A new output error method for a decoupled identification of electrical and mechanical dynamic parameters of DC motor-driven robots

To investigate the accuracy of modeling DC motor, the platform for measurement and calculation dynamic parameters is built by the Hardware-In-the-Loop(HIL) method based on dSPACE system. The running state of DC motor has to be changed with adjustment of PWM duty-cycle using ControlDesk software. Having got measurement and calculation parameters value of DC motor, we compare the test results with simulation value using the model of DC motor with cascade control in Matlab/Simulink software according to the classical mathematical model. It confirms the established model of DC motor accurately and reliability using new parameters, which provides the basis of more complex control algorithms and also indicates that the feasibility and generalization application value of measurement and calculation method for DC motor.

Download Full-text

DC motor parameter identification using equation error method

Electrical Engineering ◽

10.1007/s00202-017-0514-6 ◽

2017 ◽

Vol 100 (2) ◽

pp. 415-423 ◽

Cited By ~ 3

Author(s):

Andrius Petrovas ◽

Aurelijus Pitrėnas ◽

Zita Savickienė

Keyword(s):

Parameter Identification ◽

Dc Motor ◽

Equation Error ◽

Motor Parameter ◽

Error Method

Download Full-text

Aerodynamic coefficients modeling using Levenberg–Marquardt algorithm and network

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-03-2021-0073 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Zhigang Wang ◽

Aijun Li ◽

Lihao Wang ◽

Xiangchen Zhou ◽

Boning Wu

Keyword(s):

Neural Network ◽

Particle Swarm ◽

Particle Swarm Algorithm ◽

Aerodynamic Parameter ◽

Content Type ◽

Flight Data ◽

Output Error ◽

Aerodynamic Derivatives ◽

Levenberg Marquardt ◽

Error Method

Purpose The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm. Design/methodology/approach Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives. Findings Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data. Research limitations/implications The proposed method requires iterative calculation and can only identify parameters offline. Practical implications The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems. Originality/value In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

Download Full-text

A mathematical model of a brushed DC motor system

Data Analytics and Applied Mathematics (DAAM) ◽

10.15282/daam.v2i2.6830 ◽

2021 ◽

Vol 2 (2) ◽

pp. 60-68

Author(s):

N. N. A. Rahman ◽

N. M. Yahya

Keyword(s):

Mathematical Model ◽

Steady State ◽

Motor System ◽

Dc Motor ◽

Positioning System ◽

Pd Controller ◽

Trial And Error ◽

Trial And Error Method ◽

Low Torque ◽

Error Method

Mathematical model has been proposed for some system that involves a brushed DC motor and it is widely used in industry. Brushed DC motor ideals for applications with a low- torque, manage to change pace or speed and it is widely used in many applications such as x-y table positioning system, conveyor systems and other system that required to use the features that brushed DC motor have. Mathematical model of brushed DC motor in order to verify the performance of the DC motor. In this paper, mathematical model of brushed DC motor will be derived from a brushed DC motor circuit that consist of two parts that are electrical and mechanical part. To validate the functionality of mathematical model, the performance of the brushed DC motor without any controller will be compared with the brushed DC motor with the presence of PI-PD controller that will be tuned by trial-and-error method. Performances of both brushed DC motor with and without controller will be compared in terms of transient response which are, rise time, Tr, settling time, Ts, steady state error, ess and lastly percentage overshoot. At the end of the study, the brushed DC motor with PI-PD controller show a better performance compared to the brushed DC motor without any controller.

Download Full-text