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data-driven modeling of Wireless Power Transfer Systems With Slowly Time-Varying Parameters

IEEE TRANSACTIONS ON POWER ELECTRONICS 2020年第11期35卷 12442-12456页

作者： Chen, Fengwei Padilla, Arturo Young, Peter C. Garnier, Hugues Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Univ La Frontera Dept Mech Engn Temuco 4811230 Chile Univ Lancaster Syst & Control Grp Lancaster Environm Ctr Lancaster LA1 4YQ England Univ Lorraine Ctr Rech Automat Nancy CNRS F-54000 Nancy France

This article considers the data-driven modeling of a class of phase-controlled wireless power transfer (WPT) systems, where the load may vary slowly with respect to time. The dominant mode analysis suggests that a model of the Hammerstein type, which consists of a static nonlinearity function, followed by a linear time-varying model with a pure time delay, is the best structure to describe the input-output relationship of the system. On this basis, we derive a small-signal model that is linear in the variables in order to aid control design and allow the associated model parameters to be estimated from sampled input-output data using the standard refined instrumental variable (RIV) method. In the presence of a time-varying load, however, the plant model parameters may not be correctly estimated if the load response is not removed. In order to address this problem, a new recursive RIV method is proposed, in which an effective technique is introduced to track the load response, so allowing the parameters and time delay of the time-varying model to be accurately estimated. The effectiveness of the proposed method is verified by applying it to both a simulation model and a laboratory system.

关键词： Load modeling Integrated circuit modeling Inverters Delay effects data models Time-varying systems Analytical models data-driven modeling recursive estimation system identification time delay time-varying parameter wireless power transfer (WPT)

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data-driven modeling of Wireless Power Transfer Systems With Multiple Transmitters

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2020年第11期35卷 11363-11379页

作者： Chen, Fengwei Young, Peter C. Garnier, Hugues Deng, Qijun Kazimierczuk, Marian K. Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Univ Lancaster Lancaster Environm Ctr Syst & Control Grp Lancaster LA1 4YQ England Univ Lorraine CNRS Ctr Rech Automat Nancy F-54000 Nancy France Wuhan Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China Wright State Univ Dept Elect Engn Dayton OH 45435 USA

This article develops a new method of data-driven modeling for a class of multiple-transmitter single-receiver wireless power transfer (WPT) systems. A continuous-time multiple-input single-output (MISO) model with pure time delays is used to characterize the input-output behavior of the system, where the transfer functions associated with each input channel are not constrained to have the same denominator. Moreover, the time delays are allowed to be a fraction of the sampling interval in order to account for the delay effects that stem from circuit components and wireless communication, which are, by nature, often a fraction of the sampling interval. An optimal refined instrumental variable method is proposed to estimate the parameters and time delays of the MISO model based on sampled input-output data. In contrast to the conventional circuit-theory-based modeling methods that rely on circuit parameters and result in models which are often complex, the proposed data-based method yields parsimonious models, whose parameters are directly estimated from input-output data. Due to the easy availability of sampled data in control engineering applications, the proposed method is clearly more user-friendly, having a broad prospect for efficient operation of WPT systems, such as prediction, optimization, and control. Numerical and experimental results are presented to validate the effectiveness and merit of the proposed method.

关键词： Integrated circuit modeling Transmitters MISO communication Inverters Coils Receivers Delay effects data-driven modeling instrumental variable (IV) method multiple-input single-output (MISO) system time-delay estimation wireless power transfer (WPT)

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Iterative Learning Model Predictive Control Based on Iterative data-driven modeling

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第8期32卷 3377-3390页

作者： Ma, Lele Liu, Xiangjie Kong, Xiaobing Lee, Kwang Y. North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA

