Artículos (Ingeniería de Sistemas y Automática)

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

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Mostrando 1 - 20 de 476

Assessing SOC Estimations via Reverse-Time Kalman for Small Unmanned Aircraft
(Multidisciplinary Digital Publishing Institute (MDPI), 2024-08) Arahal, Manuel R.; Pérez Vega-Leal, Alfredo; Garrido Satué, Manuel; Esteban Roncero, Sergio; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Universidad de Sevilla. Departamento de Ingeniería Electrónica; Universidad de Sevilla. Departamento de Ingeniería Aeroespacial y Mecánica de Fluidos; Agencia Estatal de Investigación. España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
This paper presents a method to validate state of charge (SOC) estimations in batteries for their use in remotely manned aerial vehicles (UAVs). The SOC estimation must provide the mission control with a measure of the available range of the aircraft, which is critical for extended missions such as search and rescue operations. However, the uncertainty about the initial state and depth of discharge during the mission makes the estimation challenging. In order to assess the estimation provided to mission control, an a posteriori re-estimation is performed. This allows for the assessment of estimation methods. A reverse-time Kalman estimator is proposed for this task. Accurate SOC estimations are crucial for optimizing the utilization of multiple UAVs in a collaborative manner, ensuring the efficient use of energy resources and maximizing mission success rates. Experimental results for LiFePO4 batteries are provided, showing the capabilities of the proposal for the assessment of online SOC estimators.
Spatio-temporal Kriging for spatial irradiance estimation with short-term forecasting in a thermosolar power plant
(Elsevier, 2024-10) García Martín, Javier; Domínguez Frejo, José Ramón; Maestre Torreblanca, José María; Camacho, Eduardo F.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Research Council (ERC); Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Universidad de Sevilla. TEP116: Automática y Robótica Industrial
This article proposes a method to improve the efficiency of solar power plants by estimating and forecasting the spatial distribution of direct normal irradiance (DNI) using a sensor network and anemometer data. For this purpose, the proposed approach employs spatio-temporal kriging with an anisotropic spatio-temporal variogram that depends on wind speed to accurately estimate the distribution of DNI in real-time, making it useful for short-term forecast and nowcast of DNI. Finally, the method is validated using synthetic data from varying sky conditions, outperforming another state-of-the-art technique.
On the optimization of flux distribution with flat receivers: A distributed approach
(Elsevier, 2018-01-15) Gallego Len, Antonio Javier; Camacho, Eduardo F.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Junta de Andalucía; Ministerio de Educación. España
One of the most important problems in the operation of solar power towers is to achieve a uniform flux density distribution at the receiver in order to avoid hot spots. The problem can be solved by computing the aim points of the heliostats by optimizing a function which measures the uniformity of the flux density over the receiver. Due to the high number of heliostats of current commercial plants (more than 900), the number of decision variables (1800) makes the centralized approach very difficult to be implemented in real time. In this paper, a distributed optimization algorithm that computes the aim points for the heliostat field to obtain a uniform flux density distribution and maximize the solar irradiation collected by the receiver is presented. The algorithm is tested using a model of the heliostat field of the CESA-1 solar tower plant at the Plataforma Solar de Almería (PSA) in southern Spain. Simulation results show that substantial reduction of the computational time is achieved while similar performance to that obtained with the centralized approach is attained.
Estimation of effective solar irradiation using an unscented Kalman filter in a parabolic-trough field
(2012-12) Gallego Len, Antonio Javier; Camacho, Eduardo F.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Educación y Ciencia (MEC). España; Junta de Andalucía; European Union (UE)
Measurement of direct solar radiation has been shown to be very useful to improve control performance and disturbance rejection in solar fields by anticipating the effect of sudden changes in solar radiation due to clouds. Since direct solar radiation is measured locally by pyrheliometers, important errors in the estimation of the overall effective solar radiation can be produced when the pyrheliometer is covered by clouds while the rest of the solar field is not or viceversa. Furthermore, estimation of the overall efficiency affected by the reflectivity and absortance of metal tubes is very difficult because only local measurements can be obtained. This work proposes an algorithm for estimating overall solar radiation and efficiency at the field. The algorithm uses an unscented Kalman filter and it is validated by data obtained at the Plataforma Solar of Almerı´a (Spain).
