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An Integrated Observer Framework Based Mechanical Parameters Identification for Adaptive control of Permanent Magnet Synchronous Motor

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Complex System Modeling and Simulation 2022年第4期2卷 354-367页

作者： Zhong Liao Zhaohua Liu Lei Chen Mingyang Lyu Dian Wang Faming Wu Zhengheng Wang Hualiang Wei the School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China the Wind Power Business Division CRRC Zhuzhou Institute Co.Ltd.Zhuzhou 412001China. the Department of Automatic Control and Systems Engineering University of SheffieldSheffieldS13JDUK.

An integrated observer framework based mechanical parameters identification approach for adaptive control of permanent magnet synchronous motors is proposed in this ***,an integrated observer framework is established for mechanical parameters’estimation,which consists of an extended sliding mode observer(ESMO)and a Luenberger *** at minimizing the influence of parameters coupling,the viscous friction and the moment of inertia are obtained by ESMO and the load torque is identified by Luenberger observer *** obtaining estimates of the mechanical parameters,the optimal proportional integral(PI)parameters of the speed-loop are determined according to third-order best design *** a result,the controller can adjust the PI parameters in real time according to the parameter changes to realize the adaptive control of the ***,the disturbance is compensated according to the ***,the experiments were carried out on simulation platform,and the experimental results validated the reliability of parameter identification and the efficiency of the adaptive control strategy presented in this paper.

关键词： parameter identification permanent magnet synchronous motor adaptive control extended sliding mode observer Luenberger observer

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Improving Workplace Safety in the Cargo Industry through Posture Monitoring using Mediapipe and Machine Learning

Improving Workplace Safety in the Cargo Industry through Pos...

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

作者： Buket Sonbas Cobb Fethi Candan Department of Electrical-Electronics Engineering Harran University Sanliurfa Turkiye Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

This research reveals the potential of Mediapipe with machine learning methods in addressing the critical need for real-time posture monitoring, offering a promising solution for creating secure work environments and improving health and safety conditions not only in the cargo industry but also in other fields, such as healthcare, sports, and industry. The findings indicate that the Random Forest Classifier demonstrates the highest accuracy in classifying untrained movements, indicating its potential for developing a posture correction warning system. Further research is needed to validate the findings and explore real-world applications. Overall, this study contributes to the advancement of workplace safety and sets the foundation for future developments in posture monitoring systems using Mediapipe and machine learning using Python programming.

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A Review of Parameterised MPC Algorithms

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IFAC-PapersOnLine 2023年第2期56卷 7692-7697页

作者： John A. Rossiter Oscar J. Gonzalez-Villareal Mohammad A.M. Sarbini Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S1 3JD UK Electrical and Electronic Engineering Programme Area Universiti Teknologi Brunei

This paper provides an evaluation and comparison of popular parameterised model predictive control approaches that have been proposed in the literature in recent years. Using the Generalised Predictive control (GPC) algorithm as the baseline algorithm, the paper sets out a number of performance criteria to compare and contrast with several other MPC approaches. Numerical examples use 100 random samples of 2, 3, and 4-state models and the approaches are compared using the selected performance criteria.

关键词： Predictive control Model predictive and optimization-based control Monte Carlo methods

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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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Excitation Optimization for Estimating Battery Health Parameters using Reinforcement Learning considering Information Content and Bias

Excitation Optimization for Estimating Battery Health Parame...

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American control Conference (ACC)

作者： Rui Huang Morgan Jones Xinfan Lin Department of Mechanical and Aerospace Engineering University of California Davis CA USA Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK

Accurate parameter estimation has been a long-pursued objective in battery modeling and control practice. To this end, optimization of excitation to improve the estimation accuracy has been an emerging topic, since the quality of data critically determines the accuracy of estimation. However, there are several major drawbacks with existing approaches. First, the commonly used criterion for optimization, e.g., Fisher information, is limited in performance due to not considering the estimation bias caused by inevitable system uncertainties. In addition, alternative existing methods rely on a good a priori knowledge of the parameter to be estimated, which is intrinsically contradictory to the goal of estimation. To address these issues, we propose a reinforcement learning (RL) framework to learn the optimal policy for excitation generation that is robust to system uncertainties. In particular, the framework involves a non-additive objective/reward associated with the newly established optimization criterion, and a state augmentation technique is applied to address the ensuing challenge. It is shown that, when applied to estimate a key health-related battery electrochemical parameter, the RL-based approach achieves significantly higher objective value under nominal conditions, and reduces the estimation error by one-order-of-magnitude in the presence of uncertainties compared with the baseline in existing approaches.

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A study on preterm birth predictions using physiological signals,medical health record information and low-dimensional embedding methods

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IET Cyber-systems and robotics 2021年第3期3卷 228-244页

作者： Ejay Nsugbe Oluwarotimi William Samuel Ibrahim Sanusi Mojisola Grace Asogbon Guanglin Li Nsugbe Research Labs SwindonUK Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences ShenzhenChina Department of Automatic Control and Systems Engineering The University of SheffieldSheffieldUK

Preterm births have been seen to have psychological and financial implications;current surveys suggest that amongst the various methods of preterm prediction,there is yet to exist a reliable and standard means of predicting preterm *** study investigates the application of electrohysterogram and tocogram signals acquired at various points during the third pregnancy trimester,alongside information from the patients'medical health record regarding the pregnancy,towards preterm prediction and an associated delivery imminency *** addition to this,the impact of both linear and non-linear dimensional embedding methods towards the preterm prediction is *** classification exercises were carried out using a support vector machine and decision tree,both of which have a certain degree of model interpretability and have potential to be introduced into a clinical operating framework.

