Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncerta...Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncertain systems characterized by unknown control directions and time-varying input delay lacks comprehensive solutions.In this paper,we propose an observerbased adaptive tracking controller to address this gap.Neural networks are utilized to handle uncertainty,and a unique coordinate transformation is employed to untangle the coupling between input delay and unknown control directions.Subsequently,a new auxiliary signal counters the impact of time-varying input delay,while a Nussbaum function is introduced to solve the problem of unknown control directions.The leverage of an advanced dynamic surface control technique avoids the“complexity explosion”and reduces boundary layer errors.Synthesizing these techniques ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded(SGUUB),and the tracking error converges to a small region around the origin by selecting suitable parameters.Simulation examples are provided to demonstrate the feasibility of the proposed approach.展开更多
Most of the existing non-line-of-sight(NLOS)localization methods depend on the layout information of the scene which is difficult to be obtained in advance in the practical application scenarios.To solve the problem,a...Most of the existing non-line-of-sight(NLOS)localization methods depend on the layout information of the scene which is difficult to be obtained in advance in the practical application scenarios.To solve the problem,an NLOS target localization method in unknown L-shaped corridor based ultra-wideband(UWB)multiple-input multiple-output(MIMO)radar is proposed in this paper.Firstly,the multipath propagation model of Lshaped corridor is established.Then,the localization process is analyzed by the propagation characteristics of diffraction and reflection.Specifically,two different back-projection imaging processes are performed on the radar echo,and the positions of focus regions in the two images are extracted to generate candidate targets.Furthermore,the distances of propagation paths corresponding to each candidate target are calculated,and then the similarity between each candidate target and the target is evaluated by employing two matching factors.The locations of the targets and the width of the corridor are determined based on the matching rules.Finally,two experiments are carried out to demonstrate that the method can effectively obtain the target positions and unknown scene information even when partial paths are lost.展开更多
A unknown input observer （UIO） design for a class of linear time-delay systems when the observer error can＇t completely decouple from unknown input is dealt with. A sufficient condition to its existence is presente...A unknown input observer （UIO） design for a class of linear time-delay systems when the observer error can＇t completely decouple from unknown input is dealt with. A sufficient condition to its existence is presented based on Lyapunov stability method. Design problem of the proposed observer is formulated in term of linear matrix inequalities. Two design problems of the observer with internal delay and without internal delay are formulated. Based on H∞ control theory in time-delay systems, the proposed observer is designed in term of linear matrix inequalities （LMI）. A design algorithm is proposed. The effective of the proposed approach is illustrated by a numerical example.展开更多
Most existing studies about passive radar systems are based on the already known illuminator of opportunity(IO)states.However,in practice,the receiver generally has little knowledge about the IO states.Little research...Most existing studies about passive radar systems are based on the already known illuminator of opportunity(IO)states.However,in practice,the receiver generally has little knowledge about the IO states.Little research has studied this problem.This paper analyzes the observability and estimability for passive radar systems with unknown IO states under three typical scenarios.Besides,the directions of high and low estimability with respect to various states are given.Moreover,two types of observations are taken into account.The effects of different observations on both observability and estimability are well analyzed.For the observability test,linear and nonlinear methods are considered,which proves that both tests are applicable to the system.Numerical simulations confirm the correctness of the theoretical analysis.展开更多
