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Optimal fractional controller design methodology for electric drive train

INTERNATIONAL JOURNAL OF ELECTRIC AND HYBRID VEHICLES 2016年第4期8卷 335-350页

作者： Ganeshaperumal, D. . Muthukumar, N. . Ramkumar, K. . Srinivasan, Seshadhri Subathra, B. . Kalasalingam Univ Dept EEE Srivilliputhur 626126 India SASTRA Univ Elect Vehicle Engn & Robot EVER Labs Thanjavur 613401 India Kalasalingam Univ Int Res Ctr Srivilliputhur 626126 India Kalasalingam Univ Dept Instrumentat & Control Srivilliputhur 626126 India

This investigation presents an optimisation-driven fractional order PID (FOPID) controller design methodology for brushless direct current (BLDC) motor speed control for electric vehicle applications. Though the introduction of fractional terms provides additional flexibility, their optimal selection is important for achieving desired performance. For this purpose, this investigation uses two evolutionary optimisation approaches-real coded genetic algorithm (RGA) and bio-geography based optimisation (BBO). In order to illustrate the improvements provided by FOPID, its performance is compared with conventional PID controller. Our results demonstrate that the FOPID controller tuned by BBO algorithm provides up to 50% improvements in transient response over the PID controller.

关键词： BLDC brushless direct current motor BBO bio-geography based optimisation EO evolutionary optimisation FOPID fractional order proportional integral derivative controller RGA real coded genetic algorithm

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A novel social emotional optimisation algorithm for IIR system identification problem

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2014年第1期22卷 80-112页

作者： Upadhyay, Prashant Kar, Rajib Mandal, Durbadal Ghoshal, Sakti Prasad Natl Inst Technol Dept Elect & Commun Engn MG Ave Durgapur 713029 India Natl Inst Technol Dept Elect Engn Durgapur 713029 India

In this paper an evolutionary optimisation methodology based on social emotional optimisation algorithm (SEOA) is applied to the infinite impulse response (IIR) system identification problem. In SEOA methodology, behaviour of human beings for achieving higher social status in society is structured. In this virtual world, the individual with the highest rank in society gives the optimal solution in multidimensional search space. Earning the highest social status by means of cooperation and competition with others not only results in better exploration and exploitation of problem space but also ensures faster convergence to optimal solution. The proposed SEOA based system identification approach has resolved the inherent drawbacks of premature convergence and stagnation, unlike genetic algorithm (GA), particle swarm optimisation (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using SEOA over GA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for both the same order and reduced order models of adaptive IIR filters.

关键词： IIR adaptive filter real coded genetic algorithm RGA particle swarm optimisation PSO differential evolution DE social emotional optimisation algorithm SEOA evolutionary optimisation techniques mean square error MSE coefficient convergence

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Optimization of Turning Process Parameters using Multi-objective Evolutionary algorithm

Optimization of Turning Process Parameters using Multi-objec...

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2010 IEEE World Congress on Computational Intelligence

作者： Datta, Rituparna Majumder, Anima Indian Inst Technol Dept Mech Engn Kanpur Genet Algorithms Lab KanGAL Kanpur 208016 Uttar Pradesh India Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781424481262

Machining parameters optimization is very crucial in any machining process. This research focuses on Multi-objective Evolutionary algorithm based optimization technique, to determine optimal cutting parameters (cutting speed, feed, and depth of cut) in turning operation. Two conflicting objectives (operation time and tool life) with three constraints, which depends on the turning parameters, are optimized using genetic algorithm (GAs). The Pareto-optimal front of the bi-objective problem is obtained using Non-dominated Sorting genetic algorithm (NSGA-II). The extreme and intermediate points of Pareto optimal front is verified using real coded genetic algorithm (RGA) as well as epsilon-constraint method. The performance of NSGA-II is found to be more effective and efficient as compared to micro-GA. Innovization study carried out to correlate cutting parameters with the aforementioned objective functions. The effect of cutting speed is found more as compared to feed rate and depth of cut.

关键词： Pareto optimal front Pareto optimisation cutting genetic algorithms machining parameters optimization machining process multiobjective evolutionary algorithm nondominated sorting genetic algorithm optimal cutting real coded genetic algorithm turning (machining) turning process parameters optimization

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Simultaneously Placement and Sizing of Multiple DGs and Shunt Capacitor Banks in Unbalanced Distribution Systems Using real coded GA

Simultaneously Placement and Sizing of Multiple DGs and Shun...

