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In recent years, machine learning (ML) has received growing attention and it has been used in a wide range of applications. However, the ML application in renewable energies systems such as fuel cells is still limited. In this paper, a prognostic framework based on artificial neural network (ANN) is designed to predict the performance of proton exchange membrane (PEM) fuel cell system, aiming to investigate the effect of temperature and humidity on the stack characteristics and on tracking control improvements. A large part of the experimental database for various operating conditions has been used in the training operation to achieve an accurate model. Extensive tests with various ANN parameters such as number of neurons, number of hidden layers, selection of training dataset, etc., are performed to obtain the best fit in terms of prediction accuracy. The effect of temperature and humidity based on the predicted model are investigated and compared to the ones obtained from real-time experiments. The control design based on the predicted model is performed to keep the stack operating point at an adequate power stage with high-performance tracking. Experimental results have demonstrated the effectiveness of the proposed model for performance improvements of PEM fuel cell system.
Mohamed Derbeli; Cristian Napole; Oscar Barambones. Machine Learning Approach for Modeling and Control of a Commercial Heliocentris FC50 PEM Fuel Cell System. Mathematics 2021, 9, 2068 .
AMA StyleMohamed Derbeli, Cristian Napole, Oscar Barambones. Machine Learning Approach for Modeling and Control of a Commercial Heliocentris FC50 PEM Fuel Cell System. Mathematics. 2021; 9 (17):2068.
Chicago/Turabian StyleMohamed Derbeli; Cristian Napole; Oscar Barambones. 2021. "Machine Learning Approach for Modeling and Control of a Commercial Heliocentris FC50 PEM Fuel Cell System." Mathematics 9, no. 17: 2068.
In applications where high precision in micro- and nanopositioning is required, piezoelectric actuators (PEA) are an optimal micromechatronic choice. However, the accuracy of these devices is affected by a natural phenomenon called “hysteresis” that even increases the instability of the system. This anomaly can be counteracted through a material re-shape or by the design of a control strategy. Through this research, a novel control design has been developed; the structure contemplates an artificial neural network (ANN) feedforward to contract the non-linearities and a robust close-loop compensator to reduce the unmodelled dynamics, uncertainties and perturbations. The proposed scheme was embedded in a dSpace control platform with a Thorlabs PEA; the parameters were tuned online through specific metrics. The outcomes were compared with a conventional proportional-integral-derivative (PID) controller in terms of control signal and tracking performance. The experimental gathered results showed that the advanced proposed strategy had a superior accuracy and chattering reduction.
Cristian Napole; Oscar Barambones; Mohamed Derbeli; Isidro Calvo. Advanced Trajectory Control for Piezoelectric Actuators Based on Robust Control Combined with Artificial Neural Networks. Applied Sciences 2021, 11, 7390 .
AMA StyleCristian Napole, Oscar Barambones, Mohamed Derbeli, Isidro Calvo. Advanced Trajectory Control for Piezoelectric Actuators Based on Robust Control Combined with Artificial Neural Networks. Applied Sciences. 2021; 11 (16):7390.
Chicago/Turabian StyleCristian Napole; Oscar Barambones; Mohamed Derbeli; Isidro Calvo. 2021. "Advanced Trajectory Control for Piezoelectric Actuators Based on Robust Control Combined with Artificial Neural Networks." Applied Sciences 11, no. 16: 7390.
Photovoltaic (PV) panels are devices capable of converting solar energy to electrical without emissions generation, and can last for several years as there are no moving parts involved. The best performance can be achieved through maximum power point tracking (MPPT), which is challenging because it requires a sophisticated design, since the solar energy fluctuates throughout the day. The PV used in this research provided a low output voltage and, therefore, a boost-converter with a non-linear control law was implemented to reach a suitable end-used voltage. The main contribution of this research is a novel MPPT method based on a voltage reference estimator (VRE) combined with a fuzzy logic controller (FLC) in order to obtain the maximum power from the PV panel. This structure was implemented in a dSpace 1104 board for a commercial PV panel, PEIMAR SG340P. The scheme was compared with a conventional perturbation and observation (P&O) and with a sliding mode controller (SMC), where the outcomes demonstrated the superiority of the proposed advanced method.
