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作者： Azab, Ahmed M. Ahmadi, Hamed Mihaylova, Lyudmila Arvaneh, Mahnaz Department of Automatic Control and System Engineering Sheffield University Sheffield United Kingdom School of Computer Science and Electronic Engineering University of Essex Essex United Kingdom

Objective. Common spatial patterns (CSP) is a prominent feature extraction algorithm in motor imagery (MI)-based brain-computer interfaces (BCIs). However, CSP is computed using sample-based covariance-matrix estimation. Hence, its performance deteriorates if the number of training trials is small. To address this problem, this paper proposes a novel regularized covariance matrix estimation framework for CSP (i.e. DTW-RCSP) based on dynamic time warping (DTW) and transfer learning. Approach. The proposed framework combines the subject-specific covariance matrix (Σ55) estimated using the available trials from the new subject, with a novel DTW-based transferred covariance matrix (ΣDTW) estimated using previous subjects trials. In the proposed ΣDTW, the available labelled trials from the previous subjects are temporally aligned to the average of the available trials of the new subject from the same class using DTW. This alignment aims to reduce temporal variations and non-stationarities between previous subjects' trials and the available trials from the new subjects. Moreover, to tackle the problem of regularization parameter selection when only a few trials are available for training, an online method is proposed, where the best regularization parameter is selected based on the confidence scores of the trained classifier on the upcoming first few labelled testing trials. Main results. The proposed framework is evaluated on two datasets against two baseline algorithms. The obtained results reveal that DTW-RCSP significantly outperformed the baseline algorithms at various testing scenarios, particularly, when only a few trials are available for training. Significance. Impressively, our results show that successful BCI interactions could be achieved with a calibration session as small as only one trial per class. © 2020 IOP Publishing Ltd.

关键词： Covariance matrix

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Data-Driven Modelling and Model Predictive control Design of a Nonlinear Auger-Based Pyrolysis Reactor

SSRN

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SSRN 2024年

作者： Adiprabowo, Arya Bhaskara Muharto, Bambang Prabowo, Wargiantoro Saputro, Frendy Rian Anggoro, Trisno Masfuri, Imron Sugeng, Dhani Avianto Rosyadi, Erlan Lang, Zi-Qiang KST BJ Habibie Energy Building 620-625 Indonesia Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom Department of Electrical Engineering Mathematics and Computer Science University of Twente Enschede7522 NB Netherlands Vehicle System Engineering Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia Department of Chemical and Environmental Engineering Malaysia Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia

Pyrolysis is a prominent technique to harvest energy while addressing the global waste problem. Product yield relies heavily on precise reactor temperature control in a pyrolysis process;thus, a representative mathematical model is necessary to design and evaluate the control system. Hence, this research aims to create a data-driven model of a 1 kg/h Auger-based pyrolysis (APR) plant and design a robust controller accordingly. The system is excited by using a pseudo-random binary signal (PRBS) to trigger process response and is recorded for 2 hours. Various models with different orders, such as Auto Regressive Exogeneous (ARX), Auto Regressive Moving Average Exogeneous (ARMAX), Box-Jenkins (BJ), and Output Error (OE) were created from the obtained data. The models were examined using best-fit percentage, final prediction error (FPE) and mean square error (MSE) criteria and validated using cross-correlation and auto-correlation functions. Two widely used and purpose-fit controllers, Proportional-Integral-Derivate (PID) and Model Predictive control (MPC), with output constraints, were designed and tuned for this application. Several operational scenarios were investigated, such as setpoint tracking and disturbance rejection. The best-fit models were selected and validated for TIC-3, TIC-4 and TIC-5, with 96.22%, 97.4% and 84.83% fitness criteria, respectively. Both controllers successfully satisfied all the tests with less than 2% steady-state error. However, MPC surpassed PID controllers in all aspects, such as 5-7 times faster settling time, negligible overshoot, and reduced integral absolute error (IAE) and integral square error (ISE) up to 44.75% and 5.45%, respectively. Nevertheless, MPC with input and output constraints performance pales compared to other controllers despite some adjustments with massive ISE and settling time above 3000 seconds. As a result, a recommendation to change the input actuator will be beneficial to unleash the benefit of MPC fully. © 2

关键词： Model predictive control

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Constrained optimal control of monotone systems with applications to battery fast-charging

arXiv

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arXiv 2023年

作者： Drummond, Ross Courtier, Nicola E. Howey, David A. Couto, Luis D. Guiver, Chris The Department of Automatic Control and Systems Engineering University of Sheffield Mappin St SheffieldS1 3JD United Kingdom The Department of Engineering Science University of Oxford 17 Parks Road OxfordOX1 3PJ United Kingdom The Department of Control Engineering and System Analysis Université libre de Bruxelles BrusselsB-1050 Belgium The School of Engineering & the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom

Enabling fast charging for lithium ion batteries is critical to accelerating the green energy transition. As such, there has been significant interest in tailored fast-charging protocols computed from the solutions of constrained optimal control problems. Here, we derive necessity conditions for a fast charging protocol based upon monotone control systems theory. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimal control systems

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Can random proximal coordinate descent be accelerated on nonseparable convex composite minimization problems?

