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10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Gouhail, Mohamed Mazoudi, El Houssin El Issam, Salhi Doubabi, Said Cadi Ayyad University Faculy of Sciences and Techniques Departement of Applied Physics Laboratory of Computer Control for Intelligent Systems and Green Energies Marrakech Morocco University of Technology of Belfort-Montbéliard Energy Department FEMTO-ST Belfort France

ISBN: (纸本)9798350329674

This paper presents a transformation method capable of analytically converting the given differential equations of dynamic models for a four-phase interleaved boost converter (IBC) coupled with a PEMFC into a Takagi-Sugeno fuzzy inference model (TS fuzzy model). This transformation enables the utilization of various parallel distributed compensation (PDC) controller design techniques. By combining the transformation method with PDC techniques, a framework for controller design is established. The proposed controller aims to improve the robustness and stabilization of the output voltage equilibrium point while minimizing the PEMFC current overshoot within a specified limit of the PEMFC current ripple, thereby providing protection. The stability of the proposed controller is proven using the Lyapunov theorem. An LMI condition is developed to synthesize the controller gains. The effectiveness of the PDC controller is further evaluated using Matlab Simulink. © 2023 IEEE.

关键词： Proton exchange membrane fuel cells (PEMFC)

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Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models

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IFAC-PapersOnLine 2023年第2期56卷 5152-5157页

作者： Sarvin Moradi Nick Jaensson Roland Tóth Maarten Schoukens Control System group Eindhoven University of Technology Eindhoven the Netherlands Eindhoven Artificial Intelligence Systems Institute Eindhoven the Netherlands Processing and Performance of Materials Group Eindhoven University of Technology Eindhoven the Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In order to make data-driven models of physical systems interpretable and reliable, it is essential to include prior physical knowledge in the modeling framework. Hamiltonian Neural Networks (HNNs) implement Hamiltonian theory in deep learning and form a comprehensive framework for modeling autonomous energy-conservative systems. Despite being suitable to estimate a wide range of physical system behavior from data, classical HNNs are restricted to systems without inputs and require noiseless state measurements and information on the derivative of the state to be available. To address these challenges, this paper introduces an Output Error Hamiltonian Neural Network (OE-HNN) modeling approach to address the modeling of physical systems with inputs and noisy state measurements. Furthermore, it does not require the state derivatives to be known. Instead, the OE-HNN utilizes an ODE-solver embedded in the training process, which enables the OE-HNN to learn the dynamics from noisy state measurements. In addition, extending HNNs based on the generalized Hamiltonian theory enables to include external inputs into the framework which are important for engineering applications. We demonstrate via simulation examples that the proposed OE-HNNs results in superior modeling performance compared to classical HNNs.

关键词： Hamiltonian Neural Networks Nonlinear system identification Machine learning

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The Research on Sheet Metal Part Recognition Technology Based on Improved Mask R-CNN 5

The Research on Sheet Metal Part Recognition Technology Base...

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5th International Conference on Machine Learning and computer Application, ICMLCA 2024

作者： Liu, Mingfei Liu, Jun Zhou, Kecheng Wang, Jian School of Computer Science and Technology Shenyang University of Chemical Technology Key Laboratory of Industrial Intelligence Technology on Chemical Process Liaoning Province Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9798331530334

The visual recognition of sheet metal parts is a crucial component of industrial automation sorting, particularly under complex lighting and background interference conditions. To enhance the accuracy of sheet metal part recognition, this paper curates a dataset of sheet metal parts acquired by an intelligent light field image sensor and proposes a visual recognition model, PA-CBAM, based on an improved Mask R-CNN. By improving the original feature pyramid FPN and introducing the Convolutional Block Attention Module (CBAM), the model enhances multi-scale feature fusion and the focus on key features: (1) Bottom up paths and shortcut connections have been added between different layers of the traditional FPN feature pyramid, thereby improving multi-scale object detection capability. (2) CBAM further optimizes the model's channel and spatial attention mechanisms, effectively enhancing the capture of edge details and the suppression of background noise. Experimental results show that the proposed PA-CBAM model significantly improves detection and segmentation performance compared to traditional methods, particularly in terms of edge recognition accuracy for sheet metal parts under complex lighting conditions. © 2024 IEEE.

