Frontiers in control engineering最新文献_第5页

Power Allocation for Remote Estimation Over Known and Unknown Gilbert-Elliott Channels 已知和未知吉尔伯特-艾略特信道远程估计的功率分配

Frontiers in control engineering Pub Date : 2022-04-14 DOI: 10.3389/fcteg.2022.861055

Tahmoores Farjam, Themistoklis Charalambous

{"title":"Power Allocation for Remote Estimation Over Known and Unknown Gilbert-Elliott Channels","authors":"Tahmoores Farjam, Themistoklis Charalambous","doi":"10.3389/fcteg.2022.861055","DOIUrl":"https://doi.org/10.3389/fcteg.2022.861055","url":null,"abstract":"In this paper, we consider the problem of power scheduling of a sensor that transmits over a (possibly) unknown Gilbert-Elliott (GE) channel for remote state estimation. The sensor supports two power modes, namely low power, and high power. The scheduling policy determines when to use low power or high power for data transmission over a fading channel with temporal correlation while satisfying the energy constraints. Although error-free acknowledgement/negative-acknowledgement (ACK/NACK) signals are provided by the remote estimator, they only provide meaningful information about the underlying channel state when low power is utilized. This leads to a partially observable Markov decision process (POMDP) problem and we derive conditions that preserve the optimality of a stationary schedule derived for its fully observable counterpart. However, implementing this schedule requires knowledge of the parameters of the GE model which are not available in practice. To address this, we adopt a Bayesian framework to learn these parameters online and propose an algorithm that is shown to satisfy the energy constraint while achieving near-optimal performance via simulation.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42903757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilient Biomedical Systems Design Under Noise Using Logic-Based Machine Learning 基于逻辑的机器学习在噪声环境下的弹性生物医学系统设计

Frontiers in control engineering Pub Date : 2022-04-08 DOI: 10.3389/fcteg.2021.778118

Tousif Rahman, R. Shafik, Ole-Christoffer Granmo, A. Yakovlev

{"title":"Resilient Biomedical Systems Design Under Noise Using Logic-Based Machine Learning","authors":"Tousif Rahman, R. Shafik, Ole-Christoffer Granmo, A. Yakovlev","doi":"10.3389/fcteg.2021.778118","DOIUrl":"https://doi.org/10.3389/fcteg.2021.778118","url":null,"abstract":"Increased reliance on electronic health records and plethora of new sensor technologies has enabled the use of machine learning (ML) in medical diagnosis. This has opened up promising opportunities for faster and automated decision making, particularly in early and repetitive diagnostic routines. Nevertheless, there are also increased possibilities of data aberrance arising from environmentally induced noise. It is vital to create ML models that are resilient in the presence of data noise to minimize erroneous classifications that could be crucial. This study uses a recently proposed ML algorithm called the Tsetlin machine (TM) to study the robustness against noise-injected medical data. We test two different feature extraction methods, in conjunction with the TM, to explore how feature engineering can mitigate the impact of noise corruption. Our results show the TM is capable of effective classification even with a signal-to-noise ratio (SNR) of −15dB as its training parameters remain resilient to noise injection. We show that high testing data sensitivity can still be possible at very low SNRs through a balance of feature distribution–based discretization and a rule mining algorithm used as a noise filtering encoding method. Through this method we show how a smaller number of core features can be extracted from a noisy problem space resulting in reduced ML model complexity and memory footprint—in some cases up to 6x fewer training parameters while retaining equal or better performance. In addition, we investigate the cost of noise resilience in terms of energy when compared with recently proposed binarized neural networks.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49127297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

An Ellipsoidal Predictor–Corrector State Estimation Scheme for Linear Continuous-Time Systems With Bounded Parameters and Bounded Measurement Errors 具有有界参数和有界测量误差的线性连续系统的椭球预测-校正状态估计方案

Frontiers in control engineering Pub Date : 2022-03-25 DOI: 10.3389/fcteg.2022.785795

