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arXiv 2025年

作者： Sánchez-Arriaga, Néstor Eduardo Canzini, Ethan Espley-Plumb, Nathan John Farnsworth, Michael Pope, Simon Leyland, Adrian Tiwari, Ashutosh Department of Automatic Control & Systems Engineering University of Sheffield Sheffield United Kingdom Department of Materials Science & Engineering University of Sheffield Sheffield United Kingdom Airbus Robotics Airbus UK Chester United Kingdom

Thin-film inspection on large-area substrates in coating manufacture remains a critical parameter to ensure product quality;however, extending the inspection process precisely over a large area presents major challenges, due to the limitations of the available inspection equipment. An additional manipulation problem arises when automating the inspection process, as the silicon wafer requires movement constraints to ensure accurate measurements and to prevent damage. Furthermore, there are other increasingly important large-area industrial applications, such as Roll-to-Roll (R2R) manufacturing where coating thickness inspection introduces additional challenges. This paper presents an autonomous inspection system using a robotic manipulator with a novel learned constraint manifold to control a wafer to its calibration point, and a novel multi-sensor array with high potential for scalability into large substrate areas. We demonstrate that the manipulator can perform required motions whilst adhering to movement constraints. We further demonstrate that the sensor array can perform thickness measurements statically with an error of 0.5◦ from the calibration point whilst monitoring the RMSE and R2 over 1406 data points. These features are potentially useful for detecting displacement variations in R2R manufacturing processes. © 2025, CC BY-NC-ND.

关键词： Silicon wafers

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Optimisation-Based Iterative Learning control for Distributed Consensus Tracking

Optimisation-Based Iterative Learning Control for Distribute...

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UKACC International Conference on control (control)

作者： Yueqing Zhang Bin Chen Bing Chu Zhan Shu Faculty of Engineering and Physical Sciences University of Southampton Southampton Department of Automatic Control and Systems Engineering University of Sheffield Sheffield U.K. Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

This paper addresses high-performance consensus tracking of repetitively operating networked dynamical systems using an iterative learning control (ILC) algorithm. It circumvents the need for precise model information in traditional methods and guarantees the high-performance by the predictive framework with a novel performance index that takes into account both current and future performance. The proposed algorithm ensures geometric convergence of the tracking error norm to zero and can be applied to both heterogeneous and non-minimum-phase systems. A distributed implementation of the algorithm is developed using the Alternating Direction Method of Multipliers, with detailed convergence analysis and numerical examples confirming its effectiveness.

关键词： System dynamics Heuristic algorithms Scholarships Predictive models Prediction algorithms Performance analysis Reliability

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A Machine-Learning Approach for the Development of a FOWT Model Integrated with Four OWCs

A Machine-Learning Approach for the Development of a FOWT Mo...

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Circuits, systems, Communications and Computers (CSCC), International Conference on

作者： Irfan Ahmad Fares Mzoughi Payam Aboutalebi Izaskun Garrido Aitor Garrido Automatic Control Group-ACG Department of Automatic Control and Systems Engineering University of the Basque Country UPV/EHU Bilbao Spain

ISBN: (纸本)9781665481878

The wind-wave excitations cause structural vibrations on the Floating Offshore Wind Turbines (FOWT) pressing the power generation efficiency and reducing the life expectancy. In particular, tower-top displacement and barge-type platform pitch dynamics are extremely sensitive to wind speed and wave elevation to the point that may even lead to structural instability in extreme conditions. Having into account that computational techniques such as Artificial Neural Networks (ANNs) are widely used in artificial intelligence because of their strong predicting and forecasting capabilities, the aim of this article is to create a deep-layer ANN model that incorporates Oscillating Water Columns (OWCs) into the barge platform. This ANN model enables to address stability issues of the hybrid floating offshore wind platform. The proposed control-oriented model has been successfully validated to achieve adequate dynamic behavior and structural performance using FAST.

关键词： Vibrations Computational modeling Wind speed Artificial neural networks Machine learning Predictive models Aerodynamics

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A Novel Sensing Template Using Data Fusion for Large Volume Assembly

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IFAC-PapersOnLine 2022年第2期55卷 283-288页

作者： Ethan Canzini Marc Auledas Dominique Chasteau Ashutosh Tiwari Department of Automatic Control & Systems Engineering University of Sheffield UK Industrial Architecture & Manufacturing Research Airbus UK

The size of large components within manufacturing processes leads to complications with automating the processes required to assemble them into larger structures. In recent years, development of multi-sensor networks and breakthroughs in measuring algorithms have allowed for the creation of novel methods of mating large components. One major challenge with deploying sensor networks into production environments is the ability to attach sensors to large volume components. This can be remedied with the use of a sensing template that acts as a pseudo-virtual jig for the assembly process where sensors are embedded onto the template, thus not interfering with the physical assembly. The key step for this sensing template is creating an algorithmic process for accurate component localisation. This paper will introduce an innovative method of using data fusion attached to a sensing template embedded in an aerospace assembly process. A sensing algorithm utilising a Kalman filter allows for accurate component mating with a low error offset and high repeatability. The results of the sensing template show how it is capable of reducing the error offset and improves the repeatability of measurements.

