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>

scholarly journals Parameter estimation of DC motor through whale optimization algorithm

2019 ◽  
Vol 10 (1) ◽  
pp. 83 ◽  
Author(s):  
Byamakesh Nayak ◽  
Sangeeta Sahu
Keyword(s):  
Experimental Data ◽  
Cost Function ◽  
Optimization Algorithm ◽  
Dc Motor ◽  
Whale Optimization Algorithm ◽  
Model Parameters ◽  
Adaptive Model ◽  
Flux Dynamics ◽  
Whale Optimization ◽  
The Cost

This article estimates the unknown dc motor parameters by adapting the adaptive model with the reference model created by experimental data onto armature current and speed response from separately excited dc motor .The field flux dynamics, which is usually ignored, is included to model the dynamics of the motor. The block diagram including the flux dynamics and model parameters is considered as the adaptive model. The integral time square error between the instant experimental data and the corresponding adaptive model data is taken as cost function. The Whale optimization algorithm is used to minimize the cost function. Additionally, to improve the performances of optimization algorithm and for accurate result, the experimental data is divided into three intervals which form the three inequality constraints. A fixed penalty value is added to the cost function for violating these constraints. The effectiveness of estimation with two different methods is validated by convergence curve.

scholarly journals Uncertainty Quantification in Epidemiological Models for COVID-19 Pandemic

2020 ◽  
Author(s):  
Leila Taghizadeh ◽  
Ahmad Karimi ◽  
Clemens Heitzinger
Keyword(s):  
Inverse Modeling ◽  
Posteriori Probability ◽  
Model Parameters ◽  
Epidemiological Models ◽  
Unknown Parameters ◽  
Protective Measures ◽  
A Posteriori Probability ◽  
Estimated Parameters ◽  
Forward And Inverse Modeling ◽  
Inversion Techniques

AbstractThe main goal of this paper is to develop the forward and inverse modeling of the Coronavirus (COVID-19) pandemic using novel computational methodologies in order to accurately estimate and predict the pandemic. This leads to governmental decisions support in implementing effective protective measures and prevention of new outbreaks. To this end, we use the logistic equation and the SIR system of ordinary differential equations to model the spread of the COVID-19 pandemic. For the inverse modeling, we propose Bayesian inversion techniques, which are robust and reliable approaches, in order to estimate the unknown parameters of the epidemiological models. We use an adaptive Markov-chain Monte-Carlo (MCMC) algorithm for the estimation of a posteriori probability distribution and confidence intervals for the unknown model parameters as well as for the reproduction number. Furthermore, we present a fatality analysis for COVID-19 in Austria, which is also of importance for governmental protective decision making. We perform our analyses on the publicly available data for Austria to estimate the main epidemiological model parameters and to study the effectiveness of the protective measures by the Austrian government. The estimated parameters and the analysis of fatalities provide useful information for decision makers and makes it possible to perform more realistic forecasts of future outbreaks.

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.

scholarly journals DC Motor Speed Control using Internal Model Controller: Industrial Transformation Strategy

2020 ◽  
Vol 9 (5) ◽  
pp. 300-306
Keyword(s):  
Transfer Function ◽  
Pid Control ◽  
Pid Controller ◽  
Internal Model ◽  
Dc Motor ◽  
Motor Speed ◽  
Matlab Simulink ◽  
Dc Motors ◽  
Control Schemes ◽  
Motor Model

