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9th International Conference on Fluid Power and Mechatronics, FPM 2023

作者： Zhang, Xinyuan Wang, Shaoping Niu, Yu Wu, Xiao Wang, Xingjian Gavrilov, Alex I. School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Tianmushan Laboratory Hangzhou310023 China Ningbo Institute of Technology Beihang University Ningbo315800 China Research Institute of Aero-Engine Beihang University Beijing100191 China Bauman Moscow State Technical University Department of Automatic Control Systems Moscow105005 Russia

ISBN: (纸本)9798350304947

The increased demand for active control of engines has made the study of high-frequency response actuators increasingly important, and actuators based on magnetostrictive materials are promising for a wide range of applications. In this paper, the magnetic field distribution and various thermal losses including Joule heat loss, eddy current loss and hysteresis loss inside the giant magnetostrictive actuator (GMA) are analyzed, and the mathematical model of the current-driven GMA operation is derived, where the new J-A model considering the temperature effect is used to describe the hysteresis nonlinearity of the material. This paper simulates the actuator model under the action of multiple physical fields in COMSOL, focusing on the variation of the output displacement of the actuator when the thermal field and thermal expansion are integrated, and provides the theoretical basis for the design of the GMA control algorithm in the future. © 2023 IEEE.

关键词： Temperature

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A Persistent-Excitation-Free Method for System Disturbance Estimation Using Concurrent Learning

arXiv

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arXiv 2023年

作者： Zhang, Zengjie Liu, Fangzhou Liu, Tong Qiu, Jianbin Buss, Martin The Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands The Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China Automatic Control Engineering Technical University of Munich Munich80333 Germany

Observer-based methods are widely used to estimate the disturbances of different dynamic systems. However, a drawback of the conventional disturbance observers is that they all assume persistent excitation (PE) of the systems. As a result, they may lead to poor estimation precision when PE is not ensured, for instance, when the disturbance gain of the system is close to the singularity. In this paper, we propose a novel disturbance observer based on concurrent learning (CL) with time-variant history stacks, which ensures high estimation precision even in PE-free cases. The disturbance observer is designed in both continuous and discrete time. The estimation errors of the proposed method are proved to converge to a bounded set using the Lyapunov method. A history-sample-selection procedure is proposed to reduce the estimation error caused by the accumulation of old history samples. A simulation study on epidemic control shows that the proposed method produces higher estimation precision than the conventional disturbance observer when PE is not satisfied. This justifies the correctness of the proposed CL-based disturbance observer and verifies its applicability to solving practical problems. © 2023, CC BY-NC-SA.

关键词： Robust control

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Distributed Sensor FD for Sensor Networks

Distributed Sensor FD for Sensor Networks

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Jiarui Zhang Steven X. Ding Linlin Li Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg Germany School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

The main objective of this paper is to develop a distributed sensor fault detection (FD) approach for sensor networks in the presence of noise and transmission time of information exchange. To be specific, the approach optimizes the performance of FD by minimizing the detection uncertainty and the online implementation of the proposed method is in a distributed and recursive manner. Finally, a numerical example is illustrated to show that the distributed approach can successfully and efficiently accomplish the FD task.

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An $\mathcal{L}_{\mathrm{p}}$ -norm framework for event-triggered control

An $\mathcal{L}_{\mathrm{p}}$ -norm framework for event-trig...

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European control Conference (ECC)

作者： Michael Hertneck Alejandro I. Maass Dragan Nešić Frank Allgöwer University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart Germany Department of Electrical Engineering Pontificia Universidad Catolica de Chile Santiago Chile Department of Electrical and Electronic Engineering The University of Melbourne Parkville Victoria Australia

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper presents a novel event-triggered control (ETC) design framework based on measured $\mathcal{L}_{p}$ norms. We consider a class of systems with finite $\mathcal{L}_{p}$ gain from the network-induced error to a chosen output. The $\mathcal{L}_{p}$ norms of the network-induced error and the chosen output since the last sampling time are used to formulate a class of triggering rules. Based on a small-gain condition, we derive an explicit expression for the $\mathcal{L}_{p}$ gain of the resulting closed-loop systems and present a time-regularization, which can be used to guarantee a lower bound on the inter-sampling times. The proposed framework is based on a different stability- and triggering concept compared to ETC approaches from the literature, and thus may yield new types of dynamical properties for the closed-loop system. However, for specific output choices it can lead to similar triggering rules as “standard” static and dynamic ETC approaches based on input-to-state stability and yields therefore a novel interpretation for some of the existing triggering rules. We illustrate the proposed framework with a numerical example from the literature.

关键词： Europe Closed loop systems Numerical stability Standards

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Lipschitz-Bounded 1D Convolutional Neural Networks using the Cayley Transform and the controllability Gramian

Lipschitz-Bounded 1D Convolutional Neural Networks using the...