Cristian Napole; Mohamed Derbeli; Oscar Barambones. Fuzzy Logic Approach for Maximum Power Point Tracking Implemented in a Real Time Photovoltaic System. Applied Sciences 2021, 11, 5927 .
AMA StyleCristian Napole, Mohamed Derbeli, Oscar Barambones. Fuzzy Logic Approach for Maximum Power Point Tracking Implemented in a Real Time Photovoltaic System. Applied Sciences. 2021; 11 (13):5927.
Chicago/Turabian StyleCristian Napole; Mohamed Derbeli; Oscar Barambones. 2021. "Fuzzy Logic Approach for Maximum Power Point Tracking Implemented in a Real Time Photovoltaic System." Applied Sciences 11, no. 13: 5927.
Oscillating water column (OWC) systems are water power generation plants that transform wave kinetic energy into electrical energy by a surrounded air column in a chamber that changes its pressure through the waves motion. The chamber pressure output spins a Wells turbine that is linked to a doubly fed induction generator (DFIG), flexible devices that adjust the turbine speed to increase the efficiency. However, there are different nonlinearities associated with these systems such as weather conditions, uncertainties, and turbine stalling phenomenon. In this research, a fuzzy logic controller (FLC) combined with an airflow reference generator (ARG) was designed and validated in a simulation environment to display the efficiency enhancement of an OWC system by the regulation of the turbine speed. Results show that the proposed framework not only increased the system output power, but the stalling is also avoided under different pressure profiles.
Cristian Napole; Oscar Barambones; Mohamed Derbeli; José Cortajarena; Isidro Calvo; Patxi Alkorta; Pablo Bustamante. Double Fed Induction Generator Control Design Based on a Fuzzy Logic Controller for an Oscillating Water Column System. Energies 2021, 14, 3499 .
AMA StyleCristian Napole, Oscar Barambones, Mohamed Derbeli, José Cortajarena, Isidro Calvo, Patxi Alkorta, Pablo Bustamante. Double Fed Induction Generator Control Design Based on a Fuzzy Logic Controller for an Oscillating Water Column System. Energies. 2021; 14 (12):3499.
Chicago/Turabian StyleCristian Napole; Oscar Barambones; Mohamed Derbeli; José Cortajarena; Isidro Calvo; Patxi Alkorta; Pablo Bustamante. 2021. "Double Fed Induction Generator Control Design Based on a Fuzzy Logic Controller for an Oscillating Water Column System." Energies 14, no. 12: 3499.
Proton exchange membrane (PEM) fuel cell has recently attracted broad attention from many researchers due to its cleanliness, high efficiency and soundless operation. The obtention of high-performance output characteristics is required to overcome the market restrictions of the PEMFC technologies. Therefore, the main aim of this work is to maintain the system operating point at an adequate and efficient power stage with high-performance tracking. To this end, a model predictive control (MPC) based on a global minimum cost function for a two-step horizon was designed and implemented in a boost converter integrated with a fuel cell system. An experimental comparative study has been investigated between the MPC and a PI controller to reveal the merits of the proposed technique. Comparative results have indicated that a reduction of
Mohamed Derbeli; Asma Charaabi; Oscar Barambones; Cristian Napole. High-Performance Tracking for Proton Exchange Membrane Fuel Cell System PEMFC Using Model Predictive Control. Mathematics 2021, 9, 1158 .
AMA StyleMohamed Derbeli, Asma Charaabi, Oscar Barambones, Cristian Napole. High-Performance Tracking for Proton Exchange Membrane Fuel Cell System PEMFC Using Model Predictive Control. Mathematics. 2021; 9 (11):1158.
Chicago/Turabian StyleMohamed Derbeli; Asma Charaabi; Oscar Barambones; Cristian Napole. 2021. "High-Performance Tracking for Proton Exchange Membrane Fuel Cell System PEMFC Using Model Predictive Control." Mathematics 9, no. 11: 1158.
