Search results for "Feed forward"
showing 10 items of 33 documents
Strength and Latency of the HP-SAP Closed Loop Variability Interactions in Subjects Prone to Develop Postural Syncope
2019
The coupling and latency between heart period (HP) and systolic arterial pressure (SAP) variability can be investigated along the two arms of the HP-SAP closed loop, namely along the baroreflex feedback from SAP to HP, and along the feedforward pathway from HP to SAP. This study investigates the HP-SAP closed loop variability interactions through cross-correlation function (CCF). Coupling strength and delay between HP and SAP variability series were monitored in 13 subjects prone to develop orthostatic syncope (SYNC, 28±9 yrs, 5 males) and in 13 subjects with no history of postural syncope (noSYNC, age: 27±8 yrs, 5 males). Analysis was carried out at rest in supine position (REST) and durin…
Balance control in aging: improvements in anticipatory postural adjustments and updating of internal models
2015
International audience; Postural stability of older subjects can be estimated during orthostatic equilibrium. However, dynamic equilibrium is also important to investigate risks of fall. It implies different interpretations of measures given by force plates. Same dependant variables (e.g. center of pressure displacement) cannot be interpreted the same ways depending of the type of equilibrium that is investigated. In particular, sways increases during dynamic equilibrium and before movement execution may reflect an improvement of feedforward control.
Short term wind speed prediction using Multi Layer Perceptron
2012
Among renewable energy sources wind energy is having an increasing influence on the supply of energy power. However wind energy is not a stationary power, depending on the fluctuations of the wind, so that is necessary to cope with these fluctuations that may cause problems the electricity grid stability. The ability to predict short-term wind speed and consequent production patterns becomes critical for the all the operators of wind energy. This paper studies several configurations of Artificial Neural Networks (ANN), a well-known tool able to estimate wind speed starting from measured data. The presented ANNs, t have been tested through data gathered in the area of Trapani (Sicily). Diffe…
Modeling and Performance Assessment of the Split-Pi Used as a Storage Converter in All the Possible DC Microgrid Scenarios. Part II: Simulation and E…
2021
Bidirectional DC/DC converters such as the Split-pi can be used to integrate an energy storage system (ESS) into a DC microgrid providing manifold benefits. However, this integration deserves careful design because the ESS converter must behave like a stiff voltage generator, a non-stiff voltage generator, or a current generator depending on the microgrid configuration. Part I of this work presented a comprehensive theoretical analysis of the Split-pi used as an ESS converter in all the possible DC microgrid scenarios. Five typical microgrid scenarios were identified. Each of them required a specific state-space model of the Split-pi and a suitable control scheme. The present paper complete…
FEEDFORWARD CONTROL SCHEMES FOR CHEMICAL PROCESSES: AN ALGORITHMIC APPROACH
1983
Abstract A procedure for the systematic determination of feedforward control schemes for chemical processes is presented and an algorithm is derived. The proposed method makes use of the structural features of the process to be controlled and can be applied to linearized process models. Worked examples show how the algorithm can be a handy tool that systemizes the choice of manipulative and measuring variables in feedforward control schemes for multivariate processes.
Day-ahead forecasting for photovoltaic power using artificial neural networks ensembles
2016
Solar photovoltaic plants power output forecasting using machine learning techniques can be of a great advantage to energy producers when they are implemented with day-ahead energy market data. In this work a model was developed using a supervised learning algorithm of multilayer perceptron feedforward artificial neural network to predict the next twenty-four hours (day-ahead) power of a solar facility using fetched weather forecast of the following day. Each set of tested network configuration was trained by the historical power output of the plant as a target. For each configuration, one hundred networks ensembles was averaged to give the ability to generalize a better forecast. The train…
Krill herd algorithm-based neural network in structural seismic reliability evaluation
2018
ABSTRACTIn this research work, the relative displacement of the stories has been determined by means of a feedforward Artificial Neural Network (ANN) model, which employs one of the novel methods for the optimization of the artificial neural network weights, namely the krill herd algorithm. For the purpose of this work, the area, elasticity, and load parameters were the input parameters and the relative displacement of the stories was the output parameter. To assess the precision of the feedforward (FF) model optimized using the Krill Herd Optimization (FF-KH) algorithm, comparison of results has been performed relative to the results obtained by the linear regression model, the Genetic Alg…
A stacked interleaved DC-DC buck converter for proton exchange membrane electrolyzer applications: Design and experimental validation
2019
Abstract Since the two last decades, hydrogen production has been attracting the attention of the scientific community thanks to its inherent very low pollution when energy coming from renewable energy sources (RESs) are used. However, it implies the use of DC/DC converters to interface source and load. These conversion systems must meet several requirements from current ripple point of view, energy efficiency, and performance to preserve the sustainability of hydrogen production. This article proposes the design and realization of a stacked interleaved buck converter to supply a proton exchange membrane electrolyzer. The converter is designed to ensure a low output current ripple and a sui…
Boom Motion Control Using Pressure Control Valve
2014
This paper focuses on the reduction in oscillations created by counterbalance valves by using a proportional pressure control valve. The motion control of a cylinder boom is presented using this valve, which is a 4/3-way directional control valve with the main spool in series with an upstream compensator. The input to the valve control is a main spool position reference and, indirectly, the compensator downstream pressure. This gives a different flow gain and, therefore, a different feedforward scheme as compared to applications with the more common pressure compensated flow control valve. The theory behind the pressure control valve is presented and applied to both a theoretical and experi…
Regularized extreme learning machine for regression problems
2011
Extreme learning machine (ELM) is a new learning algorithm for single-hidden layer feedforward networks (SLFNs) proposed by Huang et al. [1]. Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This paper proposes an algorithm for pruning ELM networks by using regularized regression methods, thus obtaining a suitable number of the hidden nodes in the network architecture. Beginning from an initial large number of hidden nodes, irrelevant nodes are then pruned using ridge regression, elastic net and lasso methods; hence, the architectural design of ELM network can be automated. Empirical studies…