Abstract
The use of Automation Robotics and Artificial Intelligence is one of the ways for the effective use of resources, since it guarantees a constant quality and contributes to the technological discipline and in essence a driving force behind the development of the economy and the society in general. The Ministry of Science, Technology and Environment of Cuba, created a National Program of Projects to promote these scientific disciplines and the application and generalization of their results. The work highlights the importance of the interdisciplinary nature of the Program and demonstrates the need, for the successful execution of projects, of the application of correct methologies as a guide to design and development work. It shows, through the converge of Control and Machine Learning Theory in the Iterative Learning Control, how addressing these issues together helps to promote them. The operation of deep reinforcement algorithms is explained and how their applications in robotic allows themto accelerate their development and make their behavior more flexible in an autonomous way. Finally, through three projects that are developed in the program, the relevance of the interdisciplinary conception of the program is demonstrated to carry out the doctoral research topics corresponding to each project.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cosca, V.: Informe voluntario de Cuba. Informe Nacional sobre la implementación de la AGENDA 2030 (2019)
PCC.: Documentos del 7mo. Congreso del Partido aprobados por el III Pleno del Comité Central del PCC (2017)
GOE.: Constitución de la República de Cuba, Gaceta Oficial No 5 Extraordinaria, Ministerio de Justicia, 10 abril 2019, 2019-GOC-2019-406-EX5 (2019)
Wang, W., Siau, K.: Artificial intelligence, machine learning, automation, robotics, future of work and future of humanity: A review and research agenda. J. Database Manage. 30(1). https://doi.org/10.4018/JDM.2019010104 (2019)
Bristow, D., Tharayil, M., Alleyne, A.: A survey of iterative learning control: A learning-based method for high-performance tracking control. IEEE Control Syst. Mag. 26(3), 96–114 (2006)
Freeman, C.T., Tong, D., Meadmore, K., Cai, Z., Rogers, E., Hughes, A.M., Burridge, J.H.: Phase-lead iterative learning control algorithms for functional electrical stimulation-based stroke rehabilitation. Proc. Inst. Mech. Eng. Part I-J. Syst. Control Eng. 225(I6), 850–859 (2011)
Chien, C., Tayebi, A.: Further results on adaptive iterative learning control of robot manipulators. Automatica 44(3), 830–837 (2008)
Bolder, J., Kleinendorst, S., Oomen, T.: Datadriven multivariable ILC: enhanced performance by eliminating L and Q filters. Int. J. Robust Nonlinear Control 28(12), 3728–3751 (2018)
Longman, R.W., Peng, Y., Kwon, T., Lus, H., Betti, R., Juang, J.: Adaptive inverse iterative learning control. J. Chin. Soc. Mech. Eng. 32(6), 493–506 (2011)
Kinoshita, K., Sogo, T., Adachi, N.: A relationship between iterative learning control using the gradient method and stable inversion. Trans. Soc. Instrum. Control Eng. 36(12), 1117–1124 (2000)
Amann, N., Owens, D., Rogers, E.: Iterative learning control using optimal feedback and feedforward actions. Int. J. Control 65(2), 277–293 (1996)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An introduction. MIT press (2018)
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al.: A general reinforcement learning algorithm that masters chess, shogi and Go through self-play. Science 362(6419), 1140–1144 (2018)
Der, A., Ditlevsen, O.: Aleatory or epistemic? Does it matter? Struct. Saf. 31(2), 105–112 (2009)
Kurmeoto, T., et al.: A Reinforcement Learning System with Multi-Layered Fuzzy Neural Network. The Institute of Industrial Applications Engineers, Japan. https://doi.org/10.12792/icisip2017.081 (2017)
Lage, A.: La Osadía de la Ciencia. Editorial Academia, Cuba, ISBN 978-959-270-398-8 (2018)
Núñez, J., Montalvo, L.F., Figaredo, F.: Pensar Ciencia, Tecnología y Sociedad. Compilación de artículos. Editorial Felix Varela, La Habana, Cuba. ISBN 978-959-07-1008-7 (2008)
Apostel, L.: Interdisciplinarity: Problems of Teaching and Research in Universities. OECD, Paris (1972)
Wilthagen, T., et al.: A Time for Interdisciplinarity. Tilburg University (2018)
LERU.: Interdisciplinarity and the 21st Century Research-Intensive University (2016). [Online] Available on: https://www.leru.org/files/Interdisciplinarity-and-the-21st-Century-Research-Intensive-University-Full-paper.pdf [Accessed: 21 May 2021]
Fiore, S.M.: Interdisciplinarity as teamwork. How the science of teams can inform team science. Small Group Res. 39(3), 251–277 (2008). [Online] Available on: https://doi.org/10.1177/1046496408317797 [Accessed: 12 May 2021]
Klein, J.T: Interdisciplinarity. History, theory & practice. Wayne State University Press, Detroit (1990)
Plasencia, A., et al.: Consideraciones para la elaboración de una metodología de proyecto de minería de datos para el control, la supervisión y el diagnóstico. Reporte de Investigación ICIMAF (2010)
Segovia, J.: Definition and instantiation of an integrated Data Mining Process Jornadas de Seguimiento de Proyectos. Universidad Politécnica de Madrid, Programa Nacional de Tecnologías Informáticas (2007)
Gal, A., et al.: A new vision over Agile project management in the internet of things era. Procedia. Soc. Behav. Sci. 238, 277–285 (2018)
Huber, S., et al.: Data mining methodology for engineering. Procedia CIRP 79 (2019).
Hillebrand, M., Lakhani, M., Dumitrescu, R.: A design methodology for deep reinforcement learning in autonomous systems. Procedia Manufacturing 52, 266–271 (2020)
Tomin, N., et al.: Deep reinforcement learning for energy microgrids management considering flexible energy sources. EPJ Web Conf. 217, 01–16 (2019). [Online] Available on: https://doi.org/10.1051/epjconf/201921701016 [Accessed: 21 May 2021]
Rivero, Y., et al.: Web application for the analysis of the frequency deviations of the electrical system on the Isle of Youth. X Taller Internacional De Cibernética Aplicada, La Habana, Cuba (2020)
Suárez, B., et al.: Conception of an IoT Reference Model for Flight Safety. IX Taller Internacional De Cibernética Aplicada, La Habana, Cuba (2019)
Plasencia, A., et al.: Methods and Algorithms for anomalies detection in the flight safety from the Flight Data Recoreder. VII Taller Internacional De Cibernética Aplicada, La Habana, Cuba (2017)
Plasencia, A., Shichkina, Y., et al.: Parkinson’s disease classification and medication adherence monitoring using smartphone-based gait assessment and deep reinforcement learning algorithm, in 14th International Symposium «Intelligent Systems», INTELS’20, Moscow, Russia (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Salgueiro, A.P., Mateo, I.D., García, A.G., Ripoll, Y.R., Blanco, I.S. (2022). Relevance of the National Program of Automatic, Robotics and Artificial Intelligence Projects in Applications. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-97269-1_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97268-4
Online ISBN: 978-3-030-97269-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)