Adaptative state-space model predictive control of a parabolic-trough field
(2012-09) Gallego Len, Antonio Javier; Camacho, Eduardo F.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Educación y Ciencia (MEC). España; Junta de Andalucía; European Union (UE)
Model Predictive control algorithms for trough solar plants make use of solar radiation measurements and the overall efficiency knowledge which are very useful in order to reject disturbances. However, direct solar radiation, mirror reflectivity and metal absorptance which affect overall efficiency, can only be measured locally. In this paper, an adaptative model predictive control using an unscented Kalman filter (UKF) to estimate both the effective solar radiation and the metal–fluid temperature profiles is proposed. The control algorithm is validated by tests with real data taken from the ACUREX field of the PSA in Almería (Spain).
Optimal operation in solar trough plants: A case study
(Elsevier, 2013-09) Camacho, Eduardo F.; Gallego Len, Antonio Javier; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Educación. España; European Commission (EC)
One of the most important challenges in the use of solar energy is to make it economical. This can be done by reducing investment and operating costs and by increasing solar plants performance. This paper presents a three-layer hierarchical control strategy to optimize the operation of solar trough plants. The first layer deals with the operational planning of the solar plant and it computes the electrical power to be produced and delivered. The second layer computes the optimal set-point for the solar plant, aiming at producing the electrical power provided by the first layer and optimizing the economic profit. The third layer addresses the problem of tracking the set-point calculated by the second layer. A model of the Plataforma Solar de Almería (PSA) solar trough plant is used to illustrate the proposed algorithm. Simulations show that important increments in electricity production are obtained by using the proposed control scheme.
Observer-based Model Predictive Control of a parabolic-trough field
(Elsevier, 2013-11) Gallego Len, Antonio Javier; Fele, Filiberto; Camacho, Eduardo F.; Yebra, L.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Educación y Cultura (MEC). España; European Union (UE)
n this paper we propose an observer-based Model Predictive Control strategy for solar trough plants. The controller has been tested at the parabolic-trough field ACUREX at the Plataforma Solar de Almería (PSA). Results of the tests carried out at the plant are presented. Furthermore, the performance of the proposed control strategy is compared to a Gain-Scheduling Generalized Predictive controller and a PID controller.
Temperature homogenization of a solar trough field for performance improvement
(Elsevier, 2018-05-01) Sanchez, A.J.; Gallego Len, Antonio Javier; Escaño González, Juan Manuel; Camacho, Eduardo F.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Economía, Industria y Competitividad. España
Optimal operation of solar plant is a challenge due to the multiple disturbances affecting the plant. Research in optimal temperature set-point tracking is extensive whereas research concerning optimal power production under disturbances is not. A solar plant has to deal not only with temperature and radiation disturbances but also with dirt accumulated on the collectors which lead to a great disparity in collectors reflectivity. Cleaner collectors produce higher output temperature because of the higher reflectivity. In fact the temperature in this collector may be so high that they have to be defocused with the corresponding energy losses. The paradox is that the most efficient loops may be the one collecting less energy because of defocusing. This paper proposes a new solution based in the manipulation of the loops input valves in order to homogenize the temperatures of the different loops and avoid defocusing. The paper presents an original non linear model based optimization to homogenize the loop temperatures by manipulating the inlet valves of the loops. The nonlinear model is based on a distributed parameters model. In order to perform the optimization, temperature profiles of the loops and its reflectivities are needed. These are obtained by means of a Classification and Regression Trees (CARTs) trained with the full distributed parameters model. Simulations are carried out using a model of the Plataforma Solar de Almería (PSA) solar trough plant to show the results of the proposed control scheme, temperature homogenization and production benefit.
Control of thermal solar energy plants
(Elsevier, 2014-02) Camacho, Eduardo F.; Berenguel, Manuel; Gallego Len, Antonio Javier; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España; Junta de Andalucía
This work deals with the main control problems found in solar power systems and the solutions proposed in literature. The paper first describes the main solar power technologies, some of the control approaches and then describes the main challenges encountered when controlling solar power systems.
