Ponencias (Ingeniería de Sistemas y Automática)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11344

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Nonlinear MPC for Thermal Balancing of the TCP-100 Parabolic Trough Collectors Solar Plant
(Institute of Electrical and Electronics Engineers (IEEE), 2023-07) Gallego Len, Antonio Javier; Yebra, Luis J.; Sánchez del Pozo Fernández, Adolfo Juan; Escaño González, Juan Manuel; Camacho, Eduardo F.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Agencia Estatal de Investigación. España; Agencia Estatal de Investigación. España; Agencia Estatal de Investigación. España; Agencia Estatal de Investigación. España; Agencia Estatal de Investigación. España; Agencia Estatal de Investigación. España; Universidad de Sevilla. TEP116: Automatica y Robotica Industrial
The efficiency of the solar plants is conditioned by the control strategies applied in their operation. In this paper, an application of a Model Predictive Controller based on nonlinear models of the TCP-100 parabolic trough collector solar plant is presented as one example of the advanced control techniques that can contribute to enhance the efficiency of this type of plants. Both types of nonlinear models of the TCP-100 facility are applied for this application: lumped and distributed parameter ones. The objective of the proposed control strategy is to face a problem that arises in current commercial solar trough plants, with hundreds of loops, where in practice each of those loops get a different outlet temperature of the heat transfer fluid. These temperature differences might cause inefficiency in the operation and/or irreversible damages by overheating, if not properly controlled. The presented control strategy computes the set-points of the control valves of each of the loops to achieve a good thermal balance of the solar plant. The proposed strategy implements also a heuristic based algorithm when strong transients are affecting the field. The simulation results show that the application of the proposed control technique balances the outlet temperatures of the loops, protecting the TCP-100 facility from damages and increasing its efficiency in the operation.
Aprendizaje de la señal de control para un electrolizador tipo PEM
(Universidade da Coruña, 2024-07) Becerra-Mora, Yeyson; Chicaiza Salazar, William David; Barros-Queiroz, Juliana Sobral; Acosta Rodríguez, José Ángel; Escaño González, Juan Manuel; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Sevilla
Este trabajo propone el aprendizaje de una señal de control para un electrolizador tipo PEM, considerando variables de entrada y salida del sistema como la corriente eléctrica suministrada por una fuente fotovoltaica, la temperatura ambiente y la energía disipada por el caudal del sistema de refrigeración acoplado al electrolizador. Los datos de corriente eléctrica y temperatura ambiente son medidos por el sistema. Por otro lado, los datos de energía disipada por el caudal del sistema de refrigeración fueron obtenidos a partir de simulaciones de un controlador de temperatura que utiliza la técnica de Control Predictivo Basado en Modelo (MPC). Se proponen dos técnicas de aprendizaje: Gaussian Mixture Model (GMM) / Gaussian Mixture Regression (GMR) y un modelo NeuroFuzzy (NF). Los resultados de las simulaciones demuestran que las técnicas de aprendizaje modelaron con precisión el comportamiento de la señal de control en el electrolizador.
Plataforma para experimentación de controladores basada en sistema aeropropulsado de cuatro rotores coplanarios
(Editorial de la Universidad de Sevilla, 2011-09) García Rodríguez, Ramón Andrés; González Villagómez, Jesús María; Raffo, Guilherme V.; Ortega Linares, Manuel Gil; Vargas Villanueva, Manuel; Rodríguez Rubio, Francisco; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática
Este trabajo presenta el diseño e implementación de la arquitectura de un helicóptero a pequeña escala de tipo Quadrotor. Asímismo se presentan resultados de simulación de controladores robustos de estabilización en posición y orientación con realimentación de información visual.
Estimación garantista de la posición de un quadrotor con GPS
(UPC ; CEA, 2013) García Rodríguez, Ramón Andrés; Ortega Linares, Manuel Gil; Rodríguez Rubio, Francisco; Raffo, Guilherme V.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática
La aplicación de dos métodos de estimación garantistas sobre un quadrotor se presentan en este artículo. Se tiene un modelo del sistema discretizado en el que se calcula su posición cada T segundos, mientras que la medida del GPS es cada t sincro segundos. Se aplicaron los algoritmos garantistas para contemplar las posibles posiciones en las que se puede encontrar el quadrotor y posteriormente, gracias a la medida del sensor corregir y mejorar la estimación realizada.
