OPTIMAL TORQUE CONTROL STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLE WITH AUTOMATIC MECHANICAL TRANSMISSION

In parallel hybrid electrical vehicle （PHEV） equipped with automatic mechanical transmission （AMT）, the driving smoothness and the clutch abrasion are the primary considerations for powertrain control during gearshift and clutch operation. To improve these performance indexes of PHEV, a coordinated control system is proposed through the analyzing of HEV powertrain dynamic characteristics. Using the method of minimum principle, the input torque of transmission is optimized to improve the driving smoothness of vehicle. Using the methods of fuzzy logic and fuzzy-PID, the engaging speed of clutch and the throttle opening of engine are manipulated to ensure the smoothness of clutch engagement and reduce the abrasion of clutch friction plates. The motor provides the difference between the required input torque of transmission and the torque transmitted through clutch plates. Results of simulation and experiments show that the proposed control strategy performs better than the contrastive control system, the smoothness of driving and the abrasion of clutch can be improved simultaneously.

Design of Underwater Robot Lines Based on a Hybrid Automatic Optimization Strategy

In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal;the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body＇s minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.

Design of hybrid automatic repeat request (HARQ) for TDD-OFDM system

Reliability-based hybrid automatic repeat request （HARQ） （RB-HARQ） is a recently developed form of incremental-redundancy ARQ. It achieves good performance whereas large retransmission request packets should be fed back. In this paper, in order to reduce the number of the fed back bits, we propose a HARQ scheme applied in time duplex division orthogonal frequency division multiplexing （TDD- OFI）M） system over the slow fading channel which is named channel-based HARQ （CB-HARQ）. Because one bit which meets deep fading is always with small value of log likelihood ratio （LLR） during the process of decoding of LDPC code, the bits transmitted on the carrier with deep fading are retransmitted. At the receiver, the decoder will compute the locations of retransmission bits according to the channel fading values which are gotten by utilizing the feature of channel symmetry in TDD mode. So the indices of retransmission bits are avoided to be transmitted. Simulation results show that this method achieves better BER performance and requires much smaller request packets in feedback link.

Hybrid Loader Automatic Shift Strategy Based on Neural Network

Hybrid loader 's comprehensive performance mainly depends on the performance of hydraulic torque converter during its driving and working. Hybrid loader and hydraulic torque converter are taken for the research objects. The primary characteristic curve of hydraulic torque converter and the traction curve of hybrid loader are acquired by analyzing the characteristic parameters of hydraulic torque converter, the characteristic parameters of engine, the characteristic parameters of battery pack and geometric parameters of hybrid loader. The gear shift curves based on the best energy saving performance and the best power performance are acquired respectively with the opening of throttle,the speed of pump wheel and the speed of turbine as parameters. Then the two curves are combined to get the comprehensive gear shift curve. Radical basis function( RBF) neural network is applied to building the gear shift strategy to keep hybrid loader with the best power performance and energy saving performance. The experimental bench is set up for experimental verification. It proves that both of the power performance and energy saving performance of hybrid loader are improved effectively by using the automatic shift strategy.

A Novel Cascaded PID Controller for Automatic Generation Control Analysis With Renewable Sources

Present day power scenarios demand a high quality uninterrupted power supply and needs environmental issues to be addressed. Both concerns can be dealt with by the introduction of the renewable sources to the existing power system. Thus, automatic generation control(AGC) with diverse renewable sources and a modified-cascaded controller are presented in the paper.Also, a new hybrid scheme of the improved teaching learning based optimization-differential evolution(hITLBO-DE) algorithm is applied for providing optimization of controller parameters. A study of the system with a technique such as TLBO applied to a proportional integral derivative(PID), integral double derivative(IDD) and PIDD is compared to hITLBO-DE tuned cascaded controller with dynamic load change.The suggested methodology has been extensively applied to a 2-area system with a diverse source power system with various operation time non-linearities such as dead-band of, generation rate constraint and reheat thermal units. The multi-area system with reheat thermal plants, hydel plants and a unit of a wind-diesel combination is tested with the cascaded controller scheme with a different controller setting for each area. The variation of the load is taken within 1% to 5% of the connected load and robustness analysis is shown by modifying essential factors simultaneously by± 30%. Finally, the proposed scheme of controller and optimization technique is also tested with a 5-equal area thermal system with non-linearities. The simulation results demonstrate the superiority of the proposed controller and algorithm under a dynamically changing load.