Aerodynamic Interaction Minimization in Coaxial Multirotors via Optimized Control Allocation
(MDPI, 2024-09) Berra, Andrea; Trujillo Soto, Miguel Ángel; Heredia Benot, Guillermo; 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. TEP151: Robótica, visión y control
Coaxial multirotors, characterized by overlapping rotors, represent a common solution to increasing payload capacity while maintaining a compact platform size. However, the overlap between motors generates airflow disturbances that, if not taken into account properly, may decrease the system’s overall performance. In this paper, aerodynamic interactions for coaxial multirotors are analyzed and characterized. Two rotor models are introduced, which account for the aerodynamic interaction between the upper and the lower rotor. Each model is accompanied by its corresponding mixer design and analyzed with respect to the state-of-the-art mixer solution for classical multirotor systems. The proposed approaches are tested through rotor stand experiments, simulations, and implementation on an actual coaxial platform. The results demonstrate the effectiveness of these models in mitigating the adverse aerodynamic effects, thereby improving the performance and efficiency of coaxial multirotor systems.
Corrigendum to “Tuning of modern speed drives using IFOC: A case study for a five-phase induction machine
(Elsevier, 2024-11) Barrero García, Federico; Garrido Satué, Manuel; Colodro Ruiz, Francisco; Arahal, Manuel R.; Universidad de Sevilla. Departamento de Ingeniería Electrónica; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Universidad de Sevilla. TIC275: Investigación y Desarrollo en Electrónica, Automática y Tecnologías Emergentes
Tuning of modern speed drives using IFOC: A case study for a five-phase induction machine
(Elsevier, 2024-12) Barrero, Federico; Garrido Satué, Manuel; Colodro Ruiz, Francisco; Ruiz Arahal, Manuel; Universidad de Sevilla. Departamento de Ingeniería Electrónica; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICINN). España; Universidad de Sevilla. TIC275: Investigación y Desarrollo en Electrónica, Automática y Tecnologías Emergentes
Electric machines have existed since the beginning of the 19th century. Although variable-speed drive technology has evolved considerably during the last years, the indirect field-oriented control technique has been in use as a standard control method since the 1960s, using an outer speed loop and an inner current loop. This paper deals with the non-trivial problem of tuning the outer speed controller of modern variable-speed drives, where experimental results are provided to show the need for new tuning methods. The influence of non-modeled effects on the performance of the drive is illustrated considering the dynamical effect of the mechanical speed sensing procedure using optical devices. This effect has not been generally considered. However, it is shown that it has a notable effect on tuning for high performance drives. This is especially true when considering some figures of merits of relevance for drives, such as the torque ripple. A Pareto analysis is proposed to reveal trade-offs between typical figures of merit to establish new tuning methods. To focus our contribution, a five-phase induction drive is considered as the case study, using a finite-state model predictive controller for the stator current control and PI regulators for the outer speed control loop.
Trade-Off Analysis of Drive Dynamics Considering Speed and Current Loops
(MDPI, 2024-08) Arahal, Manuel R.; Garrido Satué, Manuel; Colodro Ruiz, Francisco; Martínez Heredia, Juana María; Universidad de Sevilla. Departamento de Ingeniería Electrónica; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Universidad de Sevilla. TIC275: Investigación y Desarrollo en Electrónica, Automática y Tecnologías Emergentes; Universidad de Sevilla. TEP201: Ingeniería de Automatización, Control y Robótica
Electric drive control is an important area of research due to its ubiquity. In particular, multi-phase induction machines are an important field due to their inherent robustness. Tuning of the inner loop (speed) and outer loop (current) is typically tackled separately. The problem of trade-off analysis for the tuning of both loops has never been tackled before, which motivates the present study. This paper examines the complex and non-linear relationships between commonly used performance indicators in variable speed applications. The paper shows that there are links between performance indicators for both loops. This prompts a more detailed study of concurrent tuning. Also, it is shown that said links are, in a variable speed drive, dependent on the operating point. This requires studying more than just one operating point. Experimental results for a five-phase induction motor are used to validate the analysis.