A novel LS-SVM control method is proposed for general unknown nonlinear systems. A linear kernel LS-SVM model is firstly developed for input/output(I/O) approximation. The LS-SVM control law is then derived directly f...A novel LS-SVM control method is proposed for general unknown nonlinear systems. A linear kernel LS-SVM model is firstly developed for input/output(I/O) approximation. The LS-SVM control law is then derived directly from this developed model without any approximation and assumption. It further proves that the control error is fully equal to the LS-SVM modeling error. This means that a desirable control performance can be achieved because the LS-SVM has been proven to have an outstanding modeling ability in the previous studies. Case studies finally demonstrate the effectiveness of the proposed LS-SVM control approach.展开更多
【目的】针对开放环境下野生动物红外相机监测图像中未知类别检测识别率低的问题,提出一种不依赖显式环境描述或生境元数据仅依赖已知物种标签的未知类别检测方法,以适应真实监测数据中信息受限的普遍场景。【方法】提出基于视觉语言特...【目的】针对开放环境下野生动物红外相机监测图像中未知类别检测识别率低的问题,提出一种不依赖显式环境描述或生境元数据仅依赖已知物种标签的未知类别检测方法,以适应真实监测数据中信息受限的普遍场景。【方法】提出基于视觉语言特征匹配的野生动物未知类别检测方法(EUA),通过耦合大语言模型(LLM)的生态推理能力与视觉语言模型的跨模态对齐特性,构建开放环境下的智能监测框架。首先,设计生态感知提示词,引导LLM仅基于已知物种集合推断区域生态背景,并生成具有生态合理性的潜在物种列表;其次,将潜在物种文本与已知类别共同构建扩展的视觉语言语义空间;最后,提出未知类别评分机制(ODS),通过计算图像在已知类别与潜在物种间的匹配分布偏离度,实现对未知类别的鲁棒检测。【结果】在Dataset3(D3)和North American Camera Trap Images(NACTI)2个公开数据集上的试验表明,EUA显著优于现有方法。在最具挑战性的5类未知类别场景下,EUA的平均假正例率(FPR95)为57.86%,比次优方法降低16.19%,受试者工作特征曲线下面积(AUC)达到84.31%,提升4.64个百分点。消融试验证实,基于生态推理的潜在物种生成和ODS评分机制是性能提升的核心。可视化分析进一步表明,EUA能有效分离已知与未知样本的分布,验证了其设计的有效性。【结论】本研究实现了从“被动分类”到“主动预见”的范式转变,为解决缺乏地理信息的真实监测场景下的未知类别检测问题提供了有效方案。EUA方法不仅在性能上取得突破,更探索出将生态学知识嵌入AI推理过程的可行路径,为构建具备生态感知能力的下一代野生动物智能监测系统提供了新思路。展开更多
基金National Natural Science Foundation of China(62373102)Jiangsu Natural Science Foundation(BK20221455)Anhui Provincial Key Research and Development Project(2022i01020013)。
文摘Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncertain systems characterized by unknown control directions and time-varying input delay lacks comprehensive solutions.In this paper,we propose an observerbased adaptive tracking controller to address this gap.Neural networks are utilized to handle uncertainty,and a unique coordinate transformation is employed to untangle the coupling between input delay and unknown control directions.Subsequently,a new auxiliary signal counters the impact of time-varying input delay,while a Nussbaum function is introduced to solve the problem of unknown control directions.The leverage of an advanced dynamic surface control technique avoids the“complexity explosion”and reduces boundary layer errors.Synthesizing these techniques ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded(SGUUB),and the tracking error converges to a small region around the origin by selecting suitable parameters.Simulation examples are provided to demonstrate the feasibility of the proposed approach.
基金supported by National Natural Science Foundation of China(U20B2070,62001091)Sichuan Science and Technology Program(2022YFS0531).
文摘Most of the existing non-line-of-sight(NLOS)localization methods depend on the layout information of the scene which is difficult to be obtained in advance in the practical application scenarios.To solve the problem,an NLOS target localization method in unknown L-shaped corridor based ultra-wideband(UWB)multiple-input multiple-output(MIMO)radar is proposed in this paper.Firstly,the multipath propagation model of Lshaped corridor is established.Then,the localization process is analyzed by the propagation characteristics of diffraction and reflection.Specifically,two different back-projection imaging processes are performed on the radar echo,and the positions of focus regions in the two images are extracted to generate candidate targets.Furthermore,the distances of propagation paths corresponding to each candidate target are calculated,and then the similarity between each candidate target and the target is evaluated by employing two matching factors.The locations of the targets and the width of the corridor are determined based on the matching rules.Finally,two experiments are carried out to demonstrate that the method can effectively obtain the target positions and unknown scene information even when partial paths are lost.
