Design of Second Order Sliding Mode and Sliding Mode Algorithms:A Practical Insight to DC-DC Buck Converter

This paper presents a simple and systematic approach to design second order sliding mode controller for buck converters.The second order sliding mode control(SOSMC)based on twisting algorithm has been implemented to control buck switch mode converter.The idea behind this strategy is to suppress chattering and maintain robustness and finite time convergence properties of the output voltage error to the equilibrium point under the load variations and parametric uncertainties.In addition,the influence of the twisting algorithm on the performance of closed-loop system is investigated and compared with other algorithms of first order sliding mode control such as adaptive sliding mode control(ASMC),nonsingular terminal sliding mode control(NTSMC).In comparative evaluation,the transient response of the output voltage with the step change in the load and the start-up response of the output voltage with the step change in the input voltage of buck converter were compared.Experimental results were obtained from a hardware setup constructed in laboratory.Finally,for all of the surveyed control methods,the theoretical considerations,numerical simulations,and experimental measurements from a laboratory prototype are compared for different operating points.It is shown that the proposed twisting method presents an improvement in steady state error and settling time of output voltage during load changes.

Fuzzy-second order sliding mode control optimized by genetic algorithm applied in direct torque control of dual star induction motor

The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control(SOSMC) based on the super twisting algorithm(STA) combined with the fuzzy logic control(FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimization algorithm based on the genetic algorithm(GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.

AN INFEASIBLE-INTERIOR-POINT PREDICTOR-CORRECTOR ALGORITHM FOR THE SECOND-ORDER CONE PROGRAM

A globally convergent infeasible-interior-point predictor-corrector algorithm is presented for the second-order cone programming （SOCP） by using the Alizadeh- Haeberly-Overton （AHO） search direction. This algorithm does not require the feasibility of the initial points and iteration points. Under suitable assumptions, it is shown that the algorithm can find an -approximate solution of an SOCP in at most O（√n ln（ε0/ε）） iterations. The iteration-complexity bound of our algorithm is almost the same as the best known bound of feasible interior point algorithms for the SOCP.

Two new predictor-corrector algorithms for second-order cone programming

Based on the ideas of infeasible interior-point methods and predictor-corrector algorithms, two interior-point predictor-corrector algorithms for the second-order cone programming （SOCP） are presented. The two algorithms use the Newton direction and the Euler direction as the predictor directions, respectively. The corrector directions belong to the category of the Alizadeh-Haeberly-Overton （AHO） directions. These algorithms are suitable to the cases of feasible and infeasible interior iterative points. A simpler neighborhood of the central path for the SOCP is proposed, which is the pivotal difference from other interior-point predictor-corrector algorithms. Under some assumptions, the algorithms possess the global, linear, and quadratic convergence. The complexity bound O（rln（εo/ε）） is obtained, where r denotes the number of the second-order cones in the SOCP problem. The numerical results show that the proposed algorithms are effective.

A Second Order Training Algorithm for Multilayer Feedforward Neural Networks

ＡＳｅｃｏｎｄＯｒｄｅｒＴｒａｉｎｉｎｇＡｌｇｏｒｉｔｈｍｆｏｒＭｕｌｔｉｌａｙｅｒＦｅｅｄｆｏｒｗａｒｄＮｅｕｒａｌＮｅｔｗｏｒｋｓＴａｎＹｉｎｇ（谭营）ＨｅＺｈｅｎｙａ（何振亚）（ＤｅｐａｒｔｍｅｎｔｏｆＲａｄｉｏＥｎｇｉｎｅｅｒｉｎｇ，Ｓｏｕ...

Two-grid partition of unity method for second order elliptic problems

A two-grid partition of unity method for second order elliptic problems is proposed and analyzed. The standard two-grid method is a local and parallel method usually leading to a discontinuous solution in the entire computational domain. Partition of unity method is employed to glue all the local solutions together to get the global continuous one, which is optimal in HI-norm. Furthermore, it is shown that the L^2 error can be improved by using the coarse grid correction. Numerical experiments are reported to support the theoretical results.

