Artículos (Ingeniería de Sistemas y Automática)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11342

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Mostrando 1 - 20 de 524

Bidirectional-thruster multirotor for perimeter pipe inspections (BiMPPI): A nonlinear optimal integral-SDRE design
(Elsevier, 2025-06) González Morgado, Antonio; Nekoo, Saeed Rafee; Heredia Benot, Guillermo; Ollero Baturone, Aníbal; 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); Universidad de Sevilla. TEP151: Robótica, Visión y Control
This paper presents a novel bidirectional-thruster multirotor for perimeter pipeline inspection (BiMPPI). The bidirectional thrusters enable the generation of negative collective thrust, allowing BiMPPI to land on the pipe, reverse motor thrust directions, and push against the pipe while rotating around it without losing physical contact. An integral state-dependent Riccati equation (SDRE) controller is used during the turning phase around the pipe. The integral SDRE controller is compared through simulations with the servo-SDRE controller, exhibiting similar performance while eliminating the need for an online solution to the SDRE. The platform is experimentally validated through indoor flights, demonstrating superior performance in rotational movements around a mockup pipeline compared to both SDRE and standard PID controllers.
Geometric control using the state-dependent Riccati equation: application to aerial-acrobatic maneuvers
(2021-02) Nekoo, Saeed Rafee; Acosta Rodríguez, José Ángel; Ollero Baturone, Aníbal; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Union (UE). H2020; Universidad de Sevilla. TEP995: Multi-Robot and Control Systems
Acrobatic flip is one of the most challenging representatives of aggressive maneuvers to test the performance of an aerial system’s capability or a controller. A variable-pitch rotor quadcopter generates thrust in both vertical directions for the special design of the rotor’s actuation mechanism. This research proposes two possible solutions for the flip: a regulation solution based on the geometric control approach; and tracking a predefined optimal smooth trajectory covering a turnover. The first solution uses a geometric control approach that is immune to singular points since the rotation matrix is integrated on the manifold on (Formula presented.). The second solution proposes an optimal trajectory generation for flip maneuver using open-loop optimal control, two-point boundary value problem (TPBVP) approach. Since generated open-loop state information is not applicable without a controller, the state-dependent differential Riccati equation (SDDRE) is chosen for trajectory tracking
Stochastic model predictive control of an irrigation canal with integrated performance-driven path planning of a measurement robot
(IWA Publishing, 2025) Ranjbar, Roza; García Martín, Javier; Maestre Torreblanca, José María; Etienne, Lucien; Duviella, Eric; Camacho, Eduardo F.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Research Council (ERC); Ministerio de Ciencia e Innovación (MICIN). España
This work proposes a stochastic model predictive control for an irrigation canal with uncertainties where a moving robot takes measurements across the canal considering criteria such as the robot’s velocity, energy consumption, and distances between the measuring spots. Tightened constraints are applied over the prediction horizon to the optimization so that the controller selects the optimal route for the robot from a control viewpoint. The simulations compare three different approaches, demonstrating that the proposed technique achieves superior results by reducing constraints violations and operational costs and ensuring more precise and reliable water level management across the canal compared to other methods.
Optimal Positioning Strategy for Multi-Camera Zooming Drones
(IEEE, 2024) Vargas Villanueva, Manuel; Vivas Venegas, Carlos; Alamo, Teodoro; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Universidad de Sevilla. TEP201: Ingeniería de Automatización, Control y Robótica; Universidad de Sevilla. TEP950: Estimación, Predicción, Optimización y Control
In the context of multiple-target tracking and surveillance applications, this paper investigates the challenge of determining the optimal positioning of a single autonomous aerial vehicle or agent equipped with multiple independently-steerable zooming cameras to effectively monitor a set of targets of interest. Each camera is dedicated to tracking a specific target or cluster of targets. The key innovation of this study, in comparison to existing approaches, lies in incorporating the zooming factor for the onboard cameras into the optimization problem. This enhancement offers greater flexibility during mission execution by allowing the autonomous agent to adjust the focal lengths of the on-board cameras, in exchange for varying real-world distances to the corresponding targets, thereby providing additional degrees of freedom to the optimization problem. The proposed optimization framework aims to strike a balance among various factors, including distance to the targets, verticality of viewpoints, and the required focal length for each camera. The primary focus of this paper is to establish the theoretical groundwork for addressing the non-convex nature of the optimization problem arising from these considerations. To this end, we develop an original convex approximation strategy. The paper also includes simulations of diverse scenarios, featuring varying numbers of onboard tracking cameras and target motion profiles, to validate the effectiveness of the proposed approach.
