Simulation on model predictive control for PMSM drive system based on double extended state observer

A novel double extended state observer(DESO)based on model predictive torque control(MPTC)strategy is developed for three-phase permanent magnet synchronous motor(PMSM)drive system without current sensor.In general,to achieve high-precision control,two-phase current sensors are necessary for successful implementation of MPTC.For this purpose,two ESOs are used to estimate q-axis current and stator resistance respectively,and then based on this,d-axis current is estimated.Moreover,to reduce torque and flux ripple and to improve the performance of the torque and speed,MPTC strategy is designed.The simulation results validate the feasibility and effectiveness of the proposed scheme.

Tri-state Modulation Power Driving of Electro-hydraulic Proportional Amplifier

Switch electro-hydraulic proportional amplifier（PA） widely employs single switch modulation power driving（SSMPD） or reverse discharging power driving（RDPD） at present. SSMPD has slow dynamic response, and can＇t adjust independently the dither signal＇s amplitude and frequency; RDPD accelerates the current decay; consequently, it increases current ripple and power loss. For the purpose of solving the above mentioned problem, the tri-state modulation power driving（TSMPD） scheme was proposed for improving the performance of power driving. Detailedly, the hardware circuit for the tri-state modulation power driving is designed; the tri-state modulation algorithm is realized by digital signal processor（DSP）. The tri-state modulation power driving is investigated by experiments, comparetive experiments among the single switch modulation power driving（SSMPD）, reverse discharging power driving（RDPD）, and the TSMPD are implemented, and the experimental results demonstrate that the linearity error of TSMDP meets the requirement of PA; the current response of TSMSP is the best; the amplitude of ripple current of the TSMPD can be reduced without increasing frequency of PWM, in addition, dither signal amplitude and frequency can be adjusted independently for each other. It is very meaningful to guide the development of high performance proportional amplifier for high frequency response proportional solenoid.

Speeding up generation of photon Fock state in a superconducting circuit via counterdiabatic driving

Optimal creation of photon Fock states is of importance for quantum information processing and state engineering.Here an efficient strategy is presented for speeding up generation of photon Fock state in a superconducting circuit via counterdiabatic driving.A transmon qubit is dispersively coupled to a quantized electrical field.We address a ∧-configuration interaction between the composite system and classical drivings.Based on two Gaussian-shaped drivings,a single-photon Fock state can be generated adiabatically.Instead of adding an auxiliary counterdiabatic driving,our concern is to modify these two Rabi drivings in the framework of shortcut to adiabaticity.Thus an accelerated operation with high efficiency can be realized in a much shorter time.Compared with the adiabatic counterpart,the shortcut-based operation is significantly insusceptible to decoherence effects.The scheme could offer a promising way to deterministically prepare photon Fock states with superconducting quantum circuits.

Generation of W-Type Entangled Coherent States of Three-Cavity Fields by a Driving Classical Field

A scheme is proposed to generate the W-type entangled coherent states of three-cavity field. The scheme is based on the resonant atom-field interaction, thus the interaction time between the atom and the cavity is greatly reduced, which is important in view of decoherence. Furthermore, the scheme does not need accurate adjustment of the interaction time.

Effective dynamics and quantum state engineering by periodic kicks

We study the kick dynamics of periodically driven quantum systems,and provide a time-independent effective Hamiltonian with the analytical form to reasonably describe the effective dynamics in a long timescale.It is shown that the effective coupling strength can be much larger than the coupling strength of the original system in some parameter regions,which stems from the zero time duration of kicks.Furthermore,different regimes can be transformed from and to each other in the same three-level system by only modulating the period of periodic kicks.In particular,the population of excited states can be selectively suppressed in periodic kicks,benefiting from the large detuning regime of the original system.Finally,some applications and physical implementation of periodic kicks are demonstrated in quantum systems.These unique features would make periodic kicks become a powerful tool for quantum state engineering.

Combined Estimation of Vehicle Dynamic State and Inertial Parameter for Electric Vehicles Based on Dual Central Difference Kalman Filter Method

Distributed drive electric vehicles(DDEVs)possess great advantages in the viewpoint of fuel consumption,environment protection and traffic mobility.Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size,and inertial parameter has seldom been tackled and systematically estimated.This paper presents a dual central difference Kalman filter(DCDKF)where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters,such as vehicle sideslip angle,vehicle mass,vehicle yaw moment of inertia,the distance from the front axle to centre of gravity.The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs.The four-wheel nonlinear vehicle dynamics estimation model considering payload variations,Pacejka tire model,wheel and motor dynamics model is developed,the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory.To address system nonlinearities in vehicle dynamics estimation,the DCDKF and dual extended Kalman filter(DEKF)are also investigated and compared.Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-CarsimR.The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions.This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability.

