Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results

This paper focuses on current control in a permanentmagnet synchronous motor (PMSM). The paper has two main objectives: The first objective is to develop a neural-network (NN) vector controller to overcome the decoupling inaccuracy problem associated with conventional PI-based vector-control methods. The NN is developed using the full dynamic equation of a PMSM, and trained to implement optimal control based on approximate dynamic programming. The second objective is to evaluate the robust and adaptive performance of the NN controller against that of the conventional standard vector controller under motor parameter variation and dynamic control conditions by (a) simulating the behavior of a PMSM typically used in realistic electric vehicle applications and (b) building an experimental system for hardware validation as well as combined hardware and simulation evaluation. The results demonstrate that the NN controller outperforms conventional vector controllers in both simulation and hardware implementation

Casimir interactions in graphene systems

The non-retarded Casimir interaction (van der Waals interaction) between two free standing graphene sheets as well as between a graphene sheet and a substrate is determined. An exact analytical expression is given for the dielectric function of graphene along the imaginary frequency axis within the random phase approximation for arbitrary frequency, wave vector, and doping.Comment: 4 pages, 4 figure

The Oceanic Variability Spectrum and Transport Trends

Oceanic meridional transports evaluated over the width of the Pacific Ocean from altimetric observations become incoherent surprisingly rapidly with meridional separation. Even with 15 years of data, surface slopes show no significant coherence beyond 5◦ of latitude separation at any frequency. An analysis of the frequency/zonal-wavenumber spectral density shows a broad continuum of motions at all time and space scales, with a significant excess of energy along a “non-dispersive” line extending between the simple barotropic and first baroclinic mode Rossby waves. It is speculated that much of that excess energy lies with coupled barotropic and first mode Rossby waves. The statistical significance of apparent oceanic transport trends depends upon the existence of a reliable frequency/wavenumber spectrum and for which only a few observational elements now exist.Jet Propulsion Laboratory (U.S.).United States. National Aeronautics and Space Administration (Jason-1 program)National Oceanographic Partnership Program (U.S.

Vector Control of a Grid-Connected Rectifier/Inverter Using an Artificial Neural Network

- Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations. This paper investigates how to mitigate such problems using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming (DP) algorithm and is trained using backpropagation through time. The performance of the DP-based neural controller is studied for typical vector control conditions and compared with conventional vector control methods. The paper also investigates how varying grid and power converter system parameters may affect the performance and stability of the neural control system. Future research issues regarding the control of grid-connected converters using DP-based neural networks are analyzed

Signatures of the superfluid to Mott insulator transition in equilibrium and in dynamical ramps

We investigate the equilibrium and dynamical properties of the Bose-Hubbard model and the related particle-hole symmetric spin-1 model in the vicinity of the superfluid to Mott insulator quantum phase transition. We employ the following methods: exact-diagonalization, mean field (Gutzwiller), cluster mean-field, and mean-field plus Gaussian fluctuations. In the first part of the paper we benchmark the four methods by analyzing the equilibrium problem and give numerical estimates for observables such as the density of double occupancies and their correlation function. In the second part, we study parametric ramps from the superfluid to the Mott insulator and map out the crossover from the regime of fast ramps, which is dominated by local physics, to the regime of slow ramps with a characteristic universal power law scaling, which is dominated by long wavelength excitations. We calculate values of several relevant physical observables, characteristic time scales, and an optimal protocol needed for observing universal scaling.Comment: 23 pages, 13 figure

Back-propagation of accuracy

In this paper we solve the problem: how to determine maximal allowable errors, possible for signals and parameters of each element of a network proceeding from the condition that the vector of output signals of the network should be calculated with given accuracy? "Back-propagation of accuracy" is developed to solve this problem. The calculation of allowable errors for each element of network by back-propagation of accuracy is surprisingly similar to a back-propagation of error, because it is the backward signals motion, but at the same time it is very different because the new rules of signals transformation in the passing back through the elements are different. The method allows us to formulate the requirements to the accuracy of calculations and to the realization of technical devices, if the requirements to the accuracy of output signals of the network are known.Comment: 4 pages, 5 figures, The talk given on ICNN97 (The 1997 IEEE International Conference on Neural Networks, Houston, USA

Climate change as an intergenerational problem

Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of National Academy of Sciences for personal use, not for redistribution. The definitive version was published in Proceedings of the National Academy of Sciences of the United States of America 110 (2013): 4435-4436, doi:10.1073/pnas.1302536110.Predicting climate change is a high priority for society, but such forecasts are notoriously uncertain. Why? Even should climate prove theoretically predictable---by no means certain---the near-absence of adequate observations will preclude its understanding and hence even the hope of useful predictions. Geological and cryospheric records of climate change and our brief recent record of instrumental observations show that the climate system is changeable on all time scales---from a few years out to the age of the earth. Major physical, chemical, and biological processes influence the climate system on decades, centuries, and millennia. Glaciers fluctuate on time scales of years to centuries and beyond. Since the Industrial Revolution, carbon dioxide has been emitted through fossil fuel burning, and it will be absorbed, recycled, and transferred amongst the atmosphere, ocean, and biosphere over decades to thousands of years

Training Recurrent Neural Networks With the Levenberg-Marquardt Algorithm for Optimal Control of a Grid-Connected Converter

This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an RNN using the LM algorithm, a new forward accumulation through time (FATT) algorithm is proposed to calculate the Jacobian matrix required by the LM algorithm. This paper explores how to incorporate FATT into the LM algorithm. The results show that the combination of the LM and FATT algorithms trains RNNs better than the conventional backpropagation through time algorithm. This paper presents an analytical study on the optimal control of GCCs, including theoretically ideal optimal and suboptimal controllers. To overcome the inapplicability of the optimal GCC controller under practical conditions, a new RNN controller with an improved input structure is proposed to approximate the ideal optimal controller. The performance of an ideal optimal controller and a well-trained RNN controller was compared in close to real-life power converter switching environments, demonstrating that the proposed RNN controller can achieve close to ideal optimal control performance even under low sampling rate conditions. The excellent performance of the proposed RNN controller under challenging and distorted system conditions further indicates the feasibility of using an RNN to approximate optimal control in practical applications

Neural-network based vector control of VSCHVDC transmission systems

The application of high-voltage dc (HVDC) using voltage-source converters (VSC) has surged recently in electric power transmission and distribution systems. An optimal vector control of a VSC-HVDC system which uses an artificial neural network to implement an approximate dynamic programming algorithm and is trained with Levenberg-Marquardt is introduced in this paper. The proposed neural network vector control algorithm is analyzed in comparison with standard vector control methods for various HVDC control requirements, including dc voltage, active and reactive power control, and ac system voltage support. Assessment of the resulting closed-loop control shows that the neural network vector control approach has superior performance and works efficiently within and beyond the constraints of the HVDC system, for instance, converter rated power and saturation of PWM modulation