Modeling of Energy Demand of a High-Tech Greenhouse in Warm Climate Based on Bayesian Networks

This work analyzes energy demand in a High-Tech greenhouse and its characterization, with the objective of building and evaluating classification models based on Bayesian networks. The utility of these models resides in their capacity of perceiving relations among variables in the greenhouse by identifying probabilistic dependences between them and their ability to make predictions without the need of observing all the variables present in the model. In this way they provide a useful tool for an energetic control system design. In this paper the acquisition data system used in order to collect the dataset studied is described. The energy demand distribution is analyzed and different discretization techniques are applied to reduce its dimensionality, paying particular attention to their impact on the classification model’s performance. A comparison between the different classification models applied is performed

IoT Based Smart Greenhouse Monitoring System With Fuzzy Logic

This thesis presents a smart greenhouse monitoring system based on fuzzy logic for effective control of temperature, irrigation, and lighting conditions. Traditional greenhouse monitoring practices often require manual intervention and monitoring of soil moisture, temperature, humidity, and light intensity, leading to challenges in maintaining optimal plant growth. To overcome these limitations, an automated monitoring system is developed using fuzzy logic principles. The proposed smart greenhouse monitoring system autonomously monitors the plant's growing conditions. It employs fuzzy logic algorithms to continuously assess and adjust environmental factors. For example, when the temperature exceeds predefined thresholds, the system activates the ventilation system to regulate and maintain the desired temperature range for optimal plant development. The system further addresses the issue of manual watering by monitoring soil moisture levels. When the soil moisture falls below a specified threshold, the system automatically activates the water pump, providing adequate irrigation to ensure optimal soil moisture for plant growth. Additionally, the system incorporates an intelligent lighting system to optimize light intensity. It continuously monitors the light levels within the greenhouse and activates supplementary grow lights when the intensity decreases below the desired range, thereby promoting consistent and adequate light exposure for plants, even during nighttime hours. By employing artificial intelligence techniques and fuzzy logic control, the smart greenhouse monitoring system provides an automated solution to optimize temperature, irrigation, and lighting conditions. Through its autonomous actuation of the water pump and ventilation system, the system reduces manual intervention and ensures that plants receive optimal growing conditions. The results of this study indicate improved plant growth, enhanced crop yield, and reduced labor efforts for greenhouse cultivation. In conclusion, this thesis contributes to the field of smart greenhouse technology by presenting a comprehensive monitoring system that utilizes fuzzy logic control. The system effectively monitors and regulates temperature, irrigation, and lighting conditions, alleviating the need for manual intervention, and enabling optimized plant growth. The findings highlight the efficacy of the proposed system in providing efficient and automated monitoring for greenhouse environments, fostering improved cultivation practices, and maximizing crop yields

Modelling language for biology with applications

Understanding the links between biological processes at multiple scales, from molecular regulation to populations and evolution along with their interactions with the environment, is a major challenge in understanding life. Apart from understanding this is also becoming important in attempts to engineer traits, for example in crops, starting from genetics or from genomes and at different environmental conditions (genotype x environment → trait). As systems become more complex relying on intuition alone is not enough and formal modelling becomes necessary for integrating data across different processes and allowing us to test hypotheses. The more complex the systems become, however, the harder the modelling process becomes and the harder the models become to read and write. In particular intuitive formalisms like Chemical Reaction Networks are not powerful enough to express ideas at higher levels, for example dynamic environments, dynamic state spaces, and abstraction relations between different parts of the model. Other formalisms are more powerful (for example general purpose programming languages) but they lack the readability of more domain specific approaches. The first contribution of this thesis is a modelling language with stochastic semantics, Chromar, that extends the visually intuitive formalisms of reactions, in which simple objects, called agents, are extended with attributes. Dynamics are given as stochastic rules that can operate on the level of agents (removing/adding) or at the level of attributes (updating their values). Chromar further allows the seamless integration of time and state functions with the normal set of expressions – crucial in multi-scale plant models for describing the changing environment and abstractions between scales. This leads to models that are both formal enough for simulations and easy to read and write. The second contribution of this thesis is a whole-life-cycle multi-model of the growth and reproduction of Arabidopsis Thaliana, FM-life, expressed in a declarative way in Chromar. It combines phenology models from ecology to time developmental processes and physical development, which allows to scale to the population and address ecological questions at different genotype x environment scenarios. This is a step in the path for mechanistic links between genotype x environment and higher-level crop traits. Finally, I show a way of using optimal control techniques to engineer traits of plants by controlling their growth environmental conditions. In particular we explore (i) a direct problem where the control is temperature – assuming homogeneous growth conditions and (ii) indirect problem where the control is the position of the plants – assuming inhomogeneous growth conditions

