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Stability Analysis of a Neurocontroller with Kalman Estimator for the Inverted Pendulum Case

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Applied Mathematics 2017年第11期8卷 1602-1618页

作者： Julián Antonio Pucheta cristian rodríguez rivero Carlos Alberto Salas Martín Herrera Sergio Oscar Laboret Departamentos de Electrotecnia y de Electrónica Laboratorio de Investigación Matemática Aplicada a Control (LIMAC) Facultad de Ciencias Exactas Físicas y Naturales-Universidad Nacional de Córdoba Córdoba Argentina Departamento de Ingeniería Electrónica Facultad de Tecnología y Ciencias Aplicadas Universidad Nacional de Catamarca Catamarca Argentina

In this paper, a practical analysis of stability by simulation for the effect of incorporating a Kalman estimator in the control loop of the inverted pendulum with a neurocontroller is presented. The neurocontroller is calculated by approximate optimal control, without considering the Kalman estimator in the loop following the Theorem of the separation. The results are compared with a time-varying linear controller, which in noiseless conditions in the state or in the measurement has an acceptable performance, but when it is under noise conditions its operation closes into a state space range more limited than the one proposed here.

关键词： Optimal Control Neurocontroller Inverted Pendulum State Estimation Stability

Low Voltage Daily Energy Demand Temperature Dependent Representation by Using Circular Statistics

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Applied Mathematics 2019年第3期10卷 61-74页

作者： Pucheta Julián Salas Carlos Piumetto Miguel Herrera Martín rodriguez rivero cristian Departamento de Electrónica-FCEFyN-Universidad Nacional de Córdoba Córdoba Argentina FTyCA-Universidad Nacional de Catamarca San Fernando del Valle de Catamarca Argentina Center for Neuroscience University of California at Davis-EEUU Davis Argentina

In this work, a tool that allows visualizing the probability of the power demand according to the temperature and the hours of the day is presented. This aim contributes to the decision making support for the transformer and its service administration. The objective is to represent the demand accurately as a color statistical map based on two variables: the time of day and the ambient temperature. Since the daily energy consumption is periodic regarding the hours of the day in terms of several days, its representation with Gaussian models becomes difficult, but it is simplified when working with circular statistics. The circular statistics used here is the Von Mises distribution, which has the parameters mean address and kappa concentration. Results obtained from measurements made over a year in a medium-voltage transformer with intervals of 60 minutes are shown.

关键词： Distribution Transformers Charge Estimation Time Series Dynamic Process Circular Statistics

Short and Long-Term Time Series Forecasting Stochastic Analysis for Slow Dynamic Processes

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Applied Mathematics 2019年第8期10卷 704-717页

作者： Julián Pucheta Carlos Salas Martín Herrera cristian rodriguez rivero Gustavo Alasino FCEFyN-Universidad Nacional de Córdoba Córdoba Argentina FTyCA-Universidad Nacional de Catamarca Catamarca Argentina cristian University of California Los Angeles USA Universidad Torcuato Di Tella Buenos Aires Argentina

This paper intends to develop suitable methods to provide likely scenarios in order to support decision making for slow dynamic processes such as the underlying of agribusiness. A new method to analyze the short- and long-term time series forecast and to model the behavior of the underlying process using nonlinear artificial neural networks (ANN) is presented. The algorithm can effectively forecast the time-series data by stochastic analysis (Monte Carlo) of its future behavior using fractional Gaussian noise (fGn). The algorithm was used to forecast country risk time series for several countries, both for short term that is 30 days ahead and long term 350 days ahead scenarios.

关键词： Stochastic Analysis Time Series Forecasting Decision Making Dynamic Process Process Modelling

Short and Long-Term Time Series Forecasting Stochastic Analysis for Slow Dynamic Processes

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High Roughness Time Series Forecasting Based on Energy Associated of Series

Applied Mathematics（应用数学） 2019年第8期

作者： Julián Pucheta Carlos Salas Martín Herrera cristian rodriguez rivero Gustavo Alasino

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通讯和计算机（中英文版） 2012年第5期9卷 576-586页

作者： cristian rodriguez rivero Rafael Martin Herrera Julian Anatonio Pucheta Josef Sylvester Baumgartner Hector Daniel Patino Victor Hugo Sauchelli Departments of Electrical and Electronic Engineering Mathematics Research Laboratory applied to ControlNational University of C6rdobaVelez Sarsfield Av.1611Argentina

In this study, an algorithm to adjust parameters of high roughness time series based on energy associated of series using a feed-forward NN-based model is presented. The criterion for adjustment consists of building time series values from forecasted time series area and taking into account the roughness of series. These values are approximated by the NN to make a primitive calculated as an area by the predictor filter used as a new entrance. A comparison between this work and another that involves a similar approach to test time series prediction, indicates an improvement for certain sort of series. The NN filter output is intended to approximate the current value available from the series which has the same Hurst Parameter as the real time series. The proposed approach is tested over five time series obtained from samples of Mackey-Glass delay differential equations (MG). Therefore, these results show a model performance for time series forecasting and encourage to be applied for meteorological variables measurements such as soil moisture series, daily rainfall and monthly cumulative rainfall time series forecasting..

关键词：时间序列预测模型能源使用粗糙度神经网络模型 Hurst参数预测滤波器延迟微分方程调整参数