目前已有一些针对路径跟踪控制中信号时滞问题的研究工作,但这些工作大多针对某种特定的控制方法,而在路径跟踪控制方法中,非线性模型预测控制(Nonlinear model predictive control,NMPC)具有能够显式处理系统约束、便于实现多目标优化...目前已有一些针对路径跟踪控制中信号时滞问题的研究工作,但这些工作大多针对某种特定的控制方法,而在路径跟踪控制方法中,非线性模型预测控制(Nonlinear model predictive control,NMPC)具有能够显式处理系统约束、便于实现多目标优化、能够有效利用被控对象前方参考路径信息等优势,但是针对NMPC路径跟踪控制系统中时滞问题的研究较不成熟,制约了这种控制方法的实际应用.为解决上述问题,开展了以下研究工作.首先构建了能够较好地孤立出时滞影响的类车机器人路径跟踪控制系统.接着分析了信号时滞对NMPC路径跟踪控制系统的影响机理,即时滞会导致控制器产生的控制信号不能适应类车机器人在执行控制信号时所处的位置.然后提出了基于增长NMPC预测时域的时滞影响消减方法,即在迭代周期不变的情况下,在无时滞系统较优预测步数的基础上增加二倍时滞周期比以上的整数.最后通过计算机仿真和实验验证了提出方法的有效性.仿真和实验结果表明,信号时滞对NMPC路径跟踪控制系统存在影响,未考虑时滞的NMPC控制算法能够在无时滞系统中实现高精确性路径跟踪,而在有时滞系统中控制失效.通过增长预测时域可以有效消减信号时滞的影响,在信号时滞约为0.2 s的仿真与实验系统中,基于该方法的NMPC控制器可以保证路径跟踪控制的位移误差幅值不超过0.1258 m,航向误差幅值不超过0.0583 rad.展开更多
在整体式车辆稳定性轨迹跟踪控制架构的基础之上,设计了一种引入预瞄曲率信息的自适应预测时域非线性模型预测控制(NMPC).基于预瞄的参考路径曲率点列指导控制维度变化,提升控制器对于路径曲率的动态响应能力;进一步地,引入状态协调优...在整体式车辆稳定性轨迹跟踪控制架构的基础之上,设计了一种引入预瞄曲率信息的自适应预测时域非线性模型预测控制(NMPC).基于预瞄的参考路径曲率点列指导控制维度变化,提升控制器对于路径曲率的动态响应能力;进一步地,引入状态协调优化机制,使控制器显示耦合至上一控制周期的车辆状态空间,有效避免预测时域变化造成的多步优化问题解耦效应,抑制因控制输入突变对轨迹跟踪控制任务的影响.结合两种优化方法,有效改善固定预测时域策略在高曲率轨迹跟踪中因累计误差造成的跟踪精度下降问题.最后,基于MATLAB/Simulink-CarSim联合仿真平台对算法进行了验证.经计算,高速单移线工况下,该方法在侧向偏差均值/峰值、纵向偏差均值/峰值、航向偏差均值/峰值指标中,相较于固定预测时域NMPC同比降低36.17%/15.25%、11.55%/38.58%、6.13%/25.27%;高速双移线工况下,同比降低30.28%/29.77%、25.07%/3.85%、11.02%/2.68%.此外,在高速低附着工况中,该方法仍能保证良好的控制精度及侧向稳定性,其峰值侧向偏差为0.2017 m、峰值纵向偏差为0.9744 km h^(-1)、峰值航向偏差为1.1936°、峰值质心侧偏角为1.9074°.展开更多
胆道闭锁诊断与治疗指南(2025年)于2025年9月4日在线发表在Hepatobiliary Surgery and Nutrition,该指南整合东西方国家53位专家意见,最终形成23条推荐意见,涵盖胆道闭锁的早期筛查、辅助检查、手术治疗、术后管理、肝移植及长期随访等...胆道闭锁诊断与治疗指南(2025年)于2025年9月4日在线发表在Hepatobiliary Surgery and Nutrition,该指南整合东西方国家53位专家意见,最终形成23条推荐意见,涵盖胆道闭锁的早期筛查、辅助检查、手术治疗、术后管理、肝移植及长期随访等方面。展开更多
Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable d...Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable dynamic gaps,resulting in conservative and suboptimal trajectories.To address these challenges,this paper proposes a hierarchical reinforcement learning(RL)framework that integrates global path guidance,local trajectory generation,predictive safety evaluation,and neural network-based decision-making.Specifically,the global planner provides long-term navigation guidance,and the local module then utilizes an improved 3D dynamic window approach(DWA)to generate dynamically feasible candidate trajectories.To enhance safety in dense dynamic scenarios,the algorithm introduces a predictive axis-aligned bounding box(AABB)strategy to model the future occupancy of obstacles,combined with convex hull verification for efficient trajectory safety assessment.Furthermore,a double deep Q-network(DDQN)is employed with structured feature encoding,enabling the neural network to reliably select the optimal trajectory from the candidate set,thereby improving robustness and generalization.Comparative experiments conducted in a high-fidelity simulation environment show that the algorithm outperforms existing algorithms,reducing the average number of collisions to 0.2 while shortening the average task completion time by approximately 15%,and achieving a success rate of 97%.展开更多
The definition of environmental pollution is becoming increasingly diverse,with accelerating change and exposure to complex mixtures that defy traditional detection-based monitoring approaches.We discuss the current t...The definition of environmental pollution is becoming increasingly diverse,with accelerating change and exposure to complex mixtures that defy traditional detection-based monitoring approaches.We discuss the current trends in environmental analytical chemistry whereby,rather than targeted quantification,an integrated pollutant assessment,which upholds chemical discovery,interpretability,and real-world