Implementation of a Distributed Control System

Cabrera, Stefano Ayala; RojasnMoreno, Arturo

doi:10.1109/intercon55795.2022.9870116

Cited by 1 publication

(2 citation statements)

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“…In the analysis of fuzzy systems, according to the literature [31], they are by themselves magnificent for the control of complex and highly non-linear systems since they do not need the mathematical model of the system to work. It is also possible to make use of the experience of the workers to initialize and parameterize the control system through of the process of fuzzification-fuzzy rules-defuzzification in such a way that it is used in control systems distributed by leading companies in the area of control and automation [32]. To improve the performance of these controllers, hybridizations were made with neural networks, giving them the ability to learn as time passes, and the system is subject to different operating conditions, including disturbances [33].…”

Section: Neuro-fuzzy Controllermentioning

confidence: 99%

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Temperature Control of a Chemical Reactor Based on Neuro-Fuzzy Tuned with a Metaheuristic Technique to Improve Biodiesel Production

Maya-Rodriguez,

Carvajal-Mariscal,

López-Muñoz

et al. 2023

Energies

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This work deals with the problem of choosing a controller for the production of biodiesel from the transesterification process through temperature control of the chemical reactor, from the point of view of automatic control, by considering such aspects as the performance metrics based on the error and the energy used by the controller, as well as the evaluation of the control system before disturbances. In addition, an improvement method is proposed via a neuro-fuzzy controller tuned with a metaheuristic algorithm to increase the efficiency of the chemical reaction in the reactor. A clear improvement is shown in the minimization of the integral of time multiplied squared error criterion (ITAE) performance index with respect to the proposed method (8.1657 ×104) in relation to the PID controller (7.8770 ×107). Moreover, the integral of the total control variation (TVU) performance index is also shown to evaluate the power used by the neuro-fuzzy controller (25.7697), while the PID controller obtains an index of (32.0287); this metric is especially relevant because it is related to the functional requirements of the system since it quantifies the variations of the control signal.

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Section: Neuro-fuzzy Controllermentioning

confidence: 99%

“…Lastly, at the sixth step, the individuals are compared using (32), and the best is kept for the next cycle, while the other one is discarded:…”

Section: Tuning Nfc Parameters With Dementioning

confidence: 99%