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.

Neural Network-Based State of Charge Estimation Method for Lithium-ion Batteries Based on Temperature

Lithium-ion batteries are commonly used in electric vehicles,mobile phones,and laptops.These batteries demonstrate several advantages,such as environmental friendliness,high energy density,and long life.However,battery overcharging and overdischarging may occur if the batteries are not monitored continuously.Overcharging causesfire and explosion casualties,and overdischar-ging causes a reduction in the battery capacity and life.In addition,the internal resistance of such batteries varies depending on their external temperature,elec-trolyte,cathode material,and other factors;the capacity of the batteries decreases with temperature.In this study,we develop a method for estimating the state of charge(SOC)using a neural network model that is best suited to the external tem-perature of such batteries based on their characteristics.During our simulation,we acquired data at temperatures of 25°C,30°C,35°C,and 40°C.Based on the tem-perature parameters,the voltage,current,and time parameters were obtained,and six cycles of the parameters based on the temperature were used for the experi-ment.Experimental data to verify the proposed method were obtained through a discharge experiment conducted using a vehicle driving simulator.The experi-mental data were provided as inputs to three types of neural network models:mul-tilayer neural network(MNN),long short-term memory(LSTM),and gated recurrent unit(GRU).The neural network models were trained and optimized for the specific temperatures measured during the experiment,and the SOC was estimated by selecting the most suitable model for each temperature.The experimental results revealed that the mean absolute errors of the MNN,LSTM,and GRU using the proposed method were 2.17%,2.19%,and 2.15%,respec-tively,which are better than those of the conventional method(4.47%,4.60%,and 4.40%).Finally,SOC estimation based on GRU using the proposed method was found to be 2.15%,which was the most accurate.

机耕道自动驾驶农机局部路径规划

针对机耕道场景下自动驾驶农机行驶的安全性、平稳性与规划实时性的实际需求,该研究提出了一种基于二次规划的局部路径规划方法。首先基于有限状态机构建农机机耕道行驶模式,其次采用横纵向解耦的方法,通过改进状态栅格法分别对农机速度行为和轨迹行为进行决策,随后利用二次规划方法生成满足多目标、多约束条件的农机轨迹和速度,得到最优路径,最后在多种行驶环境中进行仿真和实车试验,行驶参考速度为2 m/s。实车试验结果表明,在绕行静态障碍物场景中,规划轨迹的平均绝对曲率为0.021 m^(-1),最大绝对曲率为0.056 m^(-1),平均绝对横向误差为3.23 cm,最大绝对横向误差为8.69 cm,农机与障碍物外轮廓的距离大于0.76 m;在规避相向行驶、同向行驶和横穿机耕道的动态障碍物场景中,规划速度的平均绝对速度误差为0.08~0.12 m/s,绝对速度误差小于0.38 m/s,加速度变化范围为-0.38~0.44 m/s^(2)。在规划周期为200 ms的仿真试验中,该文算法平均耗时48 ms,最大耗时75 ms,相比采用静态状态栅格法平均耗时减少38 ms,算法效率提升44%。研究结果可为机耕道场景下的农机局部路径规划提供技术支持。

山区铁路隧道工程资源环境影响效应评估研究

山区地质复杂、环境敏感、生态脆弱,致使铁路隧道建设难度大,且在资源节约、环境保护等方面面临较高要求。绿色设计作为绿色建设的先行环节,对减少隧道建设对资源环境的扰动作用,达到资源节约、环境友好的目的起关键引领作用。为定量评估山区铁路隧道工程的资源环境影响效应、衡量隧道设计的绿色程度,提出一种山区铁路隧道工程资源环境影响效应分析方法。首先,基于“驱动力-状态-响应”模型(Driving force-State-Response,DSR),以隧道设计参数为驱动力指标、资源环境状况为状态指标、隧道设计措施为响应指标,构建山区铁路隧道工程资源环境影响效应评估指标体系,并建立各指标分级标准;其次,运用一种具有自学习自调整能力的支持向量回归模型(Support Vector Regression,SVR)对隧道工程的资源环境影响效应进行评估;再次,以驱动力指标和响应指标为分析对象,运用考虑指标间关联关系及叠加效应的敏感性分析法,甄别对隧道工程资源环境影响效应优化具有重要影响的隧道设计因素;最后,以某山区铁路隧道工程为例,得到该隧道工程的资源环境影响效应值为4.7157,对应等级为较好,表明该隧道工程绿色设计水平较好,资源集约节约利用较合理、环境保护力度较大,可为其他类似工况隧道工程的绿色设计提供借鉴。此外,分析结果显示,注浆加固效果是导致该隧道工程资源环境影响效应变化最敏感的因素,其次为清污分流比例,可着重从这2个方面进行优化设计,以实现隧道工程资源环境影响效应的进一步优化。研究结果验证了本文研究方法的适用性和可操作性,可为山区铁路隧道工程资源环境影响效应评估及明确隧道工程优化设计方向提供科学依据。