Combining Generative/Discriminative Learning for Automatic Image Annotation and Retrieval

In order to bridge the semantic gap exists in image retrieval, this paper propose an approach combining generative and discriminative learning to accomplish the task of automatic image annotation and retrieval. We firstly present continuous probabilistic latent semantic analysis (PLSA) to model continuous quantity. Furthermore, we propose a hybrid framework which employs continuous PLSA to model visual features of images in generative learning stage and uses ensembles of classifier chains to classify the multi-label data in discriminative learning stage. Since the framework combines the advantages of generative and discriminative learning, it can predict semantic annotation precisely for unseen images. Finally, we conduct a series of experiments on a standard Corel dataset. The experiment results show that our approach outperforms many state-of-the-art approaches.

A Novel Method in Wood Identification Based on Anatomical Image Using Hybrid Model

Nowadays,wood identification is made by experts using hand lenses,wood atlases,and field manuals which take a lot of cost and time for the training process.The quantity and species must be strictly set up,and accurate identification of the wood species must be made during exploitation to monitor trade and enforce regulations to stop illegal logging.With the development of science,wood identification should be supported with technology to enhance the perception of fairness of trade.An automatic wood identification system and a dataset of 50 commercial wood species from Asia are established,namely,wood anatomical images collected and used to train for the proposed model.In the convolutional neural network(CNN),the last layers are usually soft-max functions with dense layers.These layers contain the most parameters that affect the speed model.To reduce the number of parameters in the last layers of the CNN model and enhance the accuracy,the structure of the model should be optimized and developed.Therefore,a hybrid of convolutional neural network and random forest model(CNN-RF model)is introduced to wood identification.The accuracy’s hybrid model is more than 98%,and the processing speed is 3 times higher than the CNN model.The highest accuracy is 1.00 in some species,and the lowest is 0.92.These results show the excellent adaptability of the hybrid model in wood identification based on anatomical images.It also facilitates further investigations of wood cells and has implications for wood science.

受限泊车通道下自动驾驶平行泊车的路径规划方法

针对受限泊车通道下自动泊车规划时间长、成功率低等问题,提出了一种改进混合A^(*)算法的路径规划方法。首先,将泊车路径分为前进的位姿调整段与后退的倒车入库段两部分,通过建立直线-圆弧的约束优化模型规划位姿调整段,寻找合适的倒车起始点;其次,通过在混合A^(*)算法中额外引入碰撞风险代价,改进节点扩展方式,并通过判断车辆轮廓线是否与障碍物线相交来进行碰撞检测,以提高倒车入库段规划的实时性和安全性;最后,以路径长度、平滑度、偏离度为指标设计目标函数,并考虑汽车的运动学约束,使用二次规划对初始路径进行平滑,得到最终路径。使用MATLAB对改进算法与原始算法进行仿真分析,结果表明:在受限泊车通道下,改进算法能得到平滑的无碰撞泊车路径,搜索时间比混合A^(*)算法减少了23.8%,且所得路径更安全,更易进行跟踪控制。

基于圆弧样条参考路径的改进混合A^(*)泊车路径规划算法

在泊车空间狭窄的条件下,现有的基于混合A^(*)算法的泊车路径规划存在成功率低或规划速度慢等问题,为了解决这一问题,设计了一种改进混合A^(*)路径规划算法。通过将圆弧样条曲线作为参考路径,并以参考路径上的点作为混合A^(*)算法的目标点,进而搜索出成功泊入车库的路径。根据不同车位宽度进行了基于MATLAB的批量仿真测试,结果表明:改进后的混合A^(*)算法能够显著提高车辆在特定区域泊入车库的成功率,同时具有一定的规划效率。最后基于Prescan、Carsim和Simulink进行了联合仿真实验,验证了所设计算法规划的路径满足实车实验的跟踪要求。

