Vibration Suppression for Active Magnetic Bearings Using Adaptive Filter with Iterative Search Algorithm

Active Magnetic Bearing(AMB) is a kind of electromagnetic support that makes the rotor movement frictionless and can suppress rotor vibration by controlling the magnetic force. The most common approach to restrain the rotor vibration in AMBs is to adopt a notch filter or adaptive filter in the AMB controller. However, these methods cannot obtain the precise amplitude and phase of the compensation current. Thus, they are not so effective in terms of suppressing the vibrations of the fundamental and other harmonic orders over the whole speed range. To improve the vibration suppression performance of AMBs,an adaptive filter based on Least Mean Square(LMS) is applied to extract the vibration signals from the rotor displacement signal. An Iterative Search Algorithm(ISA) is proposed in this paper to obtain the corresponding relationship between the compensation current and vibration signals. The ISA is responsible for searching the compensating amplitude and shifting phase online for the LMS filter, enabling the AMB controller to generate the corresponding compensation force for vibration suppression. The results of ISA are recorded to suppress vibration using the Look-Up Table(LUT) in variable speed range. Comprehensive simulations and experimental validations are carried out in fixed and variable speed range, and the results demonstrate that by employing the ISA, vibrations of the fundamental and other harmonic orders are suppressed effectively.

Novel Retinex algorithm by interpolation and adaptive noise suppression

In order to improve image quality, a novel Retinex algorithm for image enhancement was presented. Different from conventional algorithms, it was based on certain defined points containing the illumination information in the intensity image to estimate the illumination. After locating the points, the whole illumination image was computed by an interpolation technique. When attempting to recover the reflectance image, an adaptive method which can be considered as an optimization problem was employed to suppress noise in dark environments and keep details in other areas. For color images, it was taken in the band of each channel separately. Experimental results demonstrate that the proposed algorithm is superior to the traditional Retinex algorithms in image entropy.

Optimization Design of Fairings for VIV Suppression Based on Data-Driven Models and Genetic Algorithm

Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be solved.In this paper,an optimization design methodology is presented based on data-driven models and genetic algorithm(GA).Data-driven models are introduced to substitute complex physics-based equations.GA is used to rapidly search for the optimal suppression device from all possible solutions.Taking fairings as example,VIV response database for different fairings is established based on parameterized models in which model sections of fairings are controlled by several control points and Bezier curves.Then a data-driven model,which can predict the VIV response of fairings with different sections accurately and efficiently,is trained through BP neural network.Finally,a comprehensive optimization method and process is proposed based on GA and the data-driven model.The proposed method is demonstrated by its application to a case.It turns out that the proposed method can perform the optimization design of fairings effectively.VIV can be reduced obviously through the optimization design.

A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C-Means Clustering Algorithm

Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorithm had been studied by many researchers and applied in many fields. In the algorithm, how to select the suppressed rate is a key step. In this paper, we give a method to select the fixed suppressed rate by the structure of the data itself. The experimental results show that the proposed method is a suitable way to select the suppressed rate in suppressed fuzzy c-means clustering algorithm.

Task Assignment for Forest Fire Suppression by Multiple UAVs

This paper presents a scenario of forest fire suppression using UAVs （Unmanned Aerial Vehicles） and addresses task assignment algorithm to coordinate UAVs. Forest fires are a major problem in many nations and fast extinguishing forest fires brings a lot of ecological advantages so proper use of firefighting resources is very critical. In this sense, multi UAVs forest fire suppression system can be effective way to prevent fire outbreaks. In multi agent system, an appropriate task assignment according to the SA （Situational Awareness） is the most essential to conduct mission. We should consider real time re-planning or re-scheduling of multi UAVs team because environmental situations such as wind are changeable and that changes affect the forest fire spreading. Furthermore, we have to think about convergence to a consistent SA because it may take too much time. CBBA （Consensus-Based Bundle Algorithm） is robust decentralized task assignment tool so it can be implemented in real time re-planning application. A simulation model which is the main topic in this paper shows that multi UAVs can be properly operated to suppress forest fires even if there are unpredictable random factors and partial disconnection. The simulation model includes concrete operating scenarios and recursive task re-assign algorithm until fires in the whole area are suppressed.

