MATEC Web Conf.
Volume 198, 20182018 Asia Conference on Mechanical Engineering and Aerospace Engineering (MEAE 2018)
|Number of page(s)||5|
|Section||Electronic Engineering and Mechatronics|
|Published online||12 September 2018|
Embedded Position Control of Permanent Magnet Synchronous Motor Using Model Predictive Control
School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia
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.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.