DDC Control Techniques for Three-Phase BLDC Motor Position Control
In this article, a novel hybrid control scheme is proposed for controlling the position of a three-phase brushless direct current (BLDC) motor. The hybrid controller consists of discrete time sliding mode control (SMC) with model free adaptive control (MFAC) to make a new data-driven control (DDC) s...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2017-09-01
|
Series: | Algorithms |
Subjects: | |
Online Access: | https://www.mdpi.com/1999-4893/10/4/110 |
id |
doaj-651397cb6c3044038756d6ca27332330 |
---|---|
record_format |
Article |
spelling |
doaj-651397cb6c3044038756d6ca273323302020-11-24T23:55:27ZengMDPI AGAlgorithms1999-48932017-09-0110411010.3390/a10040110a10040110DDC Control Techniques for Three-Phase BLDC Motor Position ControlRana Javed Masood0Dao Bo Wang1Zain Anwar Ali2Babar Khan3College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Information Science and Technology, Donghua University, Shanghai 201620, ChinaIn this article, a novel hybrid control scheme is proposed for controlling the position of a three-phase brushless direct current (BLDC) motor. The hybrid controller consists of discrete time sliding mode control (SMC) with model free adaptive control (MFAC) to make a new data-driven control (DDC) strategy that is able to reduce the simulation time and complexity of a nonlinear system. The proposed hybrid algorithm is also suitable for controlling the speed variations of a BLDC motor, and is also applicable for the real time simulation of platforms such as a gimbal platform. The DDC method does not require any system model because it depends on data collected by the system about its Inputs/Outputs (IOS). However, the model-based control (MBC) method is difficult to apply from a practical point of view and is time-consuming because we need to linearize the system model. The above proposed method is verified by multiple simulations using MATLAB Simulink. It shows that the proposed controller has better performance, more precise tracking, and greater robustness compared with the classical proportional integral derivative (PID) controller, MFAC, and model free learning adaptive control (MFLAC).https://www.mdpi.com/1999-4893/10/4/110model free adaptive controlsliding mode controlmodel free learning adaptive controlbrushless DC |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rana Javed Masood Dao Bo Wang Zain Anwar Ali Babar Khan |
spellingShingle |
Rana Javed Masood Dao Bo Wang Zain Anwar Ali Babar Khan DDC Control Techniques for Three-Phase BLDC Motor Position Control Algorithms model free adaptive control sliding mode control model free learning adaptive control brushless DC |
author_facet |
Rana Javed Masood Dao Bo Wang Zain Anwar Ali Babar Khan |
author_sort |
Rana Javed Masood |
title |
DDC Control Techniques for Three-Phase BLDC Motor Position Control |
title_short |
DDC Control Techniques for Three-Phase BLDC Motor Position Control |
title_full |
DDC Control Techniques for Three-Phase BLDC Motor Position Control |
title_fullStr |
DDC Control Techniques for Three-Phase BLDC Motor Position Control |
title_full_unstemmed |
DDC Control Techniques for Three-Phase BLDC Motor Position Control |
title_sort |
ddc control techniques for three-phase bldc motor position control |
publisher |
MDPI AG |
series |
Algorithms |
issn |
1999-4893 |
publishDate |
2017-09-01 |
description |
In this article, a novel hybrid control scheme is proposed for controlling the position of a three-phase brushless direct current (BLDC) motor. The hybrid controller consists of discrete time sliding mode control (SMC) with model free adaptive control (MFAC) to make a new data-driven control (DDC) strategy that is able to reduce the simulation time and complexity of a nonlinear system. The proposed hybrid algorithm is also suitable for controlling the speed variations of a BLDC motor, and is also applicable for the real time simulation of platforms such as a gimbal platform. The DDC method does not require any system model because it depends on data collected by the system about its Inputs/Outputs (IOS). However, the model-based control (MBC) method is difficult to apply from a practical point of view and is time-consuming because we need to linearize the system model. The above proposed method is verified by multiple simulations using MATLAB Simulink. It shows that the proposed controller has better performance, more precise tracking, and greater robustness compared with the classical proportional integral derivative (PID) controller, MFAC, and model free learning adaptive control (MFLAC). |
topic |
model free adaptive control sliding mode control model free learning adaptive control brushless DC |
url |
https://www.mdpi.com/1999-4893/10/4/110 |
work_keys_str_mv |
AT ranajavedmasood ddccontroltechniquesforthreephasebldcmotorpositioncontrol AT daobowang ddccontroltechniquesforthreephasebldcmotorpositioncontrol AT zainanwarali ddccontroltechniquesforthreephasebldcmotorpositioncontrol AT babarkhan ddccontroltechniquesforthreephasebldcmotorpositioncontrol |
_version_ |
1725462472939274240 |