Simulation of Full Wavefield Data with Deep Learning Approach for Delamination Identification
(MDPI, 2024-07) Ullah, Saeed; Kudela, Pawel; Ijjeh, Abdalraheem A.; Chatzi, Eleni; Ostachowicz, Wieslaw; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; National Science Center. Poland
In this work, a novel approach of guided wave-based damage identification in composite laminates is proposed. The novelty of this research lies in the implementation of ConvLSTM-based autoencoders for the generation of full wavefield data of propagating guided waves in composite structures. The developed surrogate deep learning model takes as input full wavefield frames of propagating waves in a healthy plate, along with a binary image representing delamination, and predicts the frames of propagating waves in a plate, which contains single delamination. The evaluation of the surrogate model is ultrafast (less than 1 s). Therefore, unlike traditional forward solvers, the surrogate model can be employed efficiently in the inverse framework of damage identification. In this work, particle swarm optimisation is applied as a suitable tool to this end. The proposed method was tested on a synthetic dataset, thus showing that it is capable of estimating the delamination location and size with good accuracy. The test involved full wavefield data in the objective function of the inverse method, but it should be underlined as well that partial data with measurements can be implemented. This is extremely important for practical applications in structural health monitoring where only signals at a finite number of locations are available.
Conformal predictions for probabilistically robust scalable machine learning classification
(Springer, 2024-09) Carlevaro, Alberto; Alamo, Teodoro; Dabbene, Fabrizio; Mongelli, Maurizio; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Consejo Nacional de Investigación de Italia (Consiglio Nazionale delle Ricerche)
Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be used in practice. To define a reliable learning framework for classification from the very beginning of its design, the concept of scalable classifier was introduced to generalize the concept of classical classifier by linking it to statistical order theory and probabilistic learning theory. In this paper, we analyze the similarities between scalable classifiers and conformal predictions by introducing a new definition of a score function and defining a special set of input variables, the conformal safety set, which can identify patterns in the input space that satisfy the error coverage guarantee, i.e., that the probability of observing the wrong (possibly unsafe) label for points belonging to this set is bounded by a predefined ε error level. We demonstrate the practical implications of this framework through an application in cybersecurity for identifying DNS tunneling attacks. Our work contributes to the development of probabilistically robust and reliable machine learning models.
A priori data-driven robustness guarantees on strategic deviations from generalised Nash equilibria
(Elsevier, 2024-09) Pantazis, Georgios; Fele, Filiberto; Margellos, Kostas; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Ciencia, Innovación y Universidades (MICINN). España; European Union (UE); European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Engineering and Physical Sciences Research Council (UK)
In this paper we focus on noncooperative games with uncertain constraints coupling the agents’ decisions. We consider a setting where bounded deviations of agents’ decisions from the equilibrium are possible, and uncertain constraints are inferred from data. Building upon recent advances in the so called scenario approach, we propose a randomised algorithm that returns a nominal equilibrium such that a pre-specified bound on the probability of violation for yet unseen constraints is satisfied for an entire region of admissible deviations surrounding it—thus supporting neighbourhoods of equilibria with probabilistic feasibility certificates. For the case in which the game admits a potential function, whose minimum coincides with the social welfare optimum of the population, the proposed algorithmic scheme opens the road to achieve a trade-off between the guaranteed feasibility levels of the region surrounding the nominal equilibrium, and its system-level efficiency. Detailed numerical simulations corroborate our theoretical results.
Controlled Shaking of Trees With an Aerial Manipulator
(Institute of Electrical and Electronics Engineers (IEEE), 2024) González Morgado, Antonio; Cuniato, Eugenio; Tognon, Marco; Heredia Benot, Guillermo; Siegwart, Roland; Ollero Baturone, Aníbal; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; AEROTRAIN Marie Skłodowska-Curie; MARTIN; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Universidad de Sevilla. TEP151: Robótica, Visión y Control.
In recent years, the fields of application of aerial manipulators have expanded, ranging from infrastructure inspection to physical interaction with flexible elements, such as branches and trees. This article presents the controlled shaking of a tree with an aerial manipulator. Our work aims at contributing to applications like the identification of tree parameters for environmental health monitoring or the collection of samples and fruits by vibration. To this end, we propose a control strategy for controlled shaking of flexible systems. We adopt a self-excited oscillation strategy that induces vibrations at the natural frequency of the system, at which the greatest amplification and therefore the greatest vibrations occur. Likewise, this work presents a simplified 1 degree of freedom (DoF) model based on the Rayleigh–Ritz method to analyze dynamic interaction between a tree and the aerial manipulator with the controlled shaking strategy. The proposed control strategy is evaluated through indoor experiments, where an aerial manipulator shakes an indoor tree made of bamboo canes. Experimental results show how the proposed model can estimate properly the amplitude of the vibration and the frequency of the vibration, depending on the grasping point and the control gain of the self-excited oscillation strategy.