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IEEE Conference on Decision and control

作者： Patricia Pauli Ruigang Wang Ian R. Manchester Frank Allgöwer Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany Australian Centre for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Australia

We establish a layer-wise parameterization for 1D convolutional neural networks (CNNs) with built-in end-to-end robustness guarantees. In doing so, we use the Lipschitz constant of the input-output mapping characterized by a CNN as a robustness measure. We base our parameterization on the Cayley transform that parameterizes orthogonal matrices and the controllability Gramian of the state space representation of the convolutional layers. The proposed parameterization by design fulfills linear matrix inequalities that are sufficient for Lipschitz continuity of the CNN, which further enables unconstrained training of Lipschitz-bounded 1D CNNs. Finally, we train Lipschitz-bounded 1D CNNs for the classification of heart arrythmia data and show their improved robustness.

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Exact representation and efficient approximations of linear model predictive control laws via HardTanh type deep neural networks

arXiv

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arXiv 2024年

作者： Lupu, Daniela Necoara, Ion Department of Automatic Control and Systems Engineering University Politehnica Bucharest Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest050711 Romania

Deep neural networks have revolutionized many fields, including image processing, inverse problems, text mining and more recently, give very promising results in systems and control. Neural networks with hidden layers have a strong potential as an approximation framework of predictive control laws as they usually yield better approximation quality and smaller memory requirements than existing explicit (multi-parametric) approaches. In this paper, we first show that neural networks with HardTanh activation functions can exactly represent predictive control laws of linear time-invariant systems. We derive theoretical bounds on the minimum number of hidden layers and neurons that a HardTanh neural network should have to exactly represent a given predictive control law. The choice of HardTanh deep neural networks is particularly suited for linear predictive control laws as they usually require less hidden layers and neurons than deep neural networks with ReLU units for representing exactly continuous piecewise affine (or equivalently min-max) maps. In the second part of the paper we bring the physics of the model and standard optimization techniques into the architecture design, in order to eliminate the disadvantages of the black-box HardTanh learning. More specifically, we design trainable unfolded HardTanh deep architectures for learning linear predictive control laws based on two standard iterative optimization algorithms, i.e., projected gradient descent and accelerated projected gradient descent. We also study the performance of the proposed HardTanh type deep neural networks on a linear model predictive control application. © 2024, CC BY.

关键词： Model predictive control

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Pulsating Fluidic Sensor for Sensing of Location, Pressure and Contact Area

Pulsating Fluidic Sensor for Sensing of Location, Pressure a...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Joanna Jones Marco Pontin Dana D. Damian Department of Automatic Control and Systems Engineering Sheffield Biomedical Robotics Laboratory University of Sheffield United Kingdom Sheffield Robotics University of Sheffield UK Insigneo Institute for in Silico Medicine University of Sheffield UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Designing information-rich and space-efficient sensors is a key challenge for soft robotics, and crucial for the development of safe soft robots. Sensing and understanding the environmental interactions with a minimal footprint is especially important in the medical context, where portability and unhindered patient/user movement is a priority, to move towards personalized and decentralized healthcare solutions. In this work, a pulsating fluidic soft sensor (PFS) capable of determining location, pressure and contact area of press events is shown. The sensor relies on spatio-temporal resistance changes driven by a pulsating conductive fluid. The sensor demonstrates good repeatability and distinction of single and multiple press events, detecting single indents of sizes greater than 1 cm, forces larger than 2 N, and various locations across the sensor, as well as multiple indents spaced 2 cm apart. Furthermore, the sensor is demonstrated in two applications to detect foot placement and grip location. Overall, the sensor represents an improvement towards minimizing electronic hardware, and cost of the sensing solution, without sacrificing the richness of the sensing information in the field of soft fluidic sensors.

关键词： Presses Fluids Soft sensors Medical services Soft robotics Robot sensing systems Hardware

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Design of Robust Output Feedback controllers for Discrete-Time systems with Polynomial Uncertainties

Design of Robust Output Feedback Controllers for Discrete-Ti...

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International Conference on systems and control (ICSC)

作者： Robert Dehnert Sabine Lerch Michelle Rosik Andreas Rauh Bernd Tibken Institute of Automatic Control School of Electrical Information and Media Engineering University of Wuppertal Wuppertal Germany Group: Distributed Control in Interconnected Systems Carl von Ossietzky Universität Oldenburg Oldenburg Germany

This paper addresses the robust dynamic output feedback (DOF) problem for linear parameter-varying (LPV) discrete-time systems with polynomial dependence of uncertainties. For this purpose, the robust control problem is formulated via linear matrix inequalities (LMI) using Lyapunov's theory for a stability proof. Two relaxation methods are required to solve the robust LMI problem. The first is necessary to expand the Lyapunov stability condition to the structured linear control (SLC) problem for DOF controllers. The second type of relaxation is based on the theorem of Ehlich and Zeller and is required to include the polynomial dependence of uncertainties in the LMI design. These two relaxation methods yield an iterative LMI optimization approach. In this paper, we consider the specific example of a robust I-PD controller with a predefined decay rate that satisfies $H$ ∞ conditions for a polynomial parameter-dependent uncertain system.