Improved DSE-CMA Error Function for Blind Multiuser Interference Suppression in DS/CDMA Systems

This paper presents an improved error function of dithered signed-error constant modulus algorithm （IDSE-CMA） for blind multiuser interference suppression in DS/CDMA systems. It uses a different error function to replace the former one in sign operation of the DSE-CMA and compares their performance in multiple access interference （MAI） suppression ability. Simulations indicate that the new algorithm has better performance than the similar CMA in terms of convergence speed and steady-state performance：

Radio Disturbance Suppression for Ground Wave Radar

A new method of single sample polarization filtering is proposed. The algorithm is fast and suitable for the polarization processing of stationary or nonstationary polarized disturbed signals with one or more independent disturbances. A ground wave polarimetric radar with the ability of radio disturbance suppression is then introduced. Some numerical results demonstrate the effectiveness of single sample polarization filtering method for ground wave polarimetric radar.

A Designated Harmonic Suppression Technology for Sampled SPWM

Sampled SPWM is an excellent VVVF method of motor speed control, meanwhile the harmonic components of the output wave impairs its applications in practice. A designated harmonic suppression technology is presented for sampled SPWM, which is an improved algorithm for the harmonic suppression in high voltage and high frequency spectrum. As the technology is applied in whole speed adjusting range, the voltage can be conveniently controlled and high frequency harmonic of SP WM is also improved.

光伏波动平抑下改进K-means的电池储能动态分组控制策略

针对电池储能系统(battery energy storage system,BESS)进行光伏波动平抑时寿命损耗高及荷电状态(state of charge,SOC)一致性差的问题,提出了光伏波动平抑下改进K-means的BESS动态分组控制策略。首先,采用最小最大调度方法获取光伏并网指令。其次,设计了改进侏儒猫鼬优化算法(improved dwarf mongoose optimizer,IDMO),并利用它对传统K-means聚类算法进行改进,加快了聚类速度。接着,制定了电池单元动态分组原则,并根据电池单元SOC利用改进K-means将其分为3个电池组。然后,设计了基于充放电函数的电池单元SOC一致性功率分配方法,并据此提出BESS双层功率分配策略,上层确定电池组充放电顺序及指令,下层计算电池单元充放电指令。对所提策略进行仿真验证,结果表明,所设计的IDMO具有更高的寻优精度及更快的寻优速度。所提BESS平抑光伏波动策略在有效平抑波动的同时,降低了BESS运行寿命损耗并提高了电池单元SOC的均衡性。

基于遗传模糊PID算法的倒立摆稳摆抑噪抗扰实验与分析

倒立摆稳摆控制原理应用的实际工程需求牵引,使倒立摆成为检验各类控制算法的理想替代实验研究平台。因此,将倒立摆摆杆的姿态稳定控制和抑噪抗扰作为研究内容,以理论分析、编程实现、仿真实验和实物实验验证作为研究手段,聚焦控制器设计的优化。首先,引入模糊控制对PID参数调节进行优化,改固定参数调节为在线参数调节,旨在提高系统的鲁棒性与抗扰性;然后,运用遗传算法的量化因子和比例因子完成调节参数的在线寻优,通过设计适应度函数和选择算子,经过选择、交叉、变异不断演化迭代,最终获取最佳适应度个体、找到最优解,通过仿真验证遗传模糊PID算法突出的抑噪抗扰优势;最后,基于该控制策略进行倒立摆实物实验,验证了其指令跟踪有效性的结论。该研究表明:遗传模糊PID算法具有良好的指令跟踪和抑噪抗扰性能,具有一定的工程应用价值。

抑烟沥青复掺配比优化及抑烟效果评价

为抑制沥青在高温下烟气的产生,降低环境污染及对作业人员健康的影响程度,本文进行了抑烟沥青复掺配比优化研究。基于单掺抑烟沥青的针入度、延度及软化点及自制沥青烟生成富集装置的沥青产烟结果,结合熵权法与遗传算法进行抑烟沥青复掺配比优化,并分析优化复掺抑烟剂在沥青中的分散均匀性、储存稳定性及其对沥青特征官能团的影响;基于优化复掺配比的抑烟沥青高、中、低温性能测试,并对其抑烟效果进行评价,验证优化复掺配比方法的有效性。研究表明:抑烟剂均匀分散于沥青中,且未对沥青特征官能团产生改变,优化复掺抑烟沥青满足储存稳定性要求。优化复掺抑烟剂对沥青高、低温性能具有改善作用,并提升了沥青中温抗疲劳性能。沥青烟收集前后滤管颜色变化表明该沥青烟富集方式具有较高的可靠性,且优化复掺抑烟沥青具有显著抑烟效果,最大抑烟率为99.7%。优化复掺配比下的指标预测值与实测值差异较小,证明了该优化复掺配比方法的可靠性。

