Proactive traffic responsive control based on state-space neural network and extended Kalman filter

The state-space neural network and extended Kalman filter model is used to directly predict the optimal timing plan that corresponds to futuristic traffic conditions in real time with the purposes of avoiding the lagging of the signal timing plans to traffic conditions. Utilizing the traffic conditions in current and former intervals, the network topology of the state-space neural network （SSNN）, which is derived from the geometry of urban arterial routes, is used to predict the optimal timing plan corresponding to the traffic conditions in the next time interval. In order to improve the effectiveness of the SSNN, the extended Kalman filter （EKF） is proposed to train the SSNN instead of conventional approaches. Raw traffic data of the Guangzhou Road, Nanjing and the optimal signal timing plan generated by a multi-objective optimization genetic algorithm are applied to test the performance of the proposed model. The results indicate that compared with the SSNN and the BP neural network, the proposed model can closely match the optimal timing plans in futuristic states with higher efficiency.

The Empirical Analysis on the Dynamic Effect of Rural-urban Migration on the Consumption Growth of Residents in China——Based on Varying Parameter State-space Model

The research constructed varying parameter state-space model and per- formed estimation on dynamic relationship between urban-rural migration and aggre- gate consumption expenditure on basis of dual economic structure. The results showed that urban consumption growth made the most contribution to aggregate consumption growth, followed by urban-rural migration caused consumption. The role of rural consumption growth kept stable, but consumption caused by population growth was decreasing. Therefore, China consumption growth mainly relies on urban consumption expenditure and urban-rural migration.

Recursive State-space Model Identification of Non-uniformly Sampled Systems Using Singular Value Decomposition

In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition(SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method.

基于state-space-split法的滞回非线性系统随机动力响应分析

采用SSS(state-space-split)法,建立了引入Bouc-Wen滞回模型的杜芬非线性系统在高斯白噪声激励下的概率密度函数(PDF)的近似求解方法,分析了其随机动力响应变化规律。首先,将Bouc-Wen滞回模型引入杜芬非线性系统,分别考虑非线性系统中的几何非线性和材料非线性对动力响应的影响。随后,对该模型进行了等效线性化(EQL)处理,基于等效线性化法结果,介绍了SSS法的简化方法和计算原理,并通过该方法求解了三维FPK方程的近似联合概率密度函数。最后,将该方法应用于三个案例和一个工程实例,通过求解其概率密度函数分布及动力可靠度验证了提出方法的适用性、可行性和优越性。

An Improved Time Domain Approach for Analysis of Floating Bridges Based on Dynamic Finite Element Method and State-Space Model

The floating bridge bears the dead weight and live load with buoyancy,and has wide application prospect in deep-water transportation infrastructure.The structural analysis of floating bridge is challenging due to the complicated fluid-solid coupling effects of wind and wave.In this research,a novel time domain approach combining dynamic finite element method and state-space model(SSM)is established for the refined analysis of floating bridges.The dynamic coupled effects induced by wave excitation load,radiation load and buffeting load are carefully simulated.High-precision fitted SSMs for pontoons are established to enhance the calculation efficiency of hydrodynamic radiation forces in time domain.The dispersion relation is also introduced in the analysis model to appropriately consider the phase differences of wave loads on pontoons.The proposed approach is then employed to simulate the dynamic responses of a scaled floating bridge model which has been tested under real wind and wave loads in laboratory.The numerical results are found to agree well with the test data regarding the structural responses of floating bridge under the considered environmental conditions.The proposed time domain approach is considered to be accurate and effective in simulating the structural behaviors of floating bridge under typical environmental conditions.

Least Squares Matrix Algorithm for State-Space Modelling of Dynamic Systems

This work presents a novel least squares matrix algorithm (LSM) for the analysis of rapidly changing systems using state-space modelling. The LSM algorithm is based on the Hankel structured data matrix representation. The state transition matrix is updated without the use of any forgetting function. This yields a robust estimation of model parameters in the presence of noise. The computational complexity of the LSM algorithm is comparable to the speed of the conventional recursive least squares (RLS) algorithm. The knowledge of the state transition matrix enables feasible numerical operators such as interpolation, fractional differentiation and integration. The usefulness of the LSM algorithm was proved in the analysis of the neuroelectric signal waveforms.

