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arXiv 2022年

作者： He, Zhiyu Bolognani, Saverio He, Jianping Dörfler, Florian Guan, Xinping Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Feedback optimization is a control paradigm that enables physical systems to autonomously reach efficient operating points. Its central idea is to interconnect optimization iterations in closed-loop with the physical plant. Since iterative gradient-based methods are extensively used to achieve optimality, feedback optimization controllers typically require the knowledge of the steady-state sensitivity of the plant, which may not be easily accessible in some applications. In contrast, in this paper, we develop a model-free feedback controller for efficient steady-state operation of general dynamical systems. The proposed design consists of updating control inputs via gradient estimates constructed from evaluations of the nonconvex objective at the current input and at the measured output. We study the dynamic interconnection of the proposed iterative controller with a stable nonlinear discrete-time plant. For this setup, we characterize the optimality and stability of the closed-loop behavior as functions of the problem dimension, the number of iterations, and the rate of convergence of the physical plant. To handle general constraints that affect multiple inputs, we enhance the controller with Frank-Wolfe-type updates. © 2022, CC BY-SA.

关键词： Feedback control

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A new force profile signal for a convex solution of muscle force estimation from electromyographic signals*

A new force profile signal for a convex solution of muscle f...

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

作者： Mehdi Shirzadi Arezoo Mirshamsi Alp Eşrefoğlu Monica Rojas-Martínez Hamid Reza Marateb Miquel Angel Mañanas Taian Vieira Department of Automatic Control (ESAII) Biomedical Engineering Research Centre (CREB) Universitat Politèncicna de Catalunya (UPC) Barcelona Spain Biomedical Engineering Department Engineering Faculty University of Isfahan Isfahan Iran Laboratory of Applied Biology & Research Unit in Applied Neurophysiology (LABNeuro) – Université Libre de Bruxelles Brussels Belgium CIBER of Bioengineering Biomaterilas and Nanomedicine (CIBER-BBN) Madrid Spain Department of Electronics and Telecommunication Laboratory for Engineering of the Neuromuscular System (LISiN) PolitoBIOMed Lab – Politecnico di Torino Turin Italy

High-Density Surface Electromyography (HD-sEMG) is a non-invasive technique for measuring the electrical activity of a muscle with multiple, closely spaced electrodes. Estimation of muscle force is one of the applications of HD-sEMG. Usually, validating different EMG-Force models entails simple movements limited to laboratory settings. The validity of these models in more ecological conditions, requesting force production over a wide frequency band, remains unknown. In this study, we, therefore, compare the results of force prediction using four different types of input force profiles that can be representative of daily life activities, and we investigate whether the crest factor of these different input signals affects force prediction. For predicting the force from sEMG signals, we used our real-time and convex methods. HD-sEMG signals were recorded with 144 channels from the biceps brachii, brachioradialis, and triceps (long, lateral, and medial head) muscles of 24 healthy subjects during random signal, random phase, Schroeder phase, and minimum crest factor (crestmin) signal. The correlation and coefficient of determination (R 2 ) between measured and predicted forces were calculated for the different force feedback profiles. The crestmin signal showed significantly better results based on statistical tests (P-value < 0.05), with correlation and R 2 equal to 0.92±0.03 and 0.86±0.05, respectively. The results demonstrate that the crest factor of input signals is a crucial parameter that can impact the performance of EMG-Force models and must be considered during *** Relevance— This study demonstrates that lower crest factor multisine force profiles result in improved fitness for force prediction and can be used as an alternative to random signals.

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Observability and Identifiability Analyses of Process Models for Agricultural Anaerobic Digestion Plants

Observability and Identifiability Analyses of Process Models...

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International Conference on Process control (PC)

作者： Simon Hellmann Arne-Jens Hempel Stefan Streif Sören Weinrich Biochemical Conversion Department DBFZ Deutsches Biomasseforschungszentrum gGmbH Leipzig Germany Saxon University of Cooperative Education Glauchau Germany Chair of Automatic Control and System Dynamics Chemnitz University of Technology Chemnitz Germany Faculty of Energy. Building Services. Environmental Engineering Munster University of Applied Sciences Münster Germany

Dynamic operation of anaerobic digestion plants requires advanced process monitoring and control. Different simplifications of the Anaerobic Digestion Model No. 1 (ADM1) have been proposed recently, which appear promising for model-based process automation and state estimation. As a fundamental requirement, observability and identifiability of these models are analyzed in this work, which was pursued through algebraic and geometric analysis. Manual algebraic assessment was successful for small models such as the ADM1-R4 and simplified versions of the ADM1-R3, which were derived in this context. However, for larger model classes the algebraic approach showed to be insufficient. By contrast, the geometric approach, implemented in the STRIKE GOLDD toolbox, allowed to show observability for more complex models (including ADM1-R4 and ADM1-R3), employing two independent algorithms. The present study lays the groundwork for state observer design, parameter estimation and advanced control resting upon ADM1-based models.

