AUTOMATICA自动学
AUTOMATICA(英文缩写 AUTOMATICA),ISSN 0005-1098,eISSN 1873-2836,中文译名:自动学 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 6.150 | Q1 |
| 2022 | 6.400 | Q1 |
| 2023 | 4.800 | Q1 |
| 2024 | 5.900 | Q1 |
| 2025 | 6.200 | Q1 |
AUTOMATICA 最新收录文献
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1. Tilted Least Squares Robust Estimators.
PMID:日期:2025-11-24In practical scenarios, collected data for identification may be contaminated by unexpected disturbances with large amplitudes. In such cases, the ordinary least squares estimator, commonly used for identification, may fail to deliver satisfactory performance. To address this issue, robust estimators that can withstand the influence of contaminated data become essential. This paper introduces the tilted least squares (TLS) robust estimator for handling outliers and heavy-tailed noises, which incorporates a weighted quadratic loss function, with the weights constrained by the Kullback-Leibler (KL) divergence. It is proposed that the TLS estimator assigns weights to each data point as an exponential function of the negative squared residuals, effectively mitigating the influence of unexpected disturbances with large amplitudes. Additionally, a tuning criterion is derived for automatically estimating the size of the KL divergence. Furthermore, it is demonstrated that a specific variant of the TLS estimator is equivalent to the relaxed least trimmed squares (RTLS) estimator and the almost sure convergence of the RTLS estimator is also established in the presence of heavy-tailed noises with infinite variance.
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2. Optimal Adaptive Testing for Epidemic Control: Combining Molecular and Serology Tests.
PMID:日期:2023-12-13Epidemic interventions based on surveillance testing programs are a fundamental tool to control the first stages of new epidemics, yet they are costly, invasive and rely on scarce resources, limiting their applicability. To overcome these challenges, we investigate two optimal control problems: (i) how testing needs can be minimized while maintaining the number of infected individuals below a desired threshold, and (ii) how peak infections can be minimized given a typically scarce testing budget. We find that in both cases the optimal testing policy for the well-known Susceptible-Infected-Recovered (SIR) model is adaptive, with testing rates that depend on the epidemic state, and leads to significant cost savings compared to non-adaptive policies. By using the concept of observability, we then show that a central planner can estimate the required unknown epidemic state by complementing molecular tests, which are highly sensitive but have a short detectability window, with serology tests, which are less sensitive but can detect past infections.
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3. A Markovian model for the spread of the SARS-CoV-2 virus.
PMID:日期:2023-02-15We propose a Markovian stochastic approach to model the spread of a SARS-CoV-2-like infection within a closed group of humans. The model takes the form of a Partially Observable Markov Decision Process (POMDP), whose states are given by the number of subjects in different health conditions. The model also exposes the different parameters that have an impact on the spread of the disease and the various decision variables that can be used to control it (e.g, social distancing, number of tests administered to single out infected subjects). The model describes the stochastic phenomena that underlie the spread of the epidemic and captures, in the form of deterministic parameters, some fundamental limitations in the availability of resources (hospital beds and test swabs). The model lends itself to different uses. For a given control policy, it is possible to if it satisfies an analytical property on the stochastic evolution of the state (e.g., to compute probability that the hospital beds will reach a fill level, or that a specified percentage of the population will die). If the control policy is not given, it is possible to apply POMDP techniques to identify an optimal control policy that fulfils some specified probabilistic goals. Whilst the paper primarily aims at the model description, we show with numeric examples some of its potential applications.
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4. Tracking and blind deconvolution of blood alcohol concentration from transdermal alcohol biosensor data: A population model-based LQG approach in Hilbert space.
4. 基于群体模型的希尔伯特空间LQG方法：从透皮酒精生物传感器数据中追踪和盲反卷积血液酒精浓度PMID:日期:2022-11-17LQG control in Hilbert space, a novel approach for random abstract parabolic systems, and new transdermal alcohol biosensor technology are combined to yield tracking controllers that can be used to automate inpatient management of alcohol withdrawal syndrome and human subject intravenous alcohol infusion studies, and to blindly deconvolve blood or breath alcohol concentration from biosensor measured transdermal alcohol level. The approach taken is based on a full-body alcohol population model in the form of a random, nonlinear, hybrid system of ordinary and partial differential equations and its abstract formulation in a Gelfand triple of Bochner spaces. The efficacy of the approach is demonstrated through simulation studies based on laboratory collected drinking data.
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5. Minimizing the epidemic final size while containing the infected peak prevalence in SIR systems.
5. 在SIR系统中控制感染峰值流行率的同时最小化流行病最终规模PMID:日期:2022-07-30Mathematical models are critical to understand the spread of pathogens in a population and evaluate the effectiveness of non-pharmaceutical interventions (NPIs). A plethora of optimal strategies has been recently developed to minimize either the infected peak prevalence ( ) or the epidemic final size ( ). While most of them optimize a simple cost function along a fixed finite-time horizon, no consensus has been reached about how to simultaneously handle the and the , while minimizing the intervention's side effects. In this work, based on a new characterization of the dynamical behaviour of SIR-type models under control actions (including the stability of equilibrium sets in terms of herd immunity), we study how to minimize the while keeping the controlled at any time. A procedure is proposed to tailor NPIs by separating transient from stationary control objectives: the potential benefits of the strategy are illustrated by a detailed analysis and simulation results related to the COVID-19 pandemic.
