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Topology Optimization of Ferromagnetic Components in Electrical Machines

IEEE TRANSACTIONS ON ENERGY CONVERSION 2020年第2期35卷 786-798页

作者： Ma, Bo Zheng, Jing Lei, Gang Zhu, Jianguo Jin, Ping Guo, Youguang Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia Hunan Univ Sch Mech & Vehicle Engn Changsha 410082 Hunan Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Hohai Univ Coll Energy & Elect Engn Nanjing 210098 Peoples R China

This article presents the topology optimization of ferromagnetic components in electrical machines by using the density method. Explicit expressions of machine performance based on the principle of electromechanical energy conversion are derived and incorporated in the finite element model. Because the gradient-based algorithm is employed for efficient optimization, the performance sensitivities with respect to the design variables are derived subsequently. An optimization framework is then proposed to optimize effectively the electrical machine parameters and performance, such as the flux linkage, back electromotive force, and torque profile including the torque density and torque ripples. Two design examples are reported to confirm the feasibility and effectiveness of the presented topology optimization approach. The accuracy of optimal solutions is verified by using ANSYS MAXWELL, a commercial field analysis software.

关键词： Topology optimization density method energy conversion sensitivity analysis gradient-based algorithm

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Secure communication design for multi-user peer-to-peer wireless relay networks

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IET COMMUNICATIONS 2016年第7期10卷 770-777页

作者： Gong, Xiangwu Long, Hang Dong, Feihong Yin, Hao PLA Univ Sci & Technol Coll Commun Engn Nanjing Jiangsu Peoples R China PLAAF Unit 95899 Beijing 100000 Peoples R China Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Inst China Elect Syst Engn Corp Beijing 100141 Peoples R China

In this study, physical layer security techniques are studied for multi-user peer-to-peer relay networks using amplify-and-forward protocol in the presence of an eavesdropper. Two system designs are considered: the minimum achievable secrecy rate maximisation and the total transmit power minimisation. First, considering user fairness, the authors study the max-min achievable secrecy rate problem subject to a total transmit power constraint. Mathematically, the optimisation problem is non-linear and non-convex. They propose a polynomial time algorithm based on the bisection technique to solve it. Second, they study the total transmit power minimisation problem which aims at minimising the total transmit power at relays such that individual secrecy rate for each user is satisfied. The total power optimisation problem is also non-convex, so they use the relaxation technique to transform it into a convex problem which can be efficiently solved by the gradient-based algorithms. Numerical evaluation of the obtained secrecy rate and transmit power results shows that the proposed designs can significantly improve the performance of secure wireless communications.

关键词： relay networks (telecommunication) radio networks peer-to-peer computing amplify and forward communication protocols minimisation minimax techniques nonlinear programming concave programming polynomials relaxation theory convex programming gradient methods computer network security secure wireless communication design multiuser peer-to-peer wireless relay network physical layer security technique amplify-and-forward protocol secrecy rate maximisation max-min achievable secrecy rate problem nonlinear optimisation problem nonconvex optimisation problem mathematical analysis polynomial time algorithm power minimisation problem total power optimisation problem relaxation technique convex problem gradient-based algorithm numerical evaluation

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Hybrid genetic optimization and statistical model-based approach for the classification of shadow shapes in sonar imagery

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2000年第2期22卷 129-141页

作者： Mignotte, M Collet, C Pérez, P Bouthemy, P Ecole Navale Grp Traitement Signal F-29240 Brest France Inst Natl Rech Informat & Automat IRISA F-35042 Rennes France

We present an original statistical classification method using a deformable template model to separate natural objects from man-made objects in an image provided by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is captured by a prototype template, along with a set of admissible linear transformations, to take into account the shape variability. Then, the classification problem is defined as a two-step process. First, the detection problem of a region of interest in the input image is stated as the minimization of a cost function. Second, the value of this function at convergence allows one to determine whether the desired object is present or not in the sonar image. The energy minimization problem is tackled using relaxation techniques. In this context, we compare the results obtained with a deterministic relaxation technique (a gradient-based algorithm) and two stochastic relaxation methods: Simulated Annealing (SA) and a hybrid Genetic algorithm (GA). This latter method has been successfully tested on real and synthetic sonar images, yielding very promising results.

