Homing Strategy for a 4RRR Parallel Kinematic Machine

Returning home is the most important process of a parallel kinematic machine （PKM） with incremental encoders.Currently,most corresponding articles focus on the accuracy of homing process,and there lacks the investigation of the operation＇s safety.For a 4RRR PKM,all servoaxes would be independently driven to their zero positions at the same time based on the traditional homing mode,and that can bring serious interfere of the kinematic chains.This paper systemically investigates this 4RRR PKM＇s safety of homing process.A homing strategy usually contains three parts which are the home switches＇ locations,the platform＇s initial moving space,and each links＇ homing direction,and all of them can influence the safety of homing operation.For the purpose of evaluating and describing the safety of the homing strategy,some important parameters are introduced as follows：Safely homing ratio （SHR） is used to evaluate the probability of a machine＇s successfully returning home from an initial moving space;Synchronal rotational angle （SRA） is the four links＇ largest synchronal rotational angle with given directions from a given pose.Whether a machine can safely return home from a given pose can be judged by comparing the SRA with all four home switches＇ mounting angles.By meshing the initial moving space and checking the safeties of returning home from all the initial poses on the nodes,the SHR of this initial moving space can be calculate.For the sake of convenience,the platform＇s initial moving space should be as large as possible,and in this 4RRR PKM,a square zone in the center of the workspace with a giving initial rotation range is selected as the platform＇s initial moving space.The forward direction is selected as each link＇s homing direction according to custom,and the platform＇s initial rotational angle is selected as larger than 0° based on this 4RRR PKM＇s kinematic characteristics.The platform＇s initial moving space can be defined only by the side length of the initial moving square.By setting a probable searching step and calculating the SHR of the initial moving square,an optimal procedure of searching for the largest side length of the platform＇s initial moving square is proposed.The homing strategy proposed is based on a systemic research on the safety of homing process for PKM,and the two new indexes SHR and SRA can clearly describe the safety of homing operation.The homing operation based on this strategy is fast and safe,and the method can also be used in other PKMs with the situation of serious components＇ interference.

DEVELOPMENT OF THE ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC URBAN BUSES

A novel parallel hybrid electrical urban bus （PHEUB） configuration consisting of an extra one-way clutch and an automatic mechanical transmission （AMT） is taken as the study subject. An energy management strategy combining a logic threshold approach and an instantaneous optimization algorithm is proposed for the investigated PHEUB. The objective of the energy management strategy is to achieve acceptable vehicle performance and drivability requirements while simultaneously maximizing the engine fuel consumption and maintaining the battery state of charge in its operation range at all times. Under the environment of Matlab/Simulink, a computer simulation model for the PHEUB is constructed by using the model building method combining theoretical analysis and bench test data. Simulation and experiment results for China Typical Bus Driving Schedule at Urban District （CTBDS_UD） are obtained, and the results indicate that the proposed control strategy not only controls the hybrid system efficiently but also improves the fuel economy significantly.

Performance evaluation of series and parallel strategies for financial time series forecasting

Background:Improving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision makers.Given its direct impact on related decisions,various attempts have been made to achieve more accurate and reliable forecasting results,of which the combining of individual models remains a widely applied approach.In general,individual models are combined under two main strategies:series and parallel.While it has been proven that these strategies can improve overall forecasting accuracy,the literature on time series forecasting remains vague on the choice of an appropriate strategy to generate a more accurate hybrid model.Methods:Therefore,this study’s key aim is to evaluate the performance of series and parallel strategies to determine a more accurate one.Results:Accordingly,the predictive capabilities of five hybrid models are constructed on the basis of series and parallel strategies compared with each other and with their base models to forecast stock price.To do so,autoregressive integrated moving average(ARIMA)and multilayer perceptrons(MLPs)are used to construct two series hybrid models,ARIMA-MLP and MLP-ARIMA,and three parallel hybrid models,simple average,linear regression,and genetic algorithm models.Conclusion:The empirical forecasting results for two benchmark datasets,that is,the closing of the Shenzhen Integrated Index(SZII)and that of Standard and Poor’s 500(S&P 500),indicate that although all hybrid models perform better than at least one of their individual components,the series combination strategy produces more accurate hybrid models for financial time series forecasting.

