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Sierra-Garcia, J.E. was born in Burgos, Spain. He received his M.Sc. degrees in Electronics and Telecommunications from the University of Valladolid (UVA) in 2007 and 2015, respectively, his M.Sc degree in Control Engineering from the National University for Distance Education (UNED) in 2014, and his Ph.D in Computer Science in 2019. Since 2012, he has been with the University of Burgos, where he is currently a Lecturer in System Engineering and Automatic Control in the Department of Electromechanical Engineering. His major research interests are intelligent control, robotics, signal processing, modeling, simulation, and wind energy
This work focuses on the control of the pitch angle of wind turbines. This is not an easy task due to the nonlinearity, the complex dynamics, and the coupling between the variables of these renewable energy systems. This control is even harder for floating offshore wind turbines, as they are subjected to extreme weather conditions and the disturbances of the waves. To solve it, we propose a hybrid system that combines fuzzy logic and deep learning. Deep learning techniques are used to estimate the current wind and to forecast the future wind. Estimation and forecasting are combined to obtain the effective wind which feeds the fuzzy controller. Simulation results show how including the effective wind improves the performance of the intelligent controller for different disturbances. For low and medium wind speeds, an improvement of 21% is obtained respect to the PID controller, and 7% respect to the standard fuzzy controller. In addition, an intensive analysis has been carried out on the influence of the deep learning configuration parameters in the training of the hybrid control system. It is shown how increasing the number of hidden units improves the training. However, increasing the number of cells while keeping the total number of hidden units decelerates the training.
J. Enrique Sierra-Garcia; Matilde Santos. Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control. Neural Computing and Applications 2021, 1 -15.
AMA StyleJ. Enrique Sierra-Garcia, Matilde Santos. Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control. Neural Computing and Applications. 2021; ():1-15.
Chicago/Turabian StyleJ. Enrique Sierra-Garcia; Matilde Santos. 2021. "Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control." Neural Computing and Applications , no. : 1-15.
Unmanned aerial vehicles (UAVs) have been proved very useful in civil and military sectors: defense, security, shipping, construction, agriculture, entertainment, etc. Some of these applications, especially those related to transport and logistic operations, require the use of suspended loads that may make the vehicle unstable. In order to deal with this non-linear complex system with a changing mass, further research on modelling and control must be developed. In this work, a new intelligent control strategy is proposed and applied to a quadrotor with a cable-suspended load. The UAV carrying a suspended load has two different dynamic behaviors, depending on the state of the cable. Thus, we proposed to model the complete system using the hybrid automata formalism. Using this novel UAV model approach, a hybrid control is designed based on feedback linearization controllers combined with an artificial neural network, which acts as an online estimator of the unknown mass. The suspended load is dealt with as an external disturbance. Simulation results show how the on-line learning control scheme increases the robustness of the control and it is able to stabilize the quadrotor without any information about neither the position of the load nor the tension of the cable. Additionally, the computational complexity of the proposal is studied to show the feasibility of the implementation of this intelligent control strategy on real hardware.
Jesús Enrique Sierra-García; Matilde Santos. Intelligent control of an UAV with a cable-suspended load using a neural network estimator. Expert Systems with Applications 2021, 183, 115380 .
AMA StyleJesús Enrique Sierra-García, Matilde Santos. Intelligent control of an UAV with a cable-suspended load using a neural network estimator. Expert Systems with Applications. 2021; 183 ():115380.
Chicago/Turabian StyleJesús Enrique Sierra-García; Matilde Santos. 2021. "Intelligent control of an UAV with a cable-suspended load using a neural network estimator." Expert Systems with Applications 183, no. : 115380.
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.
Jesus Enrique Sierra-Garcia; Daniel Sarabia; Matilde Santos Peñas. YOUNG STEM ONLINE TRAINING IN CONTROL ENGINEERING. INTED2021 Proceedings 2021, 4339 -4344.
AMA StyleJesus Enrique Sierra-Garcia, Daniel Sarabia, Matilde Santos Peñas. YOUNG STEM ONLINE TRAINING IN CONTROL ENGINEERING. INTED2021 Proceedings. 2021; ():4339-4344.
Chicago/Turabian StyleJesus Enrique Sierra-Garcia; Daniel Sarabia; Matilde Santos Peñas. 2021. "YOUNG STEM ONLINE TRAINING IN CONTROL ENGINEERING." INTED2021 Proceedings , no. : 4339-4344.