Iterative learning model predictive control (ILMPC) has been recognized as an effective approach to realize high-precision tracking for batch processes with repetitive nature because of its excellent learning ability and closed-loop stability property. However, as a model-based strategy, ILMPC suffers from the unavailability of accurate first principal model in many complex nonlinear batch systems. On account of the abundant process data, nonlinear dynamics of batch systems can be identified precisely along the trials by neural network (NN), making it enforceable to design a data-driven ILMPC. In this article, by using a control-affine feedforward neural network (CAFNN), the features in the process data of the former batch are extracted to form a nonlinear affine model for the controller design in the current batch. Based on the CAFNN model, the ILMPC is formulated in a tube framework to attenuate the influence of modeling errors and track the reference trajectory with sustained accuracy. Due to the control-affine structure, the gradients of the objective function can be analytically computed offline, so as to improve the online computational efficiency and optimization feasibility of the tube ILMPC. The robust stability and the convergence of the data-driven ILMPC system are analyzed theoretically. The simulation on a typical batch reactor verifies the effectiveness of the proposed control method.

关键词： Batch production systems Electron tubes Artificial neural networks Computational modeling Trajectory Predictive control Nonlinear dynamical systems Control-affine feedforward neural network (CAFNN) data-driven modeling iterative learning model predictive control (ILMPC) tube framework

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A Hybrid Physics-Based and data-driven modeling Framework for Energy and Water Use Analysis of data Centers with Spatio-Temporal Resolution

A Hybrid Physics-Based and Data-Driven Modeling Framework fo...

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作者： Lei, Nuoa Northwestern University

学位级别：Ph.D.

With the rapid growth of demand for data center services, the energy and water use of data centers has become a critical concern in the contexts of energy use, climate change, and freshwater conservation. Therefore, understanding, quantifying, and optimizing the use of energy and water resources in data centers has become an important task for data center designers, engineers, operators, and policymakers, because it provides them with useful insights on resource-efficient data center design, sustainable operations, and effective policy formulation and technology incentives. However, current models developed by the energy analysis community fail to provide a reliable method to quantify data center energy and water usage and performance under different climatic conditions, system and technology implementations, and operating practices. These models also do not provide a robust approach to optimize the energy and water usage and performance in different aspects of data center operations, including data center workload management, information technology equipment operation, and infrastructure system operation. Addressing these challenges requires an accurate and generalizable computational modelling framework that can incorporate multi-dimensional and correlated spatio-temporal (e.g., climates, power grid mixes, computing tasks), technological (e.g., server configurations, cooling system types, economizer usage), and operational (e.g., workload levels, equipment efficiencies, environment setpoints) factors that affect data center energy and water use. Accordingly, a hybrid physics-based and data-driven modelling framework was developed in this research for the quantification and optimization of resource usage and performance in data *** developed modelling framework consists of four sub-models, which were elaborated in this dissertation for model demonstration. Section 4 (case study 1) describes a statistical and thermodynamics-based model for predictive analysi

关键词： data center data-driven modeling Energy and sustainability Energy-water nexus Information technology Thermodynamics

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A Universal Automated data-driven modeling Framework for Truck Traffic Volume Prediction

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IEEE ACCESS 2021年 9卷 105341-105356页

作者： Mahdavian, Amirsaman Shojaei, Alireza Salem, Milad Laman, Haluk Eluru, Naveen Oloufa, Amr A. Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA Virginia Polytech Inst & State Univ Myers Lawson Sch Construct Blacksburg VA 24061 USA Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

Knowledge of the truck traffic volumes on state and interstate highways is critical for highway authorities and federal organizations. Increased urbanization, population growth, and economic development have led to an increased demand for freight travel. Several planning applications demand reliable and accurate truck traffic prediction. A review of the available literature indicated that limited research had been performed on the development and utilization of a universal automatic framework for truck traffic volume prediction. As a result, there is a gap to incorporate inclusive predictors, a broad dataset, a comprehensive feature selection approach, and a robust cross-validation method that utilizes both linear and non-linear algorithms. The present study uses a hyperparameter optimization framework to select the appropriate feature selection method and modeling approach among a comprehensive list of available state of the art approaches. Distinct from models based on individual case studies, the proposed framework allows for greater customization and minimized MAPE error. The developed framework automates much of the traffic count forecasting process, and the resulting method is less labor-intensive and may be utilized without the need for experienced data analysts. Florida's interstate highways historical traffic data were used to test the feasibility of the proposed framework. The results of the Florida Case Study revealed the superiority of non-linear models in the generalization and prediction of traffic volumes over linear models. The random forest algorithm results on the test dataset in this study demonstrate this model's ability to predict truck traffic with 86% accuracy. Spatial variables were the most significant variable group, followed by road characteristics.