基于扩展卡尔曼滤波的交互式多模型跟踪算法研究

在辅助驾驶系统中,行人轨迹跟踪一直是一项有挑战性的任务,因为行人的回波信号中往往存在着许多干扰噪声。此外,行人在运动过程中可能会做出突然转身或其他改变方向的行为,这将直接导致行人运动轨迹呈现出非线性特征。针对上述问题,文中提出一种基于扩展卡尔曼滤波的交互式多模型跟踪(IMM-EKF)方法,适用于毫米波雷达对行人进行轨迹跟踪。首先,在扩展卡尔曼滤波算法(EKF)的基础上重构状态预测协方差矩阵,来补偿EKF非线性化过程中引入的误差;然后将改进的EKF作为交互式多模型算法(IMM)中的滤波器,根据行人运动特性选择匀速模型和协调转弯模型作为跟踪模型,利用所提出的IMM-EKF算法进行轨迹跟踪。实验结果表明,所提出的滤波算法较典型的EKF和改进的EKF算法,在跟踪滤波精度方面均有所提升,同时具备更优的跟踪鲁棒性。

分布式驱动电动汽车状态与路面附着系数估计

【目的】准确获取车辆行驶状态参数和路面附着系数对于维持分布式驱动电动汽车的安全性和稳定性十分重要,针对于此,研究了一种能够准确估计其参数的算法,既能降低量产车辆的成本,又能达到车辆安全稳定的目的。【方法】首先,建立三自由度车辆模型,应用基于奇异值分解方法(SVD)的容积卡尔曼滤波算法搭建参数估计器。然后,搭建Carsim/Simulink联合仿真实验平台,采用高、低附着和对接路面双移线工况进行仿真验证。【结果】仿真实验结果表明,估计器能够较准确地估计出参数的真实值。基于仿真的良好估计效果,搭建实车实验平台,进一步进行验证得出,实车实验结果与仿真结果趋势一致,该算法具有较好的估计准确性和时效性。

基于云平台的车辆动态称重及监测系统研究

针对公路车辆动态称重数据具有差异性、资源利用率低和干扰多的问题,提出了一种基于云平台的车辆动态称重及监测系统。该系统结合大数据融合技术和云平台的优势,通过对所有称重设备获取的数据进行筛选和匹配,识别出车辆在道路上的行驶轨迹和重量信息,对车辆多次称重数据的核准比较来判断车辆称重数据的准确性;系统采用B/S结构进行通信,客户端和服务端采用云储存技术。测试结果表明,该系统能够实现对道路来往车辆的行为、称重数据、设备状态等全面的监测和管理,优化了数据处理流程,提高了称重过程的准确性和可靠性,为车辆超限超载检测提供了数据支撑和监督。

基于SMART数据模式的HDD硬盘状态预测方法

硬盘广泛应用于各类信息系统中,其工作状态预测对信息系统的正常运行管理有着重要意义。现有基于SMART(Self Monitoring Analysis and Reporting Technology)属性的机器学习预测算法为保证其通用性,普遍选取部分典型属性作为特征,带来一定的信息丢失。在分析SMART数据特点的基础上,提出数据模式分类后再进行机器学习预测的SMART数据处理方法。实际测试结果表明,经分类处理后,采用简单的机器学习算法即可获得与强分类器接近的性能,同时,该方法可有效简化SMART数据机器学习时的特征选择过程,有效降低算法的资源消耗。

基于动态基准的低复杂度特种车辆行驶调平控制方法

针对特种车辆行驶调平问题,提出基于动态基准的低复杂度控制方法。整车被分解为由作动器驱动的带有相互耦合特性的悬架节点。建立悬架节点动力学模型,将基于整车行驶动力学模型的位姿混合控制问题,转换为基于全驱型悬架节点动力学模型的单纯位移控制问题。提出并构建动态基准和基准误差,解决现有方法对车身铅垂高依赖和限制的技术瓶颈,同时提高车辆的通过性能。建立基于基准误差的扩张状态观测器,实现动态解耦,并提出一种低复杂度行驶调平控制方法。借助汽车系统仿真软件Carsim验证该方法的有效性。结果表明:与经典基于常值基准的整车型控制方法对比,在所提出方法作用下,车辆的行驶调平精度提高了一个数量级;特别是在路面激励幅度超过作动器行程的工况下,车辆的舒适性和安全性尤为改善。