关键词： cybernetics decision-making decision-tree classifier machine intelligence machine learning sensor fusion

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On the Approximation of the Quotient of Two Gaussian Densities for Multiple-Model Smoothing 26

On the Approximation of the Quotient of Two Gaussian Densiti...

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26th International Conference on Information Fusion, FUSION 2023

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Xue, Yanbo University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Career Science Lab Beijing China

ISBN: (纸本)9798890344854

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target. © 2023 International Society of Information Fusion.

关键词： Cost functions

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A Novel Spindle Speed and Angle Estimation Strategy in Multi-material Robotic Drilling

A Novel Spindle Speed and Angle Estimation Strategy in Multi...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Bin Chen Chaoyue Niu Erica Smith Rob Bramley Pete Crawforth Mahdi Mahfouf Visakan Kadirkamanathan Department of Automatic Control and Systems Engineering University of Sheffield Sheffield U.K. Advanced Manufacturing Research Centre with Boeing University of Sheffield Rotherham U.K.

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Aircraft assembly involves drilling vast numbers of holes, with stringent quality requirements, in multi-material stacks. To achieve high quality functional holes, one of the process parameters that is controlled carefully is the spindle speed. Variations in spindle speed from the programmed value can be indicative of the quality of the manufactured part and thus can be useful for process monitoring. Estimation of the tool angle has potential benefits in monitoring the spatial localisation of hole defects. Rotational speed estimation from encoded type detectors is an established methodology but the literature on parameter-free angle estimation is sparse. To address these challenges, this paper proposes a set of novel strategies for speed and angle estimation. The strategy includes the use of a fibre optic sensor with a novel encoding structure to facilitate angle estimation, and the analysis of the sensor signal using a staged signal processing framework. Experimental results from a real robotic drilling operation of a multi-material stack are given to demonstrate the effectiveness of the estimation strategy.

关键词： Drilling Process monitoring Velocity control Estimation Signal processing Robot sensing systems Sensors Timing Transient analysis Surface treatment

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A Review on Defense Mechanism against the Denial of Service and False Data Injection in Cyber-Physical Power systems

A Review on Defense Mechanism against the Denial of Service ...

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China International Electrical and Energy Conference (CIEEC)

作者： Zhuoran Zhou Jinning Zhang Xin Zhang Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom School of Engineering University of Leicester Leicester United Kingdom

Cyber-physical power system (CPPS), with its bi-directional power and information flows, is considered as the next generation of widely distributed and automated electrical power network. However, CPPS is vulnerable to malicious cyberattacks that can cause significant operational risk and financial loss to stakeholders, such as electricity system operators. The complexity of vulnerabilities, which arises from the heterogeneity and interdependency of physical and cyber domains, will present substantial challenges for the detection and prevention of cyberattacks. This paper provides a comprehensive review of the defense mechanisms in CPPS against coordinated and widespread cyberattacks. The most prevalent cyberattacks in the CPPS are denial-of-service (DoS) and false data injection (FDI) attacks. This paper examines the state-of-the-art prevention, detection, and mitigation technologies for these major mainstreams and coordinated cyberattacks. In particular, the advantages and disadvantages of popular cyberattack prevention sciences such as Game Theory, Optimization, control Theory, and Data-driven method are discussed. It reveals that cyberattack detection techniques have evolved from traditional state estimation to artificial intelligence-based methods. Finally, the challenges and opportunities for future research on cybersecurity in CPPS are identified.

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Pixel-based Hole Quality Evaluation in Robot Drilling Manufacturing Process

Pixel-based Hole Quality Evaluation in Robot Drilling Manufa...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Chaoyue Niu Bin Chen Erica Smith Robert Bramley Pete Crawforth Mahdi Mahfouf Visakan Kadirkamanathan Department of Automatic Control and Systems Engineering University of Sheffield Sheffield U.K. Advanced Manufacturing Research Centre with Boeing University of Sheffield Rotherham U.K.

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Aircraft assembly entails drilling numerous holes, often in multi-material stacks, then joining parts with fasteners fitted through the holes. There are stringent quality requirements on the holes, and assessment of hole quality is crucial to ensuring the integrity of the joints. Carbon fibre reinforced polymer (CFRP) is a commonly used material in aircraft structures due to its desirable properties. However, it is susceptible to defects not associated with metals, such as delamination and uncut fibres. While there have been multiple metrics for assessment of these defects proposed in literature, relatively few attempts to consolidate them have been seen. Furthermore, common measurement methods used for assessing these defects (e.g. 3D-microscopy) are well established, but can be time-consuming; manual interrogation of raw inspection data can also be highly subjective. To address these challenges, this paper proposes a set of combined metrics along with an automatic image processing framework for fast assessment of delamination and uncut fibre defects. The combined metric for the delamination region is aggregated from across six factors and uncut fibre uniform metrics from across five factors. The image processing framework receives grayscale images as inputs, taken from an optical coordinate measuring machine, and outputs the combined metrics along with eleven separate metrics. Experimental results, using a preexisting dataset from twenty-four holes on two workpieces from a real robotic drilling operation, are given to demonstrate the effectiveness of the proposed combined metrics and the corresponding image processing framework.

关键词： Measurement Optical fibers Drilling Coordinate measuring machines Robot kinematics Optical variables measurement Gray-scale Optical imaging Delamination Aircraft

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