关键词： Predictive models Forecasting Mathematical model Prediction algorithms Transportation Biological system modeling Solid modeling data-driven modeling truck traffic traffic volume prediction model regression analysis forecasting machine learning

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Heterogeneous data-driven aerodynamic modeling based on physical feature embedding

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Chinese Journal of Aeronautics 2024年第3期37卷 1-6页

作者： Weiwei ZHANG Xuhao PENG Jiaqing KOU Xu WANG School of Aeronautics Northwestern Polytechnical UniversityXi’an 710072China Institute of Aerodynamics RWTH Aachen UniversityAachen 52062Germany

Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the *** make full use of both integrated and distributed loads,a modeling paradigm,called the heterogeneous data-driven aerodynamic modeling,is *** essential concept is to incorporate the physical information of distributed loads as additional constraints within the end-to-end aerodynamic *** heterogenous data,a novel and easily applicable physical feature embedding modeling framework is *** framework extracts lowdimensional physical features from pressure distribution and then effectively enhances the modeling of the integrated loads via feature *** proposed framework can be coupled with multiple feature extraction methods,and the well-performed generalization capabilities over different airfoils are verified through a transonic *** with traditional direct modeling,the proposed framework can reduce testing errors by almost 50%.Given the same prediction accuracy,it can save more than half of the training ***,the visualization analysis has revealed a significant correlation between the discovered low-dimensional physical features and the heterogeneous aerodynamic loads,which shows the interpretability and credibility of the superior performance offered by the proposed deep learning framework.

关键词： Transonic flow data-driven modeling Feature embedding Heterogenous data Feature visualization

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data-driven modeling of Flows of Antalya Basin and Reconstruction of Missing data

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2020年第4期44卷 1335-1344页

作者： Dikbas, Fatih Yasar, Mutlu Pamukkale Univ Dept Civil Engn Denizli Turkey

The length and completeness of any hydrologic observation series increase the reliability of the results obtained by methods using the series. But the observed series generally contain gaps caused by many factors. Therefore, the imputation of missing data with appropriate methods is an important step in hydrologic analysis. This paper presents the implementation of the frequency-based imputation method in the estimation of 385 missing and 5543 observed total flow values of 23 stations in Antalya basin, Turkey. The data-driven method calculates the estimations for missing values based on the temporal and quantitative associations in existing observations by considering the frequencies of the observed value ranges. The observations of the stations in the basin show significant variations in climatic behavior. The correlations between the estimations of the frequency-based imputation method and the observations for each station vary between 0.62 and 0.98, while 87% of the correlations are higher than 0.80 and 65% of them are higher than 0.9. The obtained high correlations show that the frequency-based imputation method can be used reliably in estimating streamflow in Southern Turkey within the Mediterranean climate zone. Other statistical measures (Nash-Sutcliffe efficiency coefficient, normalized root-mean-square error and mean absolute scaled error) and the comparison with the kriging method also validate the success or the obtained results.

关键词： Antalya basin data-driven modeling Frequency-based imputation Missing data River flow

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data-driven modeling and Control of Cardiac System

Data-Driven Modeling and Control of Cardiac System

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作者： Branen, Andrew L. University of Idaho