In developed nations, industries are made to function at control engineering costs via the use of appropriate control schemes for dc motors. This paper introduces the role played by dc motors in industries thereby necessitating the analysis and performance validation of dc motor in Internal Model Control (IMC) scheme as against the Proportional– Integral–Derivative (PID) control schemes that is widely used in most industries. Theories on dc motor model, PID and IMC controller were detailed to paved the way for the methodical approach of getting specifications and transfer function for a typical dc motor (model RMCS-3011). Matlab/Simulink software was then used to tune the PID controller for the purpose of finding the values of PID gains that meets the design requirements to achieve best performance, thereby enabling the simulation of the PID controller. Using Matlab m-file environment, IMC controller transfer function was generated and simulated. The IMC controller transfer function aimed at achieving a unity gain that tracks the set-point was approximately realized. In the realization process, it was obvious that a filter is required. The aim of this work is to evaluate the performance of the IMC controller over PID controller. Simulated plots in Matlab-Simulink using the PID gains for the PID controller, and time constants and filter order for the IMC were presented. The quantitative results of the IMC method when compared with that of PID control provides a commendable performance. However, the performance in terms of rise time is small and preferred with the use of Matlab-Simulink tuned PID controller. Conclusively, IMC controller would be the preferred controller where the robustness and accuracy of the dc motor speed control counts more than faster response

DC Motor Speed Control Using Hybrid PID-Fuzzy with ITAE Polynomial Initiation

2019 ◽  
Vol 3 (1) ◽  
pp. 7
Author(s):  
Hari Wibawa ◽  
Oyas Wahyunggoro ◽  
Adha Imam Cahyadi
Keyword(s):  
Transfer Function ◽  
Fuzzy Logic Controller ◽  
Dc Motor ◽  
Industrial Sector ◽  
Pid Controllers ◽  
Motor Speed ◽  
Dc Motors ◽  
Working Parameters ◽  
Motor Operation ◽  
Load Simulation

DC motors are widely applied in industrial sector, especiallyprocesses of automation and robotics. Given its role in the sector, DC motor operation needs to be optimized. One of optimization steps is controlling speed using several control methods, for example conventional PID methods, PID Ziegler Nichols, PID based on ITAE polynomials, and Hybrid PID-Fuzzy. From these methods, Hybrid PID-Fuzzy was chosen as a method to be proposed in this paper because it can anticipate shortcomings of PID controllers and fuzzy controllers so as to produce system responses that are fast and adaptive to errors. This paper aimed to design a Hybrid PID-Fuzzy system based on ITAE polynomials (Hybrid-ITAE), to analyze its performance parameters values, and tp compare Hybrid-ITAE performance with conventional PID method. Working parameters being reviewed include overshoot, rise time, settling time, and ITAE. First of all, JGA25-370 DC motor was modeled in a form of a third order transfer function equation. Based on the transfer function, PID parameters were calculated using PID Output Feedback and ITAE polynomial methods. The best ITAE polynomial PID controllers were then be combined with a Fuzzy Logic Controller to form a Hybrid-ITAE system. Simulation and experimental stages were carried out in two conditions, namely no load and loaded. Simulation and experimental results showed that Hybrid-ITAE (l = 0.85) was the best controller for no-load simulation conditions. For loaded simulation Hybrid-ITAE (l=1) was a better controller. In no-loads experiment, the best controller was Hybrid PID-Ziegler Nichols, while for loaded condition the best controller was Hybrid PID-Ziegler Nichols.

An Analytical Mechatronic Model for Series DC Motors Using Manufacturer Test Data

2009 ◽  
Author(s):  
Clark Radcliffe
Keyword(s):  
Analytical Model ◽  
Test Data ◽  
Dc Motor ◽  
Analytical Models ◽  
Model Parameters ◽  
Market Place ◽  
Brushless Dc Motors ◽  
Dc Motors ◽  
Motor Test ◽  
Permanent Magnet Dc Motors

Direct Current (DC) Motors are one of the most common mechatronic actuators. They are important for electromechanical servo systems, drivers for battery powered appliances and tools as well as electric vehicles. Both brushless DC motors and wound DC motors are common in electric and hybrid vehicles. The series wound DC motor is commonly used for high torque vehicle applications. The literature has many papers discussing permanent magnet DC motors but a very limited number of publications on analytical models for series wound DC motors, especially motor models that fit series wound DC motor test data available in the market place. An analytical model for a series wound DC motor is developed here based on physical principles including energy conservation. The model developed will be compared with models developed by other investigators. Available commercial test data for a series motor will be used to find model parameters for the analytical model and the accuracy of this model evaluated against the original test data. The model developed displays excellent accuracy well within the accuracy of the test data available. Typical model rms deviation from test data is under 2% for the commercial series wound DC motors evaluated.

