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arXiv 2023年

作者： Verhoek, Chris Koelewijn, Patrick J.W. Haesaert, Sofie Tóth, Roland The Dept. of Electrical Engineering The Eindhoven University of Technology Netherlands The Institute for Computer Science and Control Budapest Hungary

Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop a data-driven representation of the so-called velocity-form, i.e., the time-difference dynamics, of the NL system, which is shown to admit a direct linear parameter-varying (LPV) representation. By applying the LPV extension of the Fundamental Lemma in this velocity domain, a state-feedback controller is directly synthesized to provide asymptotic stability and dissipativity of the velocity-form. By using realization theory, the synthesized controller is realized as a NL state-feedback law for the original unknown NL system with guarantees of universal shifted stability and dissipativity, i.e., stability and dissipativity w.r.t. any (forced) equilibrium point, of the closed-loop behavior. This is achieved by the use of a single sequence of data from the system and a predefined basis function set to span the scheduling map. The applicability of the results is demonstrated on a simulation example of an unbalanced disc. © 2023, CC BY-NC-SA.

关键词： Linear systems

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Tailored Output Layers of Neural Networks for Satisfaction of State Constraints in Nonlinear control Systems

Tailored Output Layers of Neural Networks for Satisfaction o...

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American control Conference (ACC)

作者： Lukas Markolf Olaf Stursberg Dept. of Electrical Engineering and Computer Science Inst. of Control and System Theory University of Kassel Germany

This work considers the synthesis of state feedback controllers established as deep artificial feed-forward neural networks for the control of discrete-time nonlinear but input-affine systems. The idea is to design output layers of particular structure to guarantee the satisfaction of state constraints in form of control-invariant ellipsoids. Since an analytical expression can be derived for the resulting neural network controller, the latter can be stored and evaluated efficiently. Moreover, the proposed output layer guarantees the satisfaction of the considered state constraints for each specification of the parameter vector. Numerical examples are provided for illustration and evaluation of the approach, in which the approximation of a nonlinear model predictive control law is considered as application.

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Design of Output and Impacts control Systems

Design of Output and Impacts Control Systems

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International Russian Automation Conference (RusAutoCon)

作者： Anatoly Gaiduk Nikolay Prokopenko Vladislav Chumakov Dept. of Automatic Control Systems Scientific Design Office Robotics and Control Systems Taganrog Southern Federal University Rostov-on-Don Russia Dept. of Scientific Research Dept. of Information Systems and Radio Engineering Don State Technical University Rostov-on-Don Russia

To design linear control systems a new method is suggested on the basis of the principle of output and impacts control. The main difference of this principle consists in the use of only measurable variables and separate operators for each input signal of the control device. The design problem has a solution if the plant is completely controllable, and at least the controllable variable is measured. The suggested approach makes it possible to eliminate the well-known contradiction between the accuracy and stability of systems with control on deviation, and to provide the required astatism orders or selective invariance to the external actions. At the same time, the conditions of ensuring the direct indicators of the transient quality and the physical implementation of the control device are taken into account. The coefficients of the equation of the output and impacts control device are determined by solving a system of linear algebraic equations. The control device can be implemented on the basis of either operational amplifiers or sufficiently high-speed digital automation tools. The suggested method can be applied for designing high-quality linear control systems for various purposes.

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Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0

arXiv

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arXiv 2023年

作者： Capogrosso, Luigi Mascolini, Alessio Girella, Federico Skenderi, Geri Gaiardelli, Sebastiano Dall’Ora, Nicola Ponzio, Francesco Fraccaroli, Enrico Di Cataldo, Santa Vinco, Sara Macii, Enrico Fummi, Franco Cristani, Marco Dept. of Engineering for Innovation Medicine University of Verona Italy Dept. of Control and Computer Engineering Polytechnic of Turin Italy Dept. of Regional and Urban Studies and Planning Polytechnic of Turin Italy Dept. of Computer Science University of North Carolina Chapel Hill United States

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before. © 2023, CC BY.