System identification and fault reconstruction in solar plants via extended Kalman filter-based training of recurrent neural networks
(Elsevier, 2025-03) Ruiz-Moreno, Sara; Bemporad, Alberto; Gallego Len, Antonio Javier; Camacho, Eduardo F.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Union (UE). H2020; Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Sevilla. TEP116: Automática y Robótica Industrial
This article proposes using the extended Kalman filter (EKF) for recurrent neural network (RNN) training and fault estimation within a parabolic-trough solar plant. The initial step involves employing an RNN to model the system. Given the challenge of fault discernibility in the collectors, parallel EKFs are employed to reconstruct the parameters of the faults. The parameters are used independently to estimate the system output, and the type of fault is isolated based on the estimation errors using another feedforward neural network. To evaluate the effectiveness of the methodology, simulations are conducted on a loop of the ACUREX plant with irradiances from sunny and cloudy days. The results reveal a fault classification accuracy of approximately 90% and a fault reconstruction error below 3%, with even better accuracies in the cloudy dataset than in the sunny dataset.
Electric power optimization in solar trough plants with deep learning-based model predictive control
(Elsevier, 2025-08) Ruiz-Moreno, Sara; Gallego Len, Antonio Javier; Domínguez Frejo, José Ramón; Camacho, Eduardo F.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Union (UE). H2020; Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Sevilla. TEP116: Automática y Robótica Industrial
On-site computational capabilities limit the optimization of electricity production in commercial plants. This work addresses the implementation of a virtual operator for electric power maximization in parabolic-trough collector plants by manipulating the flow rate circulating through the pipes with a neural network-based procedure. A model predictive control (MPC) strategy is proposed using nonlinear models to predict the system's response. The effect of including diverse parts of the plant in the prediction model and using different objective functions (the model of the pump and the pipes and the difference between optimizing thermal and electric power) is analyzed. First, two control layers are implemented: one for obtaining the temperature setpoints and one for computing the flow rate. Since the nonlinear MPC cannot be computed in real-time for medium and large plants, an artificial neural network is trained to learn the optimal solution and lower the computational burden, reducing a 2-layer MPC strategy into only one neural controller that internally decides the operating point. The simulation results obtain a mean time reduction of 99.99995%, while the electricity production for the studied cases is only reduced by around 1%, making the controller implementable in real-time in actual plants.
A Multi-UAV Approach for Fast Inspection of Overhead Power Lines: From Route Planning to Field Operation
(Springer, 2025) Caballero Gómez, Álvaro; Román-Escorza, Francisco Javier; Maza Alcañiz, Iván; Ollero Baturone, Aníbal; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Union (UE); Universidad de Sevilla. TEP151: Robótica, Visión y Control
Overhead power lines are critical infrastructures to ensure a reliable energy supply, and failures in the grid can lead to significant service disruptions. Locating these faults quickly is crucial but often challenging, especially in hard-to-reach areas such as mountainous regions. This paper presents an integrated solution for the long-range visual inspection of overhead power lines in minimum time using teams of Unmanned Aerial Vehicles (UAVs). The solution, designed for effective field operation while meeting end-user requirements, comprises route planning, autonomous execution, and monitoring of the inspection mission. Concerning route planning, a capacitated min-max multi-depot vehicle routing problem has been formulated to compute feasible routes that cover the entire grid in minimum mission time. The method can be applied to heterogeneous multiUAV teams in terms of inspection speed and battery consumption, which helps maximise the utilisation of available robots. Moreover, the planning method is complemented by an accurate battery-consumption model based on energy principles that captures the effect of parameters often overlooked such as UAV mass, inspection speed, and weather conditions. The model has shown estimates with relative errors not exceeding 1.34% compared to real measurements. The proposed solution has been experimentally validated under real-world conditions, enabling the autonomous multi-UAV inspection of more than 10 kilometres of real power lines in 13 minutes, which represents a time reduction of up to 67.21% compared to the state of the art. The resulting videos enabled the identification of a simulated power outage and its exact location.
