Embedded Position Control of Permanent Magnet Synchronous Motor Using Model Predictive Control
This paper presents an implementation embedded system for position control of Permanent Magnet Synchronous Motor (PMSM). The control system consists of raspberry pi 3 as a microcontroller, ASDA-A2 servo drive, and Delta Servo ECMA type. The software design includes simulation tool and Python include...
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2018-01-01
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Series: | MATEC Web of Conferences |
Online Access: | https://doi.org/10.1051/matecconf/201819804007 |
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doaj-2604bdd9ffb44ef399a369648e8c7d162021-03-02T09:46:40ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-011980400710.1051/matecconf/201819804007matecconf_meae2018_04007Embedded Position Control of Permanent Magnet Synchronous Motor Using Model Predictive ControlRamelan AgusRohman Arief SyaichuKelana AllenThis paper presents an implementation embedded system for position control of Permanent Magnet Synchronous Motor (PMSM). The control system consists of raspberry pi 3 as a microcontroller, ASDA-A2 servo drive, and Delta Servo ECMA type. The software design includes simulation tool and Python included on Raspbian OS. Communication between Raspberry Pi 3 and ASDA-A2 drivers using the ASCII Modbus communication protocol. Raspberry Pi 3 processes the reference data and the actual reading result and calculates the resulting error. The control algorithm used in this research is Model Predictive Control (MPC). As a Linear Quadratic Regulator, MPC aims to design and generate an optimal control signal with the ability to anticipate saturation, receding horizon, future event and take control accordingly In the design of the MPC technique to adjust the speed of the PMSM to take action of reference tracking, performance index optimization is done by adjusting the value of weighting horizon N, Q and R. The simulation and implementation results showed that the PMSM can reach the stability point on each desired setpoint and result in a near-zero steady-state error.https://doi.org/10.1051/matecconf/201819804007 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ramelan Agus Rohman Arief Syaichu Kelana Allen |
spellingShingle |
Ramelan Agus Rohman Arief Syaichu Kelana Allen Embedded Position Control of Permanent Magnet Synchronous Motor Using Model Predictive Control MATEC Web of Conferences |
author_facet |
Ramelan Agus Rohman Arief Syaichu Kelana Allen |
author_sort |
Ramelan Agus |
title |
Embedded Position Control of Permanent Magnet Synchronous Motor Using Model Predictive Control |
title_short |
Embedded Position Control of Permanent Magnet Synchronous Motor Using Model Predictive Control |
title_full |
Embedded Position Control of Permanent Magnet Synchronous Motor Using Model Predictive Control |
title_fullStr |
Embedded Position Control of Permanent Magnet Synchronous Motor Using Model Predictive Control |
title_full_unstemmed |
Embedded Position Control of Permanent Magnet Synchronous Motor Using Model Predictive Control |
title_sort |
embedded position control of permanent magnet synchronous motor using model predictive control |
publisher |
EDP Sciences |
series |
MATEC Web of Conferences |
issn |
2261-236X |
publishDate |
2018-01-01 |
description |
This paper presents an implementation embedded system for position control of Permanent Magnet Synchronous Motor (PMSM). The control system consists of raspberry pi 3 as a microcontroller, ASDA-A2 servo drive, and Delta Servo ECMA type. The software design includes simulation tool and Python included on Raspbian OS. Communication between Raspberry Pi 3 and ASDA-A2 drivers using the ASCII Modbus communication protocol. Raspberry Pi 3 processes the reference data and the actual reading result and calculates the resulting error. The control algorithm used in this research is Model Predictive Control (MPC). As a Linear Quadratic Regulator, MPC aims to design and generate an optimal control signal with the ability to anticipate saturation, receding horizon, future event and take control accordingly In the design of the MPC technique to adjust the speed of the PMSM to take action of reference tracking, performance index optimization is done by adjusting the value of weighting horizon N, Q and R. The simulation and implementation results showed that the PMSM can reach the stability point on each desired setpoint and result in a near-zero steady-state error. |
url |
https://doi.org/10.1051/matecconf/201819804007 |
work_keys_str_mv |
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