Taking into account the restricted ability of polymer electrolyte membrane fuel cell (PEMFC) to generate energy, it is compulsory to present techniques, in which an efficient operating power can be achieved. In many applications, the PEMFC is usually coupled with a high step-up DC-DC power converter which not only provides efficient power conversion, but also offers highly regulated output voltage. Due to the no-linearity of the PEMFC power systems, the application of conventional linear controllers such as proportional-integral (PI) did not succeed to drive the system to operate precisely in an adequate power point. Therefore, this paper proposes a robust non-linear integral fast terminal sliding mode control (IFTSMC) aiming to improve the power quality generated by the PEMFC; besides, a digital filter is designed and implemented to smooth the signals from the chattering effect of the IFTSMC. The stability proof of the IFTSMC is demonstrated via Lyapunov analysis. The proposed control scheme is designed for an experimental closed-loop system which consisted of a Heliocentric hy-Expert™ FC-50W, MicroLabBox dSPACE DS1202, step-up DC-DC power converter and programmable DC power supplies. Comparative results with the PI controller indicate that a reduction of 96 % in the response time could be achieved using the suggested algorithm; where, up to more than 91 % of the chattering phenomenon could be eliminated via the application of the digital filter.
Mohammed Silaa; Mohamed Derbeli; Oscar Barambones; Cristian Napole; Ali Cheknane; José Gonzalez de Durana. An Efficient and Robust Current Control for Polymer Electrolyte Membrane Fuel Cell Power System. Sustainability 2021, 13, 2360 .
AMA StyleMohammed Silaa, Mohamed Derbeli, Oscar Barambones, Cristian Napole, Ali Cheknane, José Gonzalez de Durana. An Efficient and Robust Current Control for Polymer Electrolyte Membrane Fuel Cell Power System. Sustainability. 2021; 13 (4):2360.
Chicago/Turabian StyleMohammed Silaa; Mohamed Derbeli; Oscar Barambones; Cristian Napole; Ali Cheknane; José Gonzalez de Durana. 2021. "An Efficient and Robust Current Control for Polymer Electrolyte Membrane Fuel Cell Power System." Sustainability 13, no. 4: 2360.
Piezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.
Cristian Napole; Oscar Barambones; Mohamed Derbeli; Isidro Calvo; Mohammed Silaa; Javier Velasco. High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks. Mathematics 2021, 9, 244 .
AMA StyleCristian Napole, Oscar Barambones, Mohamed Derbeli, Isidro Calvo, Mohammed Silaa, Javier Velasco. High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks. Mathematics. 2021; 9 (3):244.
Chicago/Turabian StyleCristian Napole; Oscar Barambones; Mohamed Derbeli; Isidro Calvo; Mohammed Silaa; Javier Velasco. 2021. "High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks." Mathematics 9, no. 3: 244.
Piezoelectric actuators (PEA) are devices that are used for nano- microdisplacement due to their high precision, but one of the major issues is the non-linearity phenomena caused by the hysteresis effect, which diminishes the positioning performance. This study presents a novel control structure in order to reduce the hysteresis effect and increase the PEA performance by using a fuzzy logic control (FLC) combined with a Hammerstein–Wiener (HW) black-box mapping as a feedforward (FF) compensation. In this research, a proportional-integral-derivative (PID) was contrasted with an FLC. From this comparison, the most accurate was taken and tested with a complex structure with HW-FF to verify the accuracy with the increment of complexity. All of the structures were implemented in a dSpace platform to control a commercial Thorlabs PEA. The tests have shown that an FLC combined with HW was the most accurate, since the FF compensate the hysteresis and the FLC reduced the errors; the integral of the absolute error (IAE), the root-mean-square error (RMSE), and relative root-mean-square-error (RRMSE) for this case were reduced by several magnitude orders when compared to the feedback structures. As a conclusion, a complex structure with a novel combination of FLC and HW-FF provided an increment in the accuracy for a high-precision PEA.
Cristian Napole; Oscar Barambones; Isidro Calvo; Mohamed Derbeli; Mohammed Silaa; Javier Velasco. Advances in Tracking Control for Piezoelectric Actuators Using Fuzzy Logic and Hammerstein-Wiener Compensation. Mathematics 2020, 8, 2071 .
AMA StyleCristian Napole, Oscar Barambones, Isidro Calvo, Mohamed Derbeli, Mohammed Silaa, Javier Velasco. Advances in Tracking Control for Piezoelectric Actuators Using Fuzzy Logic and Hammerstein-Wiener Compensation. Mathematics. 2020; 8 (11):2071.