A New Eighth Order Implicit Block Algorithms for the Direct Solution of Second Order Ordinary Differential Equations

This paper focuses on derivation of a uniform order 8 implicit block method for the direct solution of general second order differential equations through continuous coefficients of Linear Multi-step Method (LMM). The continuous formulation and its first derivatives were evaluated at some selected grid and off grid points to obtain our proposed method. The superiority of the method over the existing methods is established numerically.

Second-order calibration applied to quantification of two active components of Schisandra chinensis in complex matrix

The effectiveness of traditional Chinese medicine (TCM) against various diseases urges more low cost, speed and sensitive analytical methods for investigating the phamacology of TCM and providing a theoretical basis for clinical use. The potential of second-order calibration method was validated for the quantification of two effective ingredients of Schisandra chinensis in human plasma using spectrofluorimetry. The results obtained in the present study demonstrate the advantages of this strategy for multi-target determination in complex matrices. Although the spectra of the analytes are similar and a large number of interferences also exist, second-order calibration method could predict the accurate concentrations together with reasonable resolution of spectral profiles for analytes of interest owing to its ‘second-order advantage’. Moreover, the method presented in this work allows one to simply experimental procedure as well as reduces the use of harmful chemical solvents.

考虑二阶邻居信息的K-shell重要节点识别算法研究

在真实网络中,找出一些关键的节点进行保护能够有效维持系统的稳定,同时对于复杂网络中的病毒控制与传播、交通网络的故障控制、社交网络中影响力识别等都有重要作用。在现有关键节点识别算法K-shell的基础上,提出了一种考虑二阶邻居信息的K-shell的关键节点识别算法,该算法综合考虑节点的全局与局部信息,同时引入节点的一阶邻居节点和二阶邻居节点的相关信息,计算节点在网络中的重要性。为了验证该算法的性能,以随机攻击和蓄意攻击两种攻击方式在网络中的节点进行仿真实验,实验结果表明,考虑二阶邻居信息的K-shell方法能够有效地检测节点的重要性,识别网络中的关键节点。

基于XGBoost和QRLSTM的新能源出力高精度预测方法

在开展新能源出力预测阶段,由于新能源自身具有波动性和间歇性,导致预测结果的可靠性难以得到保障。为此,提出基于XGBoost和QRLSTM的新能源出力高精度预测方法。采用极限梯度提升算法(EXtreme Gradient Boosting,XGBoost)建立新能源出力数据的目标函数,利用二阶泰勒展开式对目标函数进行近似处理。结合分位数回归构(Quantile Regression,QR)改进长短期记忆(Long Short Term Memory,LSTM)递归神经网络,构建QRLSTM模型将近似处理后的数据输入至该模型中,通过逻辑门完成新能源出力预测。在测试结果中,实际方法在不同环境条件下对于新能源机组出力情况的预测结果均与实际情况保持较高的拟合度,具有较高的精准度。

IPMSM全速域无位置传感器控制策略

为了实现内置式永磁同步电机(IPMSM)无位置传感器系统全速域运行,提出一种改进型IPMSM复合控制策略。首先,在零低速域选用高频旋转电压注入法实现对电机转速和转子位置的估计;其次,在中高速域,为了削弱传统滑模观测器中开关函数造成的系统抖振问题,构造新型超螺旋滑模观测器,提高系统的观测精度;最后,引入差分进化算法优化过渡速域权重系数,实现不同观测方法间的合理切换,完成全速域下转子位置和转速的高精度辨识。研究结果表明,所提算法能够实现电机全速域运行,且具有良好的动态性能。

基于改进EKF算法的锂离子电池SOC在线估计

锂离子电池因其能量密度高、自放电率低、污染小等优势,已经在储能电站领域得到广泛应用。针对锂离子电池各项状态预测,首先搭建二阶RC等效电路模型,然后采用带遗忘因子的递推最小二乘法(FFRLS)对模型参数进行辨识,提出一种基于自适应扩展卡尔曼滤波(AEKF)算法的SOC-SOH联合估计方法。在不同电池工况下进行对比验证,结果表明,与扩展卡尔曼滤波(EKF)和无迹卡尔曼滤波(UKF)相比,所提方法预测SOC和SOH的精确度和计算效率均有所提高,具有一定的实用价值。