Jesus Enrique Sierra-Garcia; Matilde Santos Peñas. WIND ENERGY: A CLEAN MOTIVATIONAL PROBLEM FOR EDUCATION ON CONTROL ENGINEERING. INTED2021 Proceedings 2021, 1640 -1645.
AMA StyleJesus Enrique Sierra-Garcia, Matilde Santos Peñas. WIND ENERGY: A CLEAN MOTIVATIONAL PROBLEM FOR EDUCATION ON CONTROL ENGINEERING. INTED2021 Proceedings. 2021; ():1640-1645.
Chicago/Turabian StyleJesus Enrique Sierra-Garcia; Matilde Santos Peñas. 2021. "WIND ENERGY: A CLEAN MOTIVATIONAL PROBLEM FOR EDUCATION ON CONTROL ENGINEERING." INTED2021 Proceedings , no. : 1640-1645.
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.
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.
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.
In this work, a neural controller for wind turbine pitch control is presented. The controller is based on a radial basis function (RBF) network with unsupervised learning algorithm. The RBF network uses the error between the output power and the rated power and its derivative as inputs, while the integral of the error feeds the learning algorithm. A performance analysis of this neurocontrol strategy is carried out, showing the influence of the RBF parameters, wind speed, learning parameters, and control period, on the system response. The neurocontroller has been compared with a proportional-integral-derivative (PID) regulator for the same small wind turbine, obtaining better results. Simulation results show how the learning algorithm allows the neural network to adjust the proper control law to stabilize the output power around the rated power and reduce the mean squared error (MSE) over time.
Jesus Enrique Sierra-Garcia; Matilde Santos. Performance Analysis of a Wind Turbine Pitch Neurocontroller with Unsupervised Learning. Complexity 2020, 2020, 1 -15.
AMA StyleJesus Enrique Sierra-Garcia, Matilde Santos. Performance Analysis of a Wind Turbine Pitch Neurocontroller with Unsupervised Learning. Complexity. 2020; 2020 ():1-15.
Chicago/Turabian StyleJesus Enrique Sierra-Garcia; Matilde Santos. 2020. "Performance Analysis of a Wind Turbine Pitch Neurocontroller with Unsupervised Learning." Complexity 2020, no. : 1-15.
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.
In the article titled “Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors” [1], an affiliation was omitted in error. This affiliation has been added to the affiliation list above as number 2, and the author affiliations have been corrected.
Jesús Enrique Sierra; Matilde Santos. Corrigendum to “Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors”. Complexity 2020, 2020, 1 -1.
AMA StyleJesús Enrique Sierra, Matilde Santos. Corrigendum to “Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors”. Complexity. 2020; 2020 ():1-1.
Chicago/Turabian StyleJesús Enrique Sierra; Matilde Santos. 2020. "Corrigendum to “Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors”." Complexity 2020, no. : 1-1.
Jesús Enrique Sierra García; Matilde Santos. Influencia de la latencia en el control de AGVS a través de redes 5G. XL Jornadas de Automática: libro de actas (Ferrol, 4-6 de septiembre de 2019) 2020, 611 -616.
AMA StyleJesús Enrique Sierra García, Matilde Santos. Influencia de la latencia en el control de AGVS a través de redes 5G. XL Jornadas de Automática: libro de actas (Ferrol, 4-6 de septiembre de 2019). 2020; ():611-616.
Chicago/Turabian StyleJesús Enrique Sierra García; Matilde Santos. 2020. "Influencia de la latencia en el control de AGVS a través de redes 5G." XL Jornadas de Automática: libro de actas (Ferrol, 4-6 de septiembre de 2019) , no. : 611-616.
David Ramos; Jesus Enrique Sierra-Garcia. Detección de pallets mediante técnicas de visión por computador. XL Jornadas de Automática: libro de actas (Ferrol, 4-6 de septiembre de 2019) 2020, 772 -778.
AMA StyleDavid Ramos, Jesus Enrique Sierra-Garcia. Detección de pallets mediante técnicas de visión por computador. XL Jornadas de Automática: libro de actas (Ferrol, 4-6 de septiembre de 2019). 2020; ():772-778.
Chicago/Turabian StyleDavid Ramos; Jesus Enrique Sierra-Garcia. 2020. "Detección de pallets mediante técnicas de visión por computador." XL Jornadas de Automática: libro de actas (Ferrol, 4-6 de septiembre de 2019) , no. : 772-778.
