PMSM speed control using adaptive sliding mode control based on an extended state observer

In this study,a composite strategy based on sliding-mode control( SMC) is employed in a permanent-magnet synchronous motor vector control system to improve the system robustness performance against parameter variations and load disturbances. To handle the intrinsic chattering of SMC,an adaptive law and an extended state observer( ESO) are utilized in the speed SMC controller design. The adaptive law is used to estimate the internal parameter variations and compensate for the disturbances caused by model uncertainty. In addition,the ESO is introduced to estimate the load disturbance in real time. The estimated value is used as a feed-forward compensator for the speed adaptive sliding-mode controller to further increase the system's ability to resist disturbances. The proposed composite method,which combines adaptive SMC( ASMC) and ESO,is compared with PI control and ASMC. Both the simulation and experimental results demonstrate that the proposed method alleviates the chattering of SMC systems and improves the dynamic response and robustness of the speed control system against disturbances.

An Improved Method of Retrieving Sea Surface Wind Speed Based on a Four-Layer Medium Model at High Sea States

Considering about the effect of whitecaps and foams on pulse-limited Radar Altimeters, an improved algorithm of retrieving sea surface wind speed is proposed in this paper. Firstly, a four-layer dielectric model is established in order to simulate an air-sea interface. Secondly, the microwave reflectivity of a sea surface covered by spray droplets and foams at 13.5 GHz is computed based on the established model. These computed results show that the effect of spray droplets and foams in high sea state conditions shall not be negligible on retrieving sea surface wind speed. Finally, compared with the analytical algorithms proposed by Zhao and some calculated results based on a three-layer dielectric model, an improved algorithm of retrieving sea surface wind speed is presented. At a high wind speed, the improved algorithm is in a better accord with some empirical algorithms such as Brown, Young ones and et al., and also in a good agreement with ZT and other algorithms at low wind speed. This new improved algorithm will be suitable not only for low wind speed retrieval, but also for high wind speed retrieval. Better accuracy and effectiveness of wind speed retrieval can also be obtained.

Real-Time Traffic State and Boundary Flux Estimation with Distributed Speed Detecting Networks

The rapid development of 5G mobile communication and portable traffic detection technologies enhances highway transportation systems in detail and at a vehicle level. Besides the advantage of no disturbance to the regular traffic operation, these ubiquitous sensing technologies have the potential for unprecedented data collection at any temporal and spatial position. While as a typical distributed parameter system, the freeway traffic dynamics are determined by the current system states and the boundary traffic demand-supply. Using the three-step extended Kalman filtering, this paper simultaneously estimates the real-time traffic state and the boundary flux of freeway traffic with the distributed speed detector networks organized at any location of interest. In order to assess the effectiveness of the proposed approach, a freeway segment from Interstate 80 East (I-80E) in Alameda, Emeryville, and Northern California is selected. Experimental results show that the proposed method has the potential of using only speed detecting data to monitor the state of urban freeway transportation systems without access to the traditional measurement data, such as the boundary flows.

Evaluation matrix with the speed feature based on double inspiriting control lines

Methods of the comprehensive evaluation have been studied for many years. However, the change speed of evaluated objects was rarely considered by the existing evaluation methods. An evaluation matrix is proposed to remedy this deficiency. Firstly, the change speed state （CSS） of the evaluated objects is analyzed based on double inspiriting control lines （DICLs）, and a matrix of the CSS is constructed. Then, 72 elements in the matrix are analyzed, and formulas describing each CSS are given. The efficiency of the proposed evaluation matrix is proved when the CSS merges with the change speed trend （CST） in the dynamic comprehensive evaluation. Finally, a computing example shows that the proposed evaluation matrix is feasible in the dynamic comprehensive evaluation with the speed feature.