学位级别：M.S., Master of Science/Master of Surgery

Vagus nerve stimulation is an emerging therapy that seeks to offset pathological conditions by electrically stimulating the vagus nerve through cuff electrodes, where the electrical pulse is defined by several parameters such as pulse amplitude, pulse width, and pulse frequency. This electroceutical therapy has been approved for treatment resistant depression, and is currently under investigation for heart failure, heart arrhythmia, hypertension, and gastric motility disorders. Recent studies have shown the ability to selectively activate different fibers in the vagus nerve, thus allowing for a highly specific control of physiological behavior through vagal nerve stimulation. One of the major challenges with the application of this therapy involves a closed loop controller to autonomously control the behavioral responses. This problem becomes additionally challenging when multiple locations and multiple stimulation parameters are considered for optimization. Using a physiological model of a rat heart, this thesis investigates a data-driven control scheme for closed-loop control of the rat cardiac system. In the first section of this thesis, a data-driven modeling approach is used to develop a model that maps vagus nerve stimulation parameter selection to the effect on the physiological variables of heart rate and blood pressure. The second part of this thesis develops a controller that uses the data-driven model by utilizing a model predictive control framework to control the heart rate and the blood pressure in closed-loop simulations of a rat model.

关键词： data-driven modeling Cardiac system Vagus nerve stimulation Electroceutical therapy

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data-driven modeling based on Expert Knowledge for Transition Mode of Tilt Trirotor UAV

Data-driven Modeling based on Expert Knowledge for Transitio...

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Chinese Automation Congress (CAC)

作者： Yi Bao Li Yu Xiangke Wang Guang He College of Intelligence Science and Technology National University of Defense Technology Changsha China

Focusing on the high cost and complex operation in establishing the transition mode model of tilt trirotor unmanned aerial vehicle (UAV), combined with a fuzzy modeling method based on TSK fuzzy model. Firstly, the flight data of UAV are collected through simulation. Secondly, the membership function of TSK fuzzy model is designed according to the expert experience, and constant variable mass and moment of inertia is added to the data set of the neural network for Supervised learning and rapid convergence. Finally, the predictive control law is designed. Based on simulation platform, the off-line model is simulated under a fixed tilt curve which prove the effectiveness of the method. The experimental results show that this method can solve the flight state variables, such as attitude, altitude and speed, more quickly and accurately, and this method greatly reduces the cost of model identification.

关键词： Fault diagnosis data-driven modeling Costs Neural networks Supervised learning Focusing Predictive models

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A data-driven Koopman modeling Framework With Application to Soft Robots

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2025年第1期23卷 249-261页

作者： Han, Lvpeng Peng, Kerui Chen, Wangxing Liu, Zhaobing Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China

This paper presents a data-driven Koopman modeling framework for globally linearizing highly nonlinear dynamical systems in lifted infinite-dimensional state space. In this framework, three data-driven models are proposed and identified to approximate the infinite-dimensional linear Koopman operator through a method called extended dynamic mode decomposition (EDMD). The implementation of EDMD requires a data set of snap pairs and a dictionary of scalar observables, which affects the accuracy of data-driven modeling. Five basis functions are compared and discussed to illustrate their suitable application scenarios. To verify the Koopman data-driven modeling framework, we apply it to the modeling of soft robotic systems, which is thought to be an extremely difficult task due to the large and continuous deformation of soft materials. Results demonstrate that Koopman linear, bilinear, and nonlinear models for both two-dimensional (2D) and three-dimensional (3D) soft robots are superior to the existing state-space modeling approach by achieving less normalized root mean square error (NRMSE). Among the three Koopman models, the constructed nonlinear model has higher performance than the bilinear model, followed by the linear one. Furthermore, the Monomial and Hermite basis functions are the optimal choices for constructing the Koopman linear, bilinear, and nonlinear models for the investigated 2D and 3D soft robots as they have the same structure when the degree of basis function is chosen less than four. Although the Fourier basis function is expected to outperform the Hermite and Gaussian basis functions in modeling systems with oscillatory motion, it is not a preferable selection in the high dimensional soft robotic modeling in our case due to its computational complexity and tendency to be non-convergent. The Sparse Fourier basis function cannot be regarded as a good choice for modeling soft robots, as it is only suitable for generating models with sparse data. It is

关键词： data-driven modeling identification method Koopman operator nonlinear systems soft robots

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