An Improved Model Predictive Torque Control for PMSM Drives Based on Discrete Space Vector Modulation

In this article, an improved model predictive torque control (MPTC) method based on discrete space vector modulation (DSVM) is proposed for permanent magnet synchronous motor (PMSM) drives. Aiming at solving the two problems of large torque ripples and high computational complexity in conventional MPTC, the proposed method adopts a second optimization and a new simplified search strategy. The key idea of second optimization is to make the output voltage vector closer to the actual optimal solution. In this case, a more suitable voltage vector is applied in each sampling period. The simplified search strategy reduces the calculation time by cutting down the number of candidate voltage vectors without affecting drives performance. Compared to the conventional MPTC without DSVM and with DSVM, the proposed method can produce superior steady-state performance and lower computational complexity. Simulation and experimental results are presented to validate the effectiveness and feasibility of the proposed method

Model Predictive Direct Torque Control for SPMSM with Load Angle Limitation

Abstract—The purpose of this paper is to describe a model predictive direct torque control (MPDTC) with load angle limitation for surface-mounted permanent magnet synchronous motor (SPMSM) drive system. In this paper, an exact discrete-time state-space model of SPMSM is presented, which improves the state prediction accuracy comparing to simple Euler approximation. A finite control set type MPDTC is used to select the optimum voltage vectors applying to the voltage source inverter (VSI). It makes full use of the inherent discrete nature of VSI, and according to the predefined cost function it chooses the optimal solution from the possible switching states. It has been found that with the proposed scheme SPMSM drives show adequate dynamic torque performance and considerable torque ripple reduction as compared to traditional direct torque control (t-DTC). With the load angle limitation in the cost function, the proposed scheme can prevent the PMSMs falling from synchronism. 1

Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions

Color is an important feature of vehicles, and it plays a key role in intelligent traffic management and criminal investigation. Existing algorithms for vehicle color recognition are typically trained on data under good weather conditions and have poor robustness for outdoor visual tasks. Fine vehicle color recognition under rainy conditions is still a challenging problem. In this paper, an algorithm for jointly deraining and recognizing vehicle color, (JADAR), is proposed, where three layers of UNet are embedded into RetinaNet-50 to obtain joint semantic fusion information. More precisely, the UNet subnet is used for deraining, and the feature maps of the recovered clean image and the extracted feature maps of the input image are cascaded into the Feature Pyramid Net (FPN) module to achieve joint semantic learning. The joint feature maps are then fed into the class and box subnets to classify and locate objects. The RainVehicleColor-24 dataset is used to train the JADAR for vehicle color recognition under rainy conditions, and extensive experiments are conducted. Since the deraining and detecting modules share the feature extraction layers, our algorithm maintains the test time of RetinaNet-50 while improving its robustness. Testing on self-built and public real datasets, the mean average precision (mAP) of vehicle color recognition reaches 72.07%, which beats both sate-of-the-art algorithms for vehicle color recognition and popular target detection algorithms

Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions

Color is an important feature of vehicles, and it plays a key role in intelligent traffic management and criminal investigation. Existing algorithms for vehicle color recognition are typically trained on data under good weather conditions and have poor robustness for outdoor visual tasks. Fine vehicle color recognition under rainy conditions is still a challenging problem. In this paper, an algorithm for jointly deraining and recognizing vehicle color, (JADAR), is proposed, where three layers of UNet are embedded into RetinaNet-50 to obtain joint semantic fusion information. More precisely, the UNet subnet is used for deraining, and the feature maps of the recovered clean image and the extracted feature maps of the input image are cascaded into the Feature Pyramid Net (FPN) module to achieve joint semantic learning. The joint feature maps are then fed into the class and box subnets to classify and locate objects. The RainVehicleColor-24 dataset is used to train the JADAR for vehicle color recognition under rainy conditions, and extensive experiments are conducted. Since the deraining and detecting modules share the feature extraction layers, our algorithm maintains the test time of RetinaNet-50 while improving its robustness. Testing on self-built and public real datasets, the mean average precision (mAP) of vehicle color recognition reaches 72.07%, which beats both sate-of-the-art algorithms for vehicle color recognition and popular target detection algorithms

Rain Rendering and Construction of <i>Rain Vehicle Color</i>-24 Dataset

The fine identification of vehicle color can assist in criminal investigation or intelligent traffic management law enforcement. Since almost all vehicle-color datasets that are used to train models are collected in good weather, the existing vehicle-color recognition algorithms typically show poor performance for outdoor visual tasks. In this paper we construct a new RainVehicleColor-24 dataset by rain-image rendering using PS technology and a SyRaGAN algorithm based on the VehicleColor-24 dataset. The dataset contains a total of 40,300 rain images with 125 different rain patterns, which can be used to train deep neural networks for specific vehicle-color recognition tasks. Experiments show that the vehicle-color recognition algorithms trained on the new dataset RainVehicleColor-24 improve accuracy to around 72% and 90% on rainy and sunny days, respectively. The code is available at [email protected]

Grid impedance detection based on complex coefficient filter and full-order capacitor current observer for three-phase grid-connected inverters

This article proposes a new grid impedance detection method incorporating the complex coefficient filter (CCF) with full-order capacitor current observer for a T-type three-level grid-connected inverter controlled by the inverter output current feedback. Compared with conventional CCF impedance detection algorithms, the proposed method reduces the number of current sensors and detects the grid impedance accurately. First, based on the sampled inverter output current and grid-connected voltage signals, the grid-connected current is estimated. Then, the CCF method is used to extract harmonics from the grid-connected current and voltage signals to calculate the grid impedance. Finally, the correctness of the full-order capacitor current observer is verified by simulation and the feasibility and effectiveness of the proposed algorithm are verified experimentally based on a laboratory prototype. © 1986-2012 IEEE

A novel continuous control set model predictive control for lc-filtered three-phase four-wire three-level voltage-source inverter

In this article, a novel continuous control set model predictive control (CCS-MPC) is proposed for an LC-filtered three-phase three-level four-wire voltage-source inverter (3P-4W-3L-VSI). The proposed MPC algorithm provides independent control of each phase of the 3P-4W-3L-VSI under various unbalanced load conditions, which shows superior performance under unbalanced loads. Furthermore, different from conventional MPC methods, the CCS-MPC achieves fixed switching frequency, hence simplifying the design of LC filters. In addition, dc offsets are introduced to the proposed modulation to effectively balance the neutral-point voltage of the 3P-4W-3L-VSI. Experimental results are presented to verify the effectiveness of the proposed MPC algorithm. © 1986-2012 IEEE

A protein-independent fluorescent RNA aptamer reporter system for plant genetic engineering

Fluorescent RNA aptamers could potentially be used as protein-independent reporters of transgene expression in plants. Here, the authors report that an optimized RNA aptamer, developed from Broccoli, can be used to detect transgene expression in stable and transiently transformed plant tissue