基于反步法的下肢外骨骼机器人控制研究

针对下肢外骨骼预定轨迹的控制问题,在构建了二自由度机械腿的运动学和动力学模型的基础上,提出了一种非对称液压缸负载力、伸缩量与外骨骼转动扭矩、运动角度的转化关系,采用反步法控制下肢外骨骼系统的运动轨迹。首先,采用D-H法建立了二自由度下肢外骨骼系统的运动学模型,研究了外骨骼末端执行器关节速度的变化关系,并利用拉格朗日动力学方程推导了外骨骼动力学的数学模型;然后,使用了液压缸负载力控制为机械腿运动提供了相应的扭矩,进而控制了下肢外骨骼的运动姿态;其次,针对下肢外骨骼运动时的高精度要求,利用反步法控制理论,建立了阀控非对称缸系统,结合下肢外骨骼动力学系统整体的状态空间方程,利用下肢外骨骼液压伺服系统控制了下肢外骨骼进行预定轨迹运动;最后,对AMESim软件与Visual Studio软件进行了联合仿真,对比分析了PID控制与反步法控制的精度问题。研究结果表明:针对下肢外骨骼预定轨迹控制,反步法控制误差最大值为2°,对比传统PID控制,误差降低了67%;设计的反步法控制器对于下肢外骨骼具有良好的控制精度。

电力电子化电力系统多时间尺度时变动态小干扰稳定问题

面向国家能源安全和双碳战略的重大需求,大规模多样化的电力电子装备以不同的形式和功能参与到现代电力系统源-网-荷各环节中,受多样化电力电子装备多时间尺度时变动态影响的电力电子化电力系统稳定机制发生根本性改变。近年来,国内外电力系统中持续出现与电力电子装备相关的各种机理不明的振荡事故,表现为振荡频率范围较宽的多时间尺度时变动态问题,严重威胁电力电子化电力系统的安全稳定运行。该文从多样化电力电子装备原始特征出发,归纳电力电子化电力系统多时间尺度时变动态基本特性,并分别从小干扰建模和动态稳定分析归纳对不同理论方法的基本认识,随后从适用于电力电子化电力系统多时间尺度时变动态小干扰稳定快速分析计算为根本目标,凝练基于线性周期时变理论小干扰建模和动态稳定直接分析的研究思路。

基于厚尾双学生氏t分布的非线性状态空间系统鲁棒辨识方法

状态空间模型作为一种常见且重要的模型结构在自动化领域有着广泛的应用,本文针对异常值干扰下的非线性状态空间系统辨识问题开展研究.与现有的辨识方法不同,本文充分考虑了状态转移过程和输出量测过程均受到异常值干扰的情况,提出了一种更加全面的鲁棒辨识算法.首先利用两个相互独立的学生氏t分布分别对状态噪声和输出噪声进行建模以保障算法的鲁棒性;其次利用粒子平滑算法估计状态变量的后验概率分布以解决状态未知问题;最后利用期望最大化算法实现未知参数估计.在算法实现过程中使用了学生氏t分布表达式的数学分解,这样做的好处是:(1)更加有利于算法的推导和实现;(2)更清晰地解释了算法的鲁棒性能.并且本文通过数值算例和应用算例验证了该方法的有效性.

二态量子系统准确布居动力学的一类新型相空间表示及其与三角窗函数的关系

二态系统是最简单的且无经典对应的量子系统,对二态系统的同构表示的认识和研究能启发研究人员对其动力学与统计行为的更深刻理解.本文使用在[J.Chem.Phys.145,204105(2016);J.Chem.Phys.151,024105(2019);J.Phys.Chem.Lett.12,2496(2021)]等文章中发展的约束相空间严格理论、非协变相空间函数、含时权重函数与含时归一化因子来构建一类新型量子相空间表示.这类同构表示可以导出二态量子系统布居动力学的准确结果.约束相空间上的轨迹运动方程同构于含时薛定谔方程.每条相空间轨迹对于布居动力学所对应积分表达式的贡献严格半正定。进一步证明了在[J.Chem.Phys.145,144108(2016)]这篇文章中根据经验提出的三角窗函数方法在本质上可以对应于本文的这类新型相空间表示的一个特殊情况,因此同样是二态量子系统准确布居动力学的同构表示.