ALADIN-Based Distributed Model Predictive Control with Dynamic Partitioning: An Application to Solar Parabolic Trough Plants
(IEEE (Institute of Electrical and Electronics Engineers), 2024-01) Chanfreut Palacio, Paula; Maestre Torreblanca, José María; Krishnamoorthy, D.; Camacho, Eduardo F.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Union (UE); Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Sevilla. TEP-116: Automática y robótica industrial
This article presents a distributed model predictive controller with time-varying partitioning based on the augmented Lagrangian alternating direction inexact Newton method (ALADIN). In particular, we address the problem of controlling the temperature of a heat transfer fluid (HTF) in a set of loops of solar parabolic collectors by adjusting its flow rate. The control problem involves a nonlinear prediction model, decoupled inequality constraints, and coupled affine constraints on the system inputs. The application of ALADIN to address such a problem is combined with a dynamic clustering-based partitioning approach that aims at reducing, with min-imum performance losses, the number of variables to be coordinated. Numerical results on a 10-loop plant are presented.
A Coalitional MPC Approach to Control of Collaborative Vehicle Platoons
(Elsevier, 2023) Chanfreut Palacio, Paula; Keijzer, Twan; Maestre Torreblanca, José María; Ferrari, Riccardo M.G.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Universidad de Sevilla. TEP116: Automática y Robótica Industrial
This work presents a coalitional model predictive controller for collaborative vehicle platoons. The overall system is modeled as a string of locally controlled vehicles that can share data through a wireless communication network. The vehicles can dynamically form disjoint groups that coordinate their actions, i.e., the so-called coalitions. The control goals are keeping a desired reference distance between all vehicles while allowing for occasional switching of the communication topology. Likewise, the presented controller promotes a string-stable evolution of the platoon system. Numerical results are provided to illustrate the proposed approach.
Neurofuzzy Defocusing strategy for a Fresnel collector
(Elsevier, 2023) Brandao, Adriano S.M.; Chicaiza Salazar, William David; Sánchez, Adolfo J.; Normey Rico, Julio Elías; Escaño González, Juan Manuel; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España; European Union (UE). H2020; Universidad de Sevilla. TEP116: Automática y Robótica Industrial
Concentrating Solar Power systems are widely applied as a means to utilize the sun's abundant renewable energy, but the design of these processes facilitates the occurrence of overheating. This work presents two approaches for calculating the mirror angles of a fresnel collector in order to limit the amount of solar energy collected. The proposed structures are designed to receive a desired focus value from a controller and generate references for the mirror inclination controllers of the collector. The first strategy consists of the use of an optimization problem coupled with a simplified model of the collector. Whereas, the second approach consists of an ANFIS network that is trained with data from a reliable collector model made in SolTrace®. Both approaches are compared with simulations in SolTrace® the results show that the ANFIS solution presented overall better results, taking into account error and computational time.
Predictive Control of Irrigation Canals Considering Well-being of Operators
(Elsevier / International Federation of Automatic Control (IFAC), 2023-07) Ranjbar, Roza; Sadowska, Anna D.; Maestre Torreblanca, José María; Overloop, Peter Jules van; Schutter, Bart D. de; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; European Research Council (ERC); Universidad de Sevilla. TEP116: Automática y Robótica Industrial
We propose a model-predictive control (MPC) approach to solve a human-in-the-loop control problem for a non-automatic networked system with uncertain dynamics. There are no sensors or actuators installed in the system and we involve humans in the loop to travel between various nodes in the network and to provide the remote controller with measurements as well as actuating the system according to the control requirements. We compute the time instants at which the measurements and actuations should take place to yield better performance with respect to current control methods. We present simulation results using a numerical model of a real canal, the West-M canal in Arizona, and we demonstrate the superiority of the new method over previously proposed ones for such setting.
