Design optimization for cogging torque mitigation in brushless DC motor using multi-objective particle swarm optimization algorithm

Purpose – The purpose of this paper is to propose a multi-objective particle swarm optimization (MOPSO) algorithm based design optimization of Brushless DC (BLDC) motor with a view to mitigate cogging torque and enhance the efficiency. Design/methodology/approach – The suitability of MOPSO algorithm is tested on a 120 W BLDC motor considering magnet axial length, stator slot opening and air gap length as the design variables. It avails the use of MagNet 7.5.1, a Finite Element Analysis tool, to account for the geometry and the non-linearity of material for assuaging an improved design framework and operates through the boundaries of generalized regression neural network (GRNN) to advocate the optimum design. The results of MOPSO are compared with Multi-Objective Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II based formulations for claiming its place in real world applications. Findings – A MOPSO design optimization procedure has been enlivened to escalate the performance of the BLDC motor. The optimality in design has been out reached through minimizing the cogging torque, maximizing the average torque and reducing the total losses to claim an increase in the efficiency. The results have been fortified in well-distributed Pareto-optimal planes to arrive at trade-off solutions between different objectives. Research limitations/implications – The rhetoric theory of multi objective formulations has been reinforced to provide a decisive solution with regard to the choice of the design obtained from Pareto-optimal planes. Practical implications – The incorporation of a larger number of design variables together with an orientation to thermal and vibration analysis will still go a long way in bringing on board new dimensions to the fold of optimality in the design of BLDC motors. Originality/value – The proposal offers a new perspective to the design of BLDC motor in the sense it be-hives the facility of a swarm based approach to optimize the parameters in order that it serves to improve its performance. The results of a 120 W motor in terms of lowering the losses, minimizing the cogging torque and maximizing the average torque emphasize the benefits of the GRNN based multi-objective formulation and establish its viability for use in practical applications.

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Systematic design of multi-objective enhanced genetic algorithm optimized fractional order PID controller for sensorless brushless DC motor drive

Circuit World ◽

10.1108/cw-07-2020-0137 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Vanchinathan Kumarasamy ◽

Valluvan KarumanchettyThottam Ramasamy ◽

Gnanavel Chinnaraj

Keyword(s):

Genetic Algorithm ◽

Speed Control ◽

Convergence Time ◽

Systematic Design ◽

Bldc Motor ◽

Content Type ◽

Multi Objective ◽

Brushless Dc ◽

The Time Domain ◽

Intelligent Controllers

Purpose The puspose of this paper, a novel systematic design of fractional order proportional integral derivative (FOPID) controller-based speed control of sensorless brushless DC (BLDC) motor using multi-objective enhanced genetic algorithm (EGA). This scheme provides an excellent dynamic and static response, low computational burden, the robust speed control. Design/methodology/approach The EGA is a meta-heuristic-inspired algorithm for solving non-linearity problems such as sudden load disturbances, modeling errors, power fluctuations, poor stability, the maximum time of transient processes, static and dynamic errors. The conventional genetic algorithm (CGA) and modified genetic algorithm (MGA) are not very effective in solving the above-mentioned problems. Hence, a multi-objective EGA optimized FOPID (EGA-FOPID) controller is proposed for speed control of sensorless BLDC motor under various conditions such as constant load conditions, varying load conditions, varying set speed (Ns) conditions, integrated conditions and controller parameters uncertainty. Findings This systematic design of the multi-objective EGA-FOPID controller is implemented in MATLAB 2020a with Simulink models for optimal speed control of the BLDC motor. The overall performance of the EGA-FOPID controller is observed and evaluated for computational burden, time integral performance indexes, transient and steady-state characteristics. The hardware experiment results confirm that the proposed EGA-FOPID controller can precisely change the BLDC motor speed is desired range with minimal effort. Research limitations/implications The conventional real time issues such as nonlinearity characteristics, poor controllability and stability. Practical implications It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented. Originality/value It shows the effectiveness of the proposed controllers is completely verified by comparing the above three intelligent optimization algorithms. It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.

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Geometric Size Optimization of Annular Step Fin Using Multi-Objective Genetic Algorithm

Journal of Thermal Science and Engineering Applications ◽

10.1115/1.4035838 ◽

2017 ◽

Vol 9 (2) ◽

Cited By ~ 7

Author(s):

Abhijit Deka ◽

Dilip Datta

Keyword(s):

Heat Transfer ◽

Genetic Algorithm ◽

Heat Transfer Rate ◽

Transfer Rate ◽

Pareto Optimal ◽

Objective Functions ◽

Cross Sectional ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Design Variables

Although an annular stepped fin can produce better cooling effect in comparison to an annular disk fin, it is yet to be studied in detail. In the present work, one-dimensional heat transfer in a two-stepped rectangular cross-sectional annular fin with constant base temperature and variable thermal conductivity is modeled as a multi-objective optimization problem. Taking cross-sectional half-thicknesses and outer radii of the two fin steps as design variables, an attempt is made to obtain the efficient fin geometry primarily by simultaneously maximizing the heat transfer rate and minimizing the fin volume. For further assessment of the fin performance, three more objective functions are studied, which are minimization of the fin surface area and maximization of the fin efficiency and effectiveness. Evaluating the heat transfer rate through the hybrid spline difference method, the well-known multi-objective genetic algorithm, namely, nondominated sorting genetic algorithm II (NSGA-II), is employed for approximating the Pareto-optimal front containing a set of tradeoff solutions in terms of different combinations of the considered five objective functions. The Pareto-optimal sensitivity is also analyzed for studying the influences of the design variables on the objective functions. As an outcome, it can be concluded that the proposed procedure would give an open choice to designers to lead to a practical stepped fin configuration.