Long-Term Characterization of Sea Conditions in the East China Sea Using Significant Wave Height and Wind Speed

In this study, the statistical characterization of sea conditions in the East China Sea(ECS) is investigated by analyzing a significant wave height and wind speed data at a 6-hour interval for 30 years(1980–2009), which was simulated and computed using the WAVEWATCH Ⅲ(WW3) model. The monthly variations of these parameters showed that the significant wave height and wind speed have minimum values of 0.73 m and 5.15 ms^(-1) and 1.73 m and 8.24 ms^(-1) in the month of May and December, respectively. The annual, seasonal, and monthly mean sea state characterizations showed that the slight sea generally prevailed in the ECS and had nearly the highest occurrence in all seasons and months. Additionally, the moderate sea prevailed in the winter months of December and January, while the smooth(wavelets) sea prevailed in May. Furthermore, the spatial variation of sea states showed that the calm and smooth sea had the largest occurrences in the northern ECS. The slight sea occurred mostly(above 30%) in parts of the ECS and the surrounding locations, while higher occurrences of the rough and very rough seas were distributed in waters between the southwest ECS and the northeast South China Sea(SCS). The occurrences of the phenomenal sea conditions are insignificant and are distributed in the northwest Pacific and its upper region, which includes the Southern Kyushu-Palau Ridge and Ryukyu Trench.

Improvement for consensus performance of multi-agent systems based on delayed-state-derivative feedback

The delayed-state-derivative feedback （DSDF） is in- troduced into the existing consensus protocol to simultaneously improve the robustness to communication delay and accele- rate the convergence speed of achieving the consensus. The frequency-domain analysis, together with the algebra graph the- ory, is employed to derive the sufficient and necessary condition guaranteeing the average consensus. It is shown that introduc- ing the DSDF with the proper intensity in the existing consensus protocol can improve the robustness to communication delay. By analyzing the effect of DSDF on the closed-loop poles, this pa- per proves that for a supercritical-delay multi-agent system, this strategy can also accelerate the convergence speed of achieving the consensus with provided the proper intensity of the DSDE Simulations are provided to demonstrate the effectiveness of the theoretical results.

Using Artificial Neural Networks for Energy Regulation Based Variable-speed Electrohydraulic Drive

In the energy regulation based varibable-speed electrohydraulic drive system, the supply energy and the demanded energy, which will affect the control performance greatly, are crucial. However, they are hard to be obtained via conventional methods for some reasons. This paper tries to a new route： the definitive numerical values of the supply energy and the demanded energy are not required, except for their relationship which is called energy state. A three-layer back propagation（BP） neural network was built up to act as an energy analysis unit to deduce the energy state. The neural network has three inputs： the reference displacement, the actual displacement of cylinder rod and the system flowrate supply. The output of the neural network is energy state. A Chebyshev type II filter was designed to calculate the cylinder speed for the estimation of system flowrate supply. The training and testing samples of neural network were collected by the system accurate simulation model. After off-line training, the neural network was tested by the testing data. And the testing result demonstrates that the designed neural network was successful. Then, the neural network acts as the energy analysis unit in real-time experiments of cylinder position control, where it works efficiently under square-wave and sine-wave reference displacement. The experimental results validate its feasibility and adaptability. Only a position sensor and some pressure sensors, which are cheap and have quick dynamic response, are necessary for the system control. And the neural network plays the role of identifying the energy state.

Effects of Wind on Steady-state Scan Characteristics and Hit Probability of Terminal-sensitive Projectile

The wind effects on steady-state scan characteristics and hit probability of terminal-sensitive projectile were discussed in this paper. Considering wind as the constitutions of the average wind and the impulsive wind, a simplified wind field model was established for the ballistic calculation of the steady-state scan phase; under the windy condition, the effects of the range wind and the beam wind on the steady-state scan characteristics of the terminal-sensitive projectile were analyzed in detail and its hit probabilities for a certain armored target were calculated. The calculated results show that, when the wind speed exceeds a certain value, the hit probabilities of terminal-sensitive projectile drop rapidly; the wind effects must be considered in the application of the terminal-sensitive projectiles. This paper provides some theoretical references for the fire wind speed correction and the global structure optimization of the terminal-sensitive projectile.

