Name: ROGÉRIO PASSOS DO AMARAL PEREIRA

Publication date: 19/08/2022
Advisor:

Name	Role
JOSE LEANDRO FÉLIX SALLES	Advisor *
TEODIANO FREIRE BASTOS FILHO	Co-advisor *

Examining board:

Name	Role
ELIETE MARIA DE OLIVEIRA CALDEIRA	External Examiner *
JOSE LEANDRO FÉLIX SALLES	Advisor *
JUSSARA FARIAS FARDIN	Internal Examiner *
TEODIANO FREIRE BASTOS FILHO	Co advisor *

Summary: Some industrial processes have intrinsic cyclic disturbances. In these cases, it is natural to use Iterative Learning Control (ILC) or Repetitive Control (RC), or even combinations of these with other controllers, such as the Repetitive GPC (R-GPC), which integrates the RC with the Generalized Predictive Controller (GPC). One of the main weaknesses of these controllers is when the frequency of the periodic signal varies, as there is a gradual loss in its efficiency. Thus, in this Thesis, a fuzzy logic based technique is proposed to estimate the total number of samples contained in a periodic disturbance subject to small and unknown frequency variations. Therefore, the Adaptive Fuzzy ILC (AF-ILC) controller and the Adaptive Fuzzy Repetitive GPC (AFR-GPC) controller are proposed. In the AF-ILC, the proposed Fuzzy Estimator is applied to the ILC controller, while, in the AFR-GPC, the Fuzzy Estimator is applied to a structure composed of predictive controllers, allowing them to minimize periodic disturbances with frequency changes over time. The proposed controllers are adjusted with the system in closed loop (self-tuning adaptive controller) and are tested in computer simulations to compensate the bulging disturbance present in the continuous casting mold level control. In addition to the simulations, the controllers are tested in a didactic plant consisting of a resistive-capacitive circuit WHERE oscillatory disturbances are present.
Keywords: learning control, self-tunning adaptative controller, bulging, GPC, ILC, R-GPC, fuzzy, continuous casting, mold.

Access to document