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Active Flow control on Airfoils by Reinforcement Learning

SSRN

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SSRN 2023年

作者： Portal-Porras, Koldo Fernandez-Gamiz, Unai Zulueta, Ekaitz Garcia-Fernandez, Roberto Berrizbeitia, Saioa Etxebarria Nuclear Engineering and Fluid Mechanics Department Faculty of Engineering of Vitoria-Gasteiz University of the Basque Country UPV/EHU Nieves Cano 12 Araba Vitoria-Gasteiz01006 Spain Automatic Control and System Engineering Department Faculty of Engineering of Vitoria-Gasteiz University of the Basque Country UPV/EHU Nieves Cano 12 Araba Vitoria-Gasteiz01006 Spain Sunsundegui S.A. Poligono Ibarrea s/n Navarra Altsasu31800 Spain Mechanical Engineering Department Faculty of Engineering of Vitoria-Gasteiz University of the Basque Country UPV/EHU Nieves Cano 12 Araba Vitoria-Gasteiz01006 Spain

In recent years, wind energy is gaining importance, with the aim of achieving a clean and sustainable energy model. However, an enhancement in wind turbine performance is still required to compete with traditional energy sources. The implementation of flow control devices in airfoils is one of the most common solutions to achieve this improvement. Among flow control devices several alternatives are available, but devices with reactive control are the most effective, due to their capacity to adapt to instantaneous conditions of the flow. This study proposes a control strategy based on a Reinforcement Learning (RL) agent to effectively govern the motion of a rotating flap implemented on a NACA0012 airfoil. With this objective, first different Computational Fluid Dynamics (CFD) simulations are conducted to gather data about the tested case. Then, a numerical model based on Artificial Neural Networks (ANN) is developed to model the discussed case. Finally, the RL agent is trained and tested under different conditions. The results show that the trained RL agent is able to provide a fast and reliable response for every tested condition, setting the adequate position of the flap and obtaining an appropriate aerodynamic performance of the airfoil. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Improving EEG-based error detection using relative peak features

Improving EEG-based error detection using relative peak feat...

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

作者： Adrian L. Ashley Mahnaz Arvaneh Automatic control and system Engineering Department University of Sheffield Sheffield United Kingdom

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

A brain-computer interface (BCI) potentially enables a severely disabled person to communicate using brain signals. automatic detection of error-related potentials (ErrPs) in electroencephalograph (EEG) could improve BCI performance by allowing to correct the erroneous action made by the machine. However, the current low accuracy in detecting ErrPs, particularly in some users, can reduce its potential benefits. The paper addresses this problem by proposing a novel relative peak feature (RPF) selection method to improve performance and accuracy for recognising an ErrP in the EEG. Using data collected from 29 participants with a mean age of 24.14 years the relative peak features yielded an average across all classifiers of 81.63% accuracy in detecting the erroneous events and an average 78.87 % accuracy in detecting the correct events, using KNN, SVM and LDA classifiers. In comparison to the temporal feature selection, there was a gain in performance in all classifiers of 17.85% for error accuracy and a reduction of -6.16% for correct accuracy Specifically; our proposed RPF used significantly reduced the number of features by 91.7% when compared with the state of the art temporal *** the future, this work will improve the human-robot interaction by improving the accuracy of detecting errors that enable the BCI to correct any mistakes.

