| dc.creator | Rios Hernández, Ronald | |
| dc.creator | Apraez Bastidas, Alejandra | |
| dc.creator | Rodriguez Calderon, Jaime | |
| dc.creator | Tumialan Borja, José Antonio | |
| dc.date | 2020-11-04T08:00:00Z | |
| dc.date.accessioned | 2022-10-13T13:36:40Z | |
| dc.date.available | 2022-10-13T13:36:40Z | |
| dc.identifier | https://ciencia.lasalle.edu.co/scopus_unisalle/48 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/4157780 | |
| dc.description | In the industrial field, the need has arisen to use more efficient and robust controllers using artificial intelligence techniques that optimize the operation of processes within the industry. In this way, the need arises to employ adaptive controllers such as the BFOA and implement it in real systems in which its functionality can be analyzed. This article presents the implementation and analysis in a fully instrumented functional prototype with industrial sensors. The work methodology is documented from the acquisition of the physical variables through the OPC client-server communication; the synchronization of the excitation of the input variable (variable speed drive) and obtaining the evolution of the flow in time; with the experimental data, the identification methodology by relative least squares was used to obtain the transfer function. Later, the BFOA algorithm was implemented to adjust the constants of a PI controller (Kp and Ki) and analyze the response through simulation using Matlab software, in which satisfactory results were observed based on the analysis of response to disturbances and as an end final part, the controller and the BFOA algorithm were implemented in a PLC-S7-1500 controller in SCL language, and the functionality was validated with the functional prototype, changing the flow setpoints at certain times, observing a behavior according to the simulations carried out. with a minimum overshoot of approximately 5 % and an establishment time of 20s. | |
| dc.source | 2020 9th International Congress of Mechatronics Engineering and Automation, CIIMA 2020 - Conference Proceedings | |
| dc.subject | adaptative control | |
| dc.subject | Bacterial foraging | |
| dc.subject | experimental validation | |
| dc.subject | IA | |
| dc.subject | identification | |
| dc.subject | OPC | |
| dc.subject | optimization | |
| dc.subject | SCL | |
| dc.title | PI tuning based on Bacterial Foraging Algorithm for flow control | |
| dc.type | Conference Proceeding | |