关键词： Anomaly detection

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Using NLP to analyze requirements for Agriculture 4.0 applications

Using NLP to analyze requirements for Agriculture 4.0 applic...

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International Carpathian control Conference (ICCC)

作者： Jakub Jura Pavel Trnka Matous Cejnek Dept. of Automatic Control and Engineering Informatics Czech Technical University in Prague Prague Czech Republic

This contribution describes the use of Natural Language Processing (NLP) methods for the lexical analysis of requirements for control, sensors, and information systems in the Agriculture 4.0 domain. The analysis is presented on an orchard 4.0 *** proposed orchard includes a sensor network (containing mainly measurements of hydrometeorological and soil variables), camera monitoring of conditions, and yield, support for autonomous robotic care and harvesting based on machine vision, prediction of appropriate times for interventions, etc. Requirements specification for mentioned system is written in natural language.A sentence splitting, Tokenization, Lemmatization, and POS (Part-of-Speech) tagging methods are applied to the mentioned structured requirements of the system and Use Case description. From these and by means of NLP, the candidates of classes, attributes, operations, and associations of the UML (Unified Modeling Language) class diagram are filtered and the UML model is synthesized. This paper presents the application of software engineering methods to support the development of complex heterogeneous sensors, information, and control systems.

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Koopman Operators in Robot Learning

arXiv

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arXiv 2024年

作者： Shi, Lu Haseli, Masih Mamakoukas, Giorgos Bruder, Daniel Abraham, Ian Murphey, Todd Cortes, Jorge Karydis, Konstantinos Dept. of Electrical and Computer Engineering University of California Riverside United States Dept. of Mechanical and Aerospace Engineering University of California San Diego United States Dept. of Planning and Control Zoox Inc. United States Dept. of Mechanical Engineering University of Michigan United States Dept. of Mechanical Engineering Yale University United States Dept. of Mechanical Engineering Northwestern University United States Tsinghua University China

Koopman operator theory offers a rigorous treatment of dynamics and has been emerging as a powerful modeling and learning-based control method enabling significant advancements across various domains of robotics. Due to its ability to represent nonlinear dynamics as a linear operator, Koopman theory offers a fresh lens through which to understand and tackle the modeling and control of complex robotic systems. Moreover, it enables incremental updates and is computationally inexpensive making it particularly appealing for real-time applications and online active learning. This review comprehensively presents recent research results on advancing Koopman operator theory across diverse domains of robotics, encompassing aerial, legged, wheeled, underwater, soft, and manipulator robotics. Furthermore, it offers practical tutorials to help new users get started as well as a treatise of more advanced topics leading to an outlook on future directions and open research questions. Taken together, these provide insights into the potential evolution of Koopman theory as applied to the field of robotics. © 2024, CC BY.

关键词： Adversarial machine learning

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Method for Increasing the OpAmp’s Speed on the Basis of Complementary “Folded” Cascodes

Method for Increasing the OpAmp’s Speed on the Basis of Com...

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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Dmitriy V. Kleimenkin Ilya V. Pakhomov Darya Y. Denisenko dept. of Scientific Research dept. of Information Systems and Radio Engineering Don State Technical University Rostov-on-Don Russia dept. of Scientific Research dept. of Instrumentation and Biomedical Engineering Don State Technical University Rostov-on-Don Russia dept. of Scientific Research Don State Technical University Rostov-on-Don Russia dept. of Automatic Control Systems Southern Federal University Rostov-on-Don Russia

The paper presents a promising method of increasing by more than an order of magnitude the maximum slew rate (SR) of integrated operational amplifiers (OpAmps) based on two-cycle “folded“ cascodes. This method is based on the introduction of special differentiating transient correction circuits into the classical circuits of OpAmps. Developed on the basis of the MH2XA031 array chip, the high-speed OpAmp is recommended for practical use in the subclass of so-called discrete-analog SC-filters on switched capacitors, for which (in some important cases) higher SR values are required. The effect of significantly improving the OpAmp’s speed is to provide higher output current levels of the two-cycle “folded” cascode that recharges the integrating correction capacitor during the transient edge. The OpAmp’s speed performance in the large-signal mode is increased from $248.7 \mathrm{~V} / \mu \mathrm{s}$ to $4148 \mathrm{~V} / \mu \mathrm{s}$ as a result of this.