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International Seminar on Intelligent Technology and Its Applications (ISITIA) - Recent Trends in Intelligent Computational Technologies for Sustainable Energy

作者： Umar Firdaus Penangsang, Ontoseno Inst Technol Sepuluh Nopember Elect Engn Dept Surabaya Indonesia Khairun Univ Elect Engn Dept Ternate Indonesia

ISBN: (纸本)9781509017096

The allocation of local active and reactive power in distribution systems is very important to meet customer demand and maintain the quality of electrical energy. Power loss and voltage deviation can be minimized by placing Distributed Generations (DGs) or Shunt Capacitor Banks (SCBs) in the right places and with the appropriate size. This paper discusses simultaneously placement and sizing of multiple DGs and SCBs in unbalanced distribution systems for power loss and voltage deviation reduction while maintaining harmonics at the IEEE-519 standard limit. real coded genetic algorithm (RCGA) is employed to determine the location and size of SCBs and DGs. The proposed method is applied on Modified Kaliasin radial distribution systems. The results show that the placement of the DGs on the bus 7, 8, 9, 10 is the most powerful results in reducing losses and voltage deviation compared to the other scheme. Placement 4 DGs with the size of each is 135 kW reduce the active power losses of 35.80% and the reactive power of 35.81%. The minimum bus voltage before and after placement DGs are 19.906 kV and 19.928 kV respectively.

关键词： distributed generation voltage deviation individual harmonic total harmonic distortion shunt capacitor banks real coded genetic algorithm

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The Parameter Optimization of the Pulse Coupled Neural Network for the Pattern Recognition

The Parameter Optimization of the Pulse Coupled Neural Netwo...

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20th International Conference on Artificial Neural Networks

作者： Yonekawa, Masato Kurokawa, Hiroaki Tokyo Univ Technol Sch Comp Sci Tokyo Japan

ISBN: (纸本)9783642158247

The pattern recognition using Pulse Coupled Neural Network (PCNN) had been proposed. In conventional studies, the parameters in the PCNN are used to be defined empirically and the optimization of parameters has been known as a remaining problem of PCNN. In this study, we show a method to apply the real coded genetic algorithm to the parameter optimization of the PCNN and we also show performances of pattern recognition by the PCNN with learned parameters.

关键词： PCNN pattern recognition real coded genetic algorithm

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Radiation pattern synthesis of linear antenna arrays by amplitude tapering using genetic algorithm

Radiation pattern synthesis of linear antenna arrays by ampl...

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IEEE Applied Electromagnetics Conference (AEMC)

作者： Mandal, Durbadal Tapaswi, Y. Naren Natl Inst Technol Durgapur Dept Elect & Commun Engn Durgapur 713209 W Bengal India

ISBN: (纸本)9781457710995

In this paper, the method of imposing nulls and side lobe suppression in the radiation pattern of an antenna array is discussed using real coded genetic algorithm (RGA). Linear symmetric antenna array is considered. The GA adjusts the excitation amplitude of each radiating element to impose a null in the desired direction. MATLAB is used to code GA in order to facilitate the realization. Various simulated results are presented by considering particular one and two desired nulls.

关键词： Symmetric Linear Antenna Array First Null Beamwidth Side Lobe Suppression Null Insertion real coded genetic algorithm

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genetic algorithm optimization for AGC of multi-area power systems

Genetic Algorithm optimization for AGC of multi-area power s...

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IEEE Region 10 Technical Conference on Computers, Communications, Control and Power Engineering

作者： Li, PK Zhu, HJ Li, YY No Jiaotong Univ Sch Mech Elect & Control Engn Beijing 100044 Peoples R China

ISBN: (纸本)0780374908

A genetic algorithm (GA) for parameter optimization of PID sliding mode load frequency control used in Automatic Generating Control (AGC) of multiarea power systems with nonlinear elements has been proposed. The method has the advantages of both PID and sliding mode control. Instead of using traditional analysis algorithm to obtain the controller parameters, GA optimization technology is introduced. PID parameter optimization for the interconnection of the AGC loops using MATLAB/Simulink modal is developed. A real coded genetic algorithm is adopted and integrated into MATLAB/Simulink. The simulation of a two-area power systems with PI and PID controllers is reported and the results are reasonable.

关键词： automatic generating control real coded genetic algorithm sliding mode control PID control parameter optimization

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Three-Dimensional Grain Design Optimization of Solid Rocket Motors 7

Three-Dimensional Grain Design Optimization of Solid Rocket ...