Real-time monitoring and optimal vessel rescheduling in natural inland waterways
(Elsevier / International Federation of Automatic Control (IFAC), 2023-07) Moreno Nadales, Juan; Muñoz de la Peña Sequedo, David; Limón Marruedo, Daniel; Alamo, Teodoro; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; Universidad de Sevilla. TEP950: Estimación, Predicción, Optimización y Control
Despite the efforts made by the port community and the academia to develop Efficient strategies to mitigate the effect of unexpected events on the planning of vessels through natural waterways, most scheduling algorithms developed so far are not against these events unforseen events. These incidents may lead to nonoptimal operation or even to potentially dangerous situations. To tackle this issue, in this paper we propose a real-time monitoring architecture and a series of optimal rescheduling strategies to re-schedule vessels in real time when an unexpected incident is detected. The objective is to reduce the impact of the incident in the overall process while preserving safety. This is done by detecting deviations from the originally scheduled plans and taking the proper measures when incidents are detected, which will depend on the type of anomaly detected. The proposed methodology is applied to the case of the Guadalquivir river, a natural waterway located in the south of Spain.
Learning-based NMPC on SoC platforms for real-time applications using parallel Lipschitz interpolation
(Elsevier / International Federation of Automatic Control (IFAC), 2023-07) Moreno Nadales, Juan; Carnerero Panduro, Alfonso Daniel; Moreno Blázquez, Carlos; Haes-Ellis, Richard Mark; Limón Marruedo, Daniel; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Agencia Estatal de Investigación. España; Ministerio de Ciencia e Innovación (MICIN). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Universidad de Sevilla. TEP950: Estimación, Predicción, Optimización y Control
One of the main problems associated with advanced control strategies is their implementation on embedded and industrial platforms, especially when the target application requires real-time operation. Frequently, the dynamics of the system are totally or partially unknown, and data-driven methods are needed to learn an approximate model of the plant to control. On many occasions, these learning techniques use non-differentiable functions that cannot be handled by most traditional low-level gradient-based optimization methods. In addition, many data-driven techniques require the online processing of a vast amount of data, which may be exceedingly time-consuming for most real-time applications. To solve these two problems at once, we propose a low-cost solution based on a system on a chip (SoC) platform featuring an embedded microprocessor (MP) and a field programmable gate array (FPGA) to implement nonlinear model predictive control strategies. The model employed to make predictions about the future evolution of the system is learnt by means of a data-driven learning method know as parallel Lipschitz interpolation (LI) and implemented in the FPGA part. On the other hand, the optimization problem associated with the model predictive control strategy is solved by software in the MP using an adapted version of the particle swarm optimization method.
Population-Dynamics-Assisted Coalitional Model Predictive Control for Parabolic-Trough Solar Plants
(Elsevier / International Federation of Automatic Control (IFAC), 2023-07) Sánchez Amores, Ana; Martínez Piazuelo, Juan; Maestre Torreblanca, José María; Ocampo-Martínez, Carlos; Camacho, Eduardo F.; Quijano, Nicanor; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Unión Europea, Horizonte 2020; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; Universidad de Sevilla. TEP116: Automática y Robótica Industrial
This paper proposes a coalitional model predictive control method for temperature regulation in parabolic-trough solar fields. The global optimization problem is divided into a set of local subproblems that will be solved in parallel by a set of coalitions. However, these local (smaller) problems remain coupled by a common global resource constraint. In this regard, we present a population-dynamics-assisted resource allocation approach to fully decouple the local optimization problems. By doing this, each coalition can address its corresponding optimization problem without relying on the solutions of the other coalitions. To illustrate the proposed methodology, we provide simulation results for a 100-loop parabolic-trough solar collector field.
Two-layer Coalitional Model Predictive Control for Parabolic-Trough Collector Fields
(Elsevier / International Federation of Automatic Control (IFAC), 2023-07) Sánchez Amores, Ana; Maestre Torreblanca, José María; Camacho, Eduardo F.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Unión Europea, Horizonte 2020; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; Universidad de Sevilla. TEP116: Automática y Robótica Industrial
Coalitional control partitions a system into multiple clusters or coalitions that solve independent local subproblems in parallel. This paper presents a two-layer coalitional model predictive control approach for regulation in constrained-coupled subsystems. We formulate a resource allocation mechanism to distribute the coupled constraint so that the global control problem can be solved in a decentralized manner, guaranteeing the satisfaction of the common constraint. In particular, a top layer will calculate the system's partition according to a given criterion and supervise the shared resource allocation. In turn, the lower control layer will calculate the local optimization problems for every coalition in a decentralized fashion, according to the available shared resource determined by the upper layer. This strategy is applied to regulate the outlet temperature of parabolic-trough solar collector fields, which are composed of a set of loops that remain coupled through a global shared resource constraint.