Improving Retrievals of Crop Vegetation Parameters from Remote Sensing Data

Agricultural systems are difficult to model because crop growth is driven by the strongly nonlinear interaction of Genotype x Environment x Management (G x E x M) factors. Due to the nonlinearity in the interaction of these factors, the amount of data necessary to develop and utilize models to accurately predict the performance of agricultural systems at an operational scale is large. Satellite remote sensing provides the potential to vastly increase the amount of data available for modelling agricultural systems as a result of its high revisit time and spatial coverage. Unfortunately, there have been significant difficulties in deploying remote sensing for many agricultural modelling applications because of the uncertainty involved in the retrievals. In this dissertation, we show that collecting farmer-provided agro-managment information has the potential to reduce the uncertainty in the retrieval products obtained from remote sensing observations. Specifically, both field-scale and regional-scale analysis are used to show that secondary factor variability is a very significant cause of uncertainty in both crop growth modelling and agricultural remote sensing that needs to be addressed through increased data collection. In order to address this need for increased data availability, a method is developed that allows geolocated crop growth model simulations to be used to train satellite-based crop state variable retrievals, which is then validated at regional scale. The method developed provides a general robust methodology to create a large-scale platform that would allow farmers to share data with government agencies and universities to improve crop state variable retrievals and crop growth modelling and provide farmers, government, industry, and researchers with insights and predictive capability into crop growth at both field and regional scales

