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

Circuit World ◽  
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.

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

2015 ◽  
Vol 34 (4) ◽  
pp. 1302-1318 ◽  
Author(s):  
Umadevi Nagalingam ◽  
Balaji Mahadevan ◽  
Kamaraj Vijayarajan ◽  
Ananda Padmanaban Loganathan
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Pareto Optimal ◽  
Bldc Motor ◽  
Swarm Optimization ◽  
Cogging Torque ◽  
Content Type ◽  
Multi Objective ◽  
Brushless Dc ◽  
Design Variables

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.

scholarly journals Rotational Speed Control of Brushless Dc Motor Using Genetic Algorithm Optimized PD Controller

2018 ◽  
Vol 18 (2) ◽  
pp. 75
Author(s):  
Rizqi Andry Ardiansyah ◽  
Edwar Yazid
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Rotational Speed ◽  
Speed Control ◽  
Pd Controller ◽  
Bldc Motor ◽  
Steady State Condition ◽  
Brushless Dc ◽  
Absolute Encoder ◽  
Rotational Speed Control

Controlling the rotational speed of brushless DC (BLDC) motor is an essential task to improve the transient and the steady state performances under loaded condition. Rotational speed control of BLDC motor using genetic algorithm optimized proportional-derivative (PD) controller to form what the so-called the genetic algorithm-PD (GA-PD) controller is proposed in this paper. Control system is realized in a microcontroller namely a 16MHz Atmega2560 with an absolute encoder as a position sensor. The effectiveness of the GA-PD controller is tested under constant and varying step functions with the Ziegler-Nichols-PD (ZN-PD) controller as a benchmark. Experimental results show that the GA-PD controller has slower speed than the ZN-PD controller, but the latter has overshoot and small oscillations during its steady state condition as a consequent of having a fast rise time.

scholarly journals Selection methods and diversity preservation in many-objective evolutionary algorithms

2018 ◽  
Vol 52 (4) ◽  
pp. 502-519 ◽  
Author(s):  
Luis Martí ◽  
Eduardo Segredo ◽  
Nayat Sánchez-Pi ◽  
Emma Hart
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Euclidean Distance ◽  
Selection Methods ◽  
Selection Scheme ◽  
Content Type ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Diversity Preservation ◽  
Promising Line

Purpose One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, which belong to well-known multi-objective optimisers, are considered. The paper aims to discuss these issues. Design/methodology/approach Each of those mechanisms is incorporated into a common multi-objective evolutionary algorithm framework. The main goal of the study is to measure the diversity preserved by each of those selection methods when addressing many-objective optimisation problems. The Walking Fish Group test suite, a set of optimisation problems with a scalable number of objective functions, is taken into account to perform the experimental evaluation. Findings The computational results highlight that the the reference-point-based selection scheme of the Non-dominated Sorting Genetic Algorithm III and a modified version of the Non-dominated Sorting Genetic Algorithm II, where the crowding distance is replaced by the Euclidean distance, are able to provide the best performance, not only in terms of diversity preservation, but also in terms of convergence. Originality/value The performance provided by the use of the Euclidean distance as part of the selection scheme indicates this is a promising line of research and, to the best of the knowledge, it has not been investigated yet.

Multi-objective optimization of hydro-viscous flexible drive for dynamic characteristics using genetic algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jianzhong Cui ◽  
Hu Li ◽  
Dong Zhang ◽  
Yawen Xu ◽  
Fangwei Xie
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Characteristics ◽  
Transmission Efficiency ◽  
Outer Radius ◽  
Transmission Model ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Dynamic Transmission Model

Purpose The purpose of this study is to investigate the flexible dynamic characteristics about hydro-viscous drive providing meaningful insights into the credible speed-regulating behavior during the soft-start. Design/methodology/approach A comprehensive dynamic transmission model is proposed to investigate the effects of key parameters on the dynamic characteristics. To achieve a trade-off between the transmission efficiency and time proportion of hydrodynamic and mixed lubrication, a multi-objective optimization of friction pair system by genetic algorithm is presented to obtain the optimal combination of design parameters. Findings Decreasing the engagement pressure or the ratio of inner and outer radius, increasing the lubricating oil viscosity or the outer radius will result in the increase of time proportion of hydrodynamic and mixed lubrication, as well as the transmission efficiency and its maximum value. After optimization, main dynamic parameters including the oil film thickness, angular velocity of the driven disk, viscous torque and total torque show remarkable flexible transmission characteristics. Originality/value Both the dynamic transmission model and multi-objective optimization model are established to analyze the effects of main design parameters on the dynamic characteristics of hydro-viscous flexible drive.