relevance,is desired.We initially explain the conceptual change between preset sets of analytes to the chemical space exploration made possible by exploring the chemical space using high-resolution mass spectrometry,multidimensional separations,and rapid/direct analysis technologies.We next mention how the new classes of contaminants and transformation products,as well as the complexity of mixtures,reveal the long-standing gaps in sensitivity,selectivity,and confidence of the identification,especially in the non-targeted workflows.In response to such limitations,we now mention changes that combine chemical measurement with biological and data-informed aspects,such as effect-based assays,exposure-oriented metrics,chemometrics,and machine learning feature prioritization and structure annotation.We also look at the transformation of higher orders of analytical products into clean-up programs and decision programs,which should focus on continuous and in-place sensing,tiered monitoring designs,and risk-based prioritization plans that more closely reflect the changing realities of the environment.Lastly,we determine future research requirements in harmonization,open data infrastructure,and reproducibility,and the development of autonomous and intelligent analytical systems that can perform adaptive monitoring and provide insights quickly.All these changing frontiers transform environmental analysis into a detection instrument into an actionable environmental intelligence that can be used to proactively manage and protect the ecosystems and human health.展开更多
The brain's functions are governed by molecular metabolic networks.However,due to the sophisticated spatial organization and diverse activities of the brain,characterizing both the minute and large-scale metabolic...The brain's functions are governed by molecular metabolic networks.However,due to the sophisticated spatial organization and diverse activities of the brain,characterizing both the minute and large-scale metabolic activity across the entire brain and its numerous micro-regions remains incredibly challenging.Here,we offer a high-definition spatially resolved metabolomics technique to better understand the metabolic specialization and interconnection throughout the mouse brain using improved ambient mass spectrometry imaging.This method allows for the simultaneous mapping of thousands of metabolites at a 30 μm spatial resolution across the mouse brain,ranging from structural lipids to functional neurotransmitters.This approach effectively reveals the distribution patterns of delicate microregions and their distinctive metabolic characteristics.Using an integrated database,we annotated 259 metabolites,demonstrating that the metabolome and metabolic pathways are unique to each brain microregion.The distribution of metabolites,closely linked to functionally connected brain regions and their interactions,offers profound insights into the complexity of chemical processes and their roles in brain function.An initial dataset for future metabolomics research might be obtained from the high-definition mouse brain's spatial metabolome atlas.展开更多
文摘在整体式车辆稳定性轨迹跟踪控制架构的基础之上,设计了一种引入预瞄曲率信息的自适应预测时域非线性模型预测控制(NMPC).基于预瞄的参考路径曲率点列指导控制维度变化,提升控制器对于路径曲率的动态响应能力;进一步地,引入状态协调优化机制,使控制器显示耦合至上一控制周期的车辆状态空间,有效避免预测时域变化造成的多步优化问题解耦效应,抑制因控制输入突变对轨迹跟踪控制任务的影响.结合两种优化方法,有效改善固定预测时域策略在高曲率轨迹跟踪中因累计误差造成的跟踪精度下降问题.最后,基于MATLAB/Simulink-CarSim联合仿真平台对算法进行了验证.经计算,高速单移线工况下,该方法在侧向偏差均值/峰值、纵向偏差均值/峰值、航向偏差均值/峰值指标中,相较于固定预测时域NMPC同比降低36.17%/15.25%、11.55%/38.58%、6.13%/25.27%;高速双移线工况下,同比降低30.28%/29.77%、25.07%/3.85%、11.02%/2.68%.此外,在高速低附着工况中,该方法仍能保证良好的控制精度及侧向稳定性,其峰值侧向偏差为0.2017 m、峰值纵向偏差为0.9744 km h^(-1)、峰值航向偏差为1.1936°、峰值质心侧偏角为1.9074°.
