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。

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.

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。

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。

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。

含电压源换流器直流系统小信号稳定分析及测试

该文结合形成网络方程的系统化思想,提出一种基于图论和状态空间的不确定系统小信号稳定判据。该小信号稳定判据不仅减小高维系统的计算负担,而且能判断包含运行状态和结构不确定的参数不确定系统的小信号稳定性。首先,参数不确定系统表示为有向图的形式,其中每个节点是用状态矩阵、输入矩阵和输出矩阵表示的多输入多输出子系统,系统的不确定性用矩阵范数界限约束。利用节点支路关联矩阵以及支路连接矩阵,节点子系统通过支路相连。然后提出基于确定的系统动态、支路连接矩阵、不确定参数的范数界限和最大连通度的小信号稳定判据,此判据与系统结构、节点数和支路数均无关。最后将该稳定判据应用在基于电压源换流器的柔性直流电网中,通过与传统稳定判据伯德图和状态矩阵根轨迹的对比分析,验证所提小信号稳定判据有效。

上下文信息融合与噪声自适应的异常检测方法

信息物理系统(CPSs)中传感器和执行器等现场设备收集的数据中隐含复杂的上下文信息和未知分布噪声。为了提取并融合数据中的上下文信息以及减轻噪声带来的干扰,提出了上下文信息融合与噪声自适应的异常检测方法。该方法中设计了一种由自适应降噪编码器、上下文信息编码器和解码器构成的编解码网络建模CPSs状态空间模型。自适应降噪编码器在训练过程中通过拟合数据中噪声的分布模式生成自适应噪声,并利用该噪声对训练数据中的传感器数据加噪,以提升编解码网络的鲁棒性,减轻噪声带来的干扰,同时可迫使降噪自编码器学习到泛化性更强的系统的隐藏状态;上下文信息编码器利用LSTM和CNN提取数据窗口内的时序和空间上下文信息,并使用自注意力机制融合这两类上下文信息和系统隐藏状态,融合结果用于推断当前时刻系统隐藏状态,以提升此隐藏状态中的信息量;解码器利用以上系统隐藏状态可以更准确地解码出相应的传感器数据。编解码网络训练完成后,得到系统隐藏状态和传感器解码值,基于无迹卡尔曼滤波算法计算异常评分。在SWaT和PUMP两个实际CPSs数据集上的实验结果表明,所提方法的F1值均优于其他对比方法,验证了其有效性。

事件故障状态量子博弈过程的参与者收益研究

为了解事件故障状态量子博弈过程中参与者收益随各影响因素的变化情况,提出在空间故障树(Space Fault Tree, SFT)框架内,以事件故障状态为对象,对参与者收益进行研究。事件故障状态使用量子态表示,管理者和操作者的不同行为对事件故障状态的作用使用博弈表示。考虑因素包括安全产出价值、安全收益分配系数、安全措施成本。研究了事件故障状态与量子博弈的关系;纠缠与非纠缠态下的参与者收益;参与者收益受到各因素影响的特征等。研究得到了管理者和操作者考虑纠缠和非纠缠态的收益函数。结合SFT理论方法,提出了针对收益的因素重要度、因素联合重要度、收益风险区和安全区、因素区域重要度。理论上SFT可用于量子博弈参与者收益的分析。也论述了使用因素空间理论解决该问题的可能性。