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Energy, Power and Grid (ICEPG), International Conference on

作者： Fengjiao Lee Yongqing Meng Jinpeng Kan Wenjie Chen Yixin Shi Yanwei Du College of Electrical Engineering Xi’an Jiaotong University Xi’an China Electronic System Engineering AVIC Xi’an Flight Automatic Control Research Institute Xi’an China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

In response to the issues of common mode voltage and the resultant common mode currents and leakage currents in PWM motor drive systems, this paper proposes a topology using a parallel balanced inverter to drive a four-module motor. Traditional three-phase inverters, with an odd number of bridge arms, inherently struggle to eliminate common mode voltage. By employing a balanced inverter, the proposed method extends the degrees of freedom, achieving internal group common mode voltage cancellation. Furthermore, replacing the conventional three-phase motor with a four-module motor and altering the stator winding connections, combined with a carrier phaseshifting modulation strategy, allows the system to cancel common mode voltages using a parallel balanced inverter. To verify the efficacy of the proposed solution, a simulation of a parallel balanced inverter driving a four-module motor system was constructed and compared with a traditional three-phase inverter system. The simulation results demonstrate that the new topology effectively suppresses system-conducted interference, thereby improving the system’s electromagnetic interference performance.

关键词： Motor drives Phase modulation Simulation Electromagnetic interference Stator windings Voltage Motors Inverters Topology Leakage currents

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Computing the explicit MPC solution in the constrained zonotope case

Computing the explicit MPC solution in the constrained zonot...

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IEEE Conference on Decision and control

作者： Florin Stoican Ştefan S. Mihai Martin Mönnigmann Bogdan D. Ciubotaru Department of Automatic Control and Systems Engineering University “Politehnica” of Bucharest 313 Splaiul Independenţei 060042 Sector 6 Bucharest Romania Martin Mönnigmann is with Automatic Control and System Theory Ruhr University Bochum Bochum Germany

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Solving the explicit model predictive control (MPC) problem entails enumerating a list of critical regions and their ancillary feedback laws. Unfortunately, their number and the time required to compute them increase exponentially with the problem size (state-space model dimension and length of the prediction horizon). We show that when, as it is often the case, the problem’s constraints take the form of boxes or zonotopes, the resulting feasible domain can be compactly described as a constrained zonotope. Subsequently, we investigate whether, and under which circumstances, the combinatorial structure of the constrained zonotope interpretation accelerates the computation of the explicit solution.

关键词： State-space methods Predictive control

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control Method for Flywheel Energy Storage systems Based on a Novel Super-Twisting Sliding Mode Observer

Control Method for Flywheel Energy Storage Systems Based on ...

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Energy Storage and Conversion (ISESC), CPSS & IEEE International Symposium on

作者： Fengjiao Lee Yongqing Meng Wenjie Chen Yanwei Du Yixin Shi Jinpeng Kan College of Electrical Engineering Xi'an Jiaotong University Xi'an China Electronic System Engineering AVIC Xi'an Flight Automatic Control Research Institute Xi'an China

ISBN: (数字)9798350380514

ISBN: (纸本)9798350380521

To address the chattering problem caused by the discontinuity of the sign function in traditional sliding mode observers (SMO) for Permanent Magnet Synchronous Motors (PMSM) in flywheel energy storage systems (FESS), and to reduce the complexity introduced by low-pass filter and phase compensation elements, this paper proposes a novel speed-observer, the improved super-twisting sliding mode observer (STSMO). By replacing the switching function in the super-twisting sliding mode observer with a sigmoid function, the discontinuity of the system is improved, effectively suppressing torque ripple and reducing the inherent chattering of sliding modes. This approach enhances the extended back electromotive force (EMF) estimation performance in both low-speed and high-speed regions, enabling precise rotor position estimation across a wide speed range and reducing both speed and rotor position estimation errors. Comparative simulations of the improved STSMO and traditional SMO based on SiC MOSFET inverter constructed in MATLAB, demonstrate that the improved STSMO exhibits superior dynamic performance and rotor position estimation accuracy, significantly enhancing the control performance of PMSMs in flywheel energy storage systems.

关键词： Vibrations Estimation error Accuracy Heuristic algorithms Rotors Switches Observers Synchronous motors Flywheels Torque measurement

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On the Gaussian Filtering for Nonlinear Dynamic systems Using Variational Inference 27

On the Gaussian Filtering for Nonlinear Dynamic Systems Usin...

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27th International Conference on Information Fusion, FUSION 2024

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Li, Ji University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Locaris Electronic Technology Co. Ltd Zhengzhou China

ISBN: (纸本)9781737749769

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements. © 2024 ISIF.

