Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Observer-based controller for unmanned aerial vehicles in reforestation applications(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05-28) Muñoz Sepúlveda, Gustavo Alberto; Lozoya, Rafael Camilo; emimmayorquin; Castañeda, Herman; School of Engineering and Sciences; Campus Monterrey; Abaunza, HernánThis study presents a breakthrough in unmanned aerial vehicle (UAV) technology, showcas- ing the efficacy of a custom-designed controller and observer in the context of reforestation initiatives. Through meticulous experimentation and analysis, the study demonstrates the ob- server’s adeptness in mitigating external disturbances, thereby enhancing the precision and stability of UAV operations. This technological advancement not only holds promise for diverse practical applications but also holds profound implications for environmental con- servation efforts, particularly reforestation. Reforestation plays a pivotal role in mitigating climate change, preserving biodiversity, and safeguarding ecosystems. By leveraging UAV technology, this study propels forward the efficacy and efficiency of reforestation endeavors, laying the groundwork for future innovations in UAV-based interventions. The findings affirm the viability of the proposed controller and observer framework, highlighting its potential to revolutionize environmental monitoring, conservation, and sustainable resource management practices. This abstract encapsulates the significance of integrating cutting-edge technology with environmental conservation efforts, underscoring the pivotal role of UAVs in fostering a more sustainable future.
- Greenhouse irrigation control based on reinforcement learning(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Padilla Nates, Juan Pablo; Lozoya, Rafael Camilo; emimmayorquin; Orona, Luis Miguel; Medina, Sergio Armando; School of Engineering and Sciences; Campus MonterreyAccording to the United Nations, the worldwide population will grow to a vast number of 9 billion people by 2050. As the population keeps increasing, meeting the demand for food has become a tough challenge. Therefore, it is necessary to research and develop strategies on agriculture in order to keep up with demand while maintaining sustainability. Precision Irrigation, a sub-branch of precision agriculture, has gained momentum in modern times. This is an area of study about saving water while maintaining and not impacting the growth of the plant. By manipulating the irrigation schedule, one can keep the soil moisture level at the optimum level without stressing the plant. The objective of this thesis is to explore the implementation and performance of advance closed-loop control systems using artificial intelligence, such as the actor-critic from reinforcement learning, in a controlled environment to optimize the water schedule. The results will be compared against another closed-loop controller, the On-Off control, and an open-loop controller, the Time-Based Control. Water consumption analysis revealed that closed-loop controllers achieved a 40\% reduction in water use, compared to the open-loop controller. Additionally, the actor-critic controller showed a better response at maintaining the soil moisture level closer to the MAD limit compared to the On-Off and Time-based controls.