Series-parallel Hybrid Vehicle Control Strategy Design and Optimization Using Real-valued Genetic Algorithm

Despite the series-parallel hybrid electric vehicle inherits the performance advantages from both series and parallel hybrid electric vehicle, few researches about the series-parallel hybrid electric vehicle have been revealed because of its complex co nstruction and control strategy. In this paper, a series-parallel hybrid electric bus as well as its control strategy is revealed, and a control parameter optimization approach using the real-valued genetic algorithm is proposed. The optimization objective is to minimize the fuel consumption while sustain the battery state of charge, a tangent penalty function of state of charge（SOC） is embodied in the objective function to recast this multi-objective nonlinear optimization problem as a single linear optimization problem. For this strategy, the vehicle operating mode is switched based on the vehicle speed, and an ＂optimal line＂ typed strategy is designed for the parallel control. The optimization parameters include the speed threshold for mode switching, the highest state of charge allowed, the lowest state of charge allowed and the scale factor of the engine optimal torque to the engine maximum torque at a rotational speed. They are optimized through numerical experiments based on real-value genes, arithmetic crossover and mutation operators. The hybrid bus has been evaluated at the Chinese Transit Bus City Driving Cycle via road test, in which a control area network-based monitor system was used to trace the driving schedule. The test result shows that this approach is feasible for the control parameter optimization. This approach can be applied to not only the novel construction presented in this paper, but also other types of hybrid electric vehicles.

Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test function.The results show that PACS algorithmoutperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization.

An Investigation on the Effect of Migration Strategy on Parallel GA-Based Shortest Path Routing Algorithm

Genetic algorithm (GA) is one of the alternative approaches for solving the shortest path routing problem. In previous work, we have developed a coarse-grained parallel GA-based shortest path routing algorithm. With parallel GA, there is a GA operator called migration, where a chromosome is taken from one sub-population to replace a chromosome in another sub-population. Which chromosome to be taken and replaced is subjected to the migration strategy used. There are four different migration strategies that can be employed: best replace worst, best replace random, random replace worst, and random replace random. In this paper, we are going to evaluate the effect of different migration strategies on the parallel GA-based routing algorithm that has been developed in the previous work. Theoretically, the migration strategy best replace worst should perform better than the other strategies. However, result from simulation shows that even though the migration strategy best replace worst performs better most of the time, there are situations when one of the other strategies can perform just as well, or sometimes better.

Operation control strategy of series-parallel hydraulic hybrid vehicle

A series-parallel hydraulic hybrid system applied to public buses is put forward,and parameters of key components are analyzed and determined.Energy management strategy based on logic threshold is designed which is aimed at efficient operation of the overall system considering the operational characteristic of the components and taking the curves of engine,hydraulic pump/motor and hydraulic pump as the main design basis;regenerative control strategy which makes regenerative brake system and frictional brake system work harmoniously is designed to raise recovery rate of regenerative brake energy.System dynamic modeling and simulation results show that the energy control strategy designed here is able to adapt system to changes of working condition and switch the operating mode reasonably.The regenerative braking control strategy is effective in raising the utilization of energy and improving fuel economy.

Parallel scheduling strategy of web-based spatial computing tasks in multi-core environment

In order to improve the concurrent access performance of the web-based spatial computing system in cluster,a parallel scheduling strategy based on the multi-core environment is proposed,which includes two levels of parallel processing mechanisms.One is that it can evenly allocate tasks to each server node in the cluster and the other is that it can implement the load balancing inside a server node.Based on the strategy,a new web-based spatial computing model is designed in this paper,in which,a task response ratio calculation method,a request queue buffer mechanism and a thread scheduling strategy are focused on.Experimental results show that the new model can fully use the multi-core computing advantage of each server node in the concurrent access environment and improve the average hits per second,average I/O Hits,CPU utilization and throughput.Using speed-up ratio to analyze the traditional model and the new one,the result shows that the new model has the best performance.The performance of the multi-core server nodes in the cluster is optimized;the resource utilization and the parallel processing capabilities are enhanced.The more CPU cores you have,the higher parallel processing capabilities will be obtained.

大型带罩天线阵列的精确高效电磁仿真算法

针对大型带罩天线阵列系统的辐射特性仿真问题,基于等效原理和波导模式匹配法,建立了多层快速多极子算法的波端口模型,实现了对天线激励源和匹配负载的精确电磁建模,同时提出了一种适用于计算金属介质天线模型的多层快速多极子算法并行策略,通过建立多棵八叉树结构降低了计算过程中各进程间的通信量,实现了对大型带罩天线阵列系统的精确、高效一体化仿真计算。通过与高阶矩量法及有限元-边界积分方程法计算得到的天线方向图、S参数进行对比,验证了该方法的精确性及高效性。

具有两类平行顾客的灾难清空排队系统均衡分析

研究了具有两类平行顾客、不完全可靠服务台即带有灾难清空的排队系统中顾客的均衡进队策略.在该排队系统中,两种不同类型的顾客独立到达,且该过程分别形成不同参数的泊松过程.系统是不完全可靠的,系统内所有顾客因灾难发生而被全部清空,且未接受服务被迫退队的顾客可得到一定的补偿.基于顾客的收益-损失函数,在系统信息完全可见和几乎可见两种情形下,分析了两类顾客的均衡阈值策略和系统平均社会收益.最后给出数值案例,得到了系统参数对顾客策略的影响.