关键词： deformable template objective function simulated annealing gradient-based algorithm genetic optimization shape recognition sonar imagery

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On-line prediction of ferrous ion concentration in goethite process based on self-adjusting structure RBF neural network

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NEURAL NETWORKS 2019年 116卷 1-10页

作者： Xie, Yongfang Yu, Jinjing Xie, Shiwen Huang, Tingwen Gui, Weihua Cent S Univ Sch Automat Changsha 410083 Hunan Peoples R China Wayne State Univ Coll Engn Dept Elect & Comp Engn Detroit MI 48202 USA Texas A&M Univ Qatar Dept Math Doha Qatar

Outlet ferrous ion concentration is an essential indicator to manipulate the goethite process in the zinc hydrometallurgy plant. However, it cannot be measured on-line, which leads to the delay of this feedback information. In this study, a self-adjusting structure radial basis function neural network (SAS-RBFNN) is developed to predict the outlet ferrous ion concentration on-line. First, a supervised cluster algorithm is proposed to initialize the RBFNN. Then, the network structure is adjusted by the developed self-adjusting structure mechanism. This mechanism can merge or divide the hidden neurons according to the distance of the clusters to achieve the adaptability of the RBFNN. Finally, the connection weights are determined by the gradient-based algorithm. The convergence of the SAS-RBFNN is analyzed by the Lyapunov criterion. A simulation for a benchmark problem shows the effectiveness of the proposed network. The SAS-RBFNN is then applied to predict the outlet ferrous ion concentration in the goethite process. The results demonstrate that this network can provide a more accurate prediction than the mathematical model, even under the fluctuating production condition. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Radial basis function (RBF) Self-adjusting structure gradient-based algorithm On-line prediction Goethite process

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Reliability-based optimization of stochastic systems using line search

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2009年第49-52期198卷 3915-3924页

作者： Jensen, H. A. Valdebenito, M. A. Schueller, G. I. Kusanovic, D. S. Santa Maria Univ Dept Civil Engn Valparaiso Chile Univ Innsbruck Chair Engn Mech A-6020 Innsbruck Austria

This contribution presents an approach for solving reliability-based optimization problems involving structural systems under stochastic loading. The associated reliability problems to be solved during the optimization process are high-dimensional (1000 or more random variables). A standard gradient-based algorithm with line search is used in this work. Subset simulation is adopted for the purpose of estimating the corresponding failure probabilities. The gradients of the failure probability functions are estimated by an approach based on the local behavior of the performance functions that define the failure domains. Numerical results show that only a moderate number of reliability estimates has to be performed during the entire design process. Two numerical examples showing the effectiveness of the approach reported herein are presented. (C) 2009 Elsevier B.V. All rights reserved.

关键词： gradient-based algorithm High-dimensional reliability problems Non-linear systems Reliability-based optimization Sensitivity analysis Stochastic excitation

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Robustness analysis of attitude and orbit control systems for flexible satellites

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IET CONTROL THEORY AND APPLICATIONS 2010年第12期4卷 2958-2970页

作者： Wang, W. Menon, P. P. Bates, D. G. Bennani, S. Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 4QJ Devon England ESA ESTEC Guidance Nav & Control Sect Noordwijk Netherlands

In this study, an optimisation-based approach is proposed for the robustness analysis of an attitude and orbit control system (AOCS) for flexible satellites. Several optimisation methods, including local gradient-based algorithms, global evolutionary algorithms and hybrid local/global algorithms are applied to the problem of analysing the robustness of a full-authority multivariable controller with respect to several frequency and time domain performance criteria, for a 6 degree of freedom simulation model of a satellite with large sun shields. The results of our study reveal the advantages of optimisation-based worst-case analysis over traditional Monte Carlo simulations for systems with flexible dynamics. In particular, it is shown that frequency domain analysis can provide useful guidance for the formulation of subsequent time domain tests, and that hybrid local/global optimisation algorithms can produce more reliable estimates of worst-case performance, while also reducing the associated computational overheads. The proposed approach appears to have significant potential for improving the industrial flight clearance process for next-generation high-performance satellite control systems.

关键词： Spatial variables control Optimisation techniques global evolutionary algorithm Multivariable control systems industrial flight clearance process multivariable control systems evolutionary computation robustness analysis attitude control Monte Carlo simulation Aerospace control gradient methods artificial satellites multivariable controller frequency domain analysis next-generation high-performance satellite control system Mathematical analysis 6 degree of freedom simulation model Monte Carlo methods frequency-domain analysis gradient-based algorithm optimisation hybrid local-global algorithm flexible satellite orbit control system

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Static Analysis of a Small Scale Ducted-Fan UAV using Wind Tunnel Data

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2012年第1期13卷 34-42页

作者： Choi, Youn-Han Suk, Jinyoung Hong, Sang-Hwee Chungnam Natl Univ Dept Aerosp Engn Taejon 305764 South Korea Korea Aerosp Res Inst Taejon 305806 South Korea

This paper discusses the mathematical modeling of a small scale ducted-fan UAV and examines its results in comparison to the wind tunnel test. A wind tunnel test is first performed, producing a substantial amount of test data. The acquired set of wind tunnel test data is then categorized and approximated as mathematical functions. Finally, the mathematically modeled forces and moments acting on the UAV are compared with the acquired wind tunnel data. The analysis involves a gradient-based algorithm and is applied to extract trim states with respect to various flight conditions. Consequently, a numerical analysis demonstrates that there exists a reasonable flight status with respect to airspeed.