关键词： Autonomy Manufacturing Automation Internet-of-Things Sensing Enterprise

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A Learning Distributed Gaussian Process Approach for Target Tracking over Sensor Networks 25

A Learning Distributed Gaussian Process Approach for Target ...

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25th International Conference on Information Fusion, FUSION 2022

作者： Liu, Xingchi Lyu, Chenyi George, Jemin Pham, Tien Mihaylova, Lyudmila University of Sheffield Department of Automatic Control and Systems Engineering S1 3JD United Kingdom Us Army Research Laboratory AdelphiMD20783 United States

ISBN: (数字)9781737749721

ISBN: (纸本)9781737749721

Tracking manoeuvring targets often relies on complex models with non-stationary parameters. Gaussian process (GP) based model-free methods can achieve accurate performance in a data-driven manner but face scalability challenges. Aiming to address such challenges, this paper proposes a distributed GP-based tracking approach able to learn the kernel hyperparameters in an online manner, to improve the tracking performance and scalability. It caters to the inherent distributed feature of sensor networks and does not need measurements to be transmitted among sensors for target states predictions. Theoretical upper confidence bounds about the tracking error are derived within the regret bound setting. Through this theoretical analysis, the tracking error per time step is upper bounded as a function of predictive variances from local sensors. The theoretical results are supported by simulation based ones over a case study for tracking over wireless sensor networks. With evaluation on challenging target trajectories, a comparison on state-of-the-art centralised and distributed GP approaches, numerical results demonstrate that the proposed approach achieves competitively high and robust tracking performance. © 2022 International Society of Information Fusion.

关键词： Target tracking

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Prediction of Radiated Electromagnetic Interference Based on Four-module Motor Drive System

Prediction of Radiated Electromagnetic Interference Based on...

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Energy, Power and Grid (ICEPG), International Conference on

作者： Yixin Shi Wenjie Chen Fengjiao Li Jinpeng Kan Yongqing Meng Guang Yang School of Electrical Engineering Xi’an Jiaotong University Xi’an China Department of Computer and Electronic Systems Engineering AVIC Xi’an Flight Automatic Control Research Institute Xi’an China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

In recent years, wide bandgap semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) have been increasingly applied in electric drive systems, effectively enhancing system power density. However, the high-frequency and high-speed characteristics of SiC devices exacerbate electromagnetic interference (EMI) issues within the system. Additionally, the cables in electric drive systems exhibit significant antenna effects, radiating conducted EMI into the surrounding space, thereby affecting the normal operation of other sensitive equipment. Therefore, it is essential to research motor drive system topologies that can actively suppress EMI and predict radiated interference in motor drive systems. The topology of a four-module motor paired with parallel inverters can achieve active suppression of EMI at the source. This paper conducts a simulation-based prediction of radiated interference for a parallel inverter-driven four-module motor system. It analyzes the coupling mechanisms of common mode and differential mode radiated interference and validates the radiated interference from both DC and AC cables through simulations, predicting the impact on surrounding sensitive equipment in practical systems.

关键词： Motor drives Accuracy Silicon carbide Electromagnetic interference Layout Predictive models Motors Topology Gallium nitride Cables

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Detection-Triggered Recursive Impact Mitigation against Secondary False Data Injection Attacks in Microgrids

arXiv

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arXiv 2024年

作者： Liu, Mengxiang Zhang, Xin Zhang, Rui Zhou, Zhuoran Zhang, Zhenyong Deng, Ruilong The Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom The National Grid ESO Wokingham United Kingdom The State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou China

The cybersecurity of microgrid has received widespread attentions due to the frequently reported attack accidents against distributed energy resource (DER) manufactures. Numerous impact mitigation schemes have been proposed to reduce or eliminate the impacts of false data injection attacks (FDIAs). Nevertheless, the existing methods either requires at least one neighboring trustworthy agent or may bring in unacceptable cost burdens. This paper aims to propose a detection-triggered recursive impact mitigation scheme that can timely and precisely counter the secondary FDIAs (SFDIAs) against the communication links among DERs. Once triggering attack alarms, the power line current readings will be utilised to observe the voltage bias injections through the physical interconnections among DERs, based on which the current bias injections can be recursively reconstructed from the residuals generated by unknown input observers (UIOs). The attack impacts are eliminated by subtracting the reconstructed bias from the incoming compromised data. The proposed mitigation method can work even in the worst case where all communication links are under SFDIAs and only require extra current sensors. The bias reconstruction performance under initial errors and system noises is theoretically analysed and the reconstruction error is proved to be bounded regardless of the electrical parameters. To avoid deploying current sensors on all power lines, a cost-effective deployment strategy is presented to secure a spanning tree set of communication links that can guarantee the secondary control performance. Extensive validation studies are conducted in MATLAB/SIMULINK and hardware-in-the-loop (HIL) testbeds to validate the proposed method’s effectiveness against single/multiple and continuous/discontinuous SFDIAs. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost effectiveness