AOS中基于最优帧长的链路自适应联合优化方法

针对AOS中数据传输误码率高的问题,导致系统吞吐量低的问题,提出基于最优帧长的链路自适应联合优化方法。该方法以系统吞吐量最大化为目标,联合可变帧长,自适应编码调制和混合自动重传请求技术进行优化,根据信道状态信息分配下一帧传输最佳的帧长和编码调制方式,并对出错数据进行纠错和重传,最终提升系统吞吐量。仿真验证表明,与AMC-HARQ和AMC-ARQ方法相比,该方法在保证系统误帧率的同时,提高了系统的吞吐量。

泊车过程中车辆转向状态的分段位姿估计方法

针对泊车过程中转向系统非线性因素影响车辆位姿估计准确度的问题,提出了一种基于车辆转向状态的分段位姿估计方法,提高自动泊车过程中的车辆位姿估计精度。首先,通过试验对车辆运动学航迹推算法定位的误差来源进行分析,确定了转向过程的非线性因素对泊车过程中车辆位姿计算准确度的影响;其次,设计了基于车辆转向状态的分段位姿估计模型,使用LSTM对转向状态进行实时分类,分别训练混合模型进行位姿估计,并对分类结果与位姿估计结果进行后验修正;再次,构建泊车工况车辆横纵向位移数据集,基于数据集对所建模型进行训练和离线测试;最后,利用Python搭建在线测试平台,对模型进行位姿估计测试,并开展对比试验。试验结果表明:该方法能够将泊车过程中的车辆欧式距离误差控制在10 cm以内,航向角误差控制在1°以内。基于车辆转向状态的分段位姿估计方法研究,可以有效提高泊车过程中的车辆位姿估计精度,并具有较高的实时性和较好的鲁棒性。

基于重传错误率估计的自适应HARQ

自适应混合自动重传请求(hybrid automatic repeat request,HARQ)系统的性能与每次传输中的调制与编码方案(modulation and coding scheme,MCS)、发送功率选择密切相关,通过优化重传时的MCS及发送功率可以提升HARQ系统的吞吐率并降低能耗。由于不同MCS传输合并后的译码性能没有闭式解,这导致自适应HARQ优化问题不易解决。首先基于译码的判决域半径给出了一种HARQ合并后译码错误率的近似公式,进而提出了多种能权衡吞吐率和能耗性能的自适应HARQ优化策略。仿真结果验证了所提错误率估计方法的可靠性以及所提自适应HARQ的性能。与传统HARQ方案相比,所提方案能同时提高吞吐率、降低能耗,实现更好的吞吐率与能耗的性能折中。

混合教学模式下“自动控制原理”课程教学设计及思政育人路径探索

随着新一代信息技术和教学理念的发展,线上/线下混合教学模式的引入在教学成本、教学效率及学习灵活性等方面具有显著优势。以“自动控制原理”课程为例,在线上/线下混合教学背景下,以“培养家国情怀,构筑工匠精神”为课程改革目标,挖掘课程中的隐性思政元素,建立混合教学学习资源库,探索课程思政的实践路径,对现阶段自动化高等教育以线上/线下混合式教学模式为载体的课程思政的育人路径进行初步探索。

基于混合遗传算法的有源配电网故障区段自动定位

现实配电网通常由大量节点、线路和变压器组成。网络拓扑复杂,进而导致有源配电网故障区段定位精度下降。首先,对配电网故障特征进行了详细分析,包括短路故障和单相接地故障等类型。然后,依据故障特征分析结果,构建了一个适用于有源配电网的故障区段定位模型。最后,采用混合遗传算法来优化求解该定位模型,以获得精确的故障区段定位结果。实验结果表明:所提方法可以有效实现区段故障定位,且故障区段定位时间始终低于10s,最长仅为9s,表明所提方法定位性能较优,具有实用性。

Research on Hybrid Power System with Dual Stator-winding and Its Decoupled Control Strategy

For the bi-power system adopted widely in future armored vehicles,a hybrid power generator with dual stator-winding was proposed.Its structure and working principle were analyzed first,and its main parameters were determined and verified according to the power requirements.The system's mathematical model was established,and a decoupled control method was put forward on the basis of the instantaneous reactive power theory.For the voltage building-up,a voltage control strategy was designed on the basis of mixed reactive power compensation to implement stabilized 28V and 270V outputs simultaneously.The simulation results show that the stabilization accuracy and disturbance rejection ability of the system are improved much more than other ordinary generators.

