Ponencias (Ingeniería de Sistemas y Automática)

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

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Efficient Implementation of MPC for Tracking using ADMM by Decoupling its Semi-Banded Structure
(Institute of Electrical and Electronics Engineers, 2024-07) Gracia Villegas, Víctor Manuel; Krupa, Pablo; Limón Marruedo, Daniel; Alamo, Teodoro; Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; TEP950: Estimación, Predicción, Optimización y Control
Abstract—Model Predictive Control (MPC) for tracking formulation presents numerous advantages compared to standard MPC, such as a larger domain of attraction and recursive feasibility even when abrupt changes in the reference are produced. As a drawback, it includes some extra decision variables in its related optimization problem, leading to a semi-banded structure that differs from the banded structure encountered in standard MPC. This semi-banded structure prevents the direct use of the efficient algorithms available for banded problems. To address this issue, we present an algorithm based on the alternating direction method of multipliers that explicitly takes advantage of the underlying semi-banded structure of the MPC for tracking.
Adaptive Learning-Based Model Predictive Control for Thickening Processes
(Elsevier, 2025) Pinto, Thomás; Limón Marruedo, Daniel; Santos, Marcelo A.; Raffo, Guilherme V.; Ingeniería de Sistemas y Automática
This work presents an Adaptive Learning-Based Model Predictive Control (ALB-MPC) framework for a thickening process characterized by high complexity and nonlinear dynamics. The approach leverages operational data to identify an accurate process model as a Nonlinear AutoRegressive eXogenous (NARX) structure, built using a learning method known as Lazily Adaptive Constant Kinky Inference (LACKI). Additionally, a neural network is employed as an online tuning mechanism to adapt the predictive controller parameters and enhance control performance. Simulation results indicate that the proposed control framework achieves performance comparable to or better than controllers with fixed parameters.
Multivariable adaptive output regulation for a generalized second-order linear system
(Elsevier, 2025) Aguilar-Ibáñez, Carlos Fernando; Acosta Rodríguez, José Ángel; Barragán-Vázquez, Diana Patricia; Monzón, Pablo; Suárez-Castañón, Miguel Santiago; Ingeniería de Sistemas y Automática
In this work, we present the design of a novel control strategy for the stabilization of an uncertain second-order multivariable linear system of which only the generalized output vector is measurable and where the high-frequency gain is an unknown positive-definite symmetric matrix. The proposed approach corresponds to a Lyapunov-based adaptive control strategy which consists of three fundamental components: a measurable auxiliary filter that imitates the original system, an adaptive slave system that follows the filter system, and an adaptive controller that uses the states of the slave system. We provide a numerical experiment to highlight the effectiveness of the proposed adaptive control strategy in accomplishing the regulation problem.
MPC for Tracking applied to rendezvous with non-cooperative tumbling targets ensuring stability and feasibility
(IEEE, 2024-12-19) Rebollo Fernández, José Antonio; Vázquez Valenzuela, Rafael; Alvarado Aldea, Ignacio; Limón Marruedo, Daniel; Ingeniería Aeroespacial y Mecánica de Fluidos; Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Union; TEP945: Ingeniería Aeroespacial; TEP950: Estimación, Predicción, Optimización y Control
A Model Predictive Controller for Tracking is introduced for rendezvous with non-cooperative tumbling tar- gets in active debris removal applications. The target’s three- dimensional non-periodic rotational dynamics as well as other state and control constraints are considered. The approach is based on applying an intermediate coordinate transformation that eliminates the time-dependency due to rotations in the constraints. The control law is then found as the solution to a Quadratic Programming problem with linear constraints and dynamics, as derived from the Hill-Clohesy-Wiltshire equations, that provides feasibility and stability guarantees by means of a terminal Linear Quadratic Regulator and dead-beat region. The proposed control algorithm performs well in a realistic simulation scenario, namely a near rendezvous with the Envisat spacecraft.