Nonparametric estimations of the sea state bias for a radar altimeter

To estimate the sea state bias（SSB） for radar altimeter, two nonparametric models, including a Nadaraya-Watson（NW） kernel estimator and a local linear regression（LLR） estimator, are studied based on the Jason-2 altimeter data. Selecting from different combinations of the Gaussian kernel function, spherical Epanechnikov kernel function, a fixed bandwidth and a local adjustable bandwidth, it is observed that the LLR method with the spherical Epanechnikov kernel function and the local adjustable bandwidth is the optimal nonparametric model for the SSB estimation. The comparisons between the nonparametric and parametric models are conducted and the results show that the nonparametric model performs relatively better at high-latitudes of the Northern Hemisphere. This method has been applied to the HY-2A altimeter as well and the same conclusion can be obtained.

PMSM无模型滑模转速无差拍电流预测控制

针对传统永磁电机调速系统易受到参数摄动影响而导致控制精度下降的问题,提出一种基于扩张状态观测器(ESO)的永磁同步电机(PMSM)无模型滑模转速无差拍电流预测控制策略。首先用参数摄动下的PMSM调速系统数学模型归纳出系统对应的超局部模型,然后结合无差拍预测控制思想设计了电流环控制器,采用两步预测算法解决了传统无差拍算法存在的延时问题,并且提出校正算法进一步抑制实际的预测误差;利用变速率指数滑模趋近率设计了新型无模型滑模速度控制器,减弱抖振的同时提高了系统响应速度;并设计ESO对参数失配和扰动进行估计和补偿。最后,通过仿真与半实物实验验证了所提控制策略的有效性。

总相位滞后角-延时关系下的电子机械制动主控制器控制方法

电子机械制动(electromechanical brake,EMB)的主控制器延时受到多种因素的影响,影响因素与延时控制之间存在复杂的交互作用,导致对其延时控制具有较高难度。为此,提出了一种电子机械制动主控制器延时控制方法。综合考虑电机堵转转矩、电机摩擦、传动机构和制动机构等因素对电子机械制动的影响,分析制动系统的延时与滞后角的相互关系。计算主控制器延时与电子机械制动的总相位滞后角量化关系,通过状态观测器获取其估计值,利用电机转子的转动惯量、摩擦力矩、传动和制动方程获取EMB的制动状态,结合主控制器估计增益参数,实现延时的主动控制。实验结果表明,所提方法应用下主控制器的延时控制耗时较短,电子机械制动距离和滑移率得以明显降低,说明该方法能够有效地保障电子机械制动的稳定性。

基于脑电信号特征的高铁调度员疲劳状态识别

为增强铁路行车的稳定性与安全性,有效识别调度员的疲劳状态对行车组织的影响,基于脑电(EEG)信号特征,提出一种调度员疲劳状态识别方法,根据作业时间段划分调度员的疲劳状态,设计高铁调度模拟试验获取脑电信号数据,通过小波级数展开和傅里叶变换提取高铁调度被试的3种脑电波频域幅值作为特征值,结合调度员作业特征和脑电信号特征,验证疲劳状态的划分结果,通过Python语言环境搭建ResNet18+SoftMax和MobileNet V2+SoftMax这2种模型,基于深度学习方法,将输入特征转换为三维立体矩形模型,并优化调整权重,获得最优模型,从而判断高铁调度员的疲劳状态。研究结果表明:ResNet18+SoftMax和MobileNet V2+SoftMax神经网络模型对高铁调度试验参与人员的疲劳状态识别准确率分别为92.78%和99.17%;相较于支持向量机(SVM)模型,这2种模型可提升清醒状态和疲劳状态的识别精度,并降低运算时间,其中,MobileNet V2+SoftMax模型的识别准确率和运行速度最优。以MobileNet V2+SoftMax模型原理为内核,可以更快速准确地识别高铁调度员在长时间作业条件下的潜在疲劳风险。

