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Multiuser beamforming optimization for IRS-aided systems with discrete phase shifts

IET COMMUNICATIONS 2022年第13期16卷 1523-1530页

作者： Shi, Mingli Li, Xiaohui Fan, Tao Liu, Jiawen Lv, Siting Xidian Univ State Key Lab Integrated Serv Network Xian 710071 Shaanxi Peoples R China

Here, the beamforming design for multiuser multiple-input and multiple-output (MIMO) downlink transmission aided by an intelligent reflecting surface (IRS) with discrete phase shifts is studied. The authors deduce a minimum signal-to-interference-plus-noise-ratio (SINR) maximization fairness problem with the transmit beamformer, the receive beamformer and IRS phase shifts as optimization variables, which is generally NP-hard. Hence, an alternating optimization algorithm based on the gradient extrapolated majorization-minimization (GEMM) approach is employed to solve the above problem. Specifically, when the transmit beamformer and reflective phase shift are fixed, the authors give an optimal closed expression for the receive beamformer. Then, the receive beamformer is fixed, and the minimum SINR maximization problem at the transmitter is transformed into a power minimization problem. Finally, under the zero-forcing (ZF) transmitter beamformer, the GEMM algorithm is used to optimize the reflective phase shift. Simulation results show that by comparing to existing methods, the proposed GEMM algorithm can almost achieve the same performance with lower complexity.

关键词： minimum SINR maximization problem beamforming design transmitter beamformer transmit beamformer extrapolation reflective phase shift multiuser multiple-input multiple-output downlink transmission IRS phase shifts MIMO communication discrete phase shifts receive beamformer IRS-aided systems gradient extrapolated majorization-minimization approach array signal processing multiuser multiple-input minimum signal-to-interference-plus-noise-ratio maximization fairness problem power minimization problem GEMM algorithm minimisation alternating optimization algorithm multi-access systems optimization variables multiuser beamforming optimization intelligent reflecting surface

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Torque ripple reduction of brushless DC motor with convex arc-type permanent magnets based on robust optimization design

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IET ELECTRIC POWER APPLICATIONS 2022年第5期16卷 565-574页

作者： Feng, Haichao Zhang, Sheng Wei, Jinsong Xu, Xiaozhuo Gao, Caixia Ai, Liwang Henan Polytech Univ Sch Elect Engn & Automat Henan Int Joint Lab Direct Dr & Control Intellige Jiaozuo 454003 Henan Peoples R China Collaborat Innovat Ctr Coal Work Safety & Clean H Jiaozuo Henan Peoples R China

The mass production of the motors causes tolerance of shape and dimension, deviation of permanent magnet remanence and rotor eccentricity error, which affect the cogging torque and torque ripple amplitude and performance consistency. In order to reduce the torque ripple of the motor in the actual condition, the robustness optimization is performed in the paper. Firstly, an improving convex arc type permanent magnet structure is adopted to improve air gap flux density and suppress the cogging torque. Secondly, the structure parameters of the magnetic pole are selected as optimization variables, and the magnetization angle, remanence and position of the permanent magnet, static and dynamic rotor eccentricity are considered as noise factors. To improve the overall robustness of the motor under different operating conditions, the dynamic Taguchi method is used to optimize the robustness of the motor, and the test data is processed through the relation analysis method to obtain the optimal combination of control factors. Finally, the prototype is manufactured for the experiment. Compared with the single-operating conditions robust optimization results, the robust optimization method improves the robustness of the motor. The cogging torque amplitude is reduced by 39.2%. The torque ripple is 38.4% lower than that before optimization.

关键词： permanent magnets air gaps robust optimization method robust optimization design cogging torque amplitude brushless DC motor torque ripple reduction rotor eccentricity error robustness optimization permanent magnet motors static rotor eccentricity Taguchi methods structure parameters optimisation machine control torque brushless DC motors magnetic pole performance consistency remanence dynamic rotor eccentricity single-operating conditions robust optimization results magnetization angle dynamic Taguchi method improving convex arc type permanent magnet structure permanent magnet remanence air gap flux density torque ripple amplitude actual condition mass production rotors convex arc-type permanent magnets optimization variables

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Coordination of distance and directional overcurrent relays using a new algorithm: grey wolf optimizer

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2018年第6期26卷 3130-3144页

作者： Moravej, Zahra Soleimani Ooreh, Omid Semnan Univ Fac Elect & Comp Engn Semnan Iran

Optimal relay coordination has important effects on the stability, security, and reliability of a network. Hence, in this article, a method for optimal coordination of distance (D) and directional overcurrent (DOC) relays is presented. Taking into account the pickup current of the overcurrent relays as an optimization variable, in addition to the time setting multiplier (TSM) and the various characteristics of these relays, optimum and suitable settings for overcurrent and distance relays are obtained. The grey wolf optimizer algorithm has been used as an optimization tool to find optimum settings. The performance of this method has been investigated in 8-bus and 39-bus systems and compared with traditional GA and PSO algorithms. The results show that in the proposed method the operating times of the relays and the time interval between operating times of main and backup relays with respect to the coordination time interval have decreased.

