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Santos., M was born in Madrid, Spain. She received her B.Sc. and M.Sc. degrees in Physics (Computer Engineering) and her Ph.D in Physics from the University Complutense of Madrid (UCM). She is currently a Full Professor in System Engineering and Automatic Control. She is a member of the European Academy of Sciences and Arts. She has published many papers in international scientific journals and several books. She serves as member of the editorial board of journals, and she is editor-in-chief assistant of one of them. Her major research interests are intelligent control (fuzzy and neurofuzzy), pattern recognition, modeling and simulation, wind energy
Photovoltaic solar energy has evolved to be a viable and popular alternative for the generation of electricity. To analyze the profitability of these renewable energy systems, computer modelling of the solar devices has become a necessary and widespread practice in the academic and industrial world. The modelling not only allows the estimation of the electric productivity but also the estimation of the amortization of a solar installation. However, aging and deterioration of photovoltaic modules have been little studied yet and when these aging effects can be an important source of power degradation on solar cells and fault generation, and thus a cause of mismatching on amortization deadlines. In this work, based on a proposed long-term behavioral generator model, the most common aging mechanisms of solar panels have been modelled and simulated. The results have been validated against a real solar medium-high power generator designed for grid connection in Spain. Results allow to measure the efficiency of these photovoltaics energy systems, get better accuracy of their amortization and estimate the power degradation range of photovoltaic modules.
Andres Guisandez Hernandez; Santos Penas Santos. Modelling and Experimental Validation of Aging Factors of Photovoltaic Solar Cells. IEEE Latin America Transactions 2021, 19, 1270 -1277.
AMA StyleAndres Guisandez Hernandez, Santos Penas Santos. Modelling and Experimental Validation of Aging Factors of Photovoltaic Solar Cells. IEEE Latin America Transactions. 2021; 19 (8):1270-1277.
Chicago/Turabian StyleAndres Guisandez Hernandez; Santos Penas Santos. 2021. "Modelling and Experimental Validation of Aging Factors of Photovoltaic Solar Cells." IEEE Latin America Transactions 19, no. 8: 1270-1277.
Floating offshore wind turbines (FOWT) are designed to overcome some of the limitations of offshore bottom-fixed ones. The development of computational models to simulate the behavior of the structure and the turbine is key to understanding the wind energy system and demonstrating its feasibility. In this work, a general methodology for the identification of reduced dynamic models of barge-type FOWTs is presented. The method is described together with an example of the development of a dynamic model of a 5 MW floating offshore wind turbine. The novelty of the proposed identification methodology lies in the iterative loop relationship between the identification and validation processes. Diversified data sets are used to select the best-fitting identified parameters by cross evaluation of every set among all validating conditions. The data set is generated for different initial FOWT operating conditions. Indeed, an optimal initial condition for platform pitch was found to be far enough from the system at rest to allow the dynamics to be well characterized but not so far that the unmodeled system nonlinearities were so large that they affected significantly the accuracy of the model. The model has been successfully applied to structural control research to reduce fatigue on a barge-type FOWT.
Daniel Villoslada; Matilde Santos; María Tomás-Rodríguez. General Methodology for the Identification of Reduced Dynamic Models of Barge-Type Floating Wind Turbines. Energies 2021, 14, 3902 .
AMA StyleDaniel Villoslada, Matilde Santos, María Tomás-Rodríguez. General Methodology for the Identification of Reduced Dynamic Models of Barge-Type Floating Wind Turbines. Energies. 2021; 14 (13):3902.
Chicago/Turabian StyleDaniel Villoslada; Matilde Santos; María Tomás-Rodríguez. 2021. "General Methodology for the Identification of Reduced Dynamic Models of Barge-Type Floating Wind Turbines." Energies 14, no. 13: 3902.
In this study a new internal clustering validation index is proposed. It is based on a measure of the uniformity of the data in clusters. It uses the local density of each cluster, in particular, the normalized variability of the density within the clusters to find the ideal partition. The new validity measure allows it to capture the spatial pattern of the data and obtain the right number of clusters in an automatic way. This new approach, unlike the traditional one that usually identifies well-separated compact clouds, works with arbitrary-shape clusters that may be contiguous or even overlapped. The proposed clustering measure has been evaluated on nine artificial data sets, with different cluster distributions and an increasing number of classes, on three highly nonlinear data sets, and on 17 real data sets. It has been compared with nine well-known clustering validation indices with very satisfactory results. This proves that including density in the definition of clustering validation indices may be useful to identify the right partition of arbitrary-shape and different-size clusters.