Can random proximal coordinate descent be accelerated on non...

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European control Conference (ECC)

作者： Flavia Chorobura François Glineur Ion Necoara Automatic Control and System Engineering Department Spl. Independentei University Politehnica Bucharest Bucharest Romania ICTEAM Institute and CORE Université catholique de Louvain Louvain-la-Neuve Belgium Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest Romania

In this paper we consider convex composite optimization problems, where first term is smooth, while the second term is proximal easy but nonseparable (possibly non-smooth). For this problem we adapt the accelerated proximal coordinate descent algorithm from [7], initially developed for convex composite problems having the second term separable. We study convergence to a coordinate-wise minimizer point, and derive convergence rate in expected function values of order $\mathcal{O}((\mathrm{C}+(\mathbb{E}[S_{k}^{\#}])_{+})/k^{2})$. The first term, C, coincides with the usual constant appearing in the rate of accelerated gradient type methods, while the second one, $S_{k}^{\#}$, measures the nonseparability of the second term in the objective along the iterates. We conjecture that the second term, $S_{k}^{\#}$, is bounded as this is what we observe in all our numerical simulations and that coordinate descent can be accelerated.

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Impact analysis of the multi-harmonic input splicing way based on the data-driven model

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International Journal of Dynamics and control 2020年第4期8卷 1181-1188页

作者： Qiu, Yue Luo, Zhong Ge, Xiaobiao Zhu, Yunpeng Gao, Yi School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern University Shenyang110819 China State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology Dalian116024 China Department of Automatic Control and System Engineering University of Sheffield SheffieldS13JD United Kingdom

In response to the identification problem of nonlinear systems with harmonic input, this paper studies the identification effect of the different splicing way of the multi-harmonic input based model identification approach. In this method, the relationship between the input and output signals of the nonlinear system is represented by the Nonlinear Auto-Regressive model with exogenous inputs (NARX) model. Firstly, the modeling framework of the NARX model based on the multi-harmonic input method is introduced. Then the effect of the different splicing way contained sequence and period of splicing data on modeling results is researched and proved in theoretically. Finally, a case study is discussed to simultaneously validate that the different multi-harmonic signal splicing forms have no effect on the identification results. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Nonlinear systems

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

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IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Xue, Xingsi Zhang, Fuquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University New Engineering Industry College Putian China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Minjiang University College of Computer and Control Engineering Fuzhou China Sichuan Conservatory of Music Digital Media Art Key Laboratory of Sichuan Province Chengdu China Minjiang University Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Fuzhou China Fuzhou China

Driver drowsiness electroencephalogram (EEG) signal monitoring can promptly alert drivers of their drowsiness in edge-end Consumer Electronics (CE), 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. To further enhance the application of CE, we apply Adaptive Pruning Optimization (APO) to the channels and neurons of LDGCN, which nearly halves the inference latency. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization, outperforming existing state-of-the-art algorithms. © 1975-2011 IEEE.

关键词： Neurons

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Model-Free Nonlinear Feedback Optimization

arXiv

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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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Intelligent sitting postural anomaly detection system for wheelchair users with unsupervised techniques

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Digital Communications and Networks 2024年

作者： Patrick Vermander Aitziber Mancisidor Raffaele Gravina Itziar Cabanes Giancarlo Fortino Department of Automatic Control and System Engineering University of the Basque Country (UPV/EHU) Bilbao 48013 Spain Department of Informatics Modeling Electronics and Systems Engineering University of Calabria (UNICAL) Rende 87036 Italy

Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status. To date, this detection has relied on in-person observation by medical specialists. However, given the challenges faced by health specialists to carry out continuous monitoring, the development of an intelligent anomaly detection system is proposed. Unlike other authors, where they use supervised techniques, this work proposes using unsupervised techniques due to the advantages they offer. These advantages include the lack of prior labeling of data, and the detection of anomalies previously not contemplated, among others. In the present work, an individualized methodology consisting of two phases is developed: characterizing the normal sitting pattern and determining abnormal samples. An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis. It can be concluded, among other aspects, that the utilization of dimensionality reduction techniques leads to improved results. Moreover, the normality characterization phase is deemed necessary for enhancing the system's learning capabilities. Additionally, employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.

关键词： Sitting posture monitoring Anomaly detection Assistive technology Pressure sensors Unsupervised techniques Individualization Wheelchair

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