Chicago/Turabian StyleCristian Napole; Oscar Barambones; Isidro Calvo; Mohamed Derbeli; Mohammed Silaa; Javier Velasco. 2020. "Advances in Tracking Control for Piezoelectric Actuators Using Fuzzy Logic and Hammerstein-Wiener Compensation." Mathematics 8, no. 11: 2071.
Piezoelectric Actuators (PEAs) are devices that can support large actuation forces compared to their small size and are widely used in high-precision applications where micro- and nano-positioning are required. Nonetheless, these actuators have undeniable non-linearities, the well-known ones being creep, vibration dynamics, and hysteresis. The latter originate from a combination of mechanical strain and electric field action; as a consequence, these can affect the PEA tracking performance and even reach instability. The scope of this paper is to reduce the hysteresis effect using and comparing different control strategies like feedback with a Feed-Forward (FF) structure, which is often used to compensate the non-linearities and diminish the errors due to uncertainties. In this research, black-box models are analyzed; subsequently, a classic feedback control like Proportional-Integral (PI) control is combined with the FF methods proposed separately and embedded into a dSpace platform to perform real-time experiments. Results are analyzed in-depth in terms of the error, the control signal, and the Integral of the Absolute Error (IAE). It is found that with the proposed methods, the hysteresis effect could be diminished to acceptable ranges for high-precision tracking with a satisfactory control signal.
Cristian Napole; Oscar Barambones; Mohamed Derbeli; Mohammed Silaa; Isidro Calvo; Javier Velasco. Tracking Control for Piezoelectric Actuators with Advanced Feed-Forward Compensation Combined with PI Control. Proceedings of 1st International Electronic Conference on Actuator Technology: Materials, Devices and Applications 2020, 64, 29 .
AMA StyleCristian Napole, Oscar Barambones, Mohamed Derbeli, Mohammed Silaa, Isidro Calvo, Javier Velasco. Tracking Control for Piezoelectric Actuators with Advanced Feed-Forward Compensation Combined with PI Control. Proceedings of 1st International Electronic Conference on Actuator Technology: Materials, Devices and Applications. 2020; 64 (1):29.
Chicago/Turabian StyleCristian Napole; Oscar Barambones; Mohamed Derbeli; Mohammed Silaa; Isidro Calvo; Javier Velasco. 2020. "Tracking Control for Piezoelectric Actuators with Advanced Feed-Forward Compensation Combined with PI Control." Proceedings of 1st International Electronic Conference on Actuator Technology: Materials, Devices and Applications 64, no. 1: 29.
The authors introduce a new controller, aimed at industrial domains, that improves the performance and accuracy of positioning systems based on Stewart platforms. More specifically, this paper presents, and validates experimentally, a sliding mode control for precisely positioning a Stewart platform used as a mobile platform in non-destructive inspection (NDI) applications. The NDI application involves exploring the specimen surface of aeronautical coupons at different heights. In order to avoid defocusing and blurred images, the platform must be positioned accurately to keep a uniform distance between the camera and the surface of the specimen. This operation requires the coordinated control of the six electro mechanic actuators (EMAs). The platform trajectory and the EMA lengths can be calculated by means of the forward and inverse kinematics of the Stewart platform. Typically, a proportional integral (PI) control approach is used for this purpose but unfortunately this control scheme is unable to position the platform accurately enough. For this reason, a sliding mode control (SMC) strategy is proposed. The SMC requires: (1) a priori knowledge of the bounds on system uncertainties, and (2) the analysis of the system stability in order to ensure that the strategy executes adequately. The results of this work show a higher performance of the SMC when compared with the PI control strategy: the average absolute error is reduced from 3.45 mm in PI to 0.78 mm in the SMC. Additionally, the duty cycle analysis shows that although PI control demands a smoother actuator response, the power consumption is similar.
Javier Velasco; Isidro Calvo; Oscar Barambones; Pablo Venegas; Cristian Napole. Experimental Validation of a Sliding Mode Control for a Stewart Platform Used in Aerospace Inspection Applications. Mathematics 2020, 8, 2051 .