基于单片机的直流电机变频调速方法与仿真

针对电机谐波损耗及逆变器输出谐波信号影响直流电机变频调速的稳定性,导致调速性能不佳等,提出一种基于单片机的直流电机变频调速方法。建立直流电机变频调速状态空间模型,通过上述模型获取变频调速系统的运行状态;采用电流、速度双闭环PI控制器实现直流电机的变频调速初步控制,使直流电机变频调速系统可同时满足静态性能与动态性能;将直流侧电压输入至SVPWM脉宽调制系统,进一步降低电机谐波损耗和脉动转矩,实现变频调速的精确控制。仿真结果表明,所提方法的直流电机变频调速稳定性更佳、精度更高,整体效果更佳。

基于变分模态分解与鲸鱼算法优化回声状态网络的风速预测模型

风速受多种因素影响常伴随着随机性和非平稳性,给风电接入电网造成了相当大的困难,准确的风速预测对风力发电有着极大的研究意义。将变分模态分解算法与鲸鱼算法优化回声状态网络模型相结合,提出了一种风速预测模型。首先通过变分模态分解算法将风速序列分解成多个分量以减少风速内部信号间的耦合性,降低建模难度。然后对这些分量分别建立对应的回声状态网络预测模型。针对回声状态网络模型性能受储备池参数影响较大的问题,采用鲸鱼优化算法对储备池参数进行优化。风速的最终预测值由分解后各分量预测值相加得到。最后,将实际采集的短期风速数据作为研究对象,通过与其他4种预测模型的对比分析表明提出的风速预测模型具有更高的预测精度,能够更好地对风速的变化趋势进行预测。

考虑长细比的岩土颗粒系统非稳态流动特征研究

砂土等常见岩土颗粒系统在高速流动状态下具有显著的非稳态特征,其剪切速度分布与速度波动特征等均表现出显著的随机性。但目前颗粒系统细观特征对其宏观流动参数的影响尚不明确,针对岩土颗粒流动过程随机性特征参数的研究亟待加强。本文通过离散元环剪单元实验模拟,捕捉不同形状颗粒系统高速剪切流动过程中的宏微观特征。研究结果表明:(1)颗粒形状对系统宏观剪应力有较为显著的影响。颗粒长细比越大,其残余剪应力越大;(2)颗粒系统速度波动的空间分布同时受到剪切速度与边界效应两方面的影响。剪切速度越大,速度波动越大。边界对于颗粒速度波动具有一定的约束效应;(3)颗粒形状对系统速度波动大小有显著影响。颗粒越细长,微观速度波动越小;(4)形状特征对颗粒系统细观接触特征有显著影响。颗粒越细长,其平均配位数越大,出现较大粒间接触力的概率越小。

基于状态演化模型的高铁路基动力累积沉降变形预测研究

长期循环荷载作用下高铁路基会出现累积沉降变形,对轨道平顺性、列车运行的舒适性与安全性等产生不利影响。为预测高铁路基累积沉降变形,基于考虑颗粒材料系统流动性及历史应变的状态演化模型,采用有限差分法进行求解,得到预测长期循环荷载作用下路基累积变形的计算方法。开展分级荷载及长期循环荷载作用下的路基累积沉降变形缩比尺模型试验,对试验结果进行分析,并验证状态演化模型预测路基累积变形的合理性;提出三阶段拟合法,通过0~10000 s循环荷载作用下的路基累积变形试验数据拟合得到模型的关键参数,并对10000~25000 s的路基累积变形进行预测;最后深入分析初始状态模量、特征应变和特征参数对计算结果的影响。研究结果表明:路基累积沉降变形达到稳定值所需的时间随着荷载幅值的增加而增加;状态变量与荷载大小密切相关,随着荷载增大而增大;初始状态变量、特征应变及特征参数越大,路基的流动性及累积应变速率越大。所提方法可以很好地反映分级荷载及循环荷载下路基的累积变形,实现通过现有数据对路基未来累积沉降变形进行有效预测,为长期循环荷载作用引起的高铁路基累积沉降变形提供了实用的预测方法。研究结果可为高铁路基设计及变形控制提供参考,具有较高的实用价值和工程意义。