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6. Closed-form expressions and nonparametric estimation of COVID-19 infection rate.
6. COVID-19感染率的闭式表达式与非参数估计PMID:日期:2022-04-02Quantitative assessment of the infection rate of a virus is key to monitor the evolution of an epidemic. However, such variable is not accessible to direct measurement and its estimation requires the solution of a difficult inverse problem. In particular, being the result not only of biological but also of social factors, the transmission dynamics can vary significantly in time. This makes questionable the use of parametric models which could be unable to capture their full complexity. In this paper we exploit compartmental models which include important COVID-19 peculiarities (like the presence of asymptomatic individuals) and allow the infection rate to assume any continuous-time profile. We show that these models are , i.e. capable to reproduce exactly any epidemic evolution, and extract from them of the infection rate time-course. Building upon such expressions, we then design a able to reconstruct COVID-19 transmission dynamics in continuous-time. Using real data collected in Italy, our technique proves to be an useful tool to monitor COVID-19 transmission dynamics and to predict and assess the effect of lockdown restrictions.
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7. Persistence of Excitation for Identifying Switched Linear Systems.
7. 切换线性系统辨识中的激励持续性PMID:日期:2022-01-07This paper investigates the uniqueness of parameters via persistence of excitation for switched linear systems. The main contribution is a much weaker sufficient condition on the regressors to be persistently exciting that guarantees the uniqueness of the parameter sets and also provides new insights in understanding the relation among different subsystems. It is found that for uniquely determining the parameters of switched linear systems, the needed minimum number of samples derived from our sufficient condition is much smaller than that reported in the literature.
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8. A fully distributed traffic allocation algorithm for nonconcave utility maximization in connectionless communication networks.
8. 一种用于无连接通信网络中非凹效用最大化的全分布式流量分配算法PMID:日期:2019-08-01As IP video services have emerged to be the predominant Internet application, how to optimize the Internet resource allocation, while satisfying the quality of experience (QoE) for users of video services and other Internet applications becomes a challenge. This is because the QoE perceived by a user of video services can be characterized by a staircase function of the data rate, which is nonconcave and hence it is "hard" to find the optimal operating point. The work in this paper aims at tackling this challenge. It considers the packet routing problem among multiple end points in packet switching networks based on a connectionless, hop-by-hop forwarding paradigm. We model this traffic allocation problem using a fluid flow model and let the link bandwidth be the only resource to be shared. To maximize the utilization of resources and avoid congestion, we formulate the problem as a network utility maximization problem. More precisely, the objective of this paper is to design a Fully Distributed Traffic Allocation Algorithm (FDTAA) that is applicable to a large class of nonconcave utility functions. Moreover, FDTAA runs in a fully distributed way: it enables each router to independently address and route each data unit using immediate local information in parallel, without referring to any global information of the communication network. FDTAA requires minimum computation workload, since the routing decision made at each router is solely based on the destination information carried in each unit. In addition, the network utility values corresponding to the FDTAA iterate sequence converge to the optimal network utility value at the rate of (1/), where is the iteration counter. These theoretical results are exemplified by the simulation performed on an example communication network.
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9. Estimating the Distribution of Random Parameters in a Diffusion Equation Forward Model for a Transdermal Alcohol Biosensor.
PMID:日期:2019-05-16We estimate the distribution of random parameters in a distributed parameter model with unbounded input and output for the transdermal transport of ethanol in humans. The model takes the form of a diffusion equation with the input being the blood alcohol concentration and the output being the transdermal alcohol concentration. Our approach is based on the idea of reformulating the underlying dynamical system in such a way that the random parameters are now treated as additional space variables. When the distribution to be estimated is assumed to be defined in terms of a joint density, estimating the distribution is equivalent to estimating the diffusivity in a multi-dimensional diffusion equation and thus well-established finite dimensional approximation schemes, functional analytic based convergence arguments, optimization techniques, and computational methods may all be employed. We use our technique to estimate a bivariate normal distribution based on data for multiple drinking episodes from a single subject.
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10. Pharmaceutical-based entrainment of circadian phase via nonlinear model predictive control.
10. 基于药物的昼夜节律相位牵引通过非线性模型预测控制PMID:日期:2018-12-10The widespread adoption of closed-loop control in systems biology has resulted from improvements in sensors, computing, actuation, and the discovery of alternative sites of targeted drug delivery. Most control algorithms for circadian phase resetting exploit light inputs. However, recently identified small-molecule pharmaceuticals offer advantages in terms of invasiveness and potency of actuation. Herein, we develop a systematic method to control the phase of biological oscillations motivated by the recently identified small molecule circadian pharmaceutical KL001. The model-based control architecture exploits an infinitesimal parametric phase response curve (ipPRC) that is used to predict the effect of control inputs on future phase trajectories of the oscillator. The continuous time optimal control policy is first derived for phase resetting, based on the ipPRC and Pontryagin's maximum principle. Owing to practical challenges in implementing a continuous time optimal control policy, we investigate the effect of implementing the continuous time policy in a sampled time format. Specifically, we provide bounds on the errors incurred by the physiologically tractable sampled time control law. We use these results to select directions of resetting (i.e. phase advance or delay), sampling intervals, and prediction horizons for a nonlinear model predictive control (MPC) algorithm for phase resetting. The potential of this ipPRC-informed pharmaceutical nonlinear MPC is then demonstrated using real-world scenarios of jet lag or rotating shift work.