考虑新能源多重相关性的柔性配电网分布鲁棒优化策略

高比例分布式新能源与全控型柔性电力电子元器件的大量接入,给传统配电网带来更多清洁电能与调控选择的同时,其出力的时空分布不均衡性与海量器件调节的复杂性,亦使得配电网的运行面临严峻挑战。对此,提出了一种考虑新能源多重相关性的柔性配电网分布鲁棒优化策略。首先,以配电网有功损耗与电压偏移最小为目标,推导并构建含多种源网荷协同调控措施的柔性配电网最优潮流模型。然后,计及新能源在时间空间与功率维度上的多重相关性,建立了基于数据驱动的柔性配电网两阶段分布鲁棒优化模型,采用1-范数与∞-范数进行源荷不确定性集合描述,并采用二阶锥将其线性化与凸松弛。最后,采用列与约束生成算法对该模型进行求解,并以改进的IEEE 33节点测试系统为例进行仿真分析,验证了该方法的有效性和实用性。

eVTOL飞行器冗余捷联惯性导航信息融合研究

电动垂直起降(eVTOL)飞行器的运用,作为一种未来城市交通的潜在解决方案,具有缓解城市交通拥堵和提高空中交通效率的巨大潜力。在其常用的导航系统中,通常采用组合导航和传感器余度技术来提高系统可靠性与安全性。然而,在当前导航系统的传感器余度技术中,冗余传感器只起到备份数据的作用,导致测量数据的信息利用率不高。为解决这一问题,文中以三余度的捷联惯性导航系统(SINS)和全球导航卫星系统(GNSS)的组合导航系统为研究对象,提出了一种基于二阶互差分和惯性传感器(IMU)冗余信息的陀螺仪噪声实时估计方法,推导了在二余度和三余度情况下陀螺仪噪声估计的递推式;在获得陀螺仪的噪声估计值后,采用自适应扩展卡尔曼滤波方法将SINS和GNSS的导航信息进行融合,以提高组合导航系统的准确性和鲁棒性。通过对SINS/GNSS组合导航系统进行仿真和真实跑车实验,结果表明:文中方法能够准确估计陀螺仪噪声;相较于传统融合方法,引入陀螺仪噪声实时估计后,组合导航系统的导航精度和抗干扰能力得到了提高,具有实际的工程意义。

引入PID反馈的SHAEKF算法估算电池SOC

电池荷电状态(SOC)的估算精度是电动汽车电池组的重要指标。为提升SOC估算精度,在融合Sage-Husa扩展卡尔曼滤波(SHEKF)算法与自适应扩展卡尔曼滤波(AEKF)算法的基础上,增加比例积分微分(PID)反馈环节,形成改进算法。采用粒子群优化(PSO)算法对二阶RC等效电路模型进行参数辨识;用开源电池数据集对模型和算法进行实验和分析。改进的SHAEKF算法在电池动态应力测试(DST)、北京动态应力测试(BJDST)和美国联邦城市驾驶(FUDS)等工况下的平均估计误差都在1%以内,与单纯的融合算法SHAEKF算法相比,最大误差可减小5%。

二阶聚类和蜂群算法的城市生态景观用地空间布局优化方法

传统城市生态景观用地空间布局方法受到城市建设功能性、土地使用性质等多因素限制,在生态景观用地空间布局过程中存在局部偏差,所得布局结果的合理性与设计预期布局之间存在一定差距,在一定程度上影响力城市布局进度及其整体布局效果。为了彻底解决这一问题,结合城市生态景观用地空间属性,引入二阶聚类、蜂群算法两种优化方案共同对其布局进行优化。首先,城市生态景观用地空间布局优化目标函数确认;然后,蜂群算法引入及参量定义更新;最后,蜂群优化布局策略的二阶聚类优化输出;通过仿真性能对比结果,能够说明提出优化方法局部提升布局合理性,降低多因素扰动对布局结果的影响,改善布局结果精准度的能力,满足方法设计预期。