基于虚拟阻抗自适应的孤岛型微电网并联逆变器下垂控制策略

当前微电网并联逆变器下垂控制机制一般设定为独立式,控制的范围较难扩展,导致控制补偿差增加。为此,提出对基于虚拟阻抗自适应的孤岛型微电网并联逆变器下垂控制策略的设计与实践。先进行下垂控制特性提取,采用交互的方式,扩大控制的范围,设计交互控制机制。在此基础上,构建虚拟阻抗自适应微电网并联逆变器下垂控制模型,采用补偿核验的方式确保下垂控制效果。针对选定的5个测试逆变器,经过2个周期的测定得出的控制补偿差被较好地控制在1.05以下,说明此次在虚拟阻抗自适应原理的辅助下,所设计的微电网并联逆变器下垂控制方法更为高效,具有实际的应用价值。

Parallel Physics-Informed Neural Networks Method with Regularization Strategies for the Forward-Inverse Problems of the Variable Coefficient Modified KdV Equation

This paper mainly introduces the parallel physics-informed neural networks(PPINNs)method with regularization strategies to solve the data-driven forward-inverse problems of the variable coefficient modified Korteweg-de Vries(VC-MKdV)equation.For the forward problem of the VC-MKdV equation,the authors use the traditional PINN method to obtain satisfactory data-driven soliton solutions and provide a detailed analysis of the impact of network width and depth on solving accuracy and speed.Furthermore,the author finds that the traditional PINN method outperforms the one with locally adaptive activation functions in solving the data-driven forward problems of the VC-MKdV equation.As for the data-driven inverse problem of the VC-MKdV equation,the author introduces a parallel neural networks to separately train the solution function and coefficient function,successfully addressing the function discovery problem of the VC-MKdV equation.To further enhance the network’s generalization ability and noise robustness,the author incorporates two regularization strategies into the PPINNs.An amount of numerical experimental data in this paper demonstrates that the PPINNs method can effectively address the function discovery problem of the VC-MKdV equation,and the inclusion of appropriate regularization strategies in the PPINNs can improves its performance.

新型并联谐振直流环节软开关逆变器改进调制策略

针对新型并联谐振直流环节软开关逆变器的辅助电路运行过程中电流应力高、导通损耗大的问题,提出一种基于不连续的脉冲宽度调制且采用斜率正负交替锯齿载波的改进调制策略。与原调制策略相比,首先,该文所提调制策略在保留辅助电路动作频率最小化优点的基础上,不仅实现了全部开关管的软开关,而且将第二辅助开关管的电流应力降为谐振电流峰值。然后,通过合理的参数设计,进一步降低辅助电路电流应力,从而减少辅助电路损耗。然后,根据所提调制策略分析工作原理、对比不同文献、规划参数设计、进行损耗分析。最后,使用SiC MOSFET制作一台5 kW/40 kHz通用样机,通过实验验证所提调制策略对原拓扑效率提升与电流应力降低的有效性。

非线性环路补偿的交错并联BUCK变换器数字控制策略研究

针对氢能燃料电池输出低电压、大电流,不能适用通讯基站电源的问题,设计了一种电力电子交错并联BUCK变换器,同时提出了一种非线性环路补偿的数字控制策略。文中采用两个BUCK电路并联,控制信号为180°两相交错脉冲宽度控制,采用小信号模型建立了电压环、电流环数学模型,控制策略采用电流内环、电压外环双闭环控制。为了控制快准稳,分别对两环路控制策略补偿校正设计,通过环路补偿数字控制策略搭建交错并联BUCK变换器simulink模型,仿真结果表明变换器在输入范围下实现了48 V直流稳定输出,验证了本研究的有效性。

基于改进蚁群算法的凹区域无人侦察机覆盖航线规划

针对固定翼无人机的区域覆盖侦察航线规划问题,提出一种基于改进蚁群算法的凹区域覆盖航线规划方法。建立垂直载荷侦察模型,引入平行覆盖策略,通过旋转卡壳法确定飞行方向;针对不规则凹多边形区域,提出融合凹点移除和区域分解的多策略凹多边形转换方法;将覆盖航线规划问题转换为搜索最优条带遍历顺序的优化问题,建立Dubins转弯航线,通过在蚁群算法中改进启发函数、引入自适应信息素挥发系数和3-opt算法进行问题求解。实验结果表明,该方法能够高效实现凹区域转换,并获得长度更短的区域覆盖侦察航线,可为开展无人侦察机自主区域侦察研究提供理论支持。