Standardized Evaluation of Counter-Drone Systems: Methods, Technologies, and Performance Metrics
(MDPI, 2025) De Cubber, Geert; Doroftei, Daniela; Petsioti, Paraskevi; Koniaris, Alexios; Brewczyński, Konrad; Życzkowski, M.; Maza Alcañiz, Iván; Ollero Baturone, Aníbal; Popa, Cristina; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Union. Internal Security Fund Police; Universidad de Sevilla. TEP151: Robótica, Visión y Control
This paper aims to introduce a standardized test methodology for drone detection, tracking, and identification systems. It is the aim that this standardized test methodology for assessing the performance of counter-drone systems will lead to a much better understanding of the capabilities of these solutions. This is urgently needed, as there is an increase in drone threats and there are no cohesive policies to evaluate the performance of these systems and hence mitigate and manage the threat. The presented methodology has been developed within the framework of the project COURAGEOUS funded by European Union’s Internal Security Fund Police. This standardized test methodology is based upon a series of standard user-defined scenarios representing a wide set of use cases. At this moment, these standard scenarios are geared toward civil security end users. However, the proposed standard methodology provides an open architecture where the standard scenarios can be modularly extended, providing standard users the possibility to easily add new scenarios. For each of these scenarios, operational needs and functional performance requirements are provided. Using this information, an integral test methodology is presented that allows for a fair qualitative and quantitative comparison between different counter-drone systems. The standard test methodology concentrates on the qualitative and quantitative evaluation of counter-drone systems. This test methodology was validated during three user-scripted validation trials.
Control tolerante a fallos en comunidades energéticas basado en blockchain
(Universidad Politécnica de Valencia, 2025) Sivianes Castaño, Manuel; Velarde, Pablo; Zafra Cabeza, Ascensión; Bordons Alba, Carlos; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Agencia Estatal de Investigación. España
Este trabajo presenta un sistema de control distribuido que optimiza la gestión energética en una comunidad utilizando control predictivo basado en modelos. Se ha extendido el sistema para dotar a cada agente de un mecanismo tolerante a fallos capaz de detectar, aislar y reconfigurar a gentes en caso de fallos. La detección se realiza mediante el cálculo de señales residuales y umbrales basados en restricciones de probabilidad para minimizar falsos positivos. Identificado el fallo, se ajustan los parámetros del controlador predictivo de la gente para mantener la seguridad del sistema. Si la reconfiguración afecta a múltiples a gentes, la información se comparte. El algoritmo de control utiliza un contrato inteligente en una red blockchain, resolviendo el problema de manera distribuida sin un coordinador central, y asegurando la seguridad e integridad de los datos. La estrategia propuesta ha sido evaluada mediante simulaciones en una comunidad energética.
Learning port maneuvers from data for automatic guidance of Unmanned Surface Vehicles
(Elsevier, 2025-07) Becerra-Mora, Yeyson; Acosta Rodríguez, José Ángel; Rodríguez Castaño, Ángel; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Universidad de Sevilla; Universidad de Sevilla. TEP995: Multi-Robot and Control Systems
At shipping ports, some repetitive maneuvering tasks such as entering/leaving port, transporting goods inside it or just making surveillance activities, can be efficiently and quickly carried out by a domestic pilot according to his/her experience. This know-how can be seized by Unmanned Surface Vehicles (USV) in order to autonomously replicate the same tasks. However, the inherent nonlinearity of ship trajectories and environmental perturbations as wind or marine currents make it difficult to learn a model and its respective control. We therefore present a data-driven learning and control methodology for USV, which is based on Gaussian Mixture Model, Gaussian Mixture Regression and the Sontag’s universal formula. Our approach is capable to learn the nonlinear dynamics as well as guarantee the convergence toward the target with a robust controller. Real data have been collected through experiments with a vessel at the port of Ceuta. The complex trajectories followed by an expert have been learned including the robust controller. The effect of the controller over noise/perturbations are presented, a measure of error is used to compare estimates and real data trajectories, and finally, an analysis of computational complexity is performed.
Comparación de estrategias de control predictivo estocástico no lineal aplicadas a la quimioterapia
(Universidad Politécnica de Valencia, 2025) Hernández Rivera, Andrés; Velarde, Pablo; Zafra Cabeza, Ascensión; Maestre Torreblanca, José María; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Agencia Estatal de Investigación. España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
El modelado matemático de sistemas biomédicos puede ayudar a los profesionales oncológicos a diseñar ciclos de administración de fármacos más seguros y eficaces. Para lograr este objetivo, en el proceso de toma de decisiones se utiliza el modelo matemático del crecimiento tumoral y el impacto de la quimioterapia. Sin embargo, los sistemas biomédicos son propensos a un alto grado de incertidumbre, no sólo por los errores de medición, sino también por la dinámica del sistema no modelada y la variabilidad entre pacientes. Para abordar este problema, se han aplicado restricciones probabilísticas al control del proceso de administración de fármacos, haciéndolo m ́as robusto frente a perturbaciones. Este trabajo compara una versión no lineal y otra linealizada de las formulaciones estocásticas del control predictivo basado en modelo. Ambos algoritmos mejoran la eficacia y la seguridad del tratamiento, con diferencias en cuanto a conservadurismo y coste computacional.
