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All Journal Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems
Marco Antonio Márquez-Vera
Polytechnic University of Pachuca
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Interval Type-2 Fuzzy Observers Applied in Biodegradation Marco Antonio Márquez-Vera; Andrea Rodríguez-Romero; Carlos Antonio Márquez-Vera; Karla Refugio Ramos-Téllez
International Journal of Robotics and Control Systems Vol 1, No 2 (2021)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v1i2.344

Abstract

There exist processes difficult to control because of the lack of inline sensors, as occurs in biotechnology engineering. Commonly the sensor is expensive, damaged, or even they do not exist.  It is important to build an observer to have an approximation of the process output to have a closed-loop control. The biotechnological processes are nonlinear, thus in this work is proposed a fuzzy observer to endure nonlinearities. To improve the results reported in the literature, type-2 fuzzy logic was used to justify the membership functions used. The observer's gains were computed via LMIs to guarantee the observer's stability.  To facilitate the fuzzy inference computation, interval type-2 fuzzy sets were implemented. The results obtained with the interval type-2 fuzzy observer were compared with a similar technique that uses a fuzzy sliding mode observer; this new approach gives better results obtaining an error 60% lower than the obtained with the other technique. They were designed three observers that work ensemble via a fuzzy relation. The best approximation was to estimate the intermediate concentration. It is important to know this variable because this sub-product was also toxic. It was concluding that by using the oxygen concentration and the liquid volume inside the reactor, the other concentrations were estimated. Finally, this result helps to design a fuzzy controller by using the estimated state. Using this approach, the estimation errors for the phenol and biomass concentrations were 49.26% and 21.27% lower than by using sliding modes.
Adaptive threshold PCA for fault detection and isolation Marco Antonio Márquez-Vera; Omar López-Ortega; Luis Enrique Ramos-Velasco; Andrea Rodríguez-Romero; Julio César Ramos-Fernández; Jorge Adalberto Hernández-Salazar
Journal of Robotics and Control (JRC) Vol 2, No 3 (2021): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.2364

Abstract

Fault diagnosis is an important issue in industrial processes to avoid economic losses, process damage, and to guarantee safe working conditions for the operators. For high scale industrial processes the data-driven based methods are the best solution for process monitoring and fault diagnosis. Thus, in this paper, the principal component analysis is shown to detect and isolate faults. Also, a dynamic threshold is implemented to avoid false alarms because incipient faults are difficult to be detected. As a case of study, the Tennessee Eastman (TE) process is used to apply this strategy because the interaction among five units with internal control loops makes difficult to have an approached model. As results are shown the detection times, for cases where were analyzed incipient faults, the time required for fault detection must be improved, in this work, an adaptive threshold was used to reduce the false alarms but it also increases the detection times. It was concluded that the Q chart gave a better result for fault detection; the isolation times were similar to the detection ones. Two incipient faults could not be detected, the fault detection rate was similar to the shown in literature, but the detection times were better in 35% of the cases, unfortunately for four faults the detection times were bigger than the reported in other papers. It is proposed to help this method with independent component analysis due it is not guaranteed to have a Gaussian distribution in the samples.
Co-Authors Andrea Rodríguez-Romero Carlos Antonio Márquez-Vera Jorge Adalberto Hernández-Salazar Julio César Ramos-Fernández Karla Refugio Ramos-Téllez Luis Enrique Ramos-Velasco Omar López-Ortega