Computational design and development of novel Al-Mg-Sc-Cr alloy

2015 ◽  
Vol 11 (3) ◽  
pp. 401-412 ◽  
Author(s):  
Abhijit Patra ◽  
Subhas Ganguly ◽  
Partha Protim Chattopadhyay ◽  
Shubhabrata Datta
Keyword(s):  
Genetic Algorithm ◽  
Mg Alloy ◽  
Age Hardening ◽  
Pareto Solutions ◽  
Content Type ◽  
Multi Objective ◽  
Hardening Effect ◽  
Ann Models ◽  
Strength And Ductility ◽  
Alloy Systems

Purpose – The purpose of this paper is to design and develop precipitation hardened Al-Mg alloy imparting enhanced strength with acceptable ductility through minor addition of Sc and Cr by using multi-objective genetic algorithm-based searching. In earlier attempts of strengthening aluminum alloys, owing to the formation of Al3Sc and Al7Cr phase, addition of Sc and Cr have yielded attractive precipitation hardening, respectively. Both the Al-Sc and Al-Cr system are quench sensitive due to presence of a sloping solvus in their phase diagrams. It is also known that both the Al3Sc and Al7Cr phases nucleate directly from the supersaturated solid solution without formation of GP-zones or transient phases prior to the formation of the Al3Sc and Al7Cr. Sc also found to have beneficial effect on the corrosion property of such alloys. In view of the above, it is of interest to explore the possibility of enhancing the age hardening effect in Al-Mg alloy by addition of Sc and Cr. Design/methodology/approach – The paper uses an approach where experimental information of two different alloy systems (namely, Al-Mg-Sc and Al-Cr) has been combined to generate a single database involving the potential features of both the systems with the aim to formulate the suitable artificial neural network (ANN) models for strength and ductility. The models are used as the objective functions for the optimization process. The patterns of the optimized Pareto front are analyzed to recognize the optimal property of the alloy system. The hitherto unexplored Al-Mg-Sc-Cr alloy, designed from the Pareto solutions and suitably modified on the basis of prior knowledge of the system, is then synthesized and characterized. Findings – The paper has demonstrated the ANN- and genetic algorithm (GA)-based design of a hitherto unexplored alloy by utilizing the existing information concerning the component alloy systems. The paper also established that analyses of the Pareto solutions generated through multi-objective optimization using GA provide an insight of the variation of the parameters at different combination of strength and ductility. It also revealed that the Al-Mg-Sc-Cr alloy has exhibited a two-stage age hardening effect. The first and second stages are due to the precipitation of Al3Sc and Al7Cr phases, respectively. Research limitations/implications – In the present study the two alloy systems are used in tandem to develop models to describe the properties involving the distinct mechanistic features of phase evolution inherent in both the systems. Though the ANN models having the capability to capture huge non-linearity of a system have been employed to predict the convoluted effects of those characteristics when an alloy containing Mg, Sc and Cr are added simultaneously, but the ANN models predictions can be checked experimentally by the future researchers. Practical implications – The paper demonstrates the role of scandium and chromium addition on the ageing characteristics of the alloy by analyzing the age hardening behavior of the designed alloy in cast and cold rolled condition clearly. Originality/value – The approach stated in this paper is a novel one, in the sense that experimental data of two different alloy systems have been clubbed to generate a single database with the aim to formulate the suitable ANN models for strength and ductility.

scholarly journals Electromagnetic phenomena analysis in brushless DC motor with speed control using PWM method

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 907-912
Author(s):  
Marek Pawel Ciurys
Keyword(s):  
High Pressure ◽  
Single Phase ◽  
Speed Control ◽  
Dc Motor ◽  
Circuit Model ◽  
Brushless Dc Motor ◽  
Bldc Motor ◽  
Vane Pump ◽  
Brushless Dc

AbstractField-circuit model of a brushless DC motor with speed control using PWM method was developed. Waveforms of electrical and mechanical quantities of the designed motor with a high pressure vane pump built in a rotor of the motor were computed. Analysis of electromagnetic phenomena in the system: single phase AC network – converter - BLDC motor was carried out.