An Insurance Paradigm for Improving Power System Resilience via Distributed Investment
(IEEE, 2023) Billimoria, Farhad; Fele, Filiberto; Savelli, Iacopo; Morstyn, Thomas; McCulloch, Malcolm; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática
Extreme events, exacerbated by climate change, pose significant risks to the energy system and its consumers. However there are natural limits to the degree of protection that can be delivered from a centralised market architecture. Distributed energy resources provide resilience to the energy system, but their value remains inadequately recognized by regulatory frameworks. We propose an insurance framework to align residual outage risk exposure with locational incentives for distributed investment. We demonstrate that leveraging this framework in large-scale electricity systems could improve consumer welfare outcomes in the face of growing risks from extreme events via investment in distributed energy.
Predicción de voltajes en la red eléctrica por interpolación Kriging
(Universidad Politécnica de Valencia, 2025) Moreno-Blázquez, Carlos; Fele, Filiberto; Limón Marruedo, Daniel; Alamo, Teodoro; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Universidad de Sevilla
En este trabajo, abordamos el problema de la predicción en línea de las trayectorias de voltaje e intensidad nodales en la red de distribución. Para esto, proponemos una formulación basada en datos utilizando la interpolación Kriging, una técnica de aprendizaje automático que ha mostrado aplicaciones prometedoras en el campo del control basado en datos. Producimos un oráculo de predicción no paramétrico que permite inferir trayectorias futuras directamente a partir de medidas de voltaje e intensidad en tiempo real. Además, proporcionamos una implementación algorítmica simple pero efectiva basada en el conocido esquema ISTA. Demostramos la efectividad de nuestra metodología para la predicción rápida (subsegundos) de la dinámica del voltaje mediante simulaciones.
Time Interleaving in the Analogue Circuitry of Oversampled Digital-to-Analogue Converters: Proof of Concept
(The Institution of Engineering and Technology, 2025) Laguna, Marta; Marsal Pederzani, Esteban; Colodro Ruiz, Francisco; Martínez Heredia, Juana María; Arahal, Manuel R.; Universidad de Sevilla. Departamento de Ingeniería Electrónica; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Union (UE); Universidad de Sevilla. TIC275: Investigación y Desarrollo en Electrónica, Automática y Tecnologías Emergentes
Digital-to-analog converters (DACs) based on sigma-delta modulation are implemented with analog components that have low accuracy requirements. As a result, they have been widely employed in high-frequency transmitter architectures over the past decades. Time interleaving techniques allow parallel structures to operate at reduced speeds, thereby mitigating speed limitations in digital circuits. However, a common approach involves multiplexing the outputs of these parallel structures to reconstruct a single digital signal, which is subsequently converted to an analog voltage using a high-speed, low-resolution DAC. This work investigates the feasibility of employing a parallel array of low-speed DACs instead. To compensate for mismatches in analog paths, a novel dynamic element matching technique is proposed. Experimental results demonstrate that the proposed approach exhibits the characteristics required for high-frequency transmitter applications
Flight of the Future: An Experimental Analysis of Event-Based Vision for Online Perception Onboard Flapping-Wing Robots
(Wiley, 2025) Tapia López, Raúl; Luna-Santamaría, Javier; Gutiérrez Rodríguez, Iván; Rodríguez Gómez, Juan Pablo; Martínez de Dios, José Ramiro; Ollero Baturone, Aníbal; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Research Council (ERC)
Inspired by bird flight, flapping-wing robots have gained significant attention due to their high maneuverability and energy efficiency. However, the development of their perception systems faces several challenges, mainly related to payload restrictions and the effects of flapping strokes on sensor data. The limited resources of lightweight onboard processors further constrain the online processing required for autonomous flight. Event cameras exhibit several properties suitable for ornithopter perception, such as low latency, robustness to motion blur, high dynamic range, and low power consumption. This article explores the use of event-based vision for online processing onboard flapping-wing robots. First, the suitability of event cameras under flight conditions is assessed through experimental tests. Second, the integration of event-based vision systems onboard flapping-wing robots is analyzed. Finally, the performance, accuracy, and computational cost of some widely used event-based vision algorithms are experimentally evaluated when integrated into flapping-wing robots flying in indoor and outdoor scenarios under different conditions. The results confirm the benefits and suitability of event-based vision for online perception onboard ornithopters, paving the way for enhanced autonomy and safety in real-world flight operations.