Juan Carlos Rojas‐Thomas; Matilde Santos. New internal clustering validation measure for contiguous arbitrary‐shape clusters. International Journal of Intelligent Systems 2021, 36, 5506 -5529.
AMA StyleJuan Carlos Rojas‐Thomas, Matilde Santos. New internal clustering validation measure for contiguous arbitrary‐shape clusters. International Journal of Intelligent Systems. 2021; 36 (10):5506-5529.
Chicago/Turabian StyleJuan Carlos Rojas‐Thomas; Matilde Santos. 2021. "New internal clustering validation measure for contiguous arbitrary‐shape clusters." International Journal of Intelligent Systems 36, no. 10: 5506-5529.
There is strong clinical evidence from the current literature that certain psychological and physiological indicators are closely related to mood changes. However, patients with mental illnesses who present similar behavior may be diagnosed differently, which is why a personalized study of each patient is necessary. Following previous promising results in the detection of depression, in this work, supervised machine learning (ML) algorithms were applied to classify the different states of patients diagnosed with bipolar depressive disorder (BDD). The purpose of this study was to provide relevant information to medical staff and patients’ relatives in order to help them make decisions that may lead to a better management of the disease. The information used was collected from BDD patients through wearable devices (smartwatches), daily self-reports, and medical observation at regular appointments. The variables were processed and then statistical techniques of data analysis, normalization, noise reduction, and feature selection were applied. An individual analysis of each patient was carried out. Random Forest, Decision Trees, Logistic Regression, and Support Vector Machine algorithms were applied with different configurations. The results allowed us to draw some conclusions. Random Forest achieved the most accurate classification, but none of the applied models were the best technique for all patients. Besides, the classification using only selected variables produced better results than using all available information, though the amount and source of the relevant variables differed for each patient. Finally, the smartwatch was the most relevant source of information.
Pavel Llamocca; Victoria López; Matilde Santos; Milena Čukić. Personalized Characterization of Emotional States in Patients with Bipolar Disorder. Mathematics 2021, 9, 1174 .
AMA StylePavel Llamocca, Victoria López, Matilde Santos, Milena Čukić. Personalized Characterization of Emotional States in Patients with Bipolar Disorder. Mathematics. 2021; 9 (11):1174.
Chicago/Turabian StylePavel Llamocca; Victoria López; Matilde Santos; Milena Čukić. 2021. "Personalized Characterization of Emotional States in Patients with Bipolar Disorder." Mathematics 9, no. 11: 1174.
Floating offshore wind turbines (FOWT) are subjected to strong loads, mainly due to wind and waves. These disturbances cause undesirable vibrations that affect the structure of these devices, increasing the fatigue and reducing its energy efficiency. Among others, a possible way to enhance the performance of these wind energy devices installed in deep waters is to combine them with other marine energy systems, which may, in addition, improve its stability. The purpose of this work is to analyze the effects that installing some devices on the platform of a barge-type wind turbine have on the vibrations of the structure. To do so, two passive control devices, TMD (Tuned Mass Damper), have been installed on the platform of the floating device, with different positions and orientations. TMDs are usually installed in the nacelle or in the tower, which imposes space, weight, and size hard constraints. An analysis has been carried out, using the FAST software model of the NREL-5MW FOWT. The results of the suppression rate of the tower top displacement and the platform pitch have been obtained for different locations of the structural control devices. They have been compared with the system without TMD. As a conclusion, it is possible to say that these passive devices can improve the stability of the FOWT and reduce the vibrations of the marine turbine. However, it is indispensable to carry out a previous analysis to find the optimal orientation and position of the TMDs on the platform.
Antonio Galán-Lavado; Matilde Santos. Analysis of the Effects of the Location of Passive Control Devices on the Platform of a Floating Wind Turbine. Energies 2021, 14, 2850 .