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7th International Conference on Recent Advances in Space Technologies (RAST)

作者： Yucel, Osman Acik, Sevda Toker, K. Atilgan Dursunkaya, Zafer Aksel, M. Haluk Roketsan Missiles Ind Inc Prop Syst Design Dept Ankara Turkey Middle East Tech Univ Dept Mech Engn Ankara Turkey

ISBN: (纸本)9781479976973

This study investigates the ballistic design optimization of three-dimensional grains of solid rocket motors (SRMs). The geometric modeling and burnback analysis of grains are performed analytically by using basic geometries like cylinder, cone, prism, sphere, ellipsoid, and torus. For the internal ballistic analysis, a quasi-steady zero-dimensional flow solver is used. Three different optimization methods are considered: real-coded genetic algorithm (GA), binary genetic algorithm and complex method. The optimization methods are applied to the problems of finding the parameters of grain geometry satisfying an objective thrust versus time profile and maximizing the parameter of total impulse satisfying the constraints of chamber pressure and propellant mass.

关键词： Solid rocket motor grain design burnback analysis optimization binary genetic algorithm real coded genetic algorithm complex method

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Inference Strategies for Texture Parameters 20th

Inference Strategies for Texture Parameters

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20th Iberoamerican Congress on Pattern Recognition (CIARP)

作者： Martinez, Jorge Pistonesi, Silvina Georgina Flesia, Ana Univ Nacl Sur Dept Matemat RA-8000 Bahia Blanca Buenos Aires Argentina Univ Nacl Cordoba Fac Matemat Astron & Fis Ingn Medina Allende S-NCiudad Univ RA-5000 Cordoba Argentina

ISBN: (纸本)9783319257518;9783319257501

The Autobinomial model is a commonplace in Bayesian image analysis since its introduction as a convenient image model. Such model depends on a set of parameters;their value characterize texture allowing to perform classification of the whole image into regions with uniform properties of the model. This work propose a new estimator of the parameter vector of the Autobinomial model based on Conditional Least Square minimization via real coded genetic Modeling and analyze its performance compared to the classical linear approximation, which exchanges the CLS equation with a reduced Taylor equation prior to minimization. Our simulation study shows that the genetic modeling approach gives more accurate estimations when true data is provided. We also discuss its influence in a set of classification experiments with multispectral optical imagery, estimating the scalar vector parameter with our estimator and the classical linear one. Our experiments show promising results, since our approach is able to distinguish image features that the classical approach does not.

关键词： real coded genetic algorithm Conditional least squares method Gibbs Markov random field Autobinomial model Parameter estimation

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Application of Fuzzy Cognitive Maps to water demand prediction

Application of Fuzzy Cognitive Maps to water demand predicti...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Papageorgiou, Elpiniki I. Poczeta, Katarzyna Laspidou, Chrysi CERTH Ctr Res & Technol Hellas Inst Informat Technol 6th Km Charilaou Thermi Rd Thermi 57001 Greece Technol Educ Inst TEI Cent Greece Dept Comp Engn Lamia 35100 Greece Kielce Univ Technol Dept Comp Sci Applicat Kielce Poland Univ Thessaly Dept Civil Engn Volos 38334 Volos Greece

ISBN: (纸本)9781467374286

This article is focused on the issue of learning of Fuzzy Cognitive Maps designed to model and predict time series. The multi-step supervised-learning based-on-gradient methods as well as population-based learning, with the use of real coded genetic algorithms, are described. In this study, a new structure optimization genetic algorithm for fuzzy cognitive maps learning is proposed for automatic construction of FCM applied to time series prediction. The proposed learning methodologies are based on an FCM reconstruction procedure using historical time series. The main contribution of this study is the analysis of the use of FCMs with their learning algorithms based on the multi-step gradient method (MGM) and other population-based methods to predict water demand. The performance of learning algorithms is presented through the analysis of real data of daily water demand and the corresponding prediction. The multivariate analysis of historical water demand data is held for five variables, mean and high temperature, precipitation, wind speed and touristic activity. Simulation results were obtained with the ISEMK (Intelligent Expert System based on Cognitive Maps) software tool. Through the experimental analysis, we demonstrate the usefulness of the new proposed FCM learning algorithm in water demand prediction, by calculating the known prediction errors. The advantage of the optimization genetic algorithm structure is its ability to select the most significant relations between concepts for prediction.

关键词： Fuzzy Cognitive Maps Multi-Step Learning algorithms Population-Based Learning algorithms real coded genetic algorithm Structure Optimization genetic algorithm Gradient Method

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