Pareto Analysis of Electro-Mechanical Variables in Predictive Control of Drives
(MDPI, 2025) Garrido Satué, Manuel; Arahal, Manuel R.; Ortega Linares, Manuel Gil; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
Variable speed drives are often controlled by a double-loop scheme in which a proportional integral controller takes on the speed loop. The tuning of this loop is a complex job. In most cases just mechanical variables are considered for tuning. This paper presents a new Pareto analysis incorporating mechanical and electrical variables. A state of the art finite state model predictive controller is used for stator current control. The analysis is performed using experimental data from a five-phase induction motor and considers considering commonly found performance indicators derived from experimental data. The results show undocumented connections between those performance indicators. The analysis not only helps in PI tuning but, more importantly, prompts for a revision of the methods usually utilized to report performance enhancements of new methods.
Unified force and motion adaptive-integral control of flexible robot manipulators
(Elsevier, 2025-03) Rodríguez De Cos, Carlos; Acosta Rodríguez, José Ángel; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática
In this paper, we propose an adaptive nonlinear strategy for the motion and force control of manipulators with flexible joints. Our approach provides force control when in contact and robust motion control in its absence, all without the need for a control switch. This self-tuning behaviour for mixed contact/non-contact scenarios results from a unified formulation of force and motion control, with an integral transpose-based inverse kinematics core and adaptive update laws to cope with the manipulator flexibility and the contact stiffnesses. The global boundedness of all signals and the asymptotic stability of this controller are guaranteed via Lyapunov analysis. Finally, we validate its applicability experimentally by using low-cost hardware in a realistic mixed-contact scenario, demonstrating low computational demand.
Active Network Management via grid-friendly electromobility control for curtailment minimization
(Elsevier, 2025) Čičić, Mladen; Vivas Venegas, Carlos; Canudas-de-Wit, Carlos; Rodríguez Rubio, Francisco; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Research Council (ERC); Ministerio de Ciencia e Innovación (MICIN). España
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 additional virtual power grid links, transporting energy from one point to another. This new holistic approach is used as a basis for optimal control design seeking to provide Active Network Management, in order to minimize curtailment of renewable energy sources and loads at various ports of the network, while accounting for the structural limitation of the grid and other constraints necessary for the optimal operation of the EVs. The proposed control scheme is shown to be able to outperform uncoordinated EV charging in terms of total curtailment in various studied scenarios. Additionally, we study the case when public charging stations are able to incentivize or disincentivise EVs to use them, by dynamically varying their charging price throughout the day, and show that this additional control input can further reduce curtailment in certain scenarios.
Three-time-scale control for discharging rate consensus and large-signal stability analysis in AC-bus microgrids
(Elsevier, 2025-06) Merchán-Riveros, María Camila; Albea-Sánchez, Carolina; Salas Gómez, Francisco; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Agencia Estatal de Investigación. España; Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Sevilla. TEP102: Ingeniería Automática y Robótica
A distributed control scheme for islanded AC-bus microgrids is proposed, based on multi-agent system and singular perturbation theory. The goal is to achieve a balanced State Of Charge (SOC) for each Battery Energy Storage System (BESS) in discharging mode, ensuring stability properties of a large-signal model that considers the primary and secondary control loop dynamics. The power inverters are controlled through a voltage and current loop. Moreover, a droop control and consensus algorithm are proposed to ensure that the SOC of these BESSs are balanced. Furthermore, large-signal stability analysis is assessed for the complete network system by using singular perturbation theory. Indeed, through an appropriate selection of the parameters, the dynamics exhibit three-time-scale separation to fit each control goal (power converter control, droop control and consensus control). Experimental results on an Imperix power test bench validate the proposed control scheme, and verify the reliability and robustness with respect to any connection/disconnection event or communication failure through different scenarios.
Data-Driven Control Design for Power Converters Approximated as Switched Affine Systems and Experimental Validation
(IEEE, 2024) Merchán-Riveros, María Camila; Albea-Sánchez, Carolina; Seuret, Alexandre; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Agencia Estatal de Investigación. España; Universidad de Sevilla. TEP102: Ingeniería Automática y Robótica
This brief introduces a data-driven control design approach for power converters modelled as a switched affine system, that guarantees the global stability. Unlike many existing approaches, our contribution does not require a precise identification of a nonlinear model but it rather relies on prior random experimental data, which allows the design of a stabilizing control law in a direct manner. The proposed method is then evaluated on an Imperix power test bench. The experimental results not only confirm the viability of the data-driven approach for nonlinear systems but also demonstrate its applicability in real-world scenarios.

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