Predictive active control of building structures using LQR and artificial intelligence

This study presents a neural network-based model for predicting linear quadratic regulator(LQR)weighting matrices for achieving a target response reduction.Based on the expected weighting matrices,the LQR algorithm is used to determine the various responses of the structure.The responses are determined by numerically analyzing the governing equation of motion using the state-space approach.For training a neural network,four input parameters are considered:the time history of the ground motion,the percentage reduction in lateral displacement,lateral velocity,and lateral acceleration,Output parameters are LQR weighting matrices.To study the effectiveness of an LQR-based neural network(LQRNN),the actual percentage reduction in the responses obtained from using LQRNN is compared with the target percentage reductions.Furthermore,to investigate the efficacy of an active control system using LQRNN,the controlled responses of a system are compared to the corresponding uncontrolled responses.The trained neural network effectively predicts weighting parameters that can provide a percentage reduction in displacement,velocity,and acceleration close to the target percentage reduction.Based on the simulation study,it can be concluded that significant response reductions are observed in the active-controlled system using LQRNN.Moreover,the LQRNN algorithm can replace conventional LQR control with the use of an active control system.

Improved State-space Modelling for Microgrids Without Virtual Resistances

Power converters and their interfacing networks are often treated as modular state-space blocks for small-signal stability studies in microgrids;they are interconnected by matching the input and output states of the network and converters.Virtual resistors have been widely used in existing models to generate a voltage for state-space models of the network that require voltage inputs.This paper accurately quantifies the adverse impacts of adding the virtual resistance and proposes an alternative method for network modelling that eliminates the requirement of the virtual resistor when interfacing converters with microgrids.The proposed nonlinear method allows initialization,time-domain simulations of the nonlinear model,and linearization and eigenvalue generation.A numerically linearized small-signal model is used to generate eigenvalues and is compared with the eigenvalues generated using the existing modelling method with virtual resistances.Deficiencies of the existing method and improvements offered by the proposed modelling method are clearly quantified.Electromagnetic transient(EMT)simulations using detailed switching models are used for validation of the proposed modelling method.

求索大脑智慧本质,照亮类脑智能之路

宇宙创生→生命爆发→智能演生→人工智能,这是一条演化的历史长河。时代在问,脑与心智从哪里来,将到哪里去?人工智能的前途和命运是怎样的?人类文明的前途和命运是怎样的?大脑智能和人工智能怎样才能相互照亮?是否有不依赖于大数据、大算力和大模型的智能路线?人工智能可否通过完全不同于生物进化的另一条道路通向“心智”?脑智创造力如何演化为新质生产力?这些问题的核心仍然是人类大脑在整体上是怎样工作的?本文将从微观—介观—宏观—宇观的尺度上来勾勒一幅复杂性与简约性辩证统一的图景。

计及频率响应时空相关性的新能源电力系统惯量估计方法

高比例新能源电力系统惯性水平低、不确定性强,系统惯性水平与频率动态响应过程密切相关。系统内各节点之间的频率响应过程在时空上具有相关性,动态量化节点间频率的时空相关性是实时估计新型电力系统惯量的技术手段。首先,给出了面向同步机组和新能源发电机组惯量的计算方法;其次,利用Granger因果检验算法,动态分析系统内不同节点之间频率的相关性,构建随时间变化的系统频率时空因果相关集合;此外,基于系统内各节点频率响应过程的摇摆方程,建立系统惯性常数-频率状态空间模型;然后,基于无迹卡尔曼滤波器和固定滞后平滑器,提出高比例新能源电力系统惯量估计方法;最后,在改进的IEEE 39节点系统中验证了所提方法的有效性和适用性。结果表明,所提方法可以在不同新能源渗透率和频率扰动场景下保证惯量估计效果的一致性,具有工程应用潜力。

河北省洪涝灾害韧性评价及时空演进分析

为应对气候变化及非生态城市建设活动带来的洪涝灾害风险,以河北省11个地级市为研究对象,基于压力-状态-响应模型构建洪涝灾害韧性评价体系,计算指标权重及各城市韧性指数,并且对2010,2015,2020年各城市的洪涝灾害韧性进行了综合评价和时空演化分析。结果表明,河北省洪涝灾害韧性水平呈稳步上升趋势,其中压力层韧性降低,状态及响应层韧性提升明显;在空间分布上存在城市间韧性发展不平衡、差异化严重等问题。针对存在的问题提出了韧性优化措施。

电液伺服系统一种动力学建模方法

电液伺服系统具有响应速度快、输出功率大、抗冲击性好等优点,广泛应用于飞行器结构强度试验中。然而,因特殊物理结构,电液伺服系统具有复杂的非线性、时变性和不确定性,这为其动力学建模和控制器设计带来了很大的挑战。本文通过分析系统各个部件的物理学特性和工作原理,分别建立了伺服阀流量方程、非对称液压缸流量方程和液压缸力平衡方程。经过一系列数学变化,将动力学方程进一步转化为系统状态空间方程,便于未来工作中对系统进行基于模型的控制器设计。最后,进行了系统仿真平台的搭建,并采用PID控制器进行跟踪控制仿真试验,验证了所提模型的有效性。