立体化大学生素质能力贯通式分类培养方法探索与实践

人才培养是高校的主要任务之一,培养适应社会需求的高水平人才是高校义不容辞的责任。该文构建了以思想政治为引领,分类成才为标准、学科平台为支撑的3×3×3大学生立体化培养路径。首次构建“科研项目-科技成果-教学项目-教学成果-科技人才-科研创新-科研项目”的良性循环机制。人才培养过程中形成产教融合贯通“联合导师-校企课程-示范基地”、科教融合贯通“教学名师-品牌金课-科研平台”、师生共创贯通“双创导师-创业课程-孵化平台”的三类培养理念。形成产教融合可推广的新机制,学生创新创业能力显著增强,师资团队教学科研能力显著提升。

基于运动特征模式的出租车行驶状态综合研判

基于行驶轨迹全球导航卫星系统(GNSS)数据,提出了出租车运动学片段提取规则和方法。根据主成分分析(PCA)及累积贡献率,确定了8个表征运动学片段的关键指标;结合K‒均值聚类算法,挖掘出租车运动特征模式。为了确保运动特征模式关键指标权重的客观合理性,采用考虑指标关联性的CRITIC(criteria importance through intercriteria correlation)法和考虑指标离散程度的熵权法,构建了基于纳什均衡的组合赋权的多准则妥协解排序(VIKOR)评价模型,用于多时空情景下出租车运动特征模式评价和出租车行驶状态研判。结果表明,基于纳什均衡的组合赋权法可以有效融合CIRTIC法与熵权法对评价指标的优势,获得更合理的权重系数。就安全性、效率和舒适性而言,出租车行驶状态在主干路和次干路上优于在支路上。早高峰出租车行驶安全性最佳,平峰和晚高峰相对一般。

底水油藏水侵量计算新方法

针对底水油藏的开发,在缺乏试井数据的情况下采用常规油藏物质平衡法以及试井分析方法均难以确定水驱控制储量,进而无法获得水体参数和特征来计算油田的水侵量。为了更加准确的预测水驱油田水侵量,结合生产动态资料,选择符合的水驱特征曲线计算水驱控制储量。在此基础上,考虑油环油、溶解气、水体等因素,建立物质平衡方程,通过亏空体积曲线法计算获得水侵量。利用Fetkvoitch拟稳态模型,采用试错法,优化计算水体体积和水侵系数,保证Fetkvoitch拟稳态模型求解的水侵量和亏空体积曲线法求解的水侵量最接近,得到最优的水体体积和水侵系数对未来生产动态中水侵量进行预测。运用油藏数值模拟软件建立的模型与本文提出两种的计算方法拟合结果对比可知的水体体积的误差为2.6%、水侵指数的误差为2.2%,计算结果准确,为底水油藏计算水侵量提供了可靠的依据,并为预测水侵量提供了新的方法。

基于DPSIR模型的关中平原城市群城市人居环境韧性评价

为了研究关中平原城市群人居环境的韧性能力,通过综合权重法确定DPSIR(驱动力-压力-状态-影响-响应)模型权重,运用地理探测器和反距离空间插值法确定关中平原城市群2010—2021年城市人居环境韧性时空的分异和演变特征以及主要影响因素。结果显示:驱动力是韧性水平的主要影响指数;各城市的人居环境韧性在时间序列上呈现逐渐增强的趋势,韧性指数的变化趋势显著;韧性水平在空间演化上表现以西安和咸阳为中心沿着陇海铁路为发展轴的空间发展形式;在影响因素上,教育水平、经济动力为主要因素影响人居环境韧性水平,各因素之间相互作用的影响显著。

机液并联驱动的工程钻机功率分配分析及工程试验

为实现工程钻机动力系统功率的合理分配,提出机液并联驱动系统的功率分配方式。通过分析该系统的工作原理,得出其传动部分的输出转速、转矩及总传动比的特性曲线;在此基础上,运用键合图理论建立系统功率键合图的物理模型,并推导出该系统的状态方程。基于系统功率流及能量的传递,辨识出系统的功率流及能量的传递由系统分配参数与外负载共同决定。通过构建系统各负载的数学模型,经数值计算得出各负载的关系曲线,结合AMESim软件对仿真模型进行数据分析及钻机工程试验,得出机液并联驱动系统在中高转速有着良好的输出特性,此时,系统的功耗低,钻机钻凿效率高。经分析,应用于钻机的机液并联驱动系统及功率分配方式可满足工程基础开挖的需求,同时为工程装备的动力系统设计提供理论支持和技术借鉴。