基于有源阻尼方法带LLCL型滤波器的APF控制策略的研究

有源电力滤波器(Active Power Filter,APF)是一种新型的用于补偿谐波和无功功率的电力电子装置,为提高并联型APF的补偿精度。以三相三线制带LLCL型滤波器的APF为研究对象,对APF的控制策略包括电流内环的控制和直流侧电压外环的控制进行研究,分析得出电压外环采用PI控制,电流内环采用PI控制和重复控制有机结合的复合控制策略。利用Matlab/Simulink仿真工具搭建了仿真模型,验证了带LLCL型滤波器的并联APF具有良好的补偿性能,可以明显的降低电网电流的波形失真度(Total Harmonic Distortion,THD)。

三相PWM逆变器直接并联系统零序环流分析与抑制

环流抑制是实现三相PWM逆变器直接并联系统重要关键技术之一。首先在同步旋转坐标下建立了三相PWM逆变器直接并联系统的零序环流数学模型,结合逆变器空间矢量调制算法详细分析了环流抑制原理。在此基础之上,提出一种基于比例谐振与非零矢量占空比前馈控制的环流抑制策略。该策略可通过调整逆变器空间矢量中的零矢量,在减小环流幅值/有效值的同时,可进一步抑制并联零序环流中的工频波动。最后通过试验给出了不同控制策略下的环流抑制波形,充分证明了所提策略的有效性,为三相PWM逆变器直接并联系统可靠稳定运行提供技术支持。

并行RNN分组策略研究

并行RNN结构或者分组RNN结构可以显著减少模型中的参数总量,从而有效地降低模型的训练成本并提高训练效率。本文提出一种高效的并行RNN分组策略,该策略不需要对输入数据进行拆分和重组操作,并且可以降低梯度反向传播的不稳定性对于模型训练造成的负面影响。在语言建模和命名实体识别的任务中的实验结果表明,本文所提出的并行RNN分组策略,模型的参数计算总量大幅度减少,在2个任务中的表现显著提升。

基于工况识别的PHEV能量管理策略

为提升并联式混合动力汽车(parallel hybrid electric vehicle,PHEV)的燃油经济性,针对等效燃油消耗最小控制策略(equivalent fuel consumption minimum strategy,ECMS)在不同工况下适应性差的问题,以优化整车等效燃油消耗量为目标,设计基于工况识别算法的变等效因子ECMS能量管理策略。选取3类典型工况建立支持向量机分类模型,通过递归特征消除法对样本特征进行选择,采用鲸鱼算法对支持向量机进行参数优化,使用模拟退火算法分别对3类工况的ECMS等效因子进行离线全局最优求解,并分别存储于等效因子库中,通过训练好的支持向量机分类器对目标优化工况进行工况识别,不同类型的工况片段采用不同的等效因子进行转矩分配。仿真结果显示:相比于逻辑门限能量管理策略,基于工况识别算法的变等效因子ECMS能量管理策略的电池荷电状态(state of charge,SOC)变化量减少8.67%,节油率为13.11%;相比于优化前的ECMS策略电池SOC变化量减少3.47%,节油率约为6.63%。本文提出的基于工况识别算法的变等效因子ECMS能量管理策略可以有效地减少燃油消耗量,提升PHEV的整车经济性。

并联式混合动力自卸车控制策略与经济性研究

针对并联式混合动力重型自卸车的经济性评价问题,文章提出了一种基于转矩因子的逻辑门限控制策略,建立了一套基于Cruise与MATLAB/Simulink的联合仿真模型,对并联式混合动力重型自卸车的经济性进行了深入研究。首先对关键动力总成进行了选型和参数匹配并建立了整车仿真模型。然后,采用基于转矩因子的逻辑门限的方法建立了混合动力控制策略模型,并将整车模型与混合动力控制策略模型进行联合仿真;重点分析了重型商用车瞬态工况(CWTVC)和自卸汽车行驶工况(CHTC-D)下的整车经济性,并将仿真和试验结果进行了对比分析。结果表明,CWTVC循环工况下,电量消耗模式(CD)和电量保持模式(CS)下分别节油16.7%和6.4%;CHTC-D循环工况下,CD和CS模式下分别节油39.6%和25.4%。仿真与试验结果的经济性误差在为0.49 L/100 km,节油率误差低于1.5%,所建立的经济性仿真模型具有较高的精度,所提出的策略能够显著提高混合动力重型自卸车的车辆燃油经济性。