AMA StyleAntonio Galán-Lavado, Matilde Santos. Analysis of the Effects of the Location of Passive Control Devices on the Platform of a Floating Wind Turbine. Energies. 2021; 14 (10):2850.
Chicago/Turabian StyleAntonio Galán-Lavado; Matilde Santos. 2021. "Analysis of the Effects of the Location of Passive Control Devices on the Platform of a Floating Wind Turbine." Energies 14, no. 10: 2850.
Wind energy plays a key role in the sustainability of the worldwide energy system. It is forecasted to be the main source of energy supply by 2050. However, for this prediction to become reality, there are still technological challenges to be addressed. One of them is the control of the wind turbine in order to improve its energy efficiency. In this work, a new hybrid pitch-control strategy is proposed that combines a lookup table and a neural network. The table and the RBF neural network complement each other. The neural network learns to compensate for the errors in the mapping function implemented by the lookup table, and in turn, the table facilitates the learning of the neural network. This synergy of techniques provides better results than if the techniques were applied individually. Furthermore, it is shown how the neural network is able to control the pitch even if the lookup table is poorly designed. The operation of the proposed control strategy is compared with the neural control without the table, with a PID regulator, and with the combination of the PID and the lookup table. In all cases, the proposed hybrid control strategy achieves better results in terms of output power error.
Jesús Sierra-García; Matilde Santos. Lookup Table and Neural Network Hybrid Strategy for Wind Turbine Pitch Control. Sustainability 2021, 13, 3235 .
AMA StyleJesús Sierra-García, Matilde Santos. Lookup Table and Neural Network Hybrid Strategy for Wind Turbine Pitch Control. Sustainability. 2021; 13 (6):3235.
Chicago/Turabian StyleJesús Sierra-García; Matilde Santos. 2021. "Lookup Table and Neural Network Hybrid Strategy for Wind Turbine Pitch Control." Sustainability 13, no. 6: 3235.
Data about wind are usually available from different databases, for different locations. In general, this information is the average of the wind speed over time. The wind reports are crucial for designing wind turbine controllers. But when working with floating offshore wind turbines (FOWT), two problems arise regarding the wind measurement. On the one hand, there are no buoys at deep sea, but near the coast where the wind is not so strong neither so stable; so the measurements do not fully correspond to reality. On the other hand, these floating devices are subjected to extreme environmental conditions (waves, currents, $\ldots $ ) that produce disturbances and thus may distort wind measurements. To address this problem, this work presents a novel pitch neuro-control architecture based on neuro-estimators of the effective wind. The control system is composed of a proportional-integral-derivative (PID) controller, a lookup table, a neuro-estimator, and a virtual sensor. The neuro-estimator is used to estimate the effective wind in the FOWT and to forecast its future value. Both current and future wind signals are combined and power the controller. The virtual sensor also provides a measure of the effective wind based on other available signals related to the wind turbine, such as the pitch angle and the angular velocity of the generator. Neural networks are trained online to adapt to changes in the environment. Intensive simulations are carried out to validate the effectiveness of this neuro control approach. Controller performance is compared to a PID, obtaining better results. Indeed, an improvement of 16% for sinusoidal wind and an average improvement of 8% are observed.
J. Enrique Sierra-Garcia; Matilde Santos. Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators. IEEE Access 2021, 9, 10413 -10425.
AMA StyleJ. Enrique Sierra-Garcia, Matilde Santos. Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators. IEEE Access. 2021; 9 ():10413-10425.
Chicago/Turabian StyleJ. Enrique Sierra-Garcia; Matilde Santos. 2021. "Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators." IEEE Access 9, no. : 10413-10425.
Automatic guided vehicles (AGVs) are unmanned transport vehicles widely used in the industry to substitute manned industrial trucks and conveyors. They are now considered to play a key role in the development of the Industry 4.0 due to their temporal and spatial flexibility. However, in order to deal with the AGV as a potential mobile robot with high capacities and certain level of intelligence, it is necessary to develop control-oriented models of these complex and nonlinear systems. In this paper, the modelling of this vehicle as a whole is addressed. It can be considered composed of several interrelated subsystems: control, safety, driving, guiding and localization, power storage, and charging systems. The kinematics equations of a tricycle vehicle are obtained, and a controller is proposed. An extended hybrid automata formalism is used to define the behaviour of the safety and the control systems, as well as their interaction. In addition, the electrical equivalent circuit of the batteries, charger, and the motors is studied. The architecture of the holistic model is presented. Simulation results of the AGV in a workspace scenario validate the model and prove the efficiency of this approach.
