No abstract

This sequential paper aims to present studies on modelling and tip tracking control of a flexible single beam. It first outlines the flexible-beam robotic mechanism that was designed and built to be used for the force and torque sensory information-based modelling and control. It then details the vibration suppression controller strategy that is applied to this robotic system. The controller is designed with respect to a simple lumped model describing the dynamics of the system. Here the dynamics of the closed-loop controlled motor is inverted in order to obtain a system with unity dynamics. Further, the flexible-beam dynamics is input state linearized. Finally, a simple external feedback control, which is based on the measurements of beam deflections using a force and torque sensor, is implemented. The complete experimental setup was positioned by two servo-motors controlled by a proportional-integral-derivative controller for each axis. The proposed controllers allow the flexible beam to move continuously in a precise manner, so that it could be treated as an accurate positioning sensor. Simulation and experimental results provided at the end illustrate that the controllers designed and implemented produce a satisfactory control performance and adequate robustness to model uncertainties and system nonlinearities.

This paper reports some part of modelling and data analysis work carried out within the frame of a comprehensive project on the web-based development of watershed information system. This work basically aims to present the daily discharge predictions from the actual discharge along with the meteorological data using a wavelet neural network approach, which combines two methods: discrete wavelet transform and artificial neural networks. The wavelet–artificial neural network model developed provides a good fit with the measured data, in particular with zero discharge in the summer months and also with the peaks and sudden changes in discharge on the test data collected throughout the year. The results indicate that the wavelet–artificial neural network model based predictions are distinctly superior to that of conventional artificial neural network model that corresponds up to an 80% reduction in the mean-squared error between the artificial neural network model and measured data.

No abstract

Depending strongly on the wind speed, wind power exhibits significant intermittencies and fluctuations, which may affect adversely the smooth operation of the grid. Therefore, in order to provide the security of energy supply and operate the power system in accordance with the regulations, an effective wind forecast is necessary. Persistence type forecasting , which is the most basic wind power forecasting method cannot guarantee to predict the variations in wind power within the acceptable range in medium and long terms. In this paper, a new statistical approach for wind speed forecasting is presented based on independent component analysis (ICA) and autoregressive (AR) model. The purpose of using ICA is to investigate the possibility that the wind speed time-series may contain some underlying hidden factors. If exist, these hidden factors or independent components (ICs) can be used for better forecasting of wind speed time series, i.e., ICs may have more significant time structure than the actual wind speed time series. This gives motivation to try to predict the wind speed time series by first going to the ICA space, doing the forecasting there, and then transforming back to the original time series. It is understood that ICA, especially ICA methods based on time structure like second order blind identification (SOBI) can be used as a preliminary step in wind speed forecasting.