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MIRROR: miRNA regulation-level differential network to study sex and ethnic disparities in cancer

MIRROR: miRNA regulation-level differential network to study...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Caterina Alfano Marco Filetti Lorenzo Farina Manuela Petti Dept. of Experimental Medicine Sapienza University of Rome Rome Italy Phase 1 Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Dept. of Computer Control and Management Engineering PhD Program in Network Oncology and Precision Medicine Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Cancer disparities are adverse differences in cancer measures that exist among certain population groups. Given that the role they play not only in the disease prognosis but also in therapy response, there is an urgent need to understand what causes them. Most studies investigate these disparities by analyzing transcriptomic data and in particular miRNAs for their regulatory role, but only focusing on expression levels. To face this challenge we propose MIRROR, a new method which analyzes a differential co-expression network of miRNAs between patients’ cohorts, to study the role they play at the target genes’ level. Doing so, we can study the altered molecular mechanism that are linked to cancer disparities. The application of MIRROR to two different cases of cancer disparities has demonstrated its efficacy in identifying molecular players involved in the considered disparity, presenting itself as a viable option to approach this challenge.

关键词： Proteins Technological innovation Transcriptomics Medical treatment Focusing Mirrors Prostate cancer Prognostics and health management Faces Cancer

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Energy Consumption Model for LoRaWAN via Field Experiments for Smart Agriculture

Energy Consumption Model for LoRaWAN via Field Experiments f...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Satoshi Yamazaki Taiki Ogura Dept. of Control and Computer Engineering National Institute of Technology Numazu College Numazu Japan Dept. of Electrical Electronics and Information Engineering Nagaoka University of Technology Niigata Japan

This paper proposes a scheme to model the energy consumption of LoRaWAN, which is a popular example of low-power wide-area networks (LPWANs), nodes via the results of outdoor field experiments by assuming regional smart agriculture as a use case for internet of things (IoT). Specifically, we derive an experimental approximation formula to estimate the battery lifetime by introducing parameters such as spread factor and payload length. The validity of the proposed scheme is demonstrated by confirming that the results obtained by the approximate formula, the experimental and theoretical results generally agree, regardless of the node state and spread factor. Furthermore, we show that the obtained approximate formula can be used to identify the current consumption value in the sleep state that should be achieved to achieve the desired battery lifetime.

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System Identification-informed Transparent and Explainable Machine Learning with Application to Power Consumption Forecasting

System Identification-informed Transparent and Explainable M...

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Electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Hua-Liang Wei Dept. Automatic Control and Systems Engineering Energy Institute Centre of Machine Intelligence Centre for Signal Processing and Cpmplex Systems The University of Sheffield Sheffield UK

System identification (SysID) is the art and science of dealing with dynamic data modelling problems from systems science perspectives. It has been an active field and is still very active today, due to its wide range of applications, especially its basic principles of finding transparent, interpretable and parsimonious models for different purposes. The past decades have witnessed the explosive growth in machine learning (ML) and its applications in all areas of science and engineering. Meanwhile, there has been an increasing demand for the development of transparent, explainable and/or interpretable ML models. This paper proposes a new framework for developing System Identification-informed Transparent and Explainable MAchine Learning (SITEMAL) models. A case study, involving a real power consumption dataset, is presented to demonstrate the application of the proposed modelling framework and its performance for power consumption forecasting.

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