Predictive Control of Cyber-Physical Systems

[EN] Predictive control encompasses a family of controllers that continually replan the system inputs during a certain time horizon to optimize their expected evolution according to a given criterion. This methodology has among its current challenges the adaptation to the paradigm of the so-called cyber-physical systems, which are composed of computers, sensors, actuators and physical entities of various kinds, including robots and even human beings who exchange information to control physical processes. This tutorial introduces the core concepts for the application of predictive control to cyber-physical systems by reviewing a series of examples that exploit the versatility of this design framework so as to solve the challenges presented by 21st century applications.[ES] El control predictivo engloba a una familia de controladores que replanifican continuamente las entradas del sistema durante un cierto horizonte temporal con el fin de optimizar su evolución esperada conforme a un criterio dado. Esta metodología tiene entre sus retos actuales la adaptación al paradigma de los llamados sistemas ciberfísicos, que están compuestos por computadoras, sensores, actuadores y entidades físicas de diversa índole entre las que se incluyen robots e incluso seres humanos que intercambian información con el objetivo de controlar procesos físicos. Este tutorial presenta los conceptos centrales de la integración del control predictivo en este tipo de sistemas mediante el repaso a una serie de ejemplos que explotan la versatilidad de este marco de diseño de controladores para resolver los desafíos que presentan las aplicaciones del siglo XXI.Este trabajo ha sido financiado por el European Research Council (ERC) en el marco del programa de investigación e innovación Horizonte 2020 de la Unión Europea [OCONTSOLAR, ref. 789051], por el Ministerio de Economía con el proyecto C3PO [ref. DPI2017-86918-R], por el Ministerio de Ciencia, Innovación y Universidades en el marco del programa de Formación de Profesorado Universitario (FPU) [FPU17/02653 y FPU18/04476] y por la Consejería Transformación Económica, Industria, Conocimiento y Universidades en el marco del programa de Ayudas a los agentes públicos del Sistema Andaluz del Conocimiento, para la realización de proyectos de I+D+i (PAIDI 2020) [Ampliación Aquacollect, ref. P18-HO-4713].Maestre, JM.; Chanfreut, P.; García Martín, J.; Masero, E.; Inoue, M.; Camacho, EF. (2021). Control predictivo de sistemas ciberfísicos. Revista Iberoamericana de Automática e Informática industrial. 19(1):1-12. https://doi.org/10.4995/riai.2021.15771OJS112191Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., Cayirci, E., 2002. Wireless Sensor Networks: A Survey. Computer Networks 38 (4), 393-422.Angulo, A., Nachtmann, H., Waller, M. A., 2004. Supply Chain Information Sharing in a Vendor Managed Inventory Partnership. Journal of Business Logistics 25 (1), 101-120.Badal, F. R., Das, P., Sarker, S. K., Das, S. K., 2019. A Survey on Control Issues in Renewable Energy Integration and Microgrid. Protection and Control of Modern Power Systems 4 (1), 1-27.Bordons, C., García Torres, F., Valverde, L., 2015. Gestión óptima de la energía en microrredes con generación renovable. Revista Iberoamericana de Automática e Informática Industrial 12 (2), 117-132.Camacho, E. F., Berenguel, M., 2012. Control of Solar Energy Systems. IFAC Proceedings Volumes 45 (15), 848-855.Camacho, E. F., Berenguel, M., Rubio, F. R., 1997. Advanced Control of Solar Plants. Springer Berlin.Camacho, E. F., Bordons, C., 1999. Model Predictive Control. Springer, Berlin Heidelberg.Carrasco, J. M., Franquelo, L. G., Bialasiewicz, J. T., Galván, E., PortilloGuisado, R. C., Prats, M. M., Le'on, J. I., Moreno-Alfonso, N., 2006. Power-Electronic Systems for the Grid Integration of Renewable Energy Sources: A Survey. IEEE Transactions on Industrial Electronics 53 (4), 1002-1016.Castilla, M., Álvarez, J. D., Berenguel, M., Pérez, M., Rodríguez, F., Guzmán, J. L., 2010. Técnicas de control del confort en edificios. Revista Iberoamericana de Automática e Informática Industrial RIAI 7 (3), 5-24.Chanfreut, P., Maestre, J. M., Camacho, E. F., 2021. A Survey on Clustering Methods for Distributed and Networked Control Systems. Annual Reviews in Control.DOI: https://doi.org/10.1016/j.arcontrol.2021.08.002Conde, G., Quijano, N., Ocampo-Martínez, C., 2021. Modeling and Control in Open-Channel Irrigation Systems: A Review. Annual Reviews in Control.DOI: https://doi.org/10.1016/j.arcontrol.2021.01.003Dey, N., Ashour, A. S., Shi, F., Fong, S. J., Tavares, J. M., 2018. Medical Cyber-Physical Systems: A Survey. Journal of medical systems 42 (4), 1-13.Fele, F., Maestre, J. M., Camacho, E. F., 2017. Coalitional Control: