Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control
Brushed (B) and Brushless (BL) DC motors constitute the cornerstone of mechatronic systems regardless their sizes (including miniaturized), in which both position and speed control tasks require the application of sophisticated algorithms. This manuscript addresses the initial step using time series...
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MDPI AG
2022-08-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/13/8/1264 |
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| author | José A. Niembro-Ceceña Roberto A. Gómez-Loenzo Juvenal Rodríguez-Reséndiz Omar Rodríguez-Abreo Ákos Odry |
| author_facet | José A. Niembro-Ceceña Roberto A. Gómez-Loenzo Juvenal Rodríguez-Reséndiz Omar Rodríguez-Abreo Ákos Odry |
| author_sort | José A. Niembro-Ceceña |
| collection | DOAJ |
| description | Brushed (B) and Brushless (BL) DC motors constitute the cornerstone of mechatronic systems regardless their sizes (including miniaturized), in which both position and speed control tasks require the application of sophisticated algorithms. This manuscript addresses the initial step using time series analysis to forecast Back EMF values, thereby enabling the elaboration of real-time adaptive fine-tuning strategies for PID controllers in such a control system design problem. An Auto-Regressive Moving Average (ARMA) model is developed to estimate the DC motor parameter, which evolves in time due to the system’s imperfection (i.e., unpredictable duty cycle) and influences the closed-loop performance. The methodology is executed offline; thus, it highlights the applicability of collected BDC motor measurements in time series analysis. The proposed method updates the PID controller gains based on the Simulink ™ controller tuning toolbox. The contribution of this approach is shown in a comparative study that indicates an opportunity to use time series analysis to forecast DC motor parameters, to re-tune PID controller gains, and to obtain similar performance under the same perturbation conditions. The research demonstrates the practical applicability of the proposed method for fine-tuning/re-tuning controllers in real-time. The results show the inclusion of the time series analysis to recalculate controller gains as an alternative for adaptive control. |
| first_indexed | 2024-03-09T04:04:29Z |
| format | Article |
| id | doaj.art-563a034a26b146b7bb5492de07586feb |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-09T04:04:29Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-563a034a26b146b7bb5492de07586feb2023-12-03T14:08:04ZengMDPI AGMicromachines2072-666X2022-08-01138126410.3390/mi13081264Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor ControlJosé A. Niembro-Ceceña0Roberto A. Gómez-Loenzo1Juvenal Rodríguez-Reséndiz2Omar Rodríguez-Abreo3Ákos Odry4Facultad de Informática, Universidad Autónoma de Querétaro, Querétaro 76230, MexicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MexicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, MexicoIndustrial Technologies Division, Universidad Politécnica de Querétaro, Carretera Estatal 420, El Marques 76240, MexicoDepartment of Mechatronics and Automation, University of Szeged, 6724 Szeged, HungaryBrushed (B) and Brushless (BL) DC motors constitute the cornerstone of mechatronic systems regardless their sizes (including miniaturized), in which both position and speed control tasks require the application of sophisticated algorithms. This manuscript addresses the initial step using time series analysis to forecast Back EMF values, thereby enabling the elaboration of real-time adaptive fine-tuning strategies for PID controllers in such a control system design problem. An Auto-Regressive Moving Average (ARMA) model is developed to estimate the DC motor parameter, which evolves in time due to the system’s imperfection (i.e., unpredictable duty cycle) and influences the closed-loop performance. The methodology is executed offline; thus, it highlights the applicability of collected BDC motor measurements in time series analysis. The proposed method updates the PID controller gains based on the Simulink ™ controller tuning toolbox. The contribution of this approach is shown in a comparative study that indicates an opportunity to use time series analysis to forecast DC motor parameters, to re-tune PID controller gains, and to obtain similar performance under the same perturbation conditions. The research demonstrates the practical applicability of the proposed method for fine-tuning/re-tuning controllers in real-time. The results show the inclusion of the time series analysis to recalculate controller gains as an alternative for adaptive control.https://www.mdpi.com/2072-666X/13/8/1264adaptive controlBrushed DC motorProportional-Integral-Derivative controltime seriesAuto-Regressive Moving Average model |
| spellingShingle | José A. Niembro-Ceceña Roberto A. Gómez-Loenzo Juvenal Rodríguez-Reséndiz Omar Rodríguez-Abreo Ákos Odry Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control Micromachines adaptive control Brushed DC motor Proportional-Integral-Derivative control time series Auto-Regressive Moving Average model |
| title | Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control |
| title_full | Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control |
| title_fullStr | Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control |
| title_full_unstemmed | Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control |
| title_short | Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control |
| title_sort | auto regression model based off line pid controller tuning an adaptive strategy for dc motor control |
| topic | adaptive control Brushed DC motor Proportional-Integral-Derivative control time series Auto-Regressive Moving Average model |
| url | https://www.mdpi.com/2072-666X/13/8/1264 |
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