A. Rauh, Simon Rohou, L. Jaulin

引用次数: 5

Signal-to-Noise Ratio Based Fault Detection and Identification 基于信噪比的故障检测与识别

Frontiers in control engineering Pub Date : 2022-03-18 DOI: 10.3389/fcteg.2022.806558

A. Rojas, Hugo O. Garcés

引用次数: 1

Aerial Survey Robotics in Extreme Environments: Mapping Volcanic CO2 Emissions With Flocking UAVs 极端环境中的航空测量机器人:用蜂群无人机绘制火山二氧化碳排放图

Frontiers in control engineering Pub Date : 2022-03-15 DOI: 10.3389/fcteg.2022.836720

John Ericksen, G. M. Fricke, S. Nowicki, T. Fischer, Julie Hayes, Karissa Rosenberger, Samantha R. Wolf, R. Fierro, M. Moses

{"title":"Aerial Survey Robotics in Extreme Environments: Mapping Volcanic CO2 Emissions With Flocking UAVs","authors":"John Ericksen, G. M. Fricke, S. Nowicki, T. Fischer, Julie Hayes, Karissa Rosenberger, Samantha R. Wolf, R. Fierro, M. Moses","doi":"10.3389/fcteg.2022.836720","DOIUrl":"https://doi.org/10.3389/fcteg.2022.836720","url":null,"abstract":"We present methods for autonomous collaborative surveying of volcanic CO2 emissions using aerial robots. CO2 is a useful predictor of volcanic eruptions and an influential greenhouse gas. However, current CO2 mapping methods are hazardous and inefficient, as a result, only a small fraction of CO2 emitting volcanoes have been surveyed. We develop algorithms and a platform to measure volcanic CO2 emissions. The Dragonfly Unpiloted Aerial Vehicle (UAV) platform is capable of long-duration CO2 collection flights in harsh environments. We implement two survey algorithms on teams of Dragonfly robots and demonstrate that they effectively map gas emissions and locate the highest gas concentrations. Our experiments culminate in a successful field test of collaborative rasterization and gradient descent algorithms in a challenging real-world environment at the edge of the Valles Caldera supervolcano. Both algorithms treat multiple flocking UAVs as a distributed flexible instrument. Simultaneous sensing in multiple UAVs gives scientists greater confidence in estimates of gas concentrations and the locations of sources of those emissions. These methods are also applicable to a range of other airborne concentration mapping tasks, such as pipeline leak detection and contaminant localization.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44274833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Integration of Photovoltaic Generation Within a Modeling Framework for Energy Hubs 能源枢纽建模框架中的光伏发电集成

Frontiers in control engineering Pub Date : 2022-03-10 DOI: 10.3389/fcteg.2022.833146

J. Ramos-Teodoro, F. Rodríguez, M. Berenguel

{"title":"Integration of Photovoltaic Generation Within a Modeling Framework for Energy Hubs","authors":"J. Ramos-Teodoro, F. Rodríguez, M. Berenguel","doi":"10.3389/fcteg.2022.833146","DOIUrl":"https://doi.org/10.3389/fcteg.2022.833146","url":null,"abstract":"Energy efficiency is a topic with many publications related to resource exploitation at a local scale; via well-performed energy management, substantial environmental and economic benefits can be achieved. In this article, the models used to forecast the photovoltaic power yield in two distinct facilities are described. These facilities are part of the same production plant, which makes use of different heterogeneous resources (carbon dioxide, water, thermal energy, and electricity) and has already been analyzed in a problem that consists in finding the set of variables that minimize the operation cost. In order to predict the power production for both photovoltaic fields, the expressions for radiation on sloped surfaces and the equivalent circuit for solar cells are employed, and the inverters and wire-transmission loss effects are considered. Furthermore, their integration within a general-purpose modeling framework for energy hubs is demonstrated. The comparison between validation results and production real data shows that despite the slight overestimation of the energy yield, the models are suitable to forecast the production of both facilities with a suitable accuracy, as the R 2 coefficients of both facilities lie between 0.95 and 0.96.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44674970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Online Interval-Based Inertial Navigation System for Control Purposes of Autonomous Boats 一种用于自主船控制的基于区间的在线惯性导航系统