关键词： Distance relay directional overcurrent relay grey wolf optimizer algorithm pickup current relay characteristic optimization variables

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Collaborative Eco-Drive of Railway Vehicles via Switched Nonlinear Model Predictive Control⁎

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IFAC-PapersOnLine 2018年第30期51卷 626-631页

作者： Farooqi, Hafsa Incremona, Gian Paolo Colaneri, Patrizio Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Piazza Leonardo da Vinci 32 Milan20133 Italy

A switched Nonlinear Model Predictive Control (NMPC) strategy for time efficient energy control of railway vehicles, while fulfilling constraints on velocity, journey time and driving style in a collaborative fashion (collaborative eco-drive) is proposed. More specifically, the train dynamics are modeled as discrete, switched and nonlinear, while the optimization variable is the handle position which modulates the available traction/braking force and has to belong to a set of discrete values and/or operating modes, which the human driver is able to implement. Hence the aim is to choose the optimal handle position that minimizes the cost, is implementable by the driver and also fulfills the eco-driving objective, such that the driving style is constrained by predefined driving sequences. A supervisor detects the states of the trains and subsequently modifies the weights of the cost by negotiating between constraint satisfaction and control aggressiveness, in order to share the available regenerated braking energy among the connected trains in a substation network. The efficiency of the proposed switched NMPC strategy is demonstrated using realistic simulation case study. © 2016

关键词： Model predictive control Driver training Locomotives Nonlinear control systems Nonlinear systems optimization Predictive control systems Railroads Constraint Satisfaction Nonlinear model predictive control optimization variables Predictive control Realistic simulation Substation networks Switching algorithms Train control

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Computational burden reduction in set-membership identification of Wiener models

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IFAC-PapersOnLine 2018年第15期51卷 1044-1049页

作者： Cerone, Vito Razza, Valentino Regruto, Diego Dipartimento di Automatica e Informatica Politecnico di Torino Corso Duca degli Abruzzi 24 Torino10129 Italy

Recent results on set-membership identification presented in the literature show that the computation of parameter bounds requires the solution to a set of polynomial optimization problems. Although, in principle, global optimal solutions to such problems can be computed by applying suitable convex relaxation techniques, based on sum-of-squares decomposition and/or generalized moment theory, practical applicability of such methods are limited in practice by the high computational complexity. In this paper, we propose an original approach for reducing the computational load of the relaxed problems in terms of a reduction of the number of optimization variables. We also give a numerical example to show the effectiveness of the proposed technique. © 2018

关键词： Polynomials Numerical methods Relaxation processes Computational burden Computational loads Global optimal solutions optimization variables Polynomial optimization problem Set membership identification Sum of squares decompositions Wiener systems

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Analytical Solution for Energy Management of Parallel Hybrid Electric Vehicles

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IFAC-PapersOnLine 2017年第1期50卷 13872-13877页

作者： Hadj-Said, S. Colin, G. Ketfi-Cherif, A. Chamaillard, Y. Renault S.A.S. France Univ. Orléans PRISME EA 4229 OrléansF45072 France

An analytical method is proposed to solve the optimization problem of energy management for a parallel hybrid electric vehicle. This method is based on Pontryagin's Maximum Principle (PMP) for a class of Hybrid Dynamic Systems (HDS). Therefore, the analytical models are used, which are an approximation of the reference models. A numerical method based on the reference models is also used in order to validate the analytical solution by comparing their results. In this paper, two types of optimization variables are considered: continuous and discrete. The first type is the power split between the Internal Combustion Engine (ICE) and the Electric Machine (EM). The second one is the transmission ratio, which includes the ICE On/Off decision. The results show that the analytical and the numerical solutions are almost the same. In addition, the analytical approach requires less computing time and requires less memory space than the numerical method. © 2017

关键词： Hybrid vehicles Analytical models Convex optimization Energy management Internal combustion engines Maximum principle Numerical methods Analytical method Energy management strategies Hybrid dynamic system (HDS) Hybrid electric vehicle (HEV) optimization problems optimization variables Parallel hybrid electric vehicles Pontryagin's maximum principle

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Personalized Media Consumption Meets Privacy against Inference Attacks

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IEEE SOFTWARE 2015年第4期32卷 53-59页

作者： Bhamidipati, Sandilya Fawaz, Nadia Kveton, Branislav Zhang, Amy Technicolor Rennes France Adobe Res Rennes France SET Media Rennes France

The PriView video consumption system protects user privacy while recommending relevant content. It distorts a user's video ratings to prevent attackers from inferring user attributes, while maintaining the distort... 详细信息

关键词： Convex Programming Data Protection Interactive Video Quantisation Signal Recommender Systems Pri View Personalized Media Consumption Inference Attacks Interactive Personalized Video Consumption System User Privacy Protection Personalized Content Recommendation Privacy Risk Control Utility Aware Privacy Mapping User Video Ratings User Personal Attributes User Age User Gender User Political Views Convex optimization Probability Mapping Distortion Minimization Quantization Low Rank Approximation Rating Matrix optimization variables Computer Security Data Privacy TV Distortion Quantization Signal Video On Demand Databases Inference Attack Video Personalization Privacy Privacy Risk Privacy Utility Tradeoff Video Recommendations Transparency Video On Demand Pri View Software Engineering Software Development

