85 research outputs found

    Practical Modeling and Comprehensive System Identification of a BLDC Motor

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    The aim of this paper is to outline all the steps in a rigorous and simple procedure for system identification of BLDC motor. A practical mathematical model for identification is derived. Frequency domain identification techniques and time domain estimation method are combined to obtain the unknown parameters. The methods in time domain are founded on the least squares approximation method and a disturbance observer. Only the availability of experimental data for rotor speed and armature current are required for identification. The proposed identification method is systematically investigated, and the final identified model is validated by experimental results performed on a typical BLDC motor in UAV

    Flight Control Development and Test for an Unconventional VTOL UAV

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    This chapter deals with the control system development and flight test for an unconventional flight vehicle, namely, a tandem ducted-fan experimental flying platform. The first-principle modeling approach combined with the frequency system identification has been adopted to obtain a high-fidelity dynamics model. It is inherently less stable and difficult to control. To accomplish the required practical flight tasks, the flying vehicle needs to work well even in windy conditions. Moreover, for flight control engineers, simple prescribed multi-loop controller structures are preferred. To handle the multiple problems, a structured velocity controller consisting of two feedback loops is developed, where inner loop provides stability augmentation and decoupling, and the outer loop guarantees desired velocity tracking performance. The simultaneous design of the two-loop controllers under multiple performance requirements in the usual H∞ metrics can be cast as a nonsmooth optimization program. To compensate for changes in plant dynamics across the flight envelope, a smooth and compact polynomial scheduling formula is implemented as a function of the forward flight speed. Both simulations and flight test results have been presented in this work to showcase the potential for the proposed robust nonlinear control system to optimize the performance of UAV, specifically unconventional vehicles

    Silicene Nanomesh

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    Similar to graphene, zero band gap limits the application of silicene in nanoelectronics despite of its high carrier mobility. By using first-principles calculations, we reveal that a band gap is opened in silicene nanomesh (SNM) when the width W of the wall between the neighboring holes is even. The size of the band gap increases with the reduced W and has a simple relation with the ratio of the removed Si atom and the total Si atom numbers of silicene. Quantum transport simulation reveals that the sub-10 nm single-gated SNM field effect transistors show excellent performance at zero temperature but such a performance is greatly degraded at room temperature

    Perspectives on the Application of Genome-Editing Technologies in Crop Breeding

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    Most conventional and modern crop-improvement methods exploit natural or artificially induced genetic variations and require laborious characterization of the progeny of multiple generations of time-consuming genetic crosses. Genome-editing systems, in contrast, provide the means to rapidly modify genomes in a precise and predictable way, making it possible to introduce improvements directly into elite varieties. Here, we describe the range of applications available to agricultural researchers using existing genome-editing tools. In addition to providing examples of genome-editing applications in crop breeding, we discuss the technical and social challenges faced by breeders using genome-editing tools for crop improvement

    A Forecast Model Based on The BP Neural Network Used in Refinery's Steel Equipment's Corrosion

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    The forecasting of the corrosion of refinery's steel equipments shows great importance in preventing the accident. Considering the numerous factors affecting the corroding of refinery's steel equipments, which are uneasily predictable and with complex relationships, this paper proposed a new technology based on the BP neural network technology used in forecasting of the corrosion of refinery's steel equipments. A new model is also built and implemented in this paper. Finally, the experimental results prove the feasibility of the new model and the forecasted results by this new model fixes well with the sample data set

    Mode Shift Control for a Hybrid Heavy-Duty Vehicle with Power-Split Transmission

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    Given that power-split transmission (PST) is considered to be a major powertrain technology for hybrid heavy-duty vehicles (HDVs), the development and application of PST in the HDVs make mode shift control an essential aspect of powertrain system design. This paper presents a shift schedule design and torque control strategy for a hybrid HDV with PST during mode shift, intended to reduce the output torque variation and improve the shift quality (SQ). Firstly, detailed dynamic models of the hybrid HDV are developed to analyze the mode shift characteristics. Then, a gear shift schedule calculation method including a dynamic shift schedule and an economic shift schedule is provided. Based on the dynamic models and the designed shift schedule, a mode shift performance simulator is built using MATLAB/Simulink, and simulations are carried out. Through analysis of the dynamic equations, it is seen that the inertia torques of the motor–generator lead to the occurrence of transition torque. To avoid the unwanted transition torque, we use a mode shift control strategy that coordinates the motor–generator torque to compensate for the transition torque. The simulation and experimental results demonstrate that the output torque variation during mode shift is effectively reduced by the proposed control strategy, thereby improving the SQ

    A classification-based method to estimate event-related potentials from single trial EEG

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    National Natural Science Foundation of China [30670669]; National Basic Research Program of China [2007CB947703]; Natural Science Foundation of Fujian Province [2011J01344]; Science and Technology Development Foundation of Fuzhou University [2009-XQ-25]A novel method based on machine learning is developed to estimate event-related potentials from single trial electroencephalography. This paper builds a basic framework using classification and an optimization model based on this framework for estimating event-related potentials. Then the SingleTrialEM algorithm is derived by introducing a logistic regression model, which could be obtained by training before SingleTrialEM is used, to instantiate the optimization model. The simulation tests demonstrate that the proposed method is correct and solid. The advantage of this method is verified by the comparison between this method and the Woody filter in simulation tests. Also, the cognitive test results are consistent with the conclusions of cognitive science

    Genomic Analysis of the Mycoparasite Pestalotiopsis sp. PG52

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