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In-depth characterization of particulate matter in a highly polluted urban environment at the foothills of Himalaya–Karakorum Region

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Environmental Science and Pollution Research 2024年第24期31卷 35705-35726页

作者： Zeb, Bahadar Alam, Khan Huang, Zhongwei Öztürk, Fatma Wang, Peng Mihaylova, Lyudmila Khokhar, Muhammad Fahim Munir, Said Sheringal Pakistan Collaborative Innovation Centre for Western Ecological Safety College of Atmospheric Sciences Lanzhou University Lanzhou730000 China Department of Physics University of Peshawar Peshawar25120 Pakistan Faculty of Engineering Environmental Engineering Department Bolu Abant İzzet Baysal University Gölköy Campus 14030 Bolu Turkey Department of Computing and Mathematics Manchester Metropolitan University ManchesterM15 6BH United Kingdom Department of Automatic Control and Systems Engineering The University of Sheffield SheffieldS10 2TN United Kingdom Institute of Environmental Sciences and Engineering National University of Sciences and Technology Islamabad44000 Pakistan Institute for Transport Studies University of Leeds LeedsLS2 9JT United Kingdom

In recent years, the rising levels of atmospheric particulate matter (PM) have an impact on the earth’s system, leading to undesirable consequences on various aspects like human health, visibility, and climate. The present work is carried out over an insufficiently studied but polluted urban area of Peshawar, which lies at the foothills of the famous Himalaya and Karakorum area, Northern Pakistan. The particulate matter with an aerodynamic diameter of less than 10 µm, i.e., PM10 are collected and analyzed for mineralogical, morphological, and chemical properties. Diverse techniques were used to examine the PM10 samples, for instance, Fourier transform infrared spectroscopy, x-ray diffraction, and scanning electron microscopy along with energy-dispersive x-ray spectroscopy, proton-induced x-ray emission, and an OC/EC carbon analyzer. The 24 h average PM10 mass concentration along with standard deviation was investigated to be 586.83 ± 217.70 µg/m3, which was around 13 times greater than the permissible limit of the world health organization (45 µg/m3) and 4 times the Pakistan national environmental quality standards for ambient PM10 (150 µg/m3). Minerals such as crystalline silicate, carbonate, asbestiform minerals, sulfate, and clay minerals were found using FTIR and XRD investigations. Microscopic examination revealed particles of various shapes, including angular, flaky, rod-like, crystalline, irregular, rounded, porous, chain, spherical, and agglomeration structures. This proved that the particles had geogenic, anthropogenic, and biological origins. The average value of organic carbon, elemental carbon, and total carbon is found to be 91.56 ± 43.17, 6.72 ± 1.99, and 102.41 ± 44.90 µg/m3, respectively. Water-soluble ions K+ and OC show a substantial association (R = 0.71). Prominent sources identified using Principle component analysis (PCA) are anthropogenic, crustal, industrial, and electronic combustion. This research paper identified the potential sources of P

关键词： Air quality

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Utilizing Cloud Solutions for Object Recognition in the Context of Industrial Robotics Sorting Tasks

Utilizing Cloud Solutions for Object Recognition in the Cont...

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IEEE International Symposium on Innovations in Intelligent systems and Applications (INISTA)

作者： Ioana Livia Stefan Andrei Mateescu Ioana Miruna Vlasceanu Dragos Constantin Popescu Ioan Stefan Sacala Automatic Control and Systems Engineering Department Faculty of Automatic Control and Computers University POLITEHNICA of Bucharest Splaiul Independentei No. 313 Bucharest Romania Extreme Light Infrastructure (ELI-NP) & Horia Hulubei National Institute for R&D in Physics and Nuclear Engineering (IFIN-HH) Str. Reactorului No. 30 Măgurele Bucharest Romania

ISBN: (数字)9798350368130

ISBN: (纸本)9798350368147

The rapid advancements in sectors such as Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), networking and cloud computing are opening the opportunity for integrating these techniques with robots from the manufacturing field, with the promising perspective of improved performance at reduced costs, inspiring the new field of cloud robotics. In this paper, we propose an architecture for integrating industrial robots with Cloud AI object recognition services. For our proposed solution, we explore the results obtained by simulating a sorting task in RobotStudio, communicating with Microsoft Azure Custom Vision API by sending the web service calls from a computer running a Python script. We present the cycle time added by the communication with the cloud service and test the robustness of the system by comparing the results obtained with four sets of images: unaltered, altered with gaussian noise, barrel distortion and both.

关键词： Histograms Technological innovation Service robots Web services Gaussian noise Distortion Object recognition

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