Weighted network design for practical average consensus in perturbed Multi-Agent Systems
(Elsevier, 2025) Leccese, Sara; Caiazzo, Bianca; Petrillo, Alberto; Santini, Stefania; Seuret, Alexandre; Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
This paper addresses the design of an average consensus control law for perturbed multi-agent systems subject to unknown bounded-in-average disturbances. The consensus strategy is developed for both continuous-time and asynchronous event-triggered implementations. A fully distributed control approach is proposed to ensure practical stability within a specific attractor. The key novelty lies in leveraging projection matrix properties to design the Laplacian weights and triggering parameters through feasible Linear Matrix Inequalities (LMIs). Numerical results validate the theoretical analysis and demonstrate the method’s scalability and efficiency, even for large networks with random topologies.
Nonlinear model predictive control with a robot in the loop
(Elsevier, 2025) Maestre Torreblanca, José María; Ohtsuka, Toshiyuki; Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España
The interplay between heterogeneous elements in control systems, such as processes and robots, often leads to hybrid optimization problems. This work integrates the path planning of sensing agents into the process control problem while circumventing this complexity. To this end, the proposed Model Predictive Control (MPC) strategy utilizes continuous variables to model the robots’ movements and optimizes both system performance and path planning over a prediction horizon. As a result, robots move and sense in ways that maximize control performance. Moreover, the stochastic nonlinear formulation of the MPC controller allows it to dynamically adjust to constraint violations while maintaining probabilistic guarantees. To illustrate the proposed method, an academic example is employed in which a single robot monitors two separated tanks. Our simulations show that the proposed strategy enhances the flexibility of control systems with agents in the loop, providing a viable and efficient solution for applications ranging from industrial automation to resource management in uncertain environments.
Monitorización mediante vehículos aéreos multicámara. Caso de uso de Unreal Engine
(Comité Español de Automática (CEA), 2024-07) López Ruiz, José Francisco; Rico Ranea, Jaime; Alamo, Teodoro; Ortega Linares, Manuel Gil; Vargas Villanueva, Manuel; Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
El trabajo propuesto en este artículo parte de la consideración de un vehículo aéreo autónomo, dotado de múltiples cámaras, como agente capaz de proporcionar mayor versatilidad en aplicaciones de monitorización y seguimiento de múltiples objetivos móviles. Dicho agente tendría la capacidad de orientar sus c ́amaras de forma completamente independiente y ́estas estarían dotadas con capacidad de zoom, lo que permitiría ajustar la distancia focal de cada una de ellas a conveniencia. Partiendo de este concepto y de una estrategia de optimización desarrollada en trabajos anteriores, se propone una generalización de la misma que permita la colaboración de varios agentes en una misma misión. De forma complementaria, parte del trabajo se centra en explorar las posibilidades que ofrece Unreal Engine 5 como herramienta de simulación gráfica para la implementación de la propuesta.
A MILP model for the Integrated Scheduling and Energy Management problem
(Elsevier, 2025) Framiñán Torres, José Manuel; Gómez Jiménez, Javier; Escaño González, Juan Manuel; Bordons Alba, Carlos; Organización Industrial y Gestión de Empresas I; Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España
Inspired by a real-life manufacturing case study, this paper addresses the Integrated Scheduling and Energy Management (ISEM) problem. This problem consists of scheduling jobs in an heterogeneous hybrid flowshop where the machines in some stages process the jobs in batches, as well as managing the energy consumption provided by an Industrial Microgrid (IMG). The IMG includes the generation of energy using renewable Sources, a power plant operating using gas purchased to an external provider, and a Battery Energy Storage System. The objective is to minimize the operating costs of the IMG while scheduling the jobs within the time allocated. A Mixed Integer Linear Programming (MILP) model for this rather complex problem is provided, and computational experiments are carried out, showing that the MILP model is able to reach the optimal solution (or with a minimal optimality gap) for realistic cases, even if the number of stages and jobs that the model can handle within a reasonable computational effort is limited.