AMA StyleJavier Velasco, Isidro Calvo, Oscar Barambones, Pablo Venegas, Cristian Napole. Experimental Validation of a Sliding Mode Control for a Stewart Platform Used in Aerospace Inspection Applications. Mathematics. 2020; 8 (11):2051.
Chicago/Turabian StyleJavier Velasco; Isidro Calvo; Oscar Barambones; Pablo Venegas; Cristian Napole. 2020. "Experimental Validation of a Sliding Mode Control for a Stewart Platform Used in Aerospace Inspection Applications." Mathematics 8, no. 11: 2051.
Polymer electrolyte membrane (PEM) fuel cells demonstrate potential as a comprehensive and general alternative to fossil fuel. They are also considered to be the energy source of the twenty-first century. However, fuel cell systems have non-linear output characteristics because of their input variations, which causes a significant loss in the overall system output. Thus, aiming to optimize their outputs, fuel cells are usually coupled with a controlled electronic actuator (DC-DC boost converter) that offers highly regulated output voltage. High-order sliding mode (HOSM) control has been effectively used for power electronic converters due to its high tracking accuracy, design simplicity, and robustness. Therefore, this paper proposes a novel maximum power point tracking (MPPT) method based on a combination of reference current estimator (RCE) and high-order prescribed convergence law (HO-PCL) for a PEM fuel cell power system. The proposed MPPT method is implemented practically on a hardware 360W FC-42/HLC evaluation kit. The obtained experimental results demonstrate the success of the proposed method in extracting the maximum power from the fuel cell with high tracking performance.
Mohamed Derbeli; Oscar Barambones; Mohammed Silaa; Cristian Napole. Real-Time Implementation of a New MPPT Control Method for a DC-DC Boost Converter Used in a PEM Fuel Cell Power System. Actuators 2020, 9, 105 .
AMA StyleMohamed Derbeli, Oscar Barambones, Mohammed Silaa, Cristian Napole. Real-Time Implementation of a New MPPT Control Method for a DC-DC Boost Converter Used in a PEM Fuel Cell Power System. Actuators. 2020; 9 (4):105.
Chicago/Turabian StyleMohamed Derbeli; Oscar Barambones; Mohammed Silaa; Cristian Napole. 2020. "Real-Time Implementation of a New MPPT Control Method for a DC-DC Boost Converter Used in a PEM Fuel Cell Power System." Actuators 9, no. 4: 105.
This paper presents a deep analysis of different feed-forward (FF) techniques combined with two different proportional-integral-derivative (PID) control to guide a real piezoelectric actuator (PEA). These devices are well known for a non-linear effect called “hysteresis” which generates an undesirable performance during the device operation. First, the PEA was analysed under real experiments to determine the response with different frequencies and voltages. Secondly, a voltage and frequency inputs were chosen and a study of different control approaches was performed using a conventional PID in close-loop, adding a linear compensation and a FF with the same PID and an artificial neural network (ANN). Finally, the best result was contrasted with an adaptive PID which used a single neuron (SNPID) combined with Hebbs rule to update its parameters. Results were analysed in terms of guidance, error and control signal whereas the performance was evaluated with the integral of the absolute error (IAE). Experiments showed that the FF-ANN compensation combined with an SNPID was the most efficient.
Cristian Napole; Oscar Barambones; Isidro Calvo; Javier Velasco. Feedforward Compensation Analysis of Piezoelectric Actuators Using Artificial Neural Networks with Conventional PID Controller and Single-Neuron PID Based on Hebb Learning Rules. Energies 2020, 13, 3929 .
AMA StyleCristian Napole, Oscar Barambones, Isidro Calvo, Javier Velasco. Feedforward Compensation Analysis of Piezoelectric Actuators Using Artificial Neural Networks with Conventional PID Controller and Single-Neuron PID Based on Hebb Learning Rules. Energies. 2020; 13 (15):3929.
Chicago/Turabian StyleCristian Napole; Oscar Barambones; Isidro Calvo; Javier Velasco. 2020. "Feedforward Compensation Analysis of Piezoelectric Actuators Using Artificial Neural Networks with Conventional PID Controller and Single-Neuron PID Based on Hebb Learning Rules." Energies 13, no. 15: 3929.