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International Conference on Modelling, Identification and control (ICMIC)

作者： Jiansheng Yang Ling Zhao Zi-Qiang Lang Yang Zhang Jiansheng Yang is with the Electrical Engineering College Guizhou University Guiyang China Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield Department of Mechanical Engineering The University of Sheffield Sheffield

Wind turbine blade condition monitoring is necessary for planning blade maintenance. In this paper, a wind turbine blade condition monitoring and damage detection based on vibration measurement using a image-based system is proposed. Blade are measured in undamaged and damaged conditions under various vibration frequencies and displacement amplitudes. The results were compared and analysed in both time and frequency domains. Then a frequency-based index is proposed to quantify the vibration dynamics and detect the damage.

关键词： Blades Vibrations Wind turbines Condition monitoring Frequency measurement Vibration measurement Harmonic analysis

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Nonlinear dynamic predictive model selection and interference using information criteria 23

Nonlinear dynamic predictive model selection and interferenc...

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23rd IEEE International Conference on Automation and Computing, ICAC 2017

作者： Gu, Yuanlin Wei, Hua-Liang Balikhin, Michael A. Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9780701702618

Model selection is a crucial step in choosing a best model from a series of candidate models for data based modelling problems. The commonly used Akaike information criterion (AIC) and Bayesian information criteria (BIC) may not be effective for many real-world modelling problems when the true system model structure is unknown and therefore not included in the candidate model set. This study investigates the model selection issue using AIC, BIC and an adjustable prediction error sum of squares (APRESS) for nonlinear dynamic predictive modelling. Results from simulation and real data modelling case studies show that both BIC and APRESS produce good models for nonlinear modelling problems. The APRESS works slightly better than BIC in achieving a parsimonious representation of the studied system. In addition, a model averaging method is introduced, which is capable to provide an averaged model that is more robust in generalization (i.e. in representing future data) than any single model. © 2017 Chinese Automation and Computing Society in the UK - CACSUK.

关键词： Automation

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Effective parameter identification and controller design for active mechanical metamaterials 24

Effective parameter identification and controller design for...

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24th International Congress on Sound and Vibration, ICSV 2017

作者： Pope, Simon A. Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

Recently there has been increasing interest in the design of active acoustic and mechanical metamaterials. Part of the interest lies in the ability of active material systems to provide effective properties which can be tuned to meet complex or changing performance demands. They provide an interesting framework through which some of the novel devices proposed for mechanical metamaterials can be implemented. However, the introduction of control hardware such as actuators and sensors complicates the design when compared to alternative passive approaches. In particular considerable inhomogeneity can be introduced, which can lead to an effective response that differs from that which is desired. In this paper a solution is proposed that uses the information from sensors embedded in the active system to identify its effective response, including any inhomogeneity. Under certain conditions, such an approach can also be extended to passive systems. The active system can then be subsequently modified to compensate for the identified inhomogeneity. This approach is demonstrated using a simulated example of an active mechanical metamaterial.

关键词： Metamaterials

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Data Mining and Modelling of Charpy Impact Energy for Alloy Steels Using Fuzzy Rough Sets

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IFAC-PapersOnLine 2017年第1期50卷 14970-14975页

作者： Colas-Marquez, R. Mahfouf, M. Department of Automatic Control and Systems Engineering The University of Sheffield Mappin St. SheffieldS1 3JD United Kingdom

When considering data-driven modelling, uncertainties, errors and inconsistencies in the data can more often than not lead to sub-optimal predictions. A new framework based on rough sets theory is proposed and applied to an industrial data set obtained from a Charpy impact energy test for alloy steels. The inconsistent/consistent data sets are then used to train a series of artificial neural networks (ANN) for Charpy impact energy prediction for alloy steels. A k-nearest neighbor is used to classify the data points; if an object is classified as consistent, the ANN trained with the consistent data set provides a single prediction while if the object is classified as inconsistent, several ANN trained with different sets of inconsistent data are used to provide an interval prediction. Experimental results show an improvement in the consistent data set compared with a benchmark model. Also, the interval prediction provided by the various ANNs in the inconsistent data set represents a better alternative to the single point prediction results. © 2017

关键词： Classification (of information) Alloy steel Charpy impact testing Data mining Forecasting Fuzzy neural networks Fuzzy sets Nearest neighbor search Neural networks Rough set theory Statistical tests Steel metallurgy Uncertainty analysis Benchmark models Charpy impact energy Data driven modelling Data driven model Inconsistent data Interval prediction K nearest neighbors Optimal predictions

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Compact deep neural networks for computationally efficient gesture classification from electromyography signals

arXiv

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arXiv 2018年

作者： Hartwell, Adam Kadirkamanathan, Visakan Anderson, Sean R. Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom

Machine learning classifiers using surface electromyography are important for human-machine interfacing and device control. Conventional classifiers such as support vector machines (SVMs) use manually extracted features based on e.g. wavelets. These features tend to be fixed and non-person specific, which is a key limitation due to high person-to-person variability of myography signals. Deep neural networks, by contrast, can automatically extract person specific features - an important advantage. However, deep neural networks typically have the drawback of large numbers of parameters, requiring large training data sets and powerful hardware not suited to embedded systems. This paper solves these problems by introducing a compact deep neural network architecture that is much smaller than existing counterparts. The performance of the compact deep net is benchmarked against an SVM and compared to other contemporary architectures across 10 human subjects, comparing Myo and Delsys Trigno electrode sets. The accuracy of the compact deep net was found to be 84:2 ± 6% versus 70:5 ± 7% for the SVM on the Myo, and 80:3 ± 7% versus 67:8 ± 9% for the Delsys system, demonstrating the superior effectiveness of the proposed compact network, which had just 5,889 parameters - orders of magnitude less than some contemporary alternatives in this domain while maintaining better performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Learning with precise spike times: A new decoding algorithm for liquid state machines

arXiv

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arXiv 2018年

作者： Florescu, Dorian Coca, Daniel Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom

There is extensive evidence that biological neural networks encode information in the precise timing of the spikes generated and transmitted by neurons, which offers several advantages over rate-based codes. Here we adopt a vector space formulation of spike train sequences and introduce a new liquid state machine (LSM) network architecture and a new forward orthogonal regression algorithm to learn an input-output signal mapping or to decode the brain activity. The proposed algorithm uses precise spike timing to select the presynaptic neurons relevant to each learning task. We show that using precise spike timing to train the LSM and selecting the readout neurons leads to a significant increase in performance on binary classification tasks as well as in decoding neural activity from multielectrode array recordings, compared with what is achieved using the standard architecture and training method. Copyright © 2018, The Authors. All rights reserved.

关键词： Neurons

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Analysis and design of a nonlinear vibration-based energy harvester - A frequency based approach

Analysis and design of a nonlinear vibration-based energy ha...

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2017 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2017

作者： Uchenna, Diala Simon, Pope Zi-Qiang, Lang Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom

ISBN: (纸本)9781509059980

The benefits of nonlinear damping in increasing the amount of energy (power) harvested by a vibration-based energy harvester (VEH) has been reported where it was revealed that more energy can be harvested using nonlinear cubic damping when compared to a VEH with linear damping. As has been reported, this only occurs when the base excitation on the VEH, at resonance, is less than the maximum base excitation. A maximum harvester base excitation results in a maximum distance the harvester mass can move due to its size and geometric limitations. The present study is concerned with the analysis and design of a VEH using a nonlinear frequency analysis method. This method employs the concept of the output frequency response function (OFRF) to derive an explicit polynomial relationship between the harvested energy (power) and the parameter of the energy harvester of interest, i.e. the nonlinear cubic damping coefficient. Based on the OFRF, a nonlinear damping coefficient can be designed to achieve a range of desired levels of energy harvesting. It is also shown that using the OFRF the harvester throw (the displacement of the mass of the harvester), can be predicted using the designed damping coefficient. © 2017 IEEE.

关键词： Frequency response

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A Novel Robust Rauch-Tung-Striebel Smoother Based on Slash and Generalized Hyperbolic Skew Student's T-Distributions

A Novel Robust Rauch-Tung-Striebel Smoother Based on Slash a...

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International Conference on Information Fusion

作者： Yulong Huang Yonggang Zhang Yuxin Zhao Lyudmila Mihaylova Jonathon Chambers Department of Automation Harbin Engineering University Harbin China Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK Department of Engineering University of Leicester Leicester UK

In this paper, a novel robust Rauch-Tung-Striebel smoother is proposed based on the Slash and generalized hyperbolic skew Student's t-distributions. A novel hierarchical Gaussian state-space model is constructed by formulating the Slash distribution as a Gaussian scale mixture form and formulating the generalized hyperbolic skew Student's t-distribution as a Gaussian variance-mean mixture form, based on which the state trajectory, mixing parameters and unknown noise parameters are jointly inferred using the variational Bayesian approach. The posterior probability density functions of mixing parameters of the Slash and generalized hyperbolic skew Student's t-distributions are, respectively, approximated as truncated Gamma and generalized inverse Gaussian. Simulation results illustrate that the proposed robust Rauch-Tung-Striebel smoother has better estimation accuracy than existing state-of-the-art smoothers.

关键词： Noise measurement Robustness State-space methods Shape Smoothing methods Target tracking Gaussian distribution

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Encouraging student learning of control by embedding freedom into the curriculum: student perspectives and products

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IFAC-PapersOnLine 2017年第1期50卷 12149-12154页

作者： Rossiter, J.A. Barnett, L. Cartwright, E. Patterson, J. Shorten, N. Taylor, J. Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom Undergraduate students of Bioengineering at the University of Sheffield United Kingdom

Many engineering students have little inherent interest in control topics and struggle to relate these to their career goals. This paper looks at a novel mechanism for encouraging students to take more ownership of their learning of control topics and, by doing so, improve engagement and learning. Consequently it also fits well into research led teaching philosophies. The main focus here is on the student products for an assignment the author designed in the curriculum which challenged students to develop a learning resource. The paper includes description of one of these student generated resources, which could be shared with or used by the community, and the personal experiences and reflections of the students who produced this resource. © 2017

关键词： Students Curricula Feedback Philosophical aspects Professional aspects Career goals Learning resource Personal experience Student engagement Student learning Student perspectives Student satisfaction Teaching philosophy

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Stabilizing predictive control with persistence of excitation for constrained linear systems

arXiv

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arXiv 2018年

作者： Hernandez, Bernardo A. Trodden, Paul A. Department of Automatic Control and Systems Engineering University of Sheffield Mappin Street SheffieldS1 3JD United Kingdom

A new adaptive predictive controller for constrained linear systems is presented. The main feature of the proposed controller is the partition of the input in two components. The first part is used to persistently excite the system, in order to guarantee accurate and convergent parameter estimates in a deterministic framework. An MPC-inspired receding horizon optimization problem is developed to achieve the required excitation in a manner that is optimal for the plant. The remaining control action is employed by a conventional tube MPC controller to regulate the plant in the presence of parametric uncertainty and the excitation generated for estimation purposes. Constraint satisfaction, robust exponential stability, and convergence of the estimates are guaranteed under design conditions mildly more demanding than that of standard MPC implementations. Copyright © 2018, The Authors. All rights reserved.

关键词： Model predictive control

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