J. Enrique Sierra-García; Matilde Santos. Mechatronic Modelling of Industrial AGVs: A Complex System Architecture. Complexity 2020, 2020, 1 -21.
AMA StyleJ. Enrique Sierra-García, Matilde Santos. Mechatronic Modelling of Industrial AGVs: A Complex System Architecture. Complexity. 2020; 2020 ():1-21.
Chicago/Turabian StyleJ. Enrique Sierra-García; Matilde Santos. 2020. "Mechatronic Modelling of Industrial AGVs: A Complex System Architecture." Complexity 2020, no. : 1-21.
In this study, the Intelligent Infectious Diseases Algorithm (IIDA) has been developed to locate the sources of infection and survival rate of coronavirus disease 2019 (COVID-19), in order to propose health care routes for population affected by COVID-19. The main goal of this computational algorithm is to reduce the spread of the virus and decrease the number of infected people. To do so, health care routes are generated according to the priority of certain population groups. The algorithm was applied to New York state data. Based on infection rates and reported deaths, hot spots were determined by applying the kernel density estimation (KDE) to the groups that have been previously obtained using a clustering algorithm together with the elbow method. For each cluster, the survival rate —the key information to prioritize medical care— was determined using the proportional hazards model. Finally, ant colony optimization (ACO) and the traveling salesman problem (TSP) optimization algorithms were applied to identify the optimal route to the closest hospital. The results obtained efficiently covered the points with the highest concentration of COVID-19 cases. In this way, its spread can be prevented and health resources optimized.
Cesar Guevara; Matilde Santos Penas. Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm. IEEE Access 2020, 8, 201925 -201936.
AMA StyleCesar Guevara, Matilde Santos Penas. Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm. IEEE Access. 2020; 8 (99):201925-201936.
Chicago/Turabian StyleCesar Guevara; Matilde Santos Penas. 2020. "Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm." IEEE Access 8, no. 99: 201925-201936.
The generalized learning algorithm can be efficiently used as control strategy, but it has some drawbacks such as: sensitivity to the training dataset, poor robustness against changes in the system, difficulty to generate the control signals without destabilising the plant, tuning of the controller, etc. To overcome some of these issues, in this work a new switched neural adaptive control strategy is proposed. It is based on the combination of an adaptive artificial neural network, a PID regulator, an estimated inverse model of the plant and two switches to route the signals properly in the control scheme. The technique is described using the hybrid automata formalism. In order to test the validity of this proposal, it is applied to the control of a quadrotor unmanned aerial vehicle (UAV), subjected to changes in its mass and wind disturbances. Simulation results show how the on-line learning increases the robustness of the controller, reducing the effects of the mass change and of the wind on the UAV stabilization, thus improving the UAV trajectory tracking.
J. Enrique Sierra-García; Matilde Santos. Switched learning adaptive neuro-control strategy. Neurocomputing 2020, 452, 450 -464.
AMA StyleJ. Enrique Sierra-García, Matilde Santos. Switched learning adaptive neuro-control strategy. Neurocomputing. 2020; 452 ():450-464.
Chicago/Turabian StyleJ. Enrique Sierra-García; Matilde Santos. 2020. "Switched learning adaptive neuro-control strategy." Neurocomputing 452, no. : 450-464.
In this paper an intelligent controller is designed to obtain the maximum power of a large floating offshore wind turbine. The control of these turbines is more complex due to the strong loads they are subjected to and the uncertainty that comes from the environment, mainly wind and waves, and from its non-linear dynamics. In this case, the control goal is to maximize the output power of the wind turbine by controlling the rotor speed. An incremental PD-type fuzzy controller has been implemented; it generates the pitch angle reference. The performance of this control scheme on the NREL 5 MW floating offshore wind turbine has been compared with the internal control that is provided within the FAST software. Results are encouraging, showing that the intelligent control strategy is able to produce more energy.