基于Adaline算法的高速磁浮列车谐波检测及抑制方法

[目的]为了减小高速磁浮谐波对牵引网产生的影响,保障高速磁浮列车的正常运行,需研究高速磁浮牵引供电系统中的谐波特性及其相应的谐波抑制方法。[方法]分析了高速磁浮牵引供电系统的构成,并对双三相半控12脉冲多重整流电路及其谐波进行了理论分析和仿真。根据Adaline神经网络算法原理和高速磁浮谐波特性,构建基于Adaline的高速磁浮谐波检测模型。采用谐波补偿控制策略,产生与检测出的谐波方向相反、大小相等的电流作为补偿信号,对系统畸变电流进行谐波抑制。[结果及结论]仿真结果表明:采用基于Adaline算法的谐波检测及抑制方法前,谐波畸变率为11.16%,而采用该方法后,各次谐波含量明显降低,谐波畸变率下降为1.41%。研究结果表明:所提谐波检测及抑制方法可以有效减少谐波畸变,提高牵引供电系统的稳定性和工作效率。

基于改进SFLA-Elman神经网络的电离层杂波抑制方法

针对高频地波雷达目标检测中电离层杂波的干扰问题,提出了一种基于改进混合蛙跳算法优化Elman神经网络预测抑制电离层杂波的策略。为解决混合蛙跳算法初始种群分布不均匀、收敛精度低、易陷于局部极值等问题,引入Cubic混沌映射、莱维飞行策略、非线性平衡因子和复制操作,增强种群多样性,提高算法搜索能力。利用改进后的算法和其他算法分别优化Elman神经网络预测抑制模型,结果表明,改进后的算法无论是在收敛精度和稳定性上,还是在临近距离单元电离层杂波的预测抑制上,都取得了显著的提升。在基本保留目标信号的基础上,平均信杂比较原始回波提升18.52 dB,较原始混合蛙跳算法提升1.08 dB,对于电离层杂波的抑制具有较高应用价值。

双基地SAR动目标图像相位误差分析和二维稀疏重聚焦处理

双基地合成孔径雷达(Synthetic Aperture Radar,SAR)系统中,数据采集空间几何关系复杂,动目标回波距离徙动严重,重建图像中往往存在几何失真和散焦现象,需要进一步补偿运动相位误差。为此,分析并推导了双基地动目标极坐标格式图像的二维相位误差谱。根据相位误差模型建立字典,约束动目标回波谱在字典上的投影系数,进而实现杂波和动目标能量的分离和重聚焦成像。点目标仿真验证了算法的有效性。

应用模糊PID自修正的电镀槽液温度控制方法

电镀槽液温度自动控制过程属于典型的混沌过程,在温度采集的过程中存在明显的非线性波动。典型的比例-积分-微分(PID)控制算法在这种混沌特性与非线性特性干扰下,存在控制精度较低、稳定性和鲁棒性较差等问题。此外,为解决非线性问题,PID算法需要循环迭代计算,存在控制时滞问题。以模糊控制为基础,提出了基于模糊PID的电镀槽液温度自动控制方法。该方法先设计温度数据采集结构,并将移动平均滤波、温度修正算法引入到采用的数据采集结构中,完成电镀槽液的实时温度数据采集。然后将采集到的温度数据作为构建的模糊PID控制器的输入。利用模糊控制规则引入修正因子,实现电镀槽液温度的自动控制。考虑到电镀槽液的时滞特性,在控制器中加入粗糙预估参考模型,对该特性加以抑制,提升控制性能。最后应用实验证明了所提方法的先进性。实验结果表明:所提方法具有较高的控制精度和效率,能够有效降低超调量和波动,应用效果较好。