Frontiers in control engineering Pub Date : 2022-03-04 DOI: 10.3389/fcteg.2021.786188

Fabrice Le Bars, Robin Sanchez , Luc Jaulin , Simon Rohou , Andreas Rauh

引用次数: 2

Interval Extension of Neural Network Models for the Electrochemical Behavior of High-Temperature Fuel Cells 高温燃料电池电化学行为神经网络模型的区间扩展

Frontiers in control engineering Pub Date : 2022-03-02 DOI: 10.3389/fcteg.2022.785123

A. Rauh, E. Auer

{"title":"Interval Extension of Neural Network Models for the Electrochemical Behavior of High-Temperature Fuel Cells","authors":"A. Rauh, E. Auer","doi":"10.3389/fcteg.2022.785123","DOIUrl":"https://doi.org/10.3389/fcteg.2022.785123","url":null,"abstract":"In various research projects, it has been demonstrated that feedforward neural network models (possibly extended toward dynamic representations) are efficient means for identifying numerous dependencies of the electrochemical behavior of high-temperature fuel cells. These dependencies include external inputs such as gas mass flows, gas inlet temperatures, and the electric current as well as internal fuel cell states such as the temperature. Typically, the research on using neural networks in this context is focused only on point-valued training data. As a result, the neural network provides solely point-valued estimates for such quantities as the stack voltage and instantaneous fuel cell power. Although advantageous, for example, for robust control synthesis, quantifying the reliability of neural network models in terms of interval bounds for the network’s output has not yet received wide attention. In practice, however, such information is essential for optimizing the utilization of the supplied fuel. An additional goal is to make sure that the maximum power point is not exceeded since that would lead to accelerated stack degradation. To solve the data-driven modeling task with the focus on reliability assessment, a novel offline and online parameterization strategy for interval extensions of neural network models is presented in this paper. Its functionality is demonstrated using real-life measured data for a solid oxide fuel cell stack that is operated with temporally varying electric currents and fuel gas mass flows.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42650117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Control of Jump Markov Uncertain Linear Systems With General Probability Distributions 具有一般概率分布的跳跃马尔可夫不确定线性系统的控制

Frontiers in control engineering Pub Date : 2022-02-25 DOI: 10.3389/fcteg.2022.806543

Patrick Flüs, O. Stursberg

{"title":"Control of Jump Markov Uncertain Linear Systems With General Probability Distributions","authors":"Patrick Flüs, O. Stursberg","doi":"10.3389/fcteg.2022.806543","DOIUrl":"https://doi.org/10.3389/fcteg.2022.806543","url":null,"abstract":"This paper introduces a method to control a class of jump Markov linear systems with uncertain initialization of the continuous state and affected by disturbances. Both types of uncertainties are modeled as stochastic processes with arbitrarily chosen probability distributions, for which however, the expected values and (co-)variances are known. The paper elaborates on the control task of steering the uncertain system into a target set by use of continuous controls, while chance constraints have to be satisfied for all possible state sequences of the Markov chain. The proposed approach uses a stochastic model predictive control approach on moving finite-time horizons with tailored constraints to achieve the control goal with prescribed confidence. Key steps of the procedure are (i) to over-approximate probabilistic reachable sets by use of the Chebyshev inequality, and (ii) to embed a tightened version of the original constraints into the optimization problem, in order to obtain a control strategy satisfying the specifications. Convergence of the probabilistic reachable sets is attained by suitable bounding of the state covariance matrices for arbitrary Markov chain sequences. The paper presents the main steps of the solution approach, discusses its properties, and illustrates the principle for a numeric example.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41630408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Self-Triggered Control of Multi-Agent Systems With External Disturbances 具有外部干扰的多智能体系统的自触发控制

Frontiers in control engineering Pub Date : 2022-02-15 DOI: 10.3389/fcteg.2022.835052

Xiang Li, Jing Zhu

引用次数: 0