关键词： Sheet metal

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Asymptotic behavior of least squares estimators for nonlinear autoregressive models

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Science China(Information Sciences) 2023年第6期66卷 299-300页

作者： Zhaobo LIU Chanying LI College of Computer Science and Software Engineering Shenzhen University Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences

Dear editor,Estimations of nonlinear autoregressive(AR) models in the literature typically involve ergodic series. Based on this assumption,the asymptotic theory has been established accordingly(see [1–3]). However,t...

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PI-funnel and inverse hysteresis compensation cascade control of smart soft dielectric elastomer actuator

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control Theory and Technology 2024年第4期22卷 552-567页

作者： Peng Huang Yawu Wang Yue Zhang Jundong Wu Chun-Yi Su School of Automation China University of GeosciencesWuhan430074HubeiChina Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074HubeiChina Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan430074HubeiChina Gina Cody School of Engineering and Computer Science Concordia UniversityMontrealQCH3G 1M8Canada

Smart soft dielectric elastomer actuators(SSDEAs)possess wide applications in soft robotics due to their properties similar to natural muscles,including large deformation ratio,high energy density,and fast response ***,the complicated asymmetric and rate-dependent hysteresis property,creep property and quadratic input property of the SSDEA pose enormous challenges to its dynamic modeling and motion *** this paper,first,we construct the dynamic model of the SSDEA by connecting a square module,a one-sided Prandtl–Ishlinskii(OSPI)model and a linear system in series to describe the above *** key and innovative aspect of the dynamic modeling lies in cascading the square module in series with the OSPI model to construct the asymmetric hysteresis ***,a PI-funnel and inverse hysteresis compensation(PIFIHC)cascade control method of the SSDEA is proposed to actualize its tracking control *** performing the inversion operation on the asymmetric hysteresis model,the inverse hysteresis compensation controller(IHCC)is designed to compensate the asymmetric hysteresis property and quadratic input property of the *** addition,a PI-funnel controller is designed to cascade with the IHCC to construct the PIFIHC cascade controller to obtain a good tracking ***,the stability analysis of the PIFIHC cascade control system of the SSDEA is performed to theoretically prove that the tracking error can be controlled within the performance funnel and the steady-state error converges to ***,several practical tracking control experiments of the SSDEA are conducted,and RRMSEs are less than 2.30%for all *** experimental results indicate the effectiveness and feasibility of the proposed PIFIHC cascade control method of the SSDEA.

关键词： Smart soft dielectric elastomer actuator Dynamic modeling Inverse hysteresis compensation controller PI-funnel controller Cascade controller

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Baseline Results for Selected Nonlinear System Identification Benchmarks

arXiv

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arXiv 2024年

作者： Champneys, Max D. Beintema, Gerben I. Tóth, Roland Schoukens, Maarten Rogers, Timothy J. Dynamics Research Group University of Sheffield Sheffield United Kingdom Control System Group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting improvements or extensions to existing methods. It is natural, therefore, to consider how one might choose between these competing models. Benchmark datasets provide one clear way to approach this question. However, to make meaningful inference based on benchmark performance it is important to understand how well a new method performs comparatively to results available with well-established methods. This paper presents a set of ten baseline techniques and their relative performances on five popular benchmarks. The aim of this contribution is to stimulate thought and discussion regarding objective comparison of identification methodologies. © 2024, CC BY.