关键词： Ducted-Fan UAV Mathematical modeling Wind tunnel test gradient-based algorithm

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Optimal control approach to persistent monitoring problem based on monitoring index

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IET CONTROL THEORY AND APPLICATIONS 2018年第11期12卷 1628-1634页

作者： Wang, Yan-Wu Wei, Yao-Wen Yang, Wu Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Hubei Peoples R China China Three Gorges Univ Hubei Prov Collaborat Innovat Ctr New Energy Micr Yichang City Hubei Peoples R China

A class of persistent monitoring problems in one-dimensional space with the aid of multi-agent systems, which is formulated as an optimisation problem of minimising a novel performance metric is studied. The concept of the monitoring index is introduced in the performance metric to describe the importance of different targets in the space due to the requirements of the monitoring task. based on a given class of moving trajectories, an optimal control approach is used to minimise the performance metric of monitoring space. After some model transformation, the infinitesimal perturbation analysis method is used to design the gradient-based algorithm, aiming to find the optimal trajectories, where the monitoring index exhibits the effect on the monitoring time for different targets. Numerical simulations are represented to demonstrate the performance of the algorithm and reveal the effectiveness of the monitoring index.

关键词： perturbation techniques multi-agent systems optimisation gradient methods optimal control optimal control approach persistent monitoring problem monitoring index one-dimensional space optimisation problem monitoring task monitoring space monitoring time performance metric infinitesimal perturbation analysis method gradient-based algorithm optimal trajectories numerical simulations

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A model-based algorithm for maximum power point tracking of PV systems using exact analytical solution of single-diode equivalent model

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SOLAR ENERGY 2018年 162卷 117-131页

作者： Moshksar, Ehsan Ghanbari, Teymoor Shiraz Univ Sch Adv Technol Shiraz Iran

A novel model-based technique is presented for maximum power point tracking (MPPT) of photovoltaic (PV) systems. In this paper, an exact single-diode circuit model without any simplification or approximation is considered. Using datasheet information, an adaptive identification technique is utilized to find the electrical parameters uniquely and precisely. After this offline identification scheme, an estimation of solar irradiation is achieved from real-time measurements of PV voltage and current. In the next step, the gradient function of power with respect to voltage is represented from strongly concave mapping between power and voltage. Since the nonlinear gradient consists the complex Lambert W-function, a mathematical formulation is derived to approximate it with conventional logarithmic functions. Finally, a gradient update law is derived to find the unknown optimal voltage value, which results in maximum power generation for any environmental condition. The proposed approach guarantees exponential convergence with highly accurate steady-state performance.

关键词： Analytical approach gradient-based algorithm Irradiance estimation Model-based MPPT Parameter estimation

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Simultaneous Efficiency and Starting Torque Optimization of a Line-Start Permanent-Magnet Synchronous Motor Using Two Different Optimization Approaches

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第10期46卷 9953-9964页

作者： Palangar, Mousalreza Faramarzi Mahmoudi, Amin Kahourzade, Solmaz Soong, Wen L. Flinders Univ S Australia Coll Sci & Engn Adelaide SA Australia Univ South Australia STEM Adelaide SA Australia Univ Adelaide Sch Elect & Elect Engn Adelaide SA 5005 Australia

Line-start permanent-magnet synchronous motors (LSPMSMs) have poorer starting performance than induction motors. Optimization focusing only on transient performance improvement of the LSPMSM may degrade steady-state performance, and vice versa. In fact, an optimization focusing on maximizing starting torque may reduce efficiency by up to approximately 7% and optimizing efficiency may cause degradation in starting torque by 5%. Hence, simultaneous steady-state and transient performance optimization of a 4-kW LSPMSM under a multi-objective function is examined in this study. Efficiency maximization and starting torque maximization are nominated as objective functions. Two different optimization approaches, a gradient-based algorithm and gradient-free algorithm, are employed to optimize the LSPMSM. Sequential nonlinear programming is used as the gradient-based algorithm in this study, and the gradient-free algorithm used is the genetic algorithm (GA). A comparative study of the algorithms' performance is presented. To provide an inclusive comparison of both algorithms' performance, a similar optimization study is implemented for a baseline induction motor. The results demonstrate that the multi-objective optimization improves steady-state and start-up performance of both motors. Results indicate that both algorithms converge reliably to almost the same optimum (objective) value. Depending on the nature of the optimization problem, number of design variables, and degree of convergence, the genetic algorithm requires many more evaluations than the gradient-based algorithm. Accordingly, optimization time required by the GA is more than the gradient-based algorithm under similar conditions.

关键词： Finite-element analysis gradient-based algorithm Genetic algorithm Line-start permanent magnet synchronous motor (LSPMSM) Induction motor (IM) Transient Steady state Optimization

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