In this paper, a data-based fault tolerant control(FTC) scheme is investigated for unknown continuous-time(CT)affine nonlinear systems with actuator faults. First, a neural network(NN) identifier based on particle swa...In this paper, a data-based fault tolerant control(FTC) scheme is investigated for unknown continuous-time(CT)affine nonlinear systems with actuator faults. First, a neural network(NN) identifier based on particle swarm optimization(PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network(PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation(HJBE) more efficiently.Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.展开更多
In the globalized market environment,increasingly significant economic and environmental factors withincomplex industrial plants impose importance on the optimization of global production indices;such opti-mization in...In the globalized market environment,increasingly significant economic and environmental factors withincomplex industrial plants impose importance on the optimization of global production indices;such opti-mization includes improvements in production efficiency,product quality,and yield,along with reductionsof energy and resource usage.This paper briefly overviews recent progress in data-driven hybrid intelli-gence optimization methods and technologies in improving the performance of global production indicesin mineral processing.First,we provide the problem description.Next,we summarize recent progress indata-based optimization for mineral processing plants.This optimization consists of four layers:optimiza-tion of the target values for monthly global production indices,optimization of the target values for dailyglobal production indices,optimization of the target values for operational indices,and automation systemsfor unit processes.We briefly overview recent progress in each of the different layers.Finally,we point outopportunities for future works in data-based optimization for mineral processing plants.展开更多
To cope with the challenges of CoViD-19,europe has adopted relevant measures of a data-based approach to governance,on which scholars have huge differences,and the related researches are conducive to further discussio...To cope with the challenges of CoViD-19,europe has adopted relevant measures of a data-based approach to governance,on which scholars have huge differences,and the related researches are conducive to further discussion on the differences.By sorting out the challenges posed by the pandemic to public security and data protection in europe,we can summarize the“european Solution”of the data-based approach to governance,including legislation,instruments,supervision,international cooperation,and continuity.The“Solution”has curbed the spread of the pandemic to a certain extent.However,due to the influence of the traditional values of the EU,the“Solution”is too idealistic in the balance between public security and data protection,which intensifies the dilemma and causes many problems,such as ambiguous legislation,inadequate effectiveness and security of instruments,an arduous endeavor in inter national cooperation,and imperfect regulations on digital green certificates.Therefore,in a major public health crisis,there is still