关键词： Electroencephalography Feature extraction Task analysis Support vector machines Electrodes Training Electric potential

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

arXiv

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arXiv 2024年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Grau, Antoni Zhang, Fuquan The College of Computer and Big Data Fuzhou University Fuzhou China The Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain The New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Minjiang University Fuzhou China Fujian Polytechnic Normal University Fujian Province University Fuzhou China

Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://***/BryantDom/Driver-Drowsiness-Monitoring. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Study of photophysical properties of Cdq2 thin film

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Materials Today: Proceedings 2022年 66卷 202-204页

作者： Lougdali, M. El Kouari, Y. Anoua, R. Zazoui, M. El Oudghiri-Idrissi, I. Abboud, Y. Zawadzka, A. Sahraoui, B. Laboratory of Materials Energy and Control System Faculty of Sciences and Technology Hassan II University of Casablanca Mohammedia B.P 146 Morocco Department of Automatic and Measurement Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Torun Poland Laboratory of Engineering Sciences for Energy National School of Applied Sciences of El Jadida El Jadida BP 1166 Morocco Faculty of Sciences Ben M'sik University of Hassan II of Casablanca Casablanca Morocco Centre for Modern Interdisciplinary Technologies Nicolaus Copernicus University Wilenska 4 Torun 87-100 Poland MOLTECH-Anjou UMR 6200 CNRS UNIV Angers 2 bd Lavoisier Angers 49045 France

In this work, the morphological and optical properties of the cadmium (II) bis(8-hydroxyquinoline) (Cdq2) thin film are presented. The film was elaborated by physical deposition technique. The Atomic Force Microscopy (AFM) image shows a homogenous surface. The transmittance was measured in the range of 300–1200 nm with UV–visible spectroscopy. The emission of the film was characterized via the photoluminescence spectroscopy, a strong luminescence has been found. The measurements done at low temperatures show a decrease of the intensity of luminescence in the temperature range of 77–300 K. © 2022

关键词： Thin films

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Model-Based and Data-Driven control of Event- and Self-Triggered Discrete-Time LTI systems

arXiv

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arXiv 2022年

作者： Wang, Xin Berberich, Julian Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper considers the model-based and data-driven control of unknown linear time-invariant discretetime systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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Asymptotic Theory for Regularized system Identification Part I: Empirical Bayes Hyper-parameter Estimator

arXiv

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arXiv 2022年

作者： Ju, Yue Mu, Biqiang Ljung, Lennart Chen, Tianshi The School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing100190 China The Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden

Regularized techniques, also named as kernel-based techniques, are the major advances in system identification in the last decade. Although many promising results have been achieved, their theoretical analysis is far from complete and there are still many key problems to be solved. One of them is the asymptotic theory, which is about convergence properties of the model estimators as the sample size goes to infinity. The existing related results for regularized system identification are about the almost sure convergence of various hyper-parameter estimators. A common problem of those results is that they do not contain information on the factors that affect the convergence properties of those hyper-parameter estimators, e.g., the regression matrix. In this paper, we tackle problems of this kind for the regularized finite impulse response model estimation with the empirical Bayes (EB) hyper-parameter estimator and filtered white noise input. In order to expose and find those factors, we study the convergence in distribution of the EB hyper-parameter estimator, and the asymptotic distribution of its corresponding model estimator. For illustration, we run Monte Carlo simulations to show the efficacy of our obtained theoretical results. Copyright © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

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A preliminary analysis of gait performance of patients with multiple sclerosis using a sensorized crutch tip

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IFAC-PapersOnLine 2020年第2期53卷 16462-16467页

作者： Iñigo Sesar Asier Zubizarreta Itziar Cabanes Asier Brull Jon Torres-Unda Ana Rodriguez-Larrad Department of System Engineering and Automatic Control University of the Basque Country (UPV/EHU) Bilbao Spain Department of Physiology University of the Basque Country(UPV/EHU) Leioa Spain

The quality of life and functional mobility of patients with Multiple Sclerosis (MS) can significantly improve with exercise and a rehabilitation therapy adjusted to the needs of each patient. The assessment of gait and functional mobility of patients with MS is usually done based on clinical scales and tests, which have various limitations. This work presents the preliminary results of a clinical study carried out with patients with MS walking with a sensorized crutch tip. This tip allows to define new indicators that can be correlated with the clinical assessment scales and provide further objective and quantitative information to assess gait performance and level of impairment of patients with MS, and characterize their gait patterns. The results suggest that parameters such as the average cycle time and the average percentage of body weight might be useful to evaluate the gait performance and level of disability. Moreover, parameters related with the pitch angle of the crutch allow to determine crutch usage patterns and spot differences between patients with similar functional performance.

关键词： Inertial sensors sensor fusion sensorized crutch tip wearable sensors gait analysis multiple sclerosis

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