Carlos Serrano-Barreto; Matilde Santos. Intelligent Fuzzy Optimized Control for Energy Extraction in Large Wind Turbines. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 269 -276.
AMA StyleCarlos Serrano-Barreto, Matilde Santos. Intelligent Fuzzy Optimized Control for Energy Extraction in Large Wind Turbines. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():269-276.
Chicago/Turabian StyleCarlos Serrano-Barreto; Matilde Santos. 2020. "Intelligent Fuzzy Optimized Control for Energy Extraction in Large Wind Turbines." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 269-276.
Offshore wind turbines, and particularly floating wind turbines (FOWT) are subjected to strong wind and wave loads that affect the structural stability and energy efficiency of these renewable energy devices. Although wind -and less often waves- forecasting models have been developed, a deep analysis of the relationship between both external disturbances is necessary to consider the combined effect on the fatigue of the offshore WT. This work presents a study of the most relevant features of wind and waves using distribution analysis and ML techniques on wind and waves real data from an offshore buoy. Linear regression and SVM have been applied to the modelling of the data. These models may be very useful for the design of these floating structures and to study the impact of these external loads on the fatigue. The results lead us to consider the necessity of generating short-term models in specific geographical locations.
Montserrat Sacie; Rafael López; Matilde Santos. Exploratory Data Analysis of Wind and Waves for Floating Wind Turbines in Santa María, California. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 252 -259.
AMA StyleMontserrat Sacie, Rafael López, Matilde Santos. Exploratory Data Analysis of Wind and Waves for Floating Wind Turbines in Santa María, California. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():252-259.
Chicago/Turabian StyleMontserrat Sacie; Rafael López; Matilde Santos. 2020. "Exploratory Data Analysis of Wind and Waves for Floating Wind Turbines in Santa María, California." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 252-259.
The control strategy defined for a wind turbine (WT) aims to achieve the highest energy efficiency and at the same time to ensure safe operation under all wind conditions. The goal of the pitch control of a WT is to stabilize the output power around its nominal (rated) value by means of the position of the rotor blades with respect to the wind. In this work, a pitch control strategy based on reinforcement learning (RL) is proposed. The control system consists of a state estimator, a reward mechanism, a policy table and policy update algorithm. Different reward strategies and policy update algorithms for the RL controller have been tested and compared with a PID regulator. The proposed controller stabilizes the output power of the wind turbine around the rated power more accurately and with smaller overshoot than the traditional one.
J. Enrique Sierra-García; Matilde Santos. Wind Turbine Pitch Control First Approach Based on Reinforcement Learning. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 260 -268.
AMA StyleJ. Enrique Sierra-García, Matilde Santos. Wind Turbine Pitch Control First Approach Based on Reinforcement Learning. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():260-268.
Chicago/Turabian StyleJ. Enrique Sierra-García; Matilde Santos. 2020. "Wind Turbine Pitch Control First Approach Based on Reinforcement Learning." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 260-268.
In this work, a pitch controller of a wind turbine (WT) inspired by reinforcement learning (RL) is designed and implemented. The control system consists of a state estimator, a reward strategy, a policy table, and a policy update algorithm. Novel reward strategies related to the energy deviation from the rated power are defined. They are designed to improve the efficiency of the WT. Two new categories of reward strategies are proposed: “only positive” (O-P) and “positive-negative” (P-N) rewards. The relationship of these categories with the exploration-exploitation dilemma, the use of ϵ-greedy methods and the learning convergence are also introduced and linked to the WT control problem. In addition, an extensive analysis of the influence of the different rewards in the controller performance and in the learning speed is carried out. The controller is compared with a proportional-integral-derivative (PID) regulator for the same small wind turbine, obtaining better results. The simulations show how the P-N rewards improve the performance of the controller, stabilize the output power around the rated power, and reduce the error over time.
Jesús Enrique Sierra-García; Matilde Santos. Exploring Reward Strategies for Wind Turbine Pitch Control by Reinforcement Learning. Applied Sciences 2020, 10, 7462 .
