A comparative analysis of fuzzy logic and PI speed control in high-performance AC drives using experimental approach

One of the frequently discussed applications of artificial intelligence in motion control is the replacement of a standard proportional plus integral (PI) speed controller with a fuzzy logic (FL) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI and FL speed control is still missing. This paper attempts to fill in this gap, by providing an in-depth comparison of operation of a vector-controlled permanent-magnet synchronous motor, using at first an experimental rig. Speed responses, obtained under PI and FL speed control, are recorded and compared for a variety of operating conditions. The transients studied include response to a large step speed command from standstill with nominal inertia and an increased inertia, response to small step speed reference change, and response to step load torque application. The transient behavior is examined for various initial speed settings, so that a thorough comparison is enabled. Experimental results are further supplemented with a set of simulation results, obtained using a different permanent-magnet machine and a different FL controller. Better generalization of the results is enabled in this way. It is shown that superiority of the FL speed control is less pronounced than it is often portrayed in the literature on the basis of limited comparisons. Indeed, in a number of cases, PI speed control provides a superior speed response.     

Main flux saturation modelling in double-cage and deep-barinduction machines

The available models of saturated double-cage and deep-bar induction machines are the current state-space model and the flux state-space model, where state-space variables are selected either as stator current and currents of both rotor cages, or stator flux linkage and flux linkages of both rotor cages. This paper presents a number of models of saturated double-cage (deep-bar) induction machines where alternative sets of state-spate variables are selected. The method of main flux saturation modelling relies on recently introduced concept of `generalised flux space vector', which has originally been developed for modelling of saturated single-cage induction machines. The procedure and the novel models are verified by experimental study and simulation of self-excitation process in a double-cage induction generator     

Neural plasticity in adults with amblyopia

Amblyopia is a neuronal abnormality of vision that is often considered irreversible in adults. We found strong and significant improvement of Vernier acuity in human adults with naturally occurring amblyopia following practice. Learning was strongest at the trained orientation and did not transfer to an untrained task (detection), but it did transfer partially to the untrained eye (primarily at the trained orientation). We conclude that this perceptual learning reflects alterations in early neural processes that are localized beyond the site of convergence of the two eyes. Our results suggest a significant degree of plasticity in the visual system of adults with amblyopia.     

Toward automatic phenotyping of developing embryos from videos.

We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fully automated phenotyping system. The system contains three modules 1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; 2) an energy-based model, which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; 3) a set of elastic models of the embryo at various stages of development that are matched to the label images.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Surface adsorption of comb polymers by Monte Carlo simulations

This paper reports off-lattice Monte Carlo simulations of highly-branched comb homopolymers weakly adsorbed on a flat, featureless surface showing only covolume and dispersion interactions with the adsorbate. A minimal coarse-grained model, described by hard spheres connected by harmonic springs, was employed. The interaction energy of the adsorbed combs and linear chains is first discussed as a function of the molecular mass and of the number of beads in contact with the surface. The molecular size is then investigated as a function of backbone length and branching density at a fixed arm size. The apparent swelling exponents of the adsorbed combs are larger than those of the corresponding linear chains, and much larger than that of the free molecules. This result indicates a surface-induced stiffening of the comb backbone, further studied through the persistence length l_pers. It is found that l_pers increases upon adsorption over the free-molecule value, more so the larger is the branching density. Finally, the thickness of the adsorbed layer, the surface-induced molecular anisotropy and the molecular aspect ratio are investigated as a function of branching density and molecular mass.