GPS/BDS/Galileo/GLONASS实时精密单点定位性能评估

随着卫星导航系统的迅速发展及人们对实时高精度定位性能日益增大的需求,越来越多的分析中心开始提供支持多系统的状态空间表述(SSR)产品。因此评估不同分析中心产品在不同系统组合下的定位性能十分必要。本文选取全球范围内的20个测站,对中国科学院(CAS)、武汉大学(WHU)和法国空间研究中心(CNES)3个分析中心提供的实时产品进行全球定位系统(GPS)/北斗卫星导航系统(BDS)/伽利略卫星导航系统(Galileo)/格洛纳斯全球卫星导航系统(GLONASS)实时精密单点定位(PPP)处理,并从实时产品可用性、收敛时间及定位性能进行分析。实验结果表明:WHU中心提供的GPS卫星可用性最好,GLONASS系统较差,且缺失了2颗卫星,CAS播发的BDS实时产品最好,提供了全部的BDS卫星改正数;静态模式下双系统组合中GPS+BDS组合的收敛时间最短,动态模式下双系统组合中GPS+Galileo的收敛时间最短,且在动态模式下,所有分析中心的GPS+Galileo+BDS+GLONASS组合收敛时间都明显优于GPS+Galileo+BDS组合;单系统中GPS的定位精度最优,静态模式下高程方向上优于2.5 cm,水平方向上优于1.7 cm;动态模式下高程方向上优于6.5 cm,水平方向上优于3.1 cm;但加入GLONASS的四系统组合并不能明显地提高定位精度。

考虑长距离输电缆特性的逆变器并网系统谐波不稳定分析及抑制

长距离输电缆(LTC)与并网逆变器交互作用易引发系统谐波不稳定的风险,且忽略线路频变特性可能会导致稳定性误判。文中计及输电线路的分布参数特性和频变特性,研究如何评估这类逆变器并网系统的稳定性。首先,基于矢量匹配法对含LTC的交流系统进行等值,同时考虑逆变器的内外环控制、锁相环、控制延时等环节,建立dq旋转坐标系下逆变器并网系统高频状态空间模型;其次,采用参与因子分析方法辨识出导致谐波不稳定的关键影响环节,并基于根轨迹方法分析了LTC频变特性、延迟时间、控制器参数、电缆长度和电网阻抗对系统稳定性的影响;再次,提出了基于参数灵敏度分析和矩阵相似变换的系统稳定性改善方法,并对改进后系统的谐波稳定性进行分析;最后,基于RT-LAB构建硬件在环实验平台进行了验证,实验结果验证了理论分析的正确性。

航空发动机燃油系统定压活门参数优化设计

定压活门在燃油系统中为多个伺服机构供油,针对其稳定性、稳态精度、鲁棒性等设计要求,以及多个设计参数相互竞争又相互矛盾的选择,提出了一种基于优化算法的参数设计方法。建立了定压活门数学模型,基于稳态模型进行了参数设计分析。结果表明:定压活门存在流量稳态工作区,在流量稳态工作区内,阀芯截面积增大,流量敏感度增大,但阀芯截面积过大会增大定压活门的体积。根据定压活门压力范围计算了稳态参数,以调节时间和超调量为目标,取3组不同定压腔容积,将弹簧腔容积、阻尼孔径、运动阻尼、阀芯质量作为参数,基于非支配排序遗传算法(NSGA-Ⅱ)进行了动态优化。Pareto解集表明调节时间和超调量相互矛盾。选取1组解经AMESim仿真验证,优化后的结构参数能够使调节时间缩短20%以上,超调量降低15%以上,定压活门动态性能得到改善。

基于贝叶斯推理和桥梁位移响应的移动车辆荷载识别

考虑到移动车辆荷载信息对于桥梁设计、运营和维护的重要性,提出基于桥梁动态位移响应识别桥上动态车辆荷载的方法.构造桥梁动态位移响应与动态车辆荷载的关系,通过贝叶斯推理方法反演移动荷载.建立车桥耦合模型进行验证,分析传感器数量及环境噪声对识别精度的影响.结果表明,在桥上布置至少两个位移传感器时,该方法可以有效识别动态车辆荷载,且传感器数量、位置不影响识别精度;但环境噪声会增加动态车辆荷载的识别误差,通过增加传感器数量可以解决该问题.