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Design optimization of moderately thick hexagonal honeycomb sandwich plate with modified multi-objective particle swarm optimization by genetic algorithm (MOPSOGA)

Composite Structures ◽

10.1016/j.compstruct.2020.112626 ◽

2020 ◽

Vol 252 ◽

pp. 112626 ◽

Cited By ~ 4

Author(s):

A.R. Namvar ◽

A.R. Vosoughi

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Design Optimization ◽

Sandwich Plate ◽

Particle Swarm ◽

Swarm Optimization ◽

Multi Objective ◽

Honeycomb Sandwich ◽

Honeycomb Sandwich Plate

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Design optimization of metamaterial units using a genetic algorithm based optimization methodology

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering ◽

10.1108/compel-03-2020-0107 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Yiying Li ◽

Shiyou Yang

Keyword(s):

Genetic Algorithm ◽

Design Optimization ◽

Design Methodology ◽

Test Functions ◽

Content Type ◽

Multi Objective ◽

Cellular Genetic Algorithm ◽

A Cell ◽

Optimization Methodology ◽

Division Mechanism

Purpose The purpose of this paper is to develop a pertinent design optimization methodology for symmetric designs of a metamaterial (MM) unit. Design/methodology/approach A cell division mechanism is introduced and used to design a new selecting mechanism in the proposed algorithm, a non-dominated sorting cellular genetic algorithm (NSCGA). Findings The numerical results on solving standard multi-objective test functions and a prototype MM unit positively demonstrate the advantages of the proposed NSCGA. Originality/value A new NSGAII-based optimization algorithm, NSCGA, for multi-objective optimization designs of a MM unit is proposed.

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Hybrid Seven-bar Press Mechanism

Tehnički glasnik ◽

10.31803/tg-20180203202102 ◽

2018 ◽

Vol 12 (3) ◽

pp. 181-187

Author(s):

M. Erkan Kütük ◽

L. Canan Dülger

Keyword(s):

Genetic Algorithm ◽

Objective Function ◽

Design Optimization ◽

Hybrid System ◽

Degrees Of Freedom ◽

Dimensional Synthesis ◽

Design Variables ◽

Optimization Study ◽

Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

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Design optimization of noise barrier tunnels through component reuse: Minimization of costs and CO2 emissions using multi-objective genetic algorithm

Journal of Cleaner Production ◽

10.1016/j.jclepro.2021.126697 ◽

2021 ◽

Vol 298 ◽

pp. 126697

Author(s):

Seongjun Kim ◽

Sung-Ah Kim

Keyword(s):

Genetic Algorithm ◽

Design Optimization ◽

Co2 Emissions ◽

Component Reuse ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Noise Barrier

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Multi-objective optimization of a recuperative gas turbine cycle using non-dominated sorting genetic algorithm

Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy ◽

10.1177/0957650911420930 ◽

2011 ◽

Vol 225 (8) ◽

pp. 1041-1051 ◽

Cited By ~ 8

Author(s):

H Sayyaadi ◽

H R Aminian

Keyword(s):

Genetic Algorithm ◽

Gas Turbine ◽

Heat Exchangers ◽

Mixed Integer ◽

Design Stage ◽

Tube Length ◽

Pareto Optimal ◽

Multi Objective Optimization ◽

Multi Objective ◽

Gas Turbine Cycle

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.

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Application of Multi-Objective Genetic Algorithm (MOGA) for Design Optimization of Valve Timing at Various Engine Speeds

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.264-265.1719 ◽

2011 ◽

Vol 264-265 ◽

pp. 1719-1724 ◽

Cited By ~ 5

Author(s):

A.K.M. Mohiuddin ◽

Md. Ataur Rahman ◽

Yap Haw Shin

Keyword(s):

Genetic Algorithm ◽

Design Optimization ◽

Robust Design Optimization ◽

Primary Concern ◽

Variable Valve Timing ◽

Valve Timing ◽

Engine Valve ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Variable Valve

This paper aims to demonstrate the effectiveness of Multi-Objective Genetic Algorithm Optimization and its practical application on the automobile engine valve timing where the variation of performance parameters required for finest tuning to obtain the optimal engine performances. The primary concern is to acquire the clear picture of the implementation of Multi-Objective Genetic Algorithm and the essential of variable valve timing effects on the engine performances in various engine speeds. Majority of the research works in this project were in CAE software environment and method to implement optimization to 1D engine simulation. The paper conducts robust design optimization of CAMPRO 1.6L (S4PH) engine valve timing at various engine speeds using multiobjective genetic algorithm (MOGA) for the future variable valve timing (VVT) system research and development. This paper involves engine modelling in 1D software simulation environment, GT-Power. The GT-Power model is run simultaneously with mode Frontier to perform multiobjective optimization.

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