AMA StyleJesús Enrique Sierra-García, Matilde Santos. Exploring Reward Strategies for Wind Turbine Pitch Control by Reinforcement Learning. Applied Sciences. 2020; 10 (21):7462.
Chicago/Turabian StyleJesús Enrique Sierra-García; Matilde Santos. 2020. "Exploring Reward Strategies for Wind Turbine Pitch Control by Reinforcement Learning." Applied Sciences 10, no. 21: 7462.
This work aims to establish a general and optimized procedure for the initial refuelling of commercial airplanes, as this loading process is strongly related to safety and energy saving issues. The on‐ground refuelling is addressed as an optimization problem whose cost function involves expert knowledge about constraints and factors that influence the aircraft stability and performance. Several heterogeneous criteria (fuelling time, structural load, flow transfers, etc.) have been considered and weighted accordance to its importance in terms of stability. This allows us to adapt the strategy to any type and planned trip of the airplane. The priority is the positioning of the centre of mass of the civil aircraft within safety and manoeuvrability margins, and near the optimal position. Evolutive algorithms are applied, keeping feasible solutions by modifying genetic operators. As a case of study, the initial refuelling of a long range type commercial aircraft, the Airbus A330‐200, is analysed. Simulation results have proved this methodology to be efficient and optimal. Even more, this heuristic and general approach improves the traditional solution that follows a set of pre‐defined rules that are specific for each type of aircraft.
Elías Plaza; Matilde Santos. Knowledge based approach to ground refuelling optimization of commercial airplanes. Expert Systems 2020, 38, 1 .
AMA StyleElías Plaza, Matilde Santos. Knowledge based approach to ground refuelling optimization of commercial airplanes. Expert Systems. 2020; 38 (2):1.
Chicago/Turabian StyleElías Plaza; Matilde Santos. 2020. "Knowledge based approach to ground refuelling optimization of commercial airplanes." Expert Systems 38, no. 2: 1.
This paper presents a Soft Computing based system to identify and classify conventional two-lane roads according to their geometrical characteristics. The variability of input information and the uncertainty generated by the overlapping of this information make fuzzy logic a suitable technique to address this problem. A fuzzy rule-based Mamdani-type inference system and a neuro-fuzzy system are applied. The roads geometrical features are measured by vehicle sensors and are used to classify the roads according to their real conditions. The conventional two-lane roads used for this research are located in the Madrid Region, Spain. The good results obtained with the fuzzy system suggests this intelligent system can be used to update the road databases; the theoretical class of road assigned to each road should be updated according to their present characteristics, as this is key to estimate the recommended speed for a safety and comfortable driving.
Felipe Barreno; Matilde Santos; Manuel G. Romana. Fuzzy-Logic Based Identification of Conventional Two-Lane Roads. Advances in Intelligent Systems and Computing 2020, 418 -428.
AMA StyleFelipe Barreno, Matilde Santos, Manuel G. Romana. Fuzzy-Logic Based Identification of Conventional Two-Lane Roads. Advances in Intelligent Systems and Computing. 2020; ():418-428.
Chicago/Turabian StyleFelipe Barreno; Matilde Santos; Manuel G. Romana. 2020. "Fuzzy-Logic Based Identification of Conventional Two-Lane Roads." Advances in Intelligent Systems and Computing , no. : 418-428.
Autonomous and connected cars are almost here, and soon will be an everyday reality. Driver desired comfort, road conditions, travel dynamics and communication requirements between vehicles have to be considered. Simulation can help us to find how to improve road safety and comfort in traveling. Traffic flow models have been widely used in recent years to improve traffic management through understanding how current laws, with human drivers, should change in this new environment. Early attempts to driving modelling were restricted to the macroscopic level, mimicking continuous physical patterns, particularly waves. However, extensive improvements in technology have allowed the tracking of individual drivers in more detail. In this paper, the Intelligent Driver Model (IDM) is used to examine traffic flow behavior at a vehicle level with emphasis on the relation to the preceding vehicle, similarly as it is done by the Adaptive Cruise Control (ACC) systems nowadays. This traffic model has been modified to simulate vehicles at low speed and the interactions with their preceding vehicles; more specifically, in traffic congestion situations. This traffic jam scenario has been analyzed with a developed simulation tool. The results are encouraging, as they prove that automatic car speed control can potentially improve road safety and reduce driver stress.