Cooperative Game Theory and Control. IEEE Control Systems Magazine 37 (1), 53-69.Fele, F., Maestre, J. M., Hashemy, M., Mu˜noz de la Pe˜na, D., Camacho, E. F., 2014. Coalitional Model Predictive Control of an Irrigation Canal. Journal of Process Control 24 (4), 314-325.DOI: https://doi.org/10.1016/j.jprocont.2014.02.005Fernández García, I., Chanfreut, P., Jurado, I., Maestre, J. M., 2021. A Data-Based Model Predictive Decision Support System for Inventory Management in Hospitals. IEEE Journal of Biomedical and Health Informatics 25 (6), 2227-2236.DOI: https://doi.org/10.1109/JBHI.2020.3039692Frejo, J. R. D., Camacho, E. F., 2020. Centralized and Distributed Model Predictive Control for the Maximization of the Thermal Power of Solar Parabolic-Trough Plants. Solar Energy 204, 190-199.Gil, J., Roca, L., Berenguel, M., 2020. Modelado y control automático en destilación por membranas solar: fundamentos y propuestas para su desarrollo tecnológico. Revista Iberoamericana de Automática e Informática industrial 17 (4), 329-343.Guzmán, J., Acién, F., Berenguel, M., 2020. Modelado y control de la producción de microalgas en fotobiorreactores industriales. Revista Iberoamericana de Automática e Informática industrial 18 (1), 1-18.Hara, K., Inoue, M., Maestre, J. M., 2020. Data-Driven Human Modeling: Quantifying Personal Tendency Toward Laziness. IEEE Control Systems Letters 5 (4), 1219-1224.DOI: https://doi.org/10.1109/LCSYS.2020.3023337Hatanaka, T., Chopra, N., Fujita, M., 2015. Passivity-Based Bilateral Human-Swarm-Interactions for Cooperative Robotic Networks and Human Passivity Analysis. In: 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, pp. 1033-1039.DOI: https://doi.org/10.1109/CDC.2015.7402008Inoue, M., Gupta, V., 2019. "Weak" Control for Human-in-the-Loop Systems. IEEE Control Systems Letters 3 (2), 440-445.Jain, A., Chakrabortty, A., Biyik, E., 2018. Distributed Wide-Area Control of Power System Oscillations under Communication and Actuation Constraints. Control Engineering Practice 74, 132-143.Jianjun, S., Xu, W., Jizhen, G., Yangzhou, C., 2013. The Analysis of Traffic Control Cyber-Physical Systems. Procedia-Social and Behavioral Sciences 96, 2487-2496.Jurado, I., Maestre, J. M., Velarde, P., Ocampo-Martínez, C., Fernández, I., Tejera, B. I., del Prado, J. R., 2016. Stock Management in Hospital Pharmacy Using Chance-Constrained Model Predictive Control. Computers in Biology and Medicine 72, 248-255.Khamis, A., Hussein, A., Elmogy, A., 2015. Multi-robot Task Allocation: A Review of the State-of-the-art. In: Cooperative Robots and Sensor Networks. Springer, pp. 31-51.Koubâa, A., Khelil, A., 2014. Cooperative Robots and Sensor Networks. Springer.La Bella, A., Klaus, P., Ferrari-Trecate, G., Scattolini, R., 2021. Supervised Model Predictive Control of Large-Scale Electricity Networks via Clustering Methods. Optimal Control Applications and Methods.Lee, J., Bagheri, B., Kao, H.-A., 2015. A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems. Manufacturing letters 3, 18-23.Liang, X., 2016. Emerging Power Quality Challenges due to Integration of Renewable Energy Sources. IEEE Transactions on Industry Applications 53 (2), 855-866.Maestre, J. M., Fernández, M. I., Jurado, I., 2018. An application of economic model predictive control to inventory management in hospitals. Control Engineering Practice 71, 120-128.Maestre, J. M., Negenborn, R. R. (Eds.), 2014. Distributed Model Predictive Control Made Easy. Vol. 69 of Intelligent Systems, Control and Automation: Science and Engineering. Springer.Maestre, J. M., van Overloop, P. J., Hashemy, M., Sadowska, A., Camacho, E. F., 2014. Human in the Loop Model Predictive Control: An Irrigation Canal Case Study. In: 53rd IEEE Conference on Decision and Control. IEEE, pp. 4881-4886.DOI: https://doi.org/10.1109/CDC.2014.7040151Maestre, J. M., Zafra Cabeza, A., Fernández Garcáa, M. I., Isla Tejera, B., del Prado, J. R., Camacho, E. F., 2013. Control predictivo aplicado a la gestión de stocks en farmacia hospitalaria: un enfoque orientado a la minimización del riesgo. Revista Iberoamericana de Automática e Informática industrial 10 (2), 149-158.DOI: https://doi.org/10.1016/j.riai.2013.03.005Martín, J. G., Frejo, J. R. D., García, R. A., Camacho, E. F., 2021a. Multi-Robot Task Allocation Problem with Multiple Non-Linear Criteria Using Branch and Bound and Genetic Algorithms. Intelligent Service Robotics.Martín, J. G., García, R. A., Camacho, E. F., 2021b. Event-MILP-Based Task Allocation for Heterogeneous Robotic Sensor Network for Thermosolar Plants. Journal of Intelligent & Robotic Systems 102 (1), 1.DOI: https://doi.org/10.1007/s10846-021-01346-wMartín, J. G., Maestre, J. M., Camacho, E. F., 2021c. Spatial Irradiance Estimation in a Thermosolar Power Plant by a Mobile Robot Sensor