scholarly journals Online Estimation of Terminal Airspace Sector Capacity from ATC Workload

2021 ◽  
Author(s):  
Majeed Mohamed
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Experimental Data ◽  
Neural Model ◽  
Model Parameters ◽  
Air Traffic Controller ◽  
Single Output ◽  
Estimated Parameters ◽  
Model Response ◽  
Relative Standard

Neural Partial Differentiation (NPD) approach is applied to estimate terminal airspace sector capacity in real-time from the ATC (Air Traffic Controller) dynamical neural model with permissible safe separation and affordable workload. A neural model of a multi-input-single-output (MISO) ATC dynamical system is primarily established and used to estimate parameters from the experimental data using NPD. Since the relative standard deviations of these estimated parameters are lesser, the predicted neural model response is well matched with the intervention of ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters that are unknown in practice.

Parameters Calibration for Medium Hydraulic Excavator Simulation Model Based on Experimental Data

2013 ◽  
Vol 706-708 ◽  
pp. 1483-1491 ◽  
Author(s):  
An Lin Wang ◽  
Shi Ning Shi ◽  
Jun Huang
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Simulation Model ◽  
Response Surface ◽  
Model Simulation ◽  
Error Function ◽  
Model Parameters ◽  
Hydraulic Excavator ◽  
Unknown Parameters ◽  
Model Based

The authenticity and reasonability of the medium hydraulic excavator simulation model parameters was the foundation to ensure the effectiveness of the simulation model. Based on the bond graph theory, a dynamic simulation model of a medium excavator was established. By the comparison of experimental data and simulation data, the response surface of unknown parameters and the error function of the system model were built. Subsequently, the genetic algorithm was employed to optimize the response surface and obtained optimal value. And then the calibration of the unknown parameters was automatically completed. It was proved that the model simulation curve and with experimental curve fitted better when response surface-genetic algorithm method was used for automatic optimization and calibration of unknown parameters. Furthermore, this method could also function to reduce effectively the number of trials of parameter calibration.

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.

Parameter Determination for Chloride and Tritium Transport in Undisturbed Lysimeters during Steady Flow

1992 ◽  
Vol 23 (2) ◽  
pp. 89-104 ◽  
Author(s):  
Ole H. Jacobsen ◽  
Feike J. Leij ◽  
Martinus Th. van Genuchten
Keyword(s):  
Experimental Data ◽  
Unsaturated Flow ◽  
Transport Model ◽  
Time Moment ◽  
Breakthrough Curves ◽  
Coarse Sand ◽  
Retardation Factor ◽  
Parameter Determination ◽  
Model Parameters ◽  
Water Fraction

Breakthrough curves of Cl and 3H2O were obtained during steady unsaturated flow in five lysimeters containing an undisturbed coarse sand (Orthic Haplohumod). The experimental data were analyzed in terms of the classical two-parameter convection-dispersion equation and a four-parameter two-region type physical nonequilibrium solute transport model. Model parameters were obtained by both curve fitting and time moment analysis. The four-parameter model provided a much better fit to the data for three soil columns, but performed only slightly better for the two remaining columns. The retardation factor for Cl was about 10 % less than for 3H2O, indicating some anion exclusion. For the four-parameter model the average immobile water fraction was 0.14 and the Peclet numbers of the mobile region varied between 50 and 200. Time moments analysis proved to be a useful tool for quantifying the break through curve (BTC) although the moments were found to be sensitive to experimental scattering in the measured data at larger times. Also, fitted parameters described the experimental data better than moment generated parameter values.

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