Javier Echeto; Manuel G. Romana; Matilde Santos. Swarm Modelling Considering Autonomous Vehicles for Traffic Jam Assist Simulation. Advances in Intelligent Systems and Computing 2020, 429 -438.
AMA StyleJavier Echeto, Manuel G. Romana, Matilde Santos. Swarm Modelling Considering Autonomous Vehicles for Traffic Jam Assist Simulation. Advances in Intelligent Systems and Computing. 2020; ():429-438.
Chicago/Turabian StyleJavier Echeto; Manuel G. Romana; Matilde Santos. 2020. "Swarm Modelling Considering Autonomous Vehicles for Traffic Jam Assist Simulation." Advances in Intelligent Systems and Computing , no. : 429-438.
In this paper, a multi-UAV system is applied to explore a searching area. The influence of the partition of the surface and the effects of varying the number of UAVs are analyzed. The covering of the area is based on small rectangular polygon area decomposition. Each sector is assigned to an UAV and efficient coverage algorithms are applied. The UAV follows a zig-zag navigation strategy to go through the way-points located at the center of the cells of the corresponding area. The performance of the multi-UAV system is discussed for different scenarios. Simulation results in terms of travel time are presented.
Alfredo Pintado; Matilde Santos. A First Approach to Path Planning Coverage with Multi-UAVs. Advances in Intelligent Systems and Computing 2020, 667 -677.
AMA StyleAlfredo Pintado, Matilde Santos. A First Approach to Path Planning Coverage with Multi-UAVs. Advances in Intelligent Systems and Computing. 2020; ():667-677.
Chicago/Turabian StyleAlfredo Pintado; Matilde Santos. 2020. "A First Approach to Path Planning Coverage with Multi-UAVs." Advances in Intelligent Systems and Computing , no. : 667-677.
There are many control challenges in wind turbines: controlling the generator speed, blade angle adjustment (pitch control), and the rotation of the entire wind turbine (yaw control). In this work a neuro-control strategy is proposed to control the pitch angle of the wind turbine. The control architecture is based on an RBF neural network and an on-line learning algorithm. The neural network is not pre-trained but it learns from the system response (power output) in an unsupervised way. Simulation results on a small wind turbine show how the controller is able to stabilize the power output around the rated value for different wind ranges. The controller has been compared with a PID regulator with encouraging results.
Jesus Enrique Sierra Garcia; Matilde Santos. Wind Turbine Pitch Control with an RBF Neural Network. Advances in Intelligent Systems and Computing 2020, 397 -406.
AMA StyleJesus Enrique Sierra Garcia, Matilde Santos. Wind Turbine Pitch Control with an RBF Neural Network. Advances in Intelligent Systems and Computing. 2020; ():397-406.
Chicago/Turabian StyleJesus Enrique Sierra Garcia; Matilde Santos. 2020. "Wind Turbine Pitch Control with an RBF Neural Network." Advances in Intelligent Systems and Computing , no. : 397-406.
Automatic Guided Vehicles (AGV) suffer degradation in their electro-mechanical components which affect the navigation performance over time. The use of intelligent control techniques can help to alleviate this issue. In this work a new approach to control an AGV based on reinforcement learning (RL) is proposed. The space of states is defined using the guiding error, and the set of control actions provides the reference for the velocities of each wheel. Two different reward strategies are implemented, and different updating policies are tested. Simulation results show how the RL controller is able to successfully track a complex trajectory. The controller has been compared with a PID obtaining better results.
Jesus Enrique Sierra-Garcia; Matilde Santos. Control of Industrial AGV Based on Reinforcement Learning. Advances in Intelligent Systems and Computing 2020, 647 -656.
AMA StyleJesus Enrique Sierra-Garcia, Matilde Santos. Control of Industrial AGV Based on Reinforcement Learning. Advances in Intelligent Systems and Computing. 2020; ():647-656.
Chicago/Turabian StyleJesus Enrique Sierra-Garcia; Matilde Santos. 2020. "Control of Industrial AGV Based on Reinforcement Learning." Advances in Intelligent Systems and Computing , no. : 647-656.