基金This project was supported by the National Natural Science Foundation of China(60374024)
文摘A unknown input observer （UIO） design for a class of linear time-delay systems when the observer error can＇t completely decouple from unknown input is dealt with. A sufficient condition to its existence is presented based on Lyapunov stability method. Design problem of the proposed observer is formulated in term of linear matrix inequalities. Two design problems of the observer with internal delay and without internal delay are formulated. Based on H∞ control theory in time-delay systems, the proposed observer is designed in term of linear matrix inequalities （LMI）. A design algorithm is proposed. The effective of the proposed approach is illustrated by a numerical example.
基金This work was supported by the National Natural Science Foundation of China(61803379)the China Postdoctoral Science Foundation(2017M613370,2018T111129).
文摘Most existing studies about passive radar systems are based on the already known illuminator of opportunity(IO)states.However,in practice,the receiver generally has little knowledge about the IO states.Little research has studied this problem.This paper analyzes the observability and estimability for passive radar systems with unknown IO states under three typical scenarios.Besides,the directions of high and low estimability with respect to various states are given.Moreover,two types of observations are taken into account.The effects of different observations on both observability and estimability are well analyzed.For the observability test,linear and nonlinear methods are considered,which proves that both tests are applicable to the system.Numerical simulations confirm the correctness of the theoretical analysis.
基金Project(51205420)supported by the National Natural Science Foundation of ChinaProject(NCET-13-0593)supported by the Program for New Century Excellent Talents in University,ChinaProject(14C0208)supported by the Research Foundation of Education Bureau of Hunan Province,China
文摘A novel LS-SVM control method is proposed for general unknown nonlinear systems. A linear kernel LS-SVM model is firstly developed for input/output(I/O) approximation. The LS-SVM control law is then derived directly from this developed model without any approximation and assumption. It further proves that the control error is fully equal to the LS-SVM modeling error. This means that a desirable control performance can be achieved because the LS-SVM has been proven to have an outstanding modeling ability in the previous studies. Case studies finally demonstrate the effectiveness of the proposed LS-SVM control approach.
基金Supported by National Natural Science Foundation of China(60374002,60674036)the Science and Technical Development Plan of Shandong Province (2004GG4204014)the Program for New Century Excellent Talents in University of China
文摘【目的】针对开放环境下野生动物红外相机监测图像中未知类别检测识别率低的问题,提出一种不依赖显式环境描述或生境元数据仅依赖已知物种标签的未知类别检测方法,以适应真实监测数据中信息受限的普遍场景。【方法】提出基于视觉语言特征匹配的野生动物未知类别检测方法(EUA),通过耦合大语言模型(LLM)的生态推理能力与视觉语言模型的跨模态对齐特性,构建开放环境下的智能监测框架。首先,设计生态感知提示词,引导LLM仅基于已知物种集合推断区域生态背景,并生成具有生态合理性的潜在物种列表;其次,将潜在物种文本与已知类别共同构建扩展的视觉语言语义空间;最后,提出未知类别评分机制(ODS),通过计算图像在已知类别与潜在物种间的匹配分布偏离度,实现对未知类别的鲁棒检测。【结果】在Dataset3(D3)和North American Camera Trap Images(NACTI)2个公开数据集上的试验表明,EUA显著优于现有方法。在最具挑战性的5类未知类别场景下,EUA的平均假正例率(FPR95)为57.86%,比次优方法降低16.19%,受试者工作特征曲线下面积(AUC)达到84.31%,提升4.64个百分点。消融试验证实,基于生态推理的潜在物种生成和ODS评分机制是性能提升的核心。可视化分析进一步表明,EUA能有效分离已知与未知样本的分布,验证了其设计的有效性。【结论】本研究实现了从“被动分类”到“主动预见”的范式转变,为解决缺乏地理信息的真实监测场景下的未知类别检测问题提供了有效方案。EUA方法不仅在性能上取得突破,更探索出将生态学知识嵌入AI推理过程的可行路径,为构建具备生态感知能力的下一代野生动物智能监测系统提供了新思路。
