TY - GEN
T1 - On identification of a coupled four tank system
AU - Milasi, Rasoul M.
AU - Huang, Biao
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/17
Y1 - 2021/8/17
N2 - In this work, the parametric identification of a multi-input multi-output (MIMO) four tank system is investigated. The four tank system contains four coupled tanks, two pumps, and a water reservoir. The system outputs are water levels of tanks while two pumps operate as control inputs. Four steps are employed to complete the identification process: experimental data acquisition, data filtering, parametric identification, and verification. Initial input step responses are used to choose proper sampling time, filtering requirement and Pseudo Random Binary Sequence (PRBS) inputs design. Then, PRBS inputs are applied to the system for identification and verification data collection. Two parametric identification methods, transfer function and state space, are employed for system identification. Finally, the performance of the identified models is compared with actual verification data. The results validate the good accuracy of the estimated models.
AB - In this work, the parametric identification of a multi-input multi-output (MIMO) four tank system is investigated. The four tank system contains four coupled tanks, two pumps, and a water reservoir. The system outputs are water levels of tanks while two pumps operate as control inputs. Four steps are employed to complete the identification process: experimental data acquisition, data filtering, parametric identification, and verification. Initial input step responses are used to choose proper sampling time, filtering requirement and Pseudo Random Binary Sequence (PRBS) inputs design. Then, PRBS inputs are applied to the system for identification and verification data collection. Two parametric identification methods, transfer function and state space, are employed for system identification. Finally, the performance of the identified models is compared with actual verification data. The results validate the good accuracy of the estimated models.
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U2 - 10.1109/ICCSE51940.2021.9569498
DO - 10.1109/ICCSE51940.2021.9569498
M3 - Conference contribution
AN - SCOPUS:85118939193
T3 - ICCSE 2021 - IEEE 16th International Conference on Computer Science and Education
SP - 697
EP - 701
BT - ICCSE 2021 - IEEE 16th International Conference on Computer Science and Education
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Computer Science and Education, ICCSE 2021
Y2 - 17 August 2021 through 21 August 2021
ER -