考虑电压-无功调节的台区互联装置规划方法

伴随分布式能源广泛接入低压配电网,其对配电网运行灵活性和消纳能力的要求不断提高。利用低压柔性互联装置将独立运行的低压配电台区分区互联,避免传统电压调节和无功补偿装置频繁动作。考虑到柔性互联装置造价昂贵,协同传统电压-无功调节装置,文中提出低压柔性互联装置的选址定容规划方法。首先,分析低压柔性互联装置拓扑和运行方式,建立其潮流模型。其次,建立低压柔性互联装置优化配置的双层规划模型,上层规划以年综合费用最小为目标,下层规划考虑电压-无功协调控制时间序列模型,以运行成本和电压偏差最小为目标,基于粒子群优化算法和混合整数二阶锥规划算法交替求解,得出配电系统最优柔性互联方案和最优运行方式。最后,在IEEE 33节点系统上进行实例分析,验证该双层规划算法的有效性。结果表明,所提方法能有效减少柔性互联装置的过度布置,同时减少由分布式能源频繁波动造成的运行成本。将模型凸化并线性化的方法明显提高了求解效率。

基于SMD与WaOA-CNN-LSTM的短期光伏功率预测

针对当前光伏功率预测模型所面临因数据的复杂性、信号处理过程的噪声干扰、非线性特征难以提取等问题而导致的预测精度低、稳定性差等多方面挑战,提出了一种融合二次模态分解(SMD)和基于海象算法(WaOA)优化CNN-LSTM神经网络的组合预测模型。首先,利用完全自适应噪声集合经验模态分解(CEEMDAN)对光伏数据进行分解,并结合K均值聚类算法(K-means)将多个子序列重构成低频、中频以及高频序列;其次,将含有残余噪声的高频序列采用变分模态分解(VMD)进行二次分解处理;最后,对各分量分别构建CNN-LSTM模型,并利用WaOA算法对网络参数进行寻优,将各分量的预测结果进行叠加,得到最终预测结果。SMD处理方法解决了传统数据处理方法模态混叠、低频分量过多和高频分量噪声残余等问题,CNN-LSTM模型能够捕捉数据中的空间关系和长期依赖关系,WaOA算法对模型参数的优化提高了模型的性能和效率。选取陕西某地光伏电站数据进行测试,通过多组对比实验进行验证,结果表明:所提方法具有更高的预测精度。

基于FFRLS和ASR-UKF滤波算法的锂电池SOC估计

锂电池在工作过程中,其内部参数易受多种因素影响,为提高锂电池在复杂环境下荷电状态(SOC)估计精度,以二阶戴维宁(Thevenin)等效模型为基础,结合遗忘因子递推最小二乘法(FFRLS)对模型参数进行在线辨识。针对传统卡尔曼滤波算法高度非线性及系统噪声不确定性等缺点,提出了一种自适应平方根无迹卡尔曼滤波(ASR-UKF)算法,该算法利用平方根算法处理均值和协方差,确保了状态协方差的半正定性和稳定性,并引入自适应滤波算法对噪声进行实时修正,消除了系统时变噪声影响。结果表明,FFRLS能有效解决数据饱和及算法矩阵计算量大的问题,等效模型精度高达98%。在混合动力脉冲特性(HPPC)测试和北京公交动态测试工况(BBDST)下,ASR-UKF算法SOC估计最大误差分别为3.264%和0.572%,具备更好的跟踪效果,验证了改进算法良好的收敛性与自适应性。

基于二阶一致性算法的电池储能系统SOC均衡及功率分配策略

为延长电池储能系统的整体寿命,需保持储能系统中各单元的荷电状态(state of charge,SOC)均衡。为此,提出一种基于二阶一致性算法的改进下垂控制策略,通过指数函数嵌套变化系数,实现不同容量储能单元快速SOC均衡。在SOC均衡的基础上设计二次控制策略,在一定通信时延下实现频率、电压恢复和有功、无功功率合理分配。最后,以4台储能单元组成的电池储能系统为算例进行仿真,验证了所提控制策略的有效性,SOC能够快速收敛达到均衡状态,频率、电压能够恢复到额定值,有功、无功功率能够按照相应下垂系数比例进行分配。