Distributed State Estimation for Wireless Sensor Networks using Coalitional Games
(Elsevier, 2025) Masero Rubio, Eva; Maestre Torreblanca, José María; Ingeniería de Sistemas y Automática; European Union (UE); Ministerio de Ciencia e Innovación (MICIN). España
Wireless sensor networks offer diverse applications through distributed strategies. This paper investigates the fundamental properties of such networks, where agents dynamically switch communication strategies to estimate their states while minimizing communication costs. Using game-theoretic tools, we address optimal communication strategy selection, equitable cost-sharing, and identification of critical links in the network. We test the proposed distributed coalitional game framework for mobile self-localization, demonstrating its effectiveness in improving network reliability and cost efficiency in practical scenarios.
A Constraints-aware Antagonistic Controller with Disturbance-adaptive Attacks
(Elsevier, 2025) Siyyal, Shafqat A.; Maestre Torreblanca, José María; Freddi, Alessandro; Longhi, Sauro; Ingeniería de Sistemas y Automática; European Union (UE); Ministerio de Ciencia e Innovación (MICIN). España
This paper proposes an extension to attack strategies in cyber physical systems, based on adopting input sequences which can maximize an objective function typically designed for minimization. In detail, the proposed method prioritizes the violation of state constraints over cost maximization to directly driving the system into an unsafe region. To achieve this, we reformulate the cost function by introducing a slack variable into the optimization problem, explicitly encouraging constraint violations. The framework also accounts for external disturbances that may counteract the controller’s objectives. To guarantee a certain level of damage despite such disturbances, the problem is formulated as max-min optimization where the attacker optimizes for the worst-case scenario. Given that the maximization subproblem is NP-hard, we address this computational challenge using a vertex enumeration method. The effectiveness of the proposed approach is validated in a simulation scenario based on an autonomous aerial vehicle using a Model Predictive Controller (MPC), showing that constraint violations can be achieved at a reduced cost compared to existing methods.
Stability Enhancement in 4-Wheel Electric Vehicles using Deep Neural Networks
(Elsevier, 2025) Hassan, Ahmed; Ruiz-Moreno, Sara; Domínguez Frejo, José Ramón; Maestre Torreblanca, José María; Camacho, Eduardo F.; Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España
4-wheel electric vehicles (4WEVs) often face challenges in lateral stability during corning driving conditions. Nonlinear Model Predictive Control (NMPC) improves stability but struggles with high computational demands and adaptability to different operating conditions. This study proposes a novel approach to address this issue using artificial neural networks (ANNs) trained on simulated NMPC-controlled vehicle data incorporating tuning parameters as inputs. This way, the trained model represents a family of predictive controllers that can adjust the controller post-implementation to adapt efficiently to changes in operating conditions. The simulations show improvement in performance, robustness, and computation time.
RGBD-based robot localization in sewer networks
(IEEE, 2017) Alejo, David; Caballero Benítez, Fernando; Merino, Luis; Ingeniería de Sistemas y Automática
This paper presents a vision-based localization system for global pose estimation of a sewer inspection robot given prior information of the sewer network from local institutions. The system is based on a Monte-Carlo Localization system that uses RGBD odometry for the prediction stage. The update step takes into account the sewer network topology for discarding wrong hypotheses. Moreover, this step is further refined whenever a discrete element of the network (i.e. manhole) is detected. To this end, another RGBD camera pointing upwards is used for precise manhole detection. A Convolutional Neural Network has been successfully trained for classifying images with and without manholes with 96% accuracy over the tested dataset. The complete system has been validated with real data obtained from the sewers of Barcelona yielding accurate localization results. All the logs and code used in the context of this paper are publicly available.