scholarly journals Performance Analysis Stability Of Speed Control Of BLDC Motor Using PID-BAT Algorithm In Electric Vehicle

2017 ◽  
Vol 1 (1) ◽  
pp. 22
Author(s):  
Mochammad Izza Anshory
Keyword(s):  
Speed Control ◽  
Bat Algorithm ◽  
Bldc Motor ◽  
Brushless Dc ◽  
Motor Efficiency ◽  
Best Value ◽  
Performance Setting ◽  
Sound Pulses ◽  
Analysis Stability ◽  
Peak Value

The research on the development of electric vehicles includes such as power electronics, energy storage capability that the higher the battery, reducing fuel emissions, and the motor efficiency.  The electric motor efficiency requires the automatic control on the main parameters such as speed, position, and acceleration.  The performance setting of speed Brushless DC (BLDC) Motor can be improved by using the controller Proportional Integral Derivative (PID), a combination of PID using nature inspired optimization algorithms such as Bat Algorithm (BA). BA is one of the optimization algorithm that mimics the behavior of bats on the move using a vibration or sound pulses emitted a very loud (echolocation) and listen to the echoes that bounce back from the object to determine the circumstances surrounding vicinity   In this paper, simulate of Bat Algorithm to find the best value PID controller parameter to speed control BLDC motor  and analyze performance such as the value of overshoot, steady state. The result  simulation shows that values for the PID parameters without using algorithm bat is Kp = 208.1177, Ki = 1767, and Kd = -8.6025. While using the algorithm bat got value Kp = 5.4303e+04, Ki = -1.3059e+06, and Kd = 3.0193e+04. The performance of the motor obtained through value rise time of  0. 282,  settling time of 1.5, overshoot  value  of 20.5%  and the peak value of  1.21. 

scholarly journals LQR optimized BP neural network PI controller for speed control of brushless DC motor

2020 ◽  
Vol 12 (10) ◽  
pp. 168781402096898
Author(s):  
Tingting Wang ◽  
Hongzhi Wang ◽  
Huangshui Hu ◽  
Chuhang Wang
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Linear Quadratic Regulator ◽  
Speed Control ◽  
Response Speed ◽  
Back Propagation Neural Network ◽  
Pi Controller ◽  
Bldc Motor ◽  
Linear Quadratic ◽  
Brushless Dc

This paper proposes a linear quadratic regulator (LQR) optimized back propagation neural network (BPNN) PI controller called LN-PI for the speed control of brushless direct current (BLDC) motor. The controller adopts BPNN to adjust the gain [Formula: see text] and [Formula: see text] of PI, which improves the dynamic characteristics and robustness of the controller. Moreover, LQR is adopted to optimize the output of BPNN so as to make it close to the target PI gains. Finally, the optimized control output is inputted into the BLDC motor system to achieve speed control. The performance analysis of the proposed controller is presented to compare with traditional PI controller, neural network PI controller and LQR optimized PI controller under MATLAB/Simulink, the results shows that the proposed controller effectively improves the response speed, reduces the steady-state error and enhances the anti-interference ability.

Control of BLDC motor using MRPID controller with modified landsman converter

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Murali Dasari ◽  
A. Srinivasula Reddy ◽  
M. Vijaya Kumar
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Inference ◽  
Dynamic Properties ◽  
Bldc Motor ◽  
Inference System ◽  
Content Type ◽  
Brushless Dc ◽  
Simulink Model ◽  
And Performance ◽  
Bus Voltage

PurposeThe principal intention behind the activity is to regulate the speed, current and commutation of the brushless DC (BLDC) motor. Thereby, the authors can control the torque.Design/methodology/approachIn order to regulate the current and speed of the motor, the Multi-resolution PID (MRPID) controller is proposed. The altered Landsman converter is utilized in this proposed suppression circuit, and the obligation cycle is acclimated to acquire the ideal DC-bus voltage dependent on the speed of the BLDC motor. The adaptive neuro-fuzzy inference system-elephant herding optimization (ANFIS-EHO) calculation mirrors the conduct of the procreant framework in families.FindingsBrushless DC motor's dynamic properties are created, noticed and examined by MATLAB/Simulink model. The performance will be compared with existing genetic algorithms.Originality/valueThe presented approach and performance will be compared with existing genetic algorithms and optimization of different structure of BLDC motor.

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