Self-Tuning Neural Network PID With Dynamic Response Control
PID controllers are widely used and adaptable to various types of systems. However, for the response to be adequate under different conditions, the PID gains must be adjusted. The tuning is made according to the difference between the reference value and the real value (error). This work presents a...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9415738/ |
| _version_ | 1818573534800117760 |
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| author | Omar Rodriguez-Abreo Juvenal Rodriguez-Resendiz Carlos Fuentes-Silva Rodrigo Hernandez-Alvarado Maria Del Consuelo Patricia Torres Falcon |
| author_facet | Omar Rodriguez-Abreo Juvenal Rodriguez-Resendiz Carlos Fuentes-Silva Rodrigo Hernandez-Alvarado Maria Del Consuelo Patricia Torres Falcon |
| author_sort | Omar Rodriguez-Abreo |
| collection | DOAJ |
| description | PID controllers are widely used and adaptable to various types of systems. However, for the response to be adequate under different conditions, the PID gains must be adjusted. The tuning is made according to the difference between the reference value and the real value (error). This work presents a self-adjusting PID controller based on a backpropagation artificial neural network. The network calculates the appropriate gains according to the desired output, that is, the dynamic response desired which is composed of the transient part and the stationary part of the step response of a system. The contribution of the work is that in addition to using the error for network training, the maximum desired values of overshoots, settling times, and stationary errors were used as input data for the network. An offline training database was created using genetic algorithms to obtain the dynamic response data associated with PID gains. The genetic algorithm allows getting data in different operating ranges and allows using only stable gains combinations. The database was used for training. Subsequently, the neural network estimates an appropriate gain combination, adapting to the error and the desired response. The method performance is evaluated by controlling the speed of a direct current motor. The results indicate an average error of 4% for the database between the requested and system response. On the other hand, the gains estimated by the network in the test dataset (1544 combinations) did not cause instability and complying with the expected dynamic response in 86% of the dataset. |
| first_indexed | 2024-12-15T00:12:38Z |
| format | Article |
| id | doaj.art-9de470f595404d2591e3810a6a2858b4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-15T00:12:38Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9de470f595404d2591e3810a6a2858b42022-12-21T22:42:31ZengIEEEIEEE Access2169-35362021-01-019652066521510.1109/ACCESS.2021.30754529415738Self-Tuning Neural Network PID With Dynamic Response ControlOmar Rodriguez-Abreo0https://orcid.org/0000-0002-8650-1185Juvenal Rodriguez-Resendiz1https://orcid.org/0000-0001-8598-5600Carlos Fuentes-Silva2https://orcid.org/0000-0002-9476-4129Rodrigo Hernandez-Alvarado3Maria Del Consuelo Patricia Torres Falcon4Industrial technologies division, Universidad Politécnica de Querétaro, Santiago de Querétaro, MéxicoFaculty of Engineering, Universidad Autónoma de Querétaro, Santiago de Querétaro, MéxicoIndustrial technologies division, Universidad Politécnica de Querétaro, Santiago de Querétaro, MéxicoIndustrial technologies division, Universidad Politécnica de Querétaro, Santiago de Querétaro, MéxicoIndustrial technologies division, Universidad Politécnica de Querétaro, Santiago de Querétaro, MéxicoPID controllers are widely used and adaptable to various types of systems. However, for the response to be adequate under different conditions, the PID gains must be adjusted. The tuning is made according to the difference between the reference value and the real value (error). This work presents a self-adjusting PID controller based on a backpropagation artificial neural network. The network calculates the appropriate gains according to the desired output, that is, the dynamic response desired which is composed of the transient part and the stationary part of the step response of a system. The contribution of the work is that in addition to using the error for network training, the maximum desired values of overshoots, settling times, and stationary errors were used as input data for the network. An offline training database was created using genetic algorithms to obtain the dynamic response data associated with PID gains. The genetic algorithm allows getting data in different operating ranges and allows using only stable gains combinations. The database was used for training. Subsequently, the neural network estimates an appropriate gain combination, adapting to the error and the desired response. The method performance is evaluated by controlling the speed of a direct current motor. The results indicate an average error of 4% for the database between the requested and system response. On the other hand, the gains estimated by the network in the test dataset (1544 combinations) did not cause instability and complying with the expected dynamic response in 86% of the dataset.https://ieeexplore.ieee.org/document/9415738/Auto-tuningspeed controlgenetic algorithmneural networkPIDdynamic response |
| spellingShingle | Omar Rodriguez-Abreo Juvenal Rodriguez-Resendiz Carlos Fuentes-Silva Rodrigo Hernandez-Alvarado Maria Del Consuelo Patricia Torres Falcon Self-Tuning Neural Network PID With Dynamic Response Control IEEE Access Auto-tuning speed control genetic algorithm neural network PID dynamic response |
| title | Self-Tuning Neural Network PID With Dynamic Response Control |
| title_full | Self-Tuning Neural Network PID With Dynamic Response Control |
| title_fullStr | Self-Tuning Neural Network PID With Dynamic Response Control |
| title_full_unstemmed | Self-Tuning Neural Network PID With Dynamic Response Control |
| title_short | Self-Tuning Neural Network PID With Dynamic Response Control |
| title_sort | self tuning neural network pid with dynamic response control |
| topic | Auto-tuning speed control genetic algorithm neural network PID dynamic response |
| url | https://ieeexplore.ieee.org/document/9415738/ |
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