Optimization-based trajectory planning for tethered aerial robots
(IEEE, 2021-05) Martínez Rozas, Simón Ernesto; Alejo, David; Caballero Benítez, Fernando; Merino, Luis; Ingeniería de Sistemas y Automática
This paper presents a non-linear optimization method for trajectory planning of tethered aerial robots. Particularly, the paper addresses the planning problem of an unmanned aerial vehicle (UAV) linked to an unmanned ground vehicle (UGV) by means of a tether. The result is a collision-free trajectory for UAV and tether, assuming the UGV position is static. The optimizer takes into account constraints related to the UAV, UGV and tether positions, obstacles and temporal aspects of the motion such as limited robot velocities and accelerations, and finally the tether state, which is not required to be tense. The problem is formulated in a weighted multi-objective optimization framework. Results from simulated scenarios demonstrate that the approach is able to generate obstacle-free and smooth trajectories for the UAV and tether.
Stochastic Model Predictive Control of Supply Chains of Perishable Goods
(Elsevier, 2025) Bernardini, F.P; Maestre Torreblanca, José María; Velarde, P.; Negenborn, R. R.; Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España
This work presents a stochastic model predictive control approach to optimize the management of a meat supply chain with uncertain demand. The proposed approach considers the temperature-dependent deterioration of meat products and the multi-stage nature of the supply chain, including producers, warehouses, retailers, and customers. The management problem is formulated as a mixed-integer optimization problem, where the objective is to minimize the total cost of the supply chain while satisflying customer demand and quality requirements. The approach uses scenario-based optimization to account for different uncertainty sources. The results show that the proposed method effectively balances the conflicting objectives of minimizing costs and meeting demand and quality requirements while accounting for uncertainty.
Currito: A 3D printed Open-source Educational Robot
(Elsevier, 2025) Luque, Ignacio; Coronilla, F. J.; Alvarado Aldea, Ignacio; Maestre Torreblanca, José María; Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España
Currito is a low-cost, 3D-printed robot developed for educational and professional purposes. It is provided along with a comprehensive open-source framework that contains the blueprints, quick-start codes and detailed guidelines in order to facilitate a straightforward replication and adaptation of the robot, enhancing the accessibility of existing human-robot interaction models. Currito’s design efficiently integrates mechanical and electronic components that enable a real-time interaction and an organic and realistic behavior of the robot, while allowing for a wide range of physical and morphological variations. This article outlines the design of Currito and showcases its capabilities through a practical implementation in a master’s-level robotics course at the University of Seville.
Stability analysis and design of an event-triggered control scheme for a coupled ODE-heat PDE system
(Elsevier, 2025) Thomas, Arthur; Baudouin, Lucie; Seuret, Alexandre; Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
This paper tackles the stability analysis of a system driven by linear ordinary differential equation coupled to a one-dimensional heat equation. The particularity of this study concerns the nature of the coupling, which is performed using an event-triggered scheme, through a boundary condition of the partial differential equation. After proving the existence and regularity of solutions of the system, the idea is to introduce an enriched energy functional as a candidate Lyapunov functional to prove the exponential stability. Actually, we will obtain a sufficient stability condition expressed as a linear matrix inequality to satisfy. This condition is suitable for solving an emulation problem corresponding to the appropriate tuning of the event-triggered control. Our result is finally illustrated with a numerical example.
Estimación de la distribución de caudal en plantas solares de colectores cilindro-parabólicos
(Comité Español de Automática (CEA), 2025-09) Ruiz-Moreno, Sara; Gallego Len, Antonio Javier; Camacho, Eduardo F.; Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
Este trabajo presenta una metodología para la estimación del reparto de caudal en plantas termosolares de colectores cilindro-parabólicos combinando técnicas de optimización con redes neuronales recurrentes para reducir su alto coste computacional. Primero, se aplica un algoritmo para estimar la tempertura en el lazo y obtener el reparto de caudal que minimiza los errores de estimación. Después, se entrenan redes neuronales para reproducir el algoritmo. Los caudales obtenidos se utilizan como punto inicial en el proceso de optimización, limitando el espacio de búsqueda y reduciendo significativamente el tiempo de cómputo. El método se evalúa en sectores de distinto tamaño (4, 20 y 50 lazos), comparando tres variantes: optimización, combinación red neuronal+optimización, y red neuronal. Los resultados muestran que el enfoque propuesto mejora la estimación respecto a la suposición clásica de distribución uniforme, y permite una reducción significativa del tiempo de cálculo respecto al uso único del optimizador, especialmente relevante en sectores de gran escala.