关键词： Wiener filtering

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Sliding Time Window And Linguistic Ordered Weighted Averaging Based Deep Learning Pooling for Fault Diagnosis in a Wastewater Treatment Plant

SSRN

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SSRN 2024年

作者： Beneyto-Rodriguez, Alicia Sainz-Palmero, Gregorio I. Galende-Hernández, Marta Fuente, María J. Department of System Engineering and Automatic Control School of Industrial Engineering Universidad de Valladolid Paseo Prado de la Magdalena 3-5 Valladolid47011 Spain

Fault diagnosis is a key activity for any industrial process. Its frequently high complexity and large-scale require smart methodologies for fault diagnosis to optimize operation and avoid catastrophic faults. Nowadays, these large-scale and complex industrial processes are usually monitored. One of the most popular approaches for fault diagnosis is the deep learning based methodologies, which have shown a good ***, a methodology for fault diagnosis in a wastewater treatment plant is proposed based on a sliding time window and a linguistic Ordered Weighted Averaging (OWA) pooling based deep convolutional neural network. This sliding window over input data, based on the sampling time, allows an earlier fault diagnosis of the plant using a few monitoring samples. The diagnosis is carried out by the linguistic OWA pooling based Deep Convolutional Neural Network. This alternative linguistic pooling uses the well-known linguistic OWA quantifiers, which permit terms such as Most, Atleast, etc., supplying new intuitive options for the pooling *** sliding time window permits a better diagnosis performance as well as an earlier diagnosis with less training. Several linguistic OWA operators have been checked with a benchmark for wastewater treatment plants. A collection of 5 fault types has been used, taking into account 140 variables sampled at 15 minutes time intervals for sampling. The performance has been over 91% for Accuracy, Recall, F1-score, etc., and better than other methodologies. Moreover, these linguistic OWA operators for pooling have shown a better performance than the standard max and average options. © 2024, The Authors. All rights reserved.

关键词： Failure analysis

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Risk-Aware Wasserstein Distributionally Robust control of Vessels in Natural Waterways

arXiv

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arXiv 2023年

作者： Nadales, Juan Moreno Hakobyan, Astghik de la Peña, David Muñoz Limon, Daniel Yang, Insoon The Department of Automatic Control and System Engineering University of Seville Spain The Department of Electrical and Computer Engineering Seoul National University Korea Republic of

In the realm of maritime transportation, autonomous vessel navigation in natural inland waterways faces persistent challenges due to unpredictable natural factors. Existing scheduling algorithms fall short in handling these uncertainties, compromising both safety and efficiency. Moreover, these algorithms are primarily designed for non-autonomous vessels, leading to labor-intensive operations vulnerable to human error. To address these issues, this study proposes a risk-aware motion control approach for vessels that accounts for the dynamic and uncertain nature of tide islands in a distributionally robust manner. Specifically, a model predictive control method is employed to follow the reference trajectory in the time-space map while incorporating a risk constraint to prevent grounding accidents. To address uncertainties in tide islands, a novel modeling technique represents them as stochastic polytopes. Additionally, potential inaccuracies in waterway depth are addressed through a risk constraint that considers the worst-case uncertainty distribution within a Wasserstein ambiguity set around the empirical distribution. Using sensor data collected in the Guadalquivir River, we empirically demonstrate the performance of the proposed method through simulations on a vessel. As a result, the vessel successfully navigates the waterway while avoiding grounding accidents, even with a limited dataset of observations. This stands in contrast to existing non-robust controllers, highlighting the robustness and practical applicability of the proposed approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Dimensionality reduction of hyperspectral images using an ICA-based stochastic second-order optimization algorithm

Dimensionality reduction of hyperspectral images using an IC...

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European control Conference (ECC)

作者： Daniela Lupu Ion Necoara Tudor C. Ionescu Liliana Ghinea Joseph Garrett Tor A. Johansen Automatic Control and System Engineering Department University Politehnica Bucharest Bucharest Romania Automatic Control Department Dunarea de Jos University of Galati Romania Norwegian University of Science and Technology Trondheim Norway

Hyperspectral imaging is one of the advanced remote sensing techniques whose goal is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials or detecting processes. However, the high dimensional nature of hyperspectral images makes their analysis complex. Various methods have been developed to reduce the dimension of hyperspectral images. Most commonly used dimension reduction techniques are Principal Component Analysis (PCA) and Independent Component Analysis (ICA). PCA is a method to reduce the dimensionality by removing the correlation among the bands, while ICA finds additively independent components. FastICA is one of the most used ICA algorithms. It is based on maximizing a loss derived from the fourth order statistical moment (kurtosis) or negentropy, which are both non-convex functions. Moreover, FastICA can find irrelevant stationary points (no maxima) and is not scalable as it uses at each iteration the whole set of pixels. In this paper, we present a stochastic second-order Taylor-based algorithm adapted to such ICA non-convex loss functions. Our algorithm guarantees ascent, hence it usually identifies (local) maxima. Moreover, the algorithm since it is stochastic, is scalable. Detailed numerical simulations show the superior performance of our method compared to FastICA.

