scholarly journals Calibration of a Nonlinear DC Motor under Uncertainty Using Nonlinear Optimization Techniques

2021 ◽  
Vol 65 (1) ◽  
pp. 42-52
Author(s):  
Hamed Keshmiri Neghab ◽  
Hamid Keshmiri Neghab
Keyword(s):  
Nonlinear Optimization ◽  
Model Calibration ◽  
Optimization Methods ◽  
Dc Motor ◽  
Optimization Techniques ◽  
Unknown Parameters ◽  
Labview Software ◽  
Dc Motors ◽  
Industrial Software ◽  
Nonlinear Optimization Methods

The use of DC motors is increasingly high and it has more parameters which should be normalized. Now the calibration of each parameters is important for each motor, because it affects in its performance and accuracy. A lot of researches are investigated in this area. In this paper demonstrated how to estimate the parameters of a Nonlinear DC Motor using different Nonlinear Optimization techniques of fitting parameters to model, that called model calibration. First, three methods for calibration of a DC motor are defined, then unknown parameters of the mathematical model with the nonlinear optimization techniques for the fitting routines and model calibration process, are identified. In addition, three optimization techniques such as Levenberg-Marquardt, Constrained Nonlinear Optimization and Gauss-Newton, are compared. The goal of this paper is to estimate nonlinear parameters of a DC motor under uncertainty with nonlinear optimization methods by using LabVIEW software as an industrial software and compare the nonlinear optimization methods based on position, velocity and current. Finally, results are illustrated and comparison between these methods based on the results are made.

Straight-Move Robot Control System with LabView-Based Proportional Integral Derivative (PID) Control

2019 ◽  
Vol 3 (2) ◽  
pp. 13-24
Author(s):  
Andrean George W
Keyword(s):  
Pid Control ◽  
Rotational Speed ◽  
Pid Controller ◽  
Dc Motor ◽  
Motor Speed ◽  
Labview Software ◽  
Dc Motors ◽  
Dc Motor Control ◽  
Process System ◽  
Interface Devices

Abstract - Control and monitoring of the rotational speed of a wheel (DC motor) in a process system is very important role in the implementation of the industry. PWM control and monitoring for wheel rotational speed on a pair of DC motors uses computer interface devices where in the industry this is needed to facilitate operators in controlling and monitoring motor speed. In order to obtain the best controller, tuning the Integral Derifative (PID) controller parameter is done. In this tuning we can know the value of proportional gain (Kp), integral time (Ti) and derivative time (Td). The PID controller will give action to the DC motor control based on the error obtained, the desired DC motor rotation value is called the set point. LabVIEW software is used as a PE monitor, motor speed control. Keyword : LabView, Motor DC, Arduino, LabView, PID.

scholarly journals Optimization of the cross-sectional shape of the belts of trihedral lattice supports

2019 ◽  
Vol 7 (4) ◽  
pp. 5-8
Author(s):  
Linar Sabitov ◽  
Ilnar Baderddinov ◽  
Anton Chepurnenko
Keyword(s):  
Objective Function ◽  
Nonlinear Optimization ◽  
Cross Section ◽  
Optimization Methods ◽  
Cross Sectional ◽  
Maximum Value ◽  
Axial Moment ◽  
The Cross ◽  
Nonlinear Optimization Methods ◽  
Sectional Shape

The article considers the problem of optimizing the geometric parameters of the cross section of the belts of a trihedral lattice support in the shape of a pentagon. The axial moment of inertia is taken as the objective function. Relations are found between the dimensions of the pentagonal cross section at which the objective function takes the maximum value. We introduce restrictions on the constancy of the consumption of material, as well as the condition of equal stability. The solution is performed using nonlinear optimization methods in the Matlab environment.

scholarly journals Parameter Estimation of DC Motor using Adaptive Transfer Function Based on Nelder-Mead Optimisation

2018 ◽  
Vol 9 (3) ◽  
pp. 696 ◽  
Author(s):  
Byamakesh Nayak ◽  
Sangeeta Sahu ◽  
Tanmoy Roy Choudhury
Keyword(s):  
Experimental Data ◽  
Transfer Function ◽  
Adaptive Method ◽  
Dc Motor ◽  
Model Parameters ◽  
Current Response ◽  
Adaptive Model ◽  
Unknown Parameters ◽  
Dc Motors ◽  
Estimated Parameters