Bio-inspired control by overlapping adaptive clusters: a vehicle platoon case study
(Elsevier / International Federation of Automatic Control (IFAC), 2023-07) Pauca, Ovidiu; Maxim, Anca; Maestre Torreblanca, José María; Caruntu, Constantin F.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; Universidad de Sevilla. TEP116: Automática y Robótica Industrial
Clustering vehicles in a platoon by exchanging information through communication networks leads to an improvement in their performance with a moderate cooperation effort. In particular, in this paper, a bio-inspired methodology for overlapping coalitions of vehicles is proposed, which optimizes communication and control performance. The results obtained using the proposed adaptive clusters method are compared with the ones obtained by a conventional coalitional control strategy, and the comparison illustrates the improved efficiency of the proposed method.
Tube-based coalitional MPC with plug-and-play features
(Elsevier, 2023-11) Masero Rubio, Eva; Baldivieso-Monasterios, Pablo Rodolfo; Maestre Torreblanca, José María; Trodden, Paul A.; Fernández Camacho, Eduardo; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Universidad de Sevilla. TEP116: Automática y Robótica Industrial
This paper presents a distributed setting of model predictive control (MPC) to manage linear multi-agent systems consisting of coupled subsystems. Specifically, local controllers can work in coalitions to improve performance and handle plug-and-play events. This study provides insight into a coalitional MPC strategy based on optimized tubes that handles plug-in and plug-out subsystems. Moreover, we explore an inherent robustness gap to absorb disturbances not covered by the tubes without having to group local controllers. A comparison of our approach with centralized and decentralized MPC is reported using an illustrative example.
Switching Model Predictive Control of canals characterized by large operating conditions
(Elsevier, 2023-11) Anderson, Alejandro; Segovia, Pablo; García Martín, Javier; Duviella, Éric; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Universidad de Sevilla. TEP116: Automática y Robótica Industrial
This paper presents a switched linear system representation of water canal dynamics to incorporate different operating modes, which arise due to the occurrence of extreme weather phenomena such as flooding and drought episodes. To guarantee the stability during mode switching, a proper analysis on permanence regions–given by a collection of equilibrium states–for the switched linear system is presented. The permanence region is computed within a compact set, which depends on an adequate level region for the canals. A suitable algorithm is used to formulate an asymptotic stable Model Predictive Control (MPC) that steers and maintains the states of the system inside the target region indefinitely in a feasible manner. This strategy is successfully tested in a simulation using a realistic model of a canal.
Preliminary study of the impact of using hydrogen with a fuel cell for aircraft propulsion in an existing platform
(2022) Frutos, Víctor M. de; Parra Vilar, Juan Ramón; Bordons Alba, Carlos; Esteban Roncero, Sergio; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Universidad de Sevilla. Departamento de Ingeniería Aeroespacial y Mecánica de Fluidos; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; Universidad de Sevilla. TEP945: Ingeniería Aeroespacial
In the pursue of truly achieving the decarbonisation of air transport, several initiatives have been launched within the aeronautical industry with very different approaches. Despite that many studies have been conducted over the last years, including trials with demonstration flights for fuel cell-based aircraft platforms or for burning hydrogen propulsion, these studies have never gone beyond prototype status. One of the most promising presented alternatives is the incorporation of hydrogen technology in the power plant of flying platforms in order to reduce the carbon footprint. Two ways are contemplated for the introduction of hydrogen technology in the power plant of an aircraft: the burning of hydrogen in a combustion engine and the use of a fuel cell to generate energy to fly. Prior to consider the hydrogen utilization as an alternative, it is necessary to evaluate the impact that such technology might have in aircrafts utilization. The greener option for hydrogen utilization is using a fuel cell as the outcome of the energy production is only water. It is considered in this paper that this impact could be evaluated in the analysis and comparison of the PL (Pay Load) vs. R (range) diagram between two platforms: a traditional platform with engines as Power Plant and a modified platform from that original one using fuel cells and electrical motors as power plant. This article presents a comparison analysis between both platforms by presenting a methodology using preliminary study focused on the cruise phase as it is the more relevant phase to evaluate the range of the aircraft. The direct application of this methodology to a case study will give to the reader the level of the impact of the new technology introduction. Then the optimization in a computational code of this methodology will permit to perform sensitivity studies and to establish metrics and objectives to reduce the impact in the aircraft utilization because of this new technology.