MPC灰色理论算法在同步预测控制器设计中的应用

随着人工智能算法和预测模型的发展与优化,对复杂交叉耦合的环境和对象,数学模型建立困难或测试手段精度不高的场景,难以保证有效的控制方案运行和算法应用。首先,在分析干扰模型和同步控制预测原理基础上,引入MPC(模型预测控制)灰色理论算法的经验值理论,灰色预测算法的原理是约束集均方值最优解。其次,将多变的输入参数整合成若干段的模型匹配的数学模型,其控制系统的时间轴能够以离散时序建立完整的时间模型。利于分析和解决无法用传统数字精确描述的复杂模型或系统问题,尤其是信息获取不完备或者失真的参数信息。最后,分析工业控制模型多为纯滞后的二阶惯性环节的时延控制对象,通过三组建模和仿真实验,得出基于MPC灰色理论算法设计同步预测控制器具有较好的优势,属于一种多值混合动态算法。在误差调节和波动抑制具有良好的同步效果。

节流槽结构参数对阀开启压力冲击及空化特性的影响研究

液压控制阀阀芯节流槽的结构对阀的动态特性影响强烈,不良节流槽结构将导致极大的液压阀开启瞬间压力冲击。针对某型号负载敏感多路阀在甘蔗联合收割机的应用中出现啸叫和液压冲击的问题,结合动网格与RNG-k-ε湍流模型,对U+T型节流槽和V型节流槽的压力冲击及空化进行了对比分析,发现同等条件下V型槽的阀芯较U+T型槽的阀芯的压力冲击下降了19.37%,最大空化程度降低了43.7%。选取V型节流槽开展进一步研究,通过正交试验获取不同节流槽结构参数下的压力峰值;采用BP神经网络建立V型节流槽结构参数与压力冲击的代理模型,结合灰狼优化(GWO)算法得到节流槽结构参数的最优配置;依据优化结果制作阀芯并进行试验,结果显示,抑制压力冲击效果显著,系统压力超调下降至原来52.4%。

基于改进TS模糊的直流电弧炉闪变抑制优化

为提高静止无功补偿器(static var compensator,SVC)应对直流电弧炉等冲击性负载的闪变抑制性能,文中在改进Takagi-Sugeno(TS)模糊算法的基础上,提出一种SVC滚动预测控制方法。首先,建立直流电弧炉电气模型并仿真分析其无功特性;然后,针对经典TS模糊预测算法应用于波动负荷时出现的输出异常置0情况,提出一种范围自适应修正的改进方法,该方法能消除一类算法应用机理导致的异常值,从而提高TS模糊算法对波动负荷无功功率预测的可靠性和准确性;最后,基于模型训练时间约束,建立无功功率半周期滚动预测控制模型,提前10 ms预测无功功率,改善了SVC传统控制系统响应的滞后特性。仿真结果表明,相比于SVC传统控制方法,所提方法的平均闪变改善率提高了54.17%,验证了所提方法对闪变现象的抑制效果提升显著。

基于改进ORB算法的特征检测与匹配研究

针对传统ORB算法中阙值选取不具有普适性,易受光照影响,纹理信息提取能力弱且特征点易产生富集情况的缺陷,提出改进的ORB特征提取与匹配算法。首先以待检测点为中心进行区域划分,其次利用自适应阙值判断待检测点,并使用自适应范围的非极大抑制算法解决特征点富集问题,提高均匀性。然后使用各项异性扩散滤波提高纹理信息提取能力,最后结合双边滤波改进描述子点对,结合PROSAC算法消除误匹配。实验结果表明,改进后的算法对光照有较强的适应能力,提高了纹理特征提取的能力,特征点分布均匀,改善了富集效应,光照条件下的匹配精度得到提高,具有更好的适应能力。

一种无谐振C型滤波器的优化设计方法

现有无功补偿电容谐振抑制方法通常将无功补偿电容配置为无谐振C型滤波器,以降低谐振带来的谐波电压放大与滤波器有功损耗。但在抑制低频谐振时,无谐振C型滤波器的元件参数可选择范围较小,难以在实现低频谐振抑制的同时兼顾滤波器配置成本。针对这一问题,文中提出一种无谐振C型滤波器优化设计方法。首先,设计一种改进C型滤波器结构,在传统C型滤波器结构中增加低频谐振抑制单元,消除滤波器潜在低频谐振点;然后,建立计及滤波器配置成本、有功损耗、谐振抑制性能及元件参数选择范围的滤波器元件参数配置模型,求解改进C型滤波器元件参数最优值。最后,通过仿真对比证明,改进C型滤波器在控制配置成本与有功损耗的基础上,能明显降低谐波放大系数,具有更好的谐振抑制性能。