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Coordinate projected gradient descent minimization and its application to orthogonal nonnegative matrix factorization

Coordinate projected gradient descent minimization and its a...

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IEEE Conference on Decision and control

作者： Flavia Chorobura Daniela Lupu Ion Necoara Automatic Control and System Engineering Department University Politehnica Bucharest Bucharest Romania

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this paper we consider large-scale composite nonconvex optimization problems having the objective function formed as a sum of three terms, first has block coordinate-wise Lipschitz continuous gradient, second is twice differentiable but nonseparable and third is the indicator function of some separable closed convex set. Under these general settings we derive and analyze a new cyclic coordinate descent method, which uses the partial gradient of the differentiable part of the objective, yielding a coordinate gradient descent scheme with a novel adaptive stepsize rule. We prove that this stepsize rule makes the coordinate gradient scheme a descent method, provided that additional assumptions hold for the second term in the objective function. We also present a worst-case complexity analysis for this new method in the nonconvex settings. Numerical results on orthogonal nonnegative matrix factorization problem also confirm the efficiency of our algorithm.

关键词： Linear programming Minimization Complexity theory Optimization Convergence

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Data-driven modeling of heat pumps and thermal storage units for MPC

Data-driven modeling of heat pumps and thermal storage units...

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2023 International Conference on the Built Environment in Transition, CISBAT 2023

作者： Brandes, M. Cai, H. Vivian, J. Croci, L. Heer, P. Smith, R. Urban Energy Systems Laboratory Empa Dübendorf Switzerland Dept. of Industrial Engineering University of Padova Padua Italy Dept. of Power System Development Ricerca Sistema Energetico SpA Milan Italy Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Switzerland

Heat pumps can play a crucial role in the European energy strategy 2050, which aims to achieve net-zero greenhouse gas emissions. When coupled with thermal energy storage and integrated with advanced control strategies, heat pump operation can be optimized to reduce carbon footprint and respond to the needs of system operators. However, to scale in a multitude of buildings, the transferability of the modeling into heterogeneous systems is crucial. In this paper, two different interpretable linear models, a hybrid (grey-box) and a fully data-driven (black-box) model are investigated. Specifically, two regression-based identification methods (SINDYc and DMDc) are used for dynamic models and the LASSO regression is used for static models. The transferability of the approach is evaluated using two real-world facilities with heterogeneous sizing and configuration. The results show a similar simulation performance for both cases with a maximum normalized RMSE of 0.41 and 0.60, respectively. This confirms the transferability of the approach that is necessary for large-scale implementation. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Carbon footprint

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Security issues in MCPS when using Wireless Sensor Networks

Security issues in MCPS when using Wireless Sensor Networks

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E-Health and Bioengineering Conference (EHB)

作者： Balaban Béatrix-May Sacală Ioan Ştefan Petrescu-Niţă Alina-Claudia Simen Radu Department of System Engineering Faculty of Automatic Control and Computers UPB Bucharest Romania

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

Considering the evolution of technology, the need to secure data is growing fast. When we turn our attention to the healthcare field, securing data and assuring privacy are critical conditions that must be accomplished. The information is sensitive and confidential, and the exchange rate is very fast. Over the years, the healthcare domain has gradually seen a growth of interest regarding the interconnectivity of different processes to optimize and improve the services that are provided. Therefore, we need intelligent complex systems that can collect and transport sensitive data in a secure way. These systems are called cyber-physical systems. In healthcare domain, these complex systems are named medical cyber physical systems. The paper presents a brief description of the above-mentioned intelligent systems. Then, we focus on wireless sensor networks and the issues and challenges that occur in securing sensitive data and what improvements we propose on this subject. In this paper we tried to provide a detailed overview about cyber-physical systems, medical cyber-physical systems, wireless sensor networks and the security issues that can appear.

关键词： Wireless sensor networks Privacy Exchange rates Data privacy Medical services Cyber-physical systems Security

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