Estudio energético de FSMPC para un motor de 5 fases
(Comité Español de Automática (CEA), 2025-10-06) Marsal Pederzani, Esteban; Garrido Satué, Manuel; Colodro Ruiz, Francisco; Ingeniería de Sistemas y Automática; Ingeniería Electrónica; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España; TIC275: Investigación y Desarrollo en Electrónica, Automática y Tecnologías Emergentes
El control de motores mediante el manejo directo de los estados del inversor (FSMPC) ha sido objeto de estudio recientemente. El interés viene motivado por dos factores: la rapidez de respuesta comparada con el caso habitual usando moduladores y la flexibilidad para su aplicación a diversos sistemas y para incorporar diversos criterios de control. En particular ya se ha establecido previamente la posibilidad de usar FSMPC en sistemas multifásicos, aprovechando las ventajas intrínsecas de ́estos. También se ha demostrado la posibilidad de lograr un cierto equilibrio entre producción de par y contenido armónico. Queda sin embargo por explorar la capacidad de reducir las pérdidas totales mediante la sintonía del FSMPC. En este artículo se da a conocer un trabajo en curso destinado a tal fin. En particular se presentan resultados experimentales con una máquina de 5 fases y un sistema diseñado para la estimación de las pérdidas totales.
Control predictivo de motores de cinco fases mediante estrategias mono-vector ymulti-vector
(Comité Español de Automática (CEA), 2025-10-06) Garrido Satué, Manuel; Martínez Heredia, Juana María; Mora Jiménez, José Luis; Ingeniería Electrónica; Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España; TIC275: Investigación y Desarrollo en Electrónica, Automática y Tecnologías Emergentes
El control predictivo de motores polifásicos ha utilizado diversas aproximaciones en lo que respecta al uso de los estados del inversor. En algunas propuestas se usa más de una configuración del inversor dentro de un mismo periodo de muestreo. Estos métodos proporcionan una forma fácil de lidiar con el contenido del plano armónico al tiempo que disminuyen la carga computacional. En este artículo se presenta una comparación entre un método multi-vector y un método mono-vector. Para la comparación se usa la bondad de control de corrientes de estátor en el plano productor de par y en el plano armónico. Se ajusta la frecuencia de conmutación para proporcionar valores similares en ambos casos, proporcionando de este modo igual uso de inversor a ambos métodos.
Singular perturbation control of the lateral-directional flight dynamics of an UAV
(2015) Esteban Roncero, Sergio; Gavilán Jiménez, Francisco; Acosta Rodríguez, José Ángel; Ingeniería de Sistemas y Automática; Ingeniería Aeroespacial y Mecánica de Fluidos; TEP995: Multi-Robot and Control Systems; TEP945: Ingeniería Aeroespacial
This paper presents a singular perturbation control strategy for regulating the lateral-directional flight dynamics of an Unmanned Air Vehicle (UAV). The proposed control strategy is based on a four-time-scale (4TS) decomposition that includes the side-slip velocity, bank angle, yaw rate and roll rate dynamics, with the control signals being the aileron and rudder deflection. The nonlinear control strategy drives the system to follow a reference in load factor which in return provides references in bank angle, side-slip velocity and yaw rate. In addition, the control strategy permits to select the desired dynamics for all the singularly perturbed subsystems. Numerical results are included for a realistic nonlinear UAV model, including saturation on the control signals, and unmodeled dynamics.

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