高海况下客滚船风浪阻力和功率航速预报

高海况下风浪阻力对客滚船航速预报的准确性有很大影响。对此,以某型客滚船为例,分别采用二维方法和三维方法预报船舶升沉和纵摇运动,采用全浪向波浪增阻预报方法SNNM计算波浪增阻,在此基础上开展高海况下的功率航速预报,并与模型试验结果进行对比分析。研究表明:当航速较低时,采用二维方法与三维方法预报的升沉运动结果的精度接近,随着速度的增加,二者的差异变大;当航速较高时,采用三维方法预报的纵摇结果比试验结果大,采用二维方法预报的纵摇结果比试验结果小;长波范围内采用SNNM方法预报的波浪增阻偏小,当谱峰周期为7 s和9 s时,采用SNNM方法近似预报的功率与试验值较为接近,但当谱峰周期变大时,近似预报结果偏小;整体来看,采用SNNM方法得到的航速预报值比试验值大。

多源干扰下的智能车模型预测纵向运动抗干扰控制

智能车纵向运动控制面临模型失配和外部环境变化等多源干扰,影响速度跟踪的精确性,本文针对性提出一种结合扰动观测和模型预测控制(model predict control,MPC)算法的纵向运动抗干扰控制方法。首先,根据车辆纵向动力学模型分析车辆纵向加速度与各项作用力之间的关系,然后将其简化为含多源干扰的质点运动型并设计模型预测控制器作为上层控制器。其次,针对内部未建模动态干扰及外部随机干扰,设计线性扩张状态观测器(linear extended state observe,LESO)进行实时估计,并通过前馈环节实施补偿,且分析了MPC闭环稳定性和LESO收敛性,最终形成扰动补偿和状态反馈的模型预测最优调节控制律。进一步地,为确保控制策略的高效执行,提出1阶自抗扰控制器作为下层控制器,将期望加速度转换为发动机转矩,从而实现对车速的闭环控制。最后,将算法部署在车载微控制单元(microcontroller unit,MCU)上,在多个速度和道路工况下进行实车测试。实验结果表明,所提出的策略可以快速且精确跟踪目标车速,具备良好的抗干扰能力。

掺氢天然气管网数值仿真轻量化算法

目前针对管网数值仿真算法的优化主要集中在代数方程组的求解上。针对管网流动数值仿真算法提升计算效率方案中缺少针对封闭关系与状态方程的优化,分析了目前优化方案各模块的耗时情况,发现了仿真程序中耗时模块为封闭关系与状态方程求解。最后,结合计算机库函数的缺点以及现代CPU中SIMD (Single Instruction Multiple Data)模型的特点,提出了针对封闭关系与状态方程的轻量化算法,并验证了算法的高效性。研究结果表明:(1)封闭关系与状态方程模型中的耗时原因为直接调用库函数的次幂运算、指数运算、对数运算以及除法运算;(2)由于次幂运算效率远低于其他运算,通过变换消除了模型中几乎所有的次幂运算;(3)利用SIMD指令来计算指数运算与对数运算,实现了在单核CPU上同时计算多个数据的指数与对数,最后通过数学变换将所有除法转换为乘法;(4)轻量化算法加速效果显著,其整体加速比为18,而在状态方程求解模块的加速比更是高达70以上。结论认为,该轻量化算法在不影响计算精度的条件下提高了计算速度,对于提高掺氢天然气管网在线仿真与智能管网的建设具有重要的理论价值和技术支撑。

基于人工智能软件ChatGPT分析物质初始状态

逻辑是一种基础性的思维工具,也是物理学和人工智能软件的共同基础,在物理分析中引入人工智能软件,可以形成新的观察视角和思考方向。在物理分析中,人工智能软件ChatGPT可以从大量文献中获取信息,并以可理解的方式解释有关概念和理论,它可以帮助加速研究进程、提高工作效率,促进物理学知识的传播和普及。在只有一个物质的情况下,该物质的速度是光速,该物质的质量是宇宙总质量,该物质的温度是宇宙热平衡温度,这是物质初始状态。基于ChatGPT分析了与物质初始状态有关的一系列问题。