Offset-free distributed predictive control based on fuzzy logic: Application to a real four-tank plant
(Elsevier, 2023) Francisco, Mario; Masero Rubio, Eva; Morales-Rodelo, Keidy; Maestre Torreblanca, José María; Vega, Pastora; Revollar, Silvana; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Spanish government under the Predoctoral Training Program for University Staff Nº FPU18/04476; Spanish government project PID2019-105434RB-C31; Spanish government project PID2020-119476RBI00; Spanish government project FS/11-2021; European Research Council (ERCAdG) under the H2020 program OCONTSOLAR, Nº 789051; Universidad de Sevilla. TEP116: Automática y Robótica Industrial
This paper proposes an offset-free distributed implementation of a model predictive controller that employs fuzzy negotiation between agents. The scheme is based on model augmentation with additional disturbances to enable zero-offset tracking. Moreover, we code the negotiation criteria as a set of suitable fuzzy rules and consider stability and feasibility guarantees in the controller design for the linearized subsystems. We applied the method to an experimental four-tank plant, showing its effectiveness despite the coupling between subsystems and system-model mismatch.
Optimal Renewable Energy Curtailment Minimization Control Using a Combined Electromobility and Grid Model
(Elsevier, 2023) Čičić, Mladen; Vivas Venegas, Carlos; Canudas-de-Wit, Carlos; Rodríguez Rubio, Francisco; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Union’s Horizon 2020 research and innovation programme grant agreement Nº 694209; FEDER project US-1381503; Spanish National Research Agency PID2020-115561RB-C32; MCI, Spain TED2021-131604B-I00; Universidad de Sevilla. TEP201: Ingeniería de Automatización, Control y Robótica
We propose an integrated power and transportation system control framework, combining the power grid model with a macroscopic electromobility model including charging stations under V2G operation. In this framework, the electrical vehicles (EVs) act as energy storage, but also as an additional virtual power grid link, transporting energy from one point to another. This new holistic model is used as a basis for optimal control design seeking to minimize renewable energy curtailment, while accounting for the structural limitation of the grid and other SoC constraints necessary for the optimal operation of the EVs. The proposed control scheme is shown to eliminate approximately 50% of curtailment compared to uncoordinated EV charging.
Uncertainty management in peer-to-peer energy trading based on blockchain and distributed model predictive control
(Elsevier, 2023) Sivianes Castaño, Manuel; Velarde Rueda, Pablo; Zafra Cabeza, Ascensión; Maestre Torreblanca, José María; Bordons Alba, Carlos; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Union’s Horizon 2020 research and innovation program OCONTSOLAR, grant agreement Nº 789051; MCIN/AEI/ 10.13039/501100011033 Grant PID2020119476RB-I00; MCIN/AEI/ 10.13039/501100011033 Grant PID2019- 104149RB-I00; Universidad de Sevilla. TEP116: Automática y Robótica Industrial
This work presents a distributed energy management platform based on a smart contract displayed on a blockchain network to optimize the behavior of an energy community under stochastic disturbances, such as solar irradiance and agents’ energy demands. Disturbances are modeled as probability distributions and are handled by a distributed model predictive control scheme based on chance constraints. The performance of the proposed algorithm is assessed across various simulations.
Energy Demand Management in an Industrial Manufacturing Plant using MPC and Neurofuzzy Models
(Elsevier, 2023) Gómez Jiménez, Javier; Chicaiza Salazar, William David; Escaño González, Juan Manuel; Bordons Alba, Carlos; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Unions Horizon 2020 research and innovation programme under grant agreement Nº 958339; MCIN/AEI/10.13039/501100011033 Grant PID2019-104149RB-I00; Universidad de Sevilla. TEP116: Automática y Robótica Industrial
An MPC controller is proposed to maximise the use of renewable energy in a manufacturing process. The strategy has been applied in a manufacturing system which has several machines, renewable generation resources, a combined heat and power (CHP) generator for power production, and a battery bank for energy storage. The work aims to maximise the use of renewable energy sources in this process, also taking into account the price of the electricity market, to reduce the cost. The use of neurofuzzy models for the prediction of the energy produced by renewable generators allows a dynamic prediction, using input values obtained from typical forecasting variables (wind speed, global irradiance, etc.).

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