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Model predictive control of linear systems with multiplicative unbounded uncertainty and average constraints

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IFAC-PapersOnLine 2015年第23期48卷 266-271页

作者： Farina, Marcello Scattolini, Riccardo Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano via Ponzio 34/5 Milan20133 Italy

In this paper a novel Stochastic Model Predictive Control algorithm is developed for systems characterized by multiplicative and possibly unbounded model uncertainty, average constraints on the states and inputs, and quadratic cost function. The stochastic control problem, and in particular the average constraints, are reformulated in deterministic terms. The properties of the algorithm, namely the recursive feasibility and the pointwise convergence of the state, are proven by suitably selecting the terminal cost and the constraints on the mean and the variance of the state at the end of the prediction horizon and by considering the mean and the covariance of the state at the beginning of the prediction horizon as additional optimization variables. The numerical issues related to the off-line selection of the algorithm's parameters and its on-line implementation are also discussed in depth. A simulation campaign witnesses the effectiveness of the proposed control scheme. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Stochastic models Cost functions Linear systems Model predictive control Predictive control systems Stochastic control systems Stochastic systems Uncertainty analysis Line selections Model uncertainties Multiplicative uncertainty optimization variables Pointwise convergence Prediction horizon Quadratic cost functions Stochastic control

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Simple and efficient moving horizon estimation based on the fast gradient method

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IFAC-PapersOnLine 2015年第23期48卷 428-433页

作者： Morabito, Bruno Kögel, Markus Bullinger, Eric Pannocchia, Gabriele Findeisen, Rolf Department of Civil and Industrial Engineering - Chemical Engineering Section University of Pisa Italy Institute for Automation Engineering Otto-von-Guericke-University Magdeburg Germany

By now many results with respect to the fast and efficient implementation of model predictive control exist. However, for moving horizon estimation, only a few results are available. We present a simple solution algorithmtailored to moving horizon estimation of linear, discrete-time systems. In a first step the problem is reformulated such that only the states remain as optimization variables, i.e. process and measurement noise are eliminated from the optimization problem. This reformulation enables the use of the fast gradient method, which has recently received a lot of attention for the solution of model predictive control problems. In contrast to the model predictive control case, the Hessian matrix is timevarying in moving horizon estimation, due to the time-varying nature of the arrival cost. Therefore, we outline a tailored method to compute online the lower and upper eigenvalues of the Hessian matrix required by the here considered fast gradient method. In addition, we discuss stopping criteria and various implementation details. An example illustrates the efficiency of the proposed algorithm. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Gradient methods Digital control systems Discrete time control systems Eigenvalues and eigenfunctions Estimation Model predictive control State estimation Discrete time systems Efficient implementation Fast gradient methods Measurement Noise Moving horizon Moving horizon estimation optimization problems optimization variables

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ADVANCED AXIAL COMPRESSOR AIRFOILS DESIGN AND optimization

ADVANCED AXIAL COMPRESSOR AIRFOILS DESIGN AND OPTIMIZATION

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ASME Turbo Expo: Turbine Technical Conference and Exposition

作者： Jiang, Bin Zheng, Qun Zhang, Hai Zhang, Xiaolong Chen, Zhongliang Qiu, Yi Lie, Mingshi Harbin Engn Univ Harbin 150001 Peoples R China Beihang Univ Beijing Peoples R China

ISBN: (纸本)9780791856635

Advanced compressor airfoils design and optimization have been investigated for the axial compressor of gas turbine at high subsonic condition, which aim to decrease losses and to increase compressor operating ranges. The design and optimization processes are carried out based on (CDP_HEU) the Compressor Design Platform of Harbin Engineering University, in which a geometric code for the airfoil description, grid generation, blade-to-blade solver and the interface codes were combined. The influence of the higher airfoil Reynolds numbers flow of the land/marine based compressors is compared with aero engine and the impact of these differences on the location of boundary layer transition are taken into account by gamma-Re-theta transition model in ANSYS/CFX solver. The optimization objective function is a compromise of the total pressure losses at design point and at off-design conditions of a large incidence angle range. The effects of optimization variables selection, objective functions and numerical optimization algorithms on the design results are analyzed and discussed in this paper. The superior performance of the optimized airfoil is demonstrated by a comparison with conventional controlled diffusion airfoils (CDA) at a wide range of inlet flow angles, inlet Reynolds numbers and inlet Mach numbers that maximum value can reach 0.77. The aerodynamic advantage of the optimized airfoils have been presented, which include the leading edge shock wave losses and the profile losses etc. The optimized results indicate that a significant improvement in compressor efficiency and stability for land/marine gas turbine could be reached by the proposed optimized airfoils instead of conventional airfoils. Moreover, different optimized variables, objective function and optimization algorithm are recommended which could improve the optimization efficiency remarkably with the same design effect.

关键词： Compressor profile optimization design Parametric modeling optimization variables Numerical simulation

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