Floating offshore wind turbines (FOWT) are exposed to hard environmental conditions which could impose expensive maintenance operations. These costs could be alleviated by monitoring these floating devices using UAVs. Given the FOWT location, UAVs are currently the only way to do this health monitoring. But this means that UAV should be well equipped and must be accurately controlled. Rotational inertia variation is a common disturbance that affect the aerial vehicles during these inspection tasks. To address this issue, in this work we propose a new neural controller based on adaptive neuro estimators. The approach is based on the hybridization of feedback linearization, PIDs and artificial neural networks. Online learning is used to help the network to improve the estimations while the system is working. The proposal is tested by simulation with several complex trajectories when the rotational inertia is multiplied by 10. Results show the proposed UAV neural controller gets a good tracking and the neuro estimators tackle the effect of the variations of the rotational inertia.
J. Enrique Sierra-Garcia; Matilde Santos; Juan G. Victores. Neural Controller of UAVs with Inertia Variations. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 169 -177.
AMA StyleJ. Enrique Sierra-Garcia, Matilde Santos, Juan G. Victores. Neural Controller of UAVs with Inertia Variations. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():169-177.
Chicago/Turabian StyleJ. Enrique Sierra-Garcia; Matilde Santos; Juan G. Victores. 2019. "Neural Controller of UAVs with Inertia Variations." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 169-177.
In this work, a new intelligent control strategy based on neural networks is proposed to cope with some external disturbances that can affect quadrotor unmanned aerial vehicles (UAV) dynamics. Specifically, the variation of the system mass during logistic tasks and the influence of the wind are considered. An adaptive neuromass estimator and an adaptive neural disturbance estimator complement the action of a set of PID controllers, stabilizing the UAV and improving the system performance. The control strategy has been extensively tested with different trajectories: linear, helical, circular, and even a lemniscate one. During the experiments, the mass of the UAV is triplicated and winds of 6 and 9 in Beaufort’s scale are introduced. Simulation results show how the online learning of the estimator increases the robustness of the controller, reducing the effects of the changes in the mass and of the wind on the quadrotor.
Jesus Enrique Sierra-Garcia; Matilde Santos. Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors. Complexity 2019, 2019, 1 -20.
AMA StyleJesus Enrique Sierra-Garcia, Matilde Santos. Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors. Complexity. 2019; 2019 ():1-20.
Chicago/Turabian StyleJesus Enrique Sierra-Garcia; Matilde Santos. 2019. "Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors." Complexity 2019, no. : 1-20.
In this work an adaptive neuro-control is proposed to cope with some external disturbances that can affect unmanned aerial vehicles (UAV) dynamics, specifically: the variation of the system mass during logistic tasks and the influence of the wind. An intelligent control strategy based on a feedforward neural networks is applied. In particular, a variant of the generalized learning algorithm has been used. Simulation results show how the on-line learning increases the robustness of the controller, reducing the effects of the changes in mass and the effects of wind on the UAV stabilization, thus improving the system response. It has been compared with a PID controller obtaining better results.
J. Enrique Sierra; Matilde Santos. Disturbances Based Adaptive Neuro-Control for UAVs: A First Approach. Advances in Intelligent Systems and Computing 2018, 293 -302.
AMA StyleJ. Enrique Sierra, Matilde Santos. Disturbances Based Adaptive Neuro-Control for UAVs: A First Approach. Advances in Intelligent Systems and Computing. 2018; ():293-302.
Chicago/Turabian StyleJ. Enrique Sierra; Matilde Santos. 2018. "Disturbances Based Adaptive Neuro-Control for UAVs: A First Approach." Advances in Intelligent Systems and Computing , no. : 293-302.
J. Enrique Sierra; Matilde Santos. Modelling engineering systems using analytical and neural techniques: Hybridization. Neurocomputing 2018, 271, 70 -83.
AMA StyleJ. Enrique Sierra, Matilde Santos. Modelling engineering systems using analytical and neural techniques: Hybridization. Neurocomputing. 2018; 271 ():70-83.
Chicago/Turabian StyleJ. Enrique Sierra; Matilde Santos. 2018. "Modelling engineering systems using analytical and neural techniques: Hybridization." Neurocomputing 271, no. : 70-83.