The Experts below are selected from a list of 2122275 Experts worldwide ranked by ideXlab platform
Javier Velasco - One of the best experts on this subject based on the ideXlab platform.
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feedforward compensation analysis of piezoelectric actuators using artificial neural networks with conventional pid controller and single neuron pid based on hebb learning rules
Energies, 2020Co-Authors: Cristian Napole, Oscar Barambones, Isidro Calvo, Javier VelascoAbstract: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 - One of the best experts on this subject based on the ideXlab platform.
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feedforward compensation analysis of piezoelectric actuators using artificial neural networks with conventional pid controller and single neuron pid based on hebb learning rules
Energies, 2020Co-Authors: Cristian Napole, Oscar Barambones, Isidro Calvo, Javier VelascoAbstract: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.
Edward Miguel - One of the best experts on this subject based on the ideXlab platform.
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global non linear Effect of temperature on economic production
Nature, 2015Co-Authors: Marshall Burke, Solomon Hsiang, Edward MiguelAbstract:Economic productivity is shown to peak at an annual average temperature of 13 °C and decline at high temperatures, indicating that climate change is expected to lower global incomes more than 20% by 2100. Temperature, and therefore climate change, can affect a country's economic productivity, but it has not been clear if rich and poor countries, or different aspects of economic productivity, show similar relationships. These authors use economic data from 166 countries for the years 1960 to 2010 to uncover a universal nonlinear relationship that reconciles earlier results. Economic productivity peaks at an annual average temperature of 13 °C, and the authors explore the likelihood of global economic contraction under future warming scenarios. Growing evidence demonstrates that climatic conditions can have a profound impact on the functioning of modern human societies1,2, but Effects on economic activity appear inconsistent. Fundamental productive elements of modern economies, such as workers and crops, exhibit highly Non-Linear responses to local temperature even in wealthy countries3,4. In contrast, aggregate macroeconomic productivity of entire wealthy countries is reported not to respond to temperature5, while poor countries respond only linearly5,6. Resolving this conflict between micro and macro observations is critical to understanding the role of wealth in coupled human–natural systems7,8 and to anticipating the global impact of climate change9,10. Here we unify these seemingly contradictory results by accounting for Non-Linearity at the macro scale. We show that overall economic productivity is Non-Linear in temperature for all countries, with productivity peaking at an annual average temperature of 13 °C and declining strongly at higher temperatures. The relationship is globally generalizable, unchanged since 1960, and apparent for agricultural and non-agricultural activity in both rich and poor countries. These results provide the first evidence that economic activity in all regions is coupled to the global climate and establish a new empirical foundation for modelling economic loss in response to climate change11,12, with important implications. If future adaptation mimics past adaptation, unmitigated warming is expected to reshape the global economy by reducing average global incomes roughly 23% by 2100 and widening global income inequality, relative to scenarios without climate change. In contrast to prior estimates, expected global losses are approximately linear in global mean temperature, with median losses many times larger than leading models indicate.
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global non linear Effect of temperature on economic production
Nature, 2015Co-Authors: Marshall Burke, Solomon Hsiang, Edward MiguelAbstract:Growing evidence demonstrates that climatic conditions can have a profound impact on the functioning of modern human societies, but Effects on economic activity appear inconsistent. Fundamental productive elements of modern economies, such as workers and crops, exhibit highly Non-Linear responses to local temperature even in wealthy countries. In contrast, aggregate macroeconomic productivity of entire wealthy countries is reported not to respond to temperature, while poor countries respond only linearly. Resolving this conflict between micro and macro observations is critical to understanding the role of wealth in coupled human-natural systems and to anticipating the global impact of climate change. Here we unify these seemingly contradictory results by accounting for Non-Linearity at the macro scale. We show that overall economic productivity is Non-Linear in temperature for all countries, with productivity peaking at an annual average temperature of 13 °C and declining strongly at higher temperatures. The relationship is globally generalizable, unchanged since 1960, and apparent for agricultural and non-agricultural activity in both rich and poor countries. These results provide the first evidence that economic activity in all regions is coupled to the global climate and establish a new empirical foundation for modelling economic loss in response to climate change, with important implications. If future adaptation mimics past adaptation, unmitigated warming is expected to reshape the global economy by reducing average global incomes roughly 23% by 2100 and widening global income inequality, relative to scenarios without climate change. In contrast to prior estimates, expected global losses are approximately linear in global mean temperature, with median losses many times larger than leading models indicate.
Isidro Calvo - One of the best experts on this subject based on the ideXlab platform.
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feedforward compensation analysis of piezoelectric actuators using artificial neural networks with conventional pid controller and single neuron pid based on hebb learning rules
Energies, 2020Co-Authors: Cristian Napole, Oscar Barambones, Isidro Calvo, Javier VelascoAbstract: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.
Oscar Barambones - One of the best experts on this subject based on the ideXlab platform.
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feedforward compensation analysis of piezoelectric actuators using artificial neural networks with conventional pid controller and single neuron pid based on hebb learning rules
Energies, 2020Co-Authors: Cristian Napole, Oscar Barambones, Isidro Calvo, Javier VelascoAbstract: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.