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Prato, R., Méndez, M., Sulbaran, R.: Efecto de las técnicas de desacoplamiento en sistemas multivariables. Ingeniería y Desarrollo 35(2), 471–490 (2017)

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Teneda F.I., Villacís J.I., Espinosa E.G., Andaluz V.H.: Conversational agent for industrial processes through virtual environments. Advances in Intelligent Systems and Computing, vol 1368. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72654-6_21

Stankovski, S., Ostojić, G., Baranovski, I., Babić, M., Stanojević, M.: The impact of edge computing on industrial`automation. In: 19th International Symposium INFOTEH- JAHORINA (INFOTEH), pp. 1–4 (2020)

The authors would like to thank the Universidad de las Fuerzas Armadas ESPE; Universidad Tecnológica Indoamérica; SISAu Research Group, and the Research Group ARSI, for the support for the development of this work.

Porras, A.P., Solis, C.R., Andaluz, V.H., Sánchez, J.S., Naranjo, C.A.: Virtual training system for an industrial pasteurization process. In: Augmented Reality, Virtual Reality, and Computer Graphics, pp. 430–441. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-25999-0_35

Ahmed, K., Blech, O., Gregory, A., Schmidt, W.: Software defined networks in industrial automation. J. Sens. Actuator Netw., pp. 1–3 (2018)

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Quispe, M.A., Molina, M.C., Castillo, F., Andaluz, V.H. (2022). Advanced Controllers for Level Processes: Hardware-in-the-Loop Technique. In: Mesquita, A., Abreu, A., Carvalho, J.V. (eds) Perspectives and Trends in Education and Technology. Smart Innovation, Systems and Technologies, vol 256. Springer, Singapore. https://doi.org/10.1007/978-981-16-5063-5_59

Zamani, M., Rahmani, Z., Rezaie, B.: A novel framework of model predictive control for controlling a class of nonlinear system in the presence of uncertainty. J. Vib. Control (2020)

Camacho, O., Martinez, J., Patiño, K., Ulloa, F., Aro, K., Mejía, C.: Comparación de diferentes estrategias de control para sistemas con retardo. Rev. Cienc. Ing. 40(3), 303–310 (2019)

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Ortiz, J.S., Palacios-Navarro, G., Andaluz, V.H., Guevara, B.S.: Virtual reality-based framework to simulate control algorithms for robotic assistance and rehabilitation tasks through a standing wheelchair. Sensors 21(15), 5083 (2021). https://doi.org/10.3390/s21155083

Bosquea, G., Campob, L., Echanobeb, J.: Fuzzy systems, neural networks and neuro-fuzzy systems: a vision on their hardware implementation and platforms over two decades. Eng. Appl. Artif. Intell. 32, 283–331 (2015)

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Pros: Bright light, Multiple mode settings, Rechargeable capability, Impact-resistant, Good price

Sarkar, A., Mandal, J.: Secured wireless communication using fuzzy logic based high speed public-key cryptography (FLHSPKC). (IJACSA) Int. J. Adv. Comput. Sci. Appl. 3(10), 137

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Pros: Powerful and bright light, Long battery life, Portability and ease of use, Reasonable price, Comfortable fit

Chabni, F., Taleb, R., Benbouali, A., Bouthiba, M.: The application of fuzzy control in water tank level using Arduino. Int. J. Adv. Comput. Sci. Appl. 7(4), 261–265 (2016)

Alexandre, G., Inácio, J., Fernandes, T., De Sousa, F.: Comparativo entre controladores PID, PID Fuzzy e Preditivo avaliados a una planta industrial de nível e temperatura. Congresso Técnico Científico da Engenharia e da Agronomia CONTECC’ , nº Paraná, Brasil (2016)

Bradon, E.: Geographies of the labour process: automation and the spatiality of mining. Work, Employ. Soc. 30(6), 932–948 (2016)

Rodrígueza, T.: Modelado y control de turbinas eólicas marinas flotantes. Rev. Iberoamericana de Automática e Informática ind. 16(4), 381–390 (2018)

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Ortiz, J.S., et al.: Virtual training for industrial automation processes through pneumatic controls. In: Augmented Reality, Virtual Reality, and Computer Graphics, pp. 516–532. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95282-6_37

Soto, G.: Control de procesos industriales con minimización del consumo energetico. Escuela de Ingeniería Eléctrica y Mecánica., Medellín-Colombia (2019)

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One of the most important variables in the automation process is the liquid level; therefore, this work presents the design of advanced control algorithms such as model-based predictive control and fuzzy for a level process with multiple inputs and multiple outputs. These will be implemented under the hardware-in-the-loop technique. Where each controller is distributed in embedded devices (Raspberry Pi 4), in turn the mathematical model that presents the dynamics of the process is located in a main computer, in which a virtual environment is developed to contribute with a more realistic representation of the process. This is done in order to evaluate the behavior of the process, as well as to assess the performance of each control algorithm implemented. Finally, the experimental results obtained are presented, based on previously established parameters, which allow the identification of the controller with the best efficiency for process optimization.

Zambrano, J.I. et al.: Multi-user virtual system for training of the production and bottling process of soft drinks. In: 2020 15th Iberian Conference on Information Systems and Technologies (CISTI). IEEE (2020)

Rymarczyk, T., Kłosowski, G.: Innovative methods of neural reconstruction for tomographic images in maintenance of tank industrial reactors. Eksploatacja i Niezawodnosc – Maintenance and Reliability 21(2), 261–263 (2019)

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Bahrin, M., Othman, M., Azli, N., Talib, M.: Industry 4.0: a review on industrial automation and robotic. Jurnal Teknologi, pp. 137–139 (2016)

Andaluz, G.M., et al.: Modeling dynamic of the human-wheelchair system applied to NMPC. In: Lecture Notes in Computer Science, vol 9835. Springer, Cham. (2016). https://doi.org/10.1007/978-3-319-43518-3_18

Coello, L., Casas, L., Laura, Pérez, O., Caballero, Y.: Redes neuronales artificiales en la producción de tecnología. Rev. Acad. Virtualidad 8(1), 15 (2016)