基于混合神经网络的无线电信号自动调制识别

随着无线电信号数据海量增加,复杂电磁环境下面临着未知威胁和目标侦察识别复杂度高的问题,本文针对未知无线电信号的特征提取任务,设计了一种混合神经网络以提高目标无线电信号的识别能力。先通过胶囊神经网络对未知信号的空间信息进行提取,再进一步运用门控循环单元提取信号在时间上的特征信息。设计混合网络模型将信号的时间和空间特征相结合,提高对目标信号的分类精度。通过RML2016.04C调制信号数据集,验证了混合神经网络的识别性能。结果表明:当信噪比为6 dB时,混合网络模型对多种不同调制信号的分类精度大于95%。因此,本文所设计的混合神经网络能够有效对不同调制信号进行准确分类。

考虑舒适性的自动驾驶轨道列车牵引电机节能控制

为提升乘客在自动驾驶轨道列车中的体验感,保证自动驾驶轨道列车的安全运行,提出考虑舒适性的自动驾驶轨道列车牵引电机节能控制技术。构建自动驾驶轨道列车运动学模型,分析牵引和制动电机能耗。依据分析结果建立列车牵引电机节能控制目标函数,并以行驶速度、舒适性、准时性和精确停车设置约束条件。利用混沌算法和蛙跳算法改进标准差分进化算法求解目标函数,获取最优解,实现自动驾驶轨道列车牵引电机节能控制。实验结果表明,所提方法在预期速度跟踪性能、节能性、舒适性、精准停车和准点中表现更为优异,验证了所提方法应用下的自动驾驶轨道列车在满足乘客舒适性要求的基础上,获取了较好的牵引电机节能控制效果。

带时间窗约束的AGV集配货绿色路径规划问题研究

针对柔性制造车间背景下带时间窗约束的自动化导引运输车(automated guided vehicle,AGV)集配货绿色路径规划问题,以最小化AGV集配货过程能耗及时间偏离能耗作为组合优化目标,构建AGV绿色车辆路径规划模型,根据所研究问题特性,提出了一种改进变邻域搜索的混合遗传算法(GA-VNS)对其进行求解,并设计了5种邻域结构来提高算法寻优能力。通过对Solomon算例测试集进行求解,并与国际已知最优解进行数据对比,验证文章所提算法的可行性;进一步以某柔性制造车间某一生产时段的AGV物流运输任务作为实验案例,分别使用所设计的算法、GA和VNS算法对问题进行求解,数值实验结果表明了文章所提模型及算法的优化、适用性,为车间实现节能减排的发展目标提供一种可行方案。

基于改进混合A*算法的自动泊车系统路径搜索方法

针对混合A*算法在短距离泊车场景下由于终点附近环境复杂,难以用无碰撞的RS曲线加速搜索的情况,提出一种改进混合A*算法,从目标位姿开始反向搜索路径,结合通过A*算法计算得到的代价地图查表获得启发值,通过判断车身轮廓线与简化后的障碍物线是否相交进行碰撞检测以节省搜索时间,并通过设置合适的车辆转向角度分辨率增加节点扩展方向的数量,保证路径的平滑性。最后使用MATLAB对改进算法与原始算法进行仿真对比分析,结果表明,在平行和垂直车位泊车场景下,该改进混合A*算法均可有效缩短路径搜索时间,获得的路径更短、更平滑。

基于DBSCAN与二分法的混合A*路径规划方法

在多障碍物非结构化场景中,传统混合A*算法存在计算效率低、路径平滑性差的问题。针对该问题,本文提出了一种基于密度聚类算法(density-based clustering,简称DBSCAN)与二分法的混合A*路径规划方法。首先,设计基于DBSCAN算法的障碍物聚类方法,简化多障碍物非结构化场景,避免混合A*算法在类U形障碍物群附近的无效节点拓展,有效提高算法效率。然后,提出基于二分法的状态节点拓展策略,能够在不显著增加混合A*算法计算复杂度的前提下,搜索出一条更平滑的路径。最后,基于MATLAB进行仿真。结果表明,在多障碍物非结构化场景中,本文提出的改进混合A*算法可以显著提升计算效率并改善路径平滑性。