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Offline Goal-Conditioned Reinforcement Learning for Shape control of Deformable Linear Objects

arXiv

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arXiv 2024年

作者： Laezza, Rita Shetab-Bushehri, Mohammadreza Waltersson, Gabriel Arslan Özgür, Erol Mezouar, Youcef Karayiannidis, Yiannis Division of Systems and Control Department of Electrical Engineering Chalmers University of Technology Sweden CNRS Clermont Auvergne INP Institut Pascal Université Clermont Auvergne France Department of Automatic Control Lund University Sweden

Deformable objects present several challenges to the field of robotic manipulation. One of the tasks that best encapsulates the difficulties arising due to non-rigid behavior is shape control, which requires driving an object to a desired shape. While shape-servoing methods have been shown successful in contexts with approximately linear behavior, they can fail in tasks with more complex dynamics. We investigate an alternative approach, using offline RL to solve a planar shape control problem of a Deformable Linear Object (DLO). To evaluate the effect of material properties, two DLOs are tested namely a soft rope and an elastic cord. We frame this task as a goal-conditioned offline RL problem, and aim to learn to generalize to unseen goal shapes. Data collection and augmentation procedures are proposed to limit the amount of experimental data which needs to be collected with the real robot. We evaluate the amount of augmentation needed to achieve the best results, and test the effect of regularization through behavior cloning on the TD3+BC algorithm. Finally, we show that the proposed approach is able to outperform a shape-servoing baseline in a curvature inversion experiment. © 2024, CC BY.

关键词： Reinforcement learning

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Segmentation of Retina Vessels in 2D OCT-Reconstructed Fundus Images with 3D UNet

Segmentation of Retina Vessels in 2D OCT-Reconstructed Fundu...

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Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Tomasz Marciniak Agnieszka Stankiewicz Przemysław Zaradzki Department of Control Robotics and Electrical Engineering Division of Electronic Systems and Signal Processing Institute of Automatic Control and Robotics Poznan University of Technology Poznan Poland

This paper presents an analysis of the effectiveness of blood vessel segmentation in human retinal images. The segmentation process was carried out for three-dimensional optical coherence tomography (OCT) scans using the author’s publicly available CAVRI-C database. A 3D version of the U-Net artificial neural network was used. Various dimensions of the input blocks were investigated to determine their influence on the segmentation process. The effectiveness of the proposed solutions was evaluated using various metrics and the results obtained were compared with the 2D solution, also based on neural networks. The use of the proposed solutions allowed us to improve the segmentation even by 15% (in the case of the F1-score).

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Stochastic subgradient projection methods for composite optimization with functional constraints

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 12146-12180页

作者： Ion Necoara Nitesh Kumar Singh Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest Romania and Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest Romania Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest Romania

In this paper we consider optimization problems with stochastic composite objective function subject to (possibly) infinite intersection of constraints. The objective function is expressed in terms of expectation operator over a sum of two terms satisfying a stochastic bounded gradient condition, with or without strong convexity type properties. In contrast to the classical approach, where the constraints are usually represented as intersection of simple sets, in this paper we consider that each constraint set is given as the level set of a convex but not necessarily differentiable function. Based on the exibility offered by our general optimization model we consider a stochastic subgradient method with random feasibility updates. At each iteration, our algorithm takes a stochastic proximal (sub)gradient step aimed at minimizing the objective function and then a subsequent subgradient step minimizing the feasibility violation of the observed random constraint. We analyze the convergence behavior of the proposed algorithm for diminishing stepsizes and for the case when the objective function is convex or has a quadratic functional growth, unifying the nonsmooth and smooth cases. We prove sublinear convergence rates for this stochastic subgradient algorithm, which are known to be optimal for subgradient methods on this class of problems. When the objective function has a linear least-square form and the constraints are polyhedral, it is shown that the algorithm converges linearly. Numerical evidence supports the effectiveness of our method in real problems.

关键词： stochastic optimization convex functional constraints stochastic subgradient rate of convergence constrained least-squares robust/sparse svm

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