Network. Solar Energy 220, 735-744.DOI: https://doi.org/10.1016/j.solener.2021.03.038Martínez, O. E. B., 2004. Evolución de una idea: de la cibernética a la cibercultura la filosofía griega y la cibernética. Cuadernos de Filosofía Latinoamericana 25 (91), 1.Masero, E., Frejo, J. R. D., Maestre, J. M., Camacho, E. F., 2020. A Light Clustering Model Predictive Control Approach to Maximize Thermal Power in Solar Parabolic-Trough Plants. Solar Energy 214, 531-541.Masero, E., Maestre, J. M., Camacho, E. F., 2022. Market-based clustering of model predictive controllers for maximizing collected energy by parabolictrough solar collector fields. Applied Energy 306, 117936.Nagahara, M., Quevedo, D. E., Nesi'c, D., 2015. Maximum Hands-Off Control: A Paradigm of Control Effort Minimization. IEEE Transactions on Automatic Control 61 (3), 735-747.DOI: https://doi.org/10.1109/TAC.2015.2452831Negenborn, R. R., Maestre, J. M., 2014. Distributed Model Predictive Control: An Overview and Roadmap of Future Research Opportunities. IEEE Control Systems Magazine 34 (4), 87-97.Negenborn, R. R., van Overloop, P. J., Keviczky, T., De Schutter, B., 2009. Distributed Model Predictive Control of Irrigation Canals. Network and Heterogeneus Media 4 (2), 359-380.Priess, M. C., Conway, R., Choi, J., Popovich, J. M., Radcliffe, C., 2014. Solutions to the Inverse LQR Problem with Application to Biological Systems Analysis. IEEE Transactions on Control Systems Technology 23 (2), 770-777.Protte, M., Fahr, R., Quevedo, D. E., 2020. Behavioral Economics for Humanin-the-Loop Control Systems Design: Overconfidence and the Hot Hand Fallacy. IEEE Control Systems Magazine 40 (6), 57-76.DOI: https://doi.org/10.1109/MCS.2020.3019723Qin, S. J., Badgwell, T. A., 2003. A Survey of Industrial Model Predictive Control Technology. Control Engineering Practice 11 (7), 733-764.DOI: https://doi.org/10.1016/S0967-0661(02)00186-7Ramadan, A., Choi, J., Radcliffe, C. J., 2016. Inferring Human Subject Motor Control Intent Using Inverse MPC. In: 2016 American Control Conference (ACC). IEEE, pp. 5791-5796.Ramadan, A., Choi, J., Radcliffe, C. J., Popovich, J. M., Reeves, N. P., 2018. Inferring Control Intent During Seated Balance Using Inverse Model Predictive Control. IEEE Robotics and Automation Letters 4 (2), 224-230.Ramírez-Arias, A., Rodríguez, F., Guzmán, J. L., Berenguel, M., 2012. Multiobjective Hierarchical Control Architecture for Greenhouse Crop Growth. Automatica 48 (3), 490-498.Sadowska, A., van Overloop, P. J., Maestre, J. M., De Schutter, B., 2015. Human-in-the-Loop Control of an Irrigation Canal Using Time Instant Optimization Model Predictive Control. In: Proceedings of the 2015 European Control Conference (ECC). IEEE, pp. 3274-3279.DOI: https://doi.org/10.1109/ECC.2015.7331039Sánchez, A. J., Gallego, A. J., Escaño, J. M., Camacho, E. F., 2018. Temperature Homogenization of a Solar Trough Field for Performance Improvement. Solar Energy 165, 1-9.Schmidt, M., Åhlund, C., 2018. Smart Buildings as Cyber-Physical Systems: Data-Driven Predictive Control Strategies for Energy Efficiency. Renewable and Sustainable Energy Reviews 90, 742-756.Shibasaki, S., Inoue, M., Arahata, M., Gupta, V., 2020. Weak Control Approach to Consumer-Preferred Energy Management. IFAC-PapersOnLine 53 (2), 17083-17088.Sun, C., Puig, V., Cembrano, G., 2020. Real-Time Control of Urban Water Cycle under Cyber-Physical Systems Framework. Water 12 (2), 406.Van Overloop, P. J., Maestre, J. M., Sadowska, A. D., Camacho, E. F., De Schutter, B., 2015. Human-in-the-Loop Model Predictive Control of an Irrigation Canal [Applications of Control]. IEEE Control Systems Magazine 35 (4),19-29.DOI: https://doi.org/10.1109/MCS.2015.2427040Wang, G., Gunasekaran, A., Ngai, E. W., Papadopoulos, T., 2016. Big Data Analytics in Logistics and Supply Chain Management: Certain Investigations for Research and Applications. International Journal of Production Economics 176, 98-110.Wiener, N., 1948. Cybernetics or Control and Communication in the Animal and the Machine.Wolf, W., 2009. Cyber-physical systems. IEEE Annals of the History of Computing 42 (03), 88-89.Wu, F.-J., Kao, Y.-F., Tseng, Y.-C., 2011. From Wireless Sensor Networks Towards Cyber Physical Systems. Pervasive and Mobile computing 7 (4), 397-413.Zafra-Cabeza, A., Maestre, J. M., Ridao, M. A., Camacho, E. F., Sánchez, L.,2011. A Hierarchical Distributed Model Predictive Control Approach in Irrigation Canals: A Risk Mitigation Perspective. Journal of Process Control 21 (5), 787-799.DOI: https://doi.org/10.1016/j.jprocont.2010.12.012Zhong, R. Y., Newman, S. T., Huang, G. Q., Lan, S., 2016. Big Data for Supply Chain Management in the Service and Manufacturing Sectors: Challenges, Opportunities, and Future Perspectives. Computers & Industrial Engineering 101, 572-591