Data-driven learning and control of nonlinear system dynamics: A robust-learning approach via Sontag’s control formula
(Springer Nature, 2024) Becerra-Mora, Yeyson; Acosta Rodríguez, José Ángel; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Universidad de Sevilla; Universidad de Sevilla. TEP995: Multi-robot and Control Systems
This work falls into the field of discovering the dynamic equations of stabilizable nonlinear systems, via a learning-and-control algorithm to process the data sets of trajectories previously obtained. To this end, an interlaced method to learn and control nonlinear system dynamics from a set of demonstrations is proposed, under a constrained optimization framework for the unsupervised learning process. The nonlinear system is modeled as a mixture of Gaussians and Sontag’s formula together with its associated Control Lyapunov Function is proposed for learning and control. Lyapunov stability and robustness in noisy data environments are guaranteed, as a result of the inclusion of control in the learning-optimization problem. The performances are validated through a well-known dataset of demonstrations with handwriting complex trajectories, succeeding in all of them and outperforming previous methods under bounded disturbances, possibly coming from inaccuracies, imperfect demonstrations, or noisy datasets. As a result, the proposed interlaced solution yields a good performance trade-off between reproductions and robustness. Therefore, this work sheds some more light on the automatic discovery of nonlinear dynamics from noisy raw data.
Digital twin of an absorption chiller for solar cooling
(Elsevier, 2023-05) Ortiz Machado, Diogo; Chicaiza Salazar, William David; Escaño González, Juan Manuel; Gallego Len, Antonio Javier; Gustavo, Andrade A. de; Normey Rico, Julio Elías; Bordons Alba, Carlos; Camacho, Eduardo F.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Sevilla.TEP116: Automatica y Robotica Industrial
The aim of this study is to create a digital twin of a commercial absorption chiller for control and optimization purposes. The chiller is a complex system that is affected by solar intermittency and non-linearities. The authors use Adaptive Neuro-fuzzy Inference System (ANFIS) to model the chiller's behavior during transients and part-load events. The chiller is divided into four sub-models, each modeled by ANFIS, and trained and validated using data from 15 days of operation. The ANFIS models are precise, accurate, and fast, with a worst-case Mean Absolute Percentage Error (MAPE) of 3.30% and reduced error dispersion (σE=0.88) and Standard Error (SE=0.01). The models outperformed literature models in terms of MAPE, with MAPEs of 1.12%, 2.21%, and 3.24% for the High Temperature Generator (HTG), absorber + condenser, and evaporator outlet temperatures, respectively. The computational execution time of the model is also a valuable asset, with an average simulation step taking less than 0.20 ms and a total simulation time of 8.9 s for three days of operation. The resulting digital twin is suitable for Model Predictive Control applications and fast what-if analysis and optimization due to its gray-box representation and computational speed.
Model-Based Control for Power Converters With Variable Sampling Time: A Case Example Using Five-Phase Induction Motor Drives
(Institute of Electrical and Electronics Engineers Inc. (IEEE), 2019-08) Arahal, Manuel R.; Martín Torres, Cristina; Barrero, Federico; González Prieto, Ignacio; Durán, Mario J.; Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; Universidad de Sevilla. Departamento de Ingeniería Electrónica; Ministerio de Economía y Competitividad (MINECO). España; Universidad de Sevilla; Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica; Universidad de Sevilla. TIC201: ACE-TI
Discrete-time control of power converters without modulation blocks have been considered in recent times in modern high-performance electromechanical drives, particularly with the appearance of model predictive control in its finite set version. The shortcomings produced by the fixed discretization of time used in this kind of control systems has been analyzed, and several methods have been put forward to deal with them. Most of the alternatives increase the complexity of the controller introducing different analytical modulation methods. However, a variable sampling time can be a simpler and more natural solution, at the expense of using a less-known paradigm for implementation. This paper introduces a new control approach based on a model of the system as in predictive controllers but using variable sampling time. It can be applied to modern power converters and drives, including conventional three-phase or advanced multiphase ones. Experimental results are provided to test the ability of the controller using a five-phase induction motor drive as a case example.

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