino Lagoa, Stephanie T Lanza, Mario Sznaier

Considerable effort has been devoted to the development of algorithms for identification of parsimonious discrete time models from noisy input/output data sets since this facilitates controller design. Several methods, such as nuclear norm minimization, have been used to provide approximate solutions to this non-convex problem. However, even though the field of continuous time system identification is now mature, results on parsimonious model identification of continuous time systems are still very limited. In this paper, an atomic norm minimization method is proposed for this purpose that can handle non-uniformly sampled data without preprocessing. The proposed approach provides an efficient way to use noisy, non-uniformly sampled data to determine a reliable, low-order continuous time model. Numerical performance is illustrated using academic examples and simulated behavioral data from a smoking cessation study.

人们一直致力于开发从噪声输入/输出数据集中识别离散时间模型的算法，因为这有助于控制器的设计。有几种方法（如核规范最小化）已被用于为这一非凸问题提供近似解。然而，尽管连续时间系统识别领域目前已经非常成熟，但有关连续时间系统准模型识别的成果仍然非常有限。本文为此提出了一种原子规范最小化方法，无需预处理即可处理非均匀采样数据。所提出的方法提供了一种有效的方法，利用有噪声的非均匀采样数据来确定可靠的低阶连续时间模型。利用学术实例和戒烟研究中的模拟行为数据对数值性能进行了说明。

引用次数: 0

The Business of America Is Lobbying 美国的商业是游说

Proceedings of the IFAC World Congress. International Federation of Automatic Control. World Congress

Pub Date : 2015-05-01 DOI: 10.1093/ACPROF:OSO/9780190215514.003.0010

Lee Drutman

引用次数: 132

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