基金supported by the Postgraduate Research&Practice Innovation Program of Nanjing University of Aeronautics and Astronautics(NUAA)(No.xcxjh20251502)。
文摘Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable dynamic gaps,resulting in conservative and suboptimal trajectories.To address these challenges,this paper proposes a hierarchical reinforcement learning(RL)framework that integrates global path guidance,local trajectory generation,predictive safety evaluation,and neural network-based decision-making.Specifically,the global planner provides long-term navigation guidance,and the local module then utilizes an improved 3D dynamic window approach(DWA)to generate dynamically feasible candidate trajectories.To enhance safety in dense dynamic scenarios,the algorithm introduces a predictive axis-aligned bounding box(AABB)strategy to model the future occupancy of obstacles,combined with convex hull verification for efficient trajectory safety assessment.Furthermore,a double deep Q-network(DDQN)is employed with structured feature encoding,enabling the neural network to reliably select the optimal trajectory from the candidate set,thereby improving robustness and generalization.Comparative experiments conducted in a high-fidelity simulation environment show that the algorithm outperforms existing algorithms,reducing the average number of collisions to 0.2 while shortening the average task completion time by approximately 15%,and achieving a success rate of 97%.
文摘The definition of environmental pollution is becoming increasingly diverse,with accelerating change and exposure to complex mixtures that defy traditional detection-based monitoring approaches.We discuss the current trends in environmental analytical chemistry whereby,rather than targeted quantification,an integrated pollutant assessment,which upholds chemical discovery,interpretability,and real-world relevance,is desired.We initially explain the conceptual change between preset sets of analytes to the chemical space exploration made possible by exploring the chemical space using high-resolution mass spectrometry,multidimensional separations,and rapid/direct analysis technologies.We next mention how the new classes of contaminants and transformation products,as well as the complexity of mixtures,reveal the long-standing gaps in sensitivity,selectivity,and confidence of the identification,especially in the non-targeted workflows.In response to such limitations,we now mention changes that combine chemical measurement with biological and data-informed aspects,such as effect-based assays,exposure-oriented metrics,chemometrics,and machine learning feature prioritization and structure annotation.We also look at the transformation of higher orders of analytical products into clean-up programs and decision programs,which should focus on continuous and in-place sensing,tiered monitoring designs,and risk-based prioritization plans that more closely reflect the changing realities of the environment.Lastly,we determine future research requirements in harmonization,open data infrastructure,and reproducibility,and the development of autonomous and intelligent analytical systems that can perform adaptive monitoring and provide insights quickly.All these changing frontiers transform environmental analysis into a detection instrument into an actionable environmental intelligence that can be used to proactively manage and protect the ecosystems and human health.
基金financial support from the National Natural Science Foundation of China (Nos.82473887 and 21927808)the Scientific and Technological Innovation Program of Shanghai (No.23DZ2202500)the CAMS Innovation Fund for Medical Sciences (No.2021-1-I2M-026)。
文摘The brain's functions are governed by molecular metabolic networks.However,due to the sophisticated spatial organization and diverse activities of the brain,characterizing both the minute and large-scale metabolic activity across the entire brain and its numerous micro-regions remains incredibly challenging.Here,we offer a high-definition spatially resolved metabolomics technique to better understand the metabolic specialization and interconnection throughout the mouse brain using improved ambient mass spectrometry imaging.This method allows for the simultaneous mapping of thousands of metabolites at a 30 μm spatial resolution across the mouse brain,ranging from structural lipids to functional neurotransmitters.This approach effectively reveals the distribution patterns of delicate microregions and their distinctive metabolic characteristics.Using an integrated database,we annotated 259 metabolites,demonstrating that the metabolome and metabolic pathways are unique to each brain microregion.The distribution of metabolites,closely linked to functionally connected brain regions and their interactions,offers profound insights into the complexity of chemical processes and their roles in brain function.An initial dataset for future metabolomics research might be obtained from the high-definition mouse brain's spatial metabolome atlas.