<p>This paper explains an adaptive method for estimation of unknown parameters of transfer function model of any system for finding the parameters. The transfer function of the model with unknown model parameters is considered as the adaptive model whose values are adapted with the experimental data. The minimization of error between the experimental data and the output of the adaptive model have been realised by choosing objective function based on different error criterions. Nelder-Mead optimisation Method is used for adaption algorithm. To prove the method robustness and for students learning, the simple system of separately excited dc motor is considered in this paper. The experimental data of speed response and corresponding current response are taken and transfer function parameters of  dc motors are adapted based on Nelder-Mead optimisation to match with the experimental data. The effectiveness of estimated parameters with different objective functions are compared and validated with machine specification parameters.</p>

Crashworthy structures for future vehicle architecture of autonomous pods and heavy quadricycles on public roads: A review

2019 ◽  
Vol 234 (1) ◽  
pp. 3-16
Author(s):  
Andrew Harrison ◽  
Jesper Christensen ◽  
Christophe Bastien ◽  
Stratis Kanarachos
Keyword(s):  
Nonlinear Optimization ◽  
Design Methodology ◽  
Significant Risk ◽  
Optimization Methods ◽  
Functionally Graded ◽  
Vehicle Architecture ◽  
Design Methodologies ◽  
Expensive Problems ◽  
Computationally Expensive ◽  
Nonlinear Optimization Methods

With the development and deployment of lightweight vehicles to the market, inclusive of autonomous pods, a review of advanced crashworthy structures and the design methodology has been conducted as it is thought that super-lightweight vehicles may pose significant risk to the occupants if they are involved in a crash. It is suggested that tests should include oblique and multiple velocity impacts to cater for the effects of assisted driving systems of future vehicles. A review of current crash structures and design methodologies revealed that the most recent research do not cater to multiple crash scenarios, nor a shorter crush allowance, therefore resulting in poor crashworthiness performance. In addition, the arbitrary seat positioning shown in autonomous pods’ concepts vastly increases the risk to occupants. Greater enhancements to passive crashworthiness are imperative. To this end, functionally graded vehicle structures should be designed as it has been found that these can provide optimized solutions. Research into nonlinear optimization methods for computationally expensive problems will become central to this.

Nonlinear optimization methods applied to magnetic actuators design

1992 ◽  
Vol 28 (2) ◽  
pp. 1581-1584 ◽  
Author(s):  
R.R. Saldanha ◽  
S. Pelissier ◽  
K. Kadded ◽  
Y.P. Yonnet ◽  
J.-L. Coulomb
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Methods ◽  
Magnetic Actuators ◽  
Nonlinear Optimization Methods

Numerical Optimization of a Gas Turbine Cogeneration Plant

2003 ◽  
Author(s):  
Jose´ C. F. Teixeira ◽  
Senhorinha F. C. F. Teixeira ◽  
Aˆngela M. E. Silva
Keyword(s):  
Nonlinear Optimization ◽  
Gas Turbine ◽  
Heat Recovery ◽  
Optimization Methods ◽  
Thermodynamic Quantities ◽  
Cogeneration Plant ◽  
Fortran Code ◽  
Cogeneration System ◽  
Nonlinear Optimization Methods ◽  
Textile Factory

Amongst the various alternatives for the combined production of heat and power, the systems based on a gas turbine are becoming increasingly attractive. In addition to the high thermal efficiency, they can also operate on a wide variety of fuels. The basic configuration of a simple cogeneration system consisting of a gas turbine and a heat recovery steam generator has been used to illustrate the application of nonlinear optimization numerical methods as a tool to evaluate and optimize complex energy systems. The problem was formulated as the minimization of costs as the objective function, subject to the constraints imposed by the physical and thermodynamic quantities. Two numerical nonlinear optimization methods with constraints have been tested using a Fortran code and the MATLAB® environment. The model has been evaluated on a real cogeneration plant consisting of a gas turbine heat recovery system of a local textile factory.

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