关键词： Nonlinear simulations

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A Tube-Based Reinforcement Learning Approach for Optimal Motion Planning in Unknown Workspaces

A Tube-Based Reinforcement Learning Approach for Optimal Mot...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Department of Electrical and Computer Engineering University of Patras

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this work, a tube-based nearly optimal solution to motion planning in unknown workspaces is presented. The advantages of reactive motion planning are combined with a Policy Iteration Reinforcement Learning scheme to yield a novel solution for unknown workspaces that inherits provable safety, convergence and optimality. Moreover, in simply-connected workspaces, our method is proven to asymptotically provide the globally optimal path. Our method is compared against a provably asymptotically optimal RRT ⋆ method, as well as a relevant reactive method and provides satisfactory performance, closely matching or outperforming the former.

关键词： Reinforcement learning Planning Safety Robotics and automation Convergence

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Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1692-1703页

作者： Xingxia Wang Jing Yang Yutong Wang Qinghai Miao Fei-Yue Wang Aijun Zhao Jian-Ling Deng Lingxi Li Xiaoxiang Na Ljubo Vlacic School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE Macao University of Science and Technology Macao 999078China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China North Automatic Control Technology Institute Taiyuan 030006China China Huaneng Group Co. Ltd.Beijing 100031China Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis(IUPUI)IndianapolisIN 46202 USA Department of Engineering University of CambridgeCambridgeCB21PZUK School of Engineering and Built Environment Griffith UniversityGold CoastQLD 4222Australia

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the ***,there is no consensus on why and what is Industry 5.0 *** this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future *** believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart *** are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

关键词： ACP artificial intelligence CPS CPSS Industry 4.0 Industry 5.0 parallel industries parallel intelligence

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Distributed Prescribed-Time Observer for Nonlinear systems in Block-Triangular Form

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IEEE control systems Letters 2025年 9卷 222-227页

作者： Vincent de Heij M. Umar B. Niazi Karl H. Johansson Saeed Ahmed Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen The Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

This letter proposes a design of a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified.

关键词： Observers Convergence Nonlinear systems Symmetric matrices Observability Directed graphs control systems Vectors Linear systems Electronic mail

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Optimizing the Structural Parameters of the Mechanical Sensing Element of a Microoptoelectromechanical Micro-g Accelerometer

Optimizing the Structural Parameters of the Mechanical Sensi...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Evgenij S. Barbin Tamara G. Nesterenko Aleksei N. Koleda Evgeniy V. Shesterikov Ivan V. Kulinich Andrey A. Kokolov Laboratory of Radiophotonics The Tomsk State University of Control Systems and Radioelectronics. Laboratory of Intelligent Computer Systems Institute of Optics and Atmosphere Siberian Branch of the Russian Academy of Sciences Tomsk Russia Laboratory of Radiophotonics The Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

The paper investigates a microoptoelectromechanical accelerometer that implements optical measuring transducer built on the optical tunneling effect. The article discusses the issues of micromechanical sensing element (MSE) optimization for an MOEM- accelerometer fabricated by silicon on insulator (SOI) technology. The results represent the requirements to the design to achieve a sensitivity of 1 μg m/s 2 at a calculated eigenvalue of the MSE. The main parameters of the mechanical part of a MOEM-accelerometer affecting its sensitivity are dimensions, mass, resonance frequency and corresponding stiffness of the spring suspension. Following the Brownian noise that characterizes the accelerometer's sensitivity, the relations between the parameters are determined. Two designs of the MSE are considered according to the fabrication process. One design contains only a device layer; the second one implements a SOI-wafer basic layer as a weighting additive. The latter design occupies less area. To prevent MSE deflection stipulated by its larger dimensions and to increase the number of sensors fabricated from a single SOI wafer, MSE with additional mass are more reasonable to use. The MSE mass at an eigenfrequency of 10, 20, 50 and 100 Hz should exceed 0.01.10 −6 kg, while at a frequency of 200 Hz, the mass should exceed 0.023.10 −6 kg to comply with the requirement of minimal measurable acceleration of 1 μg m/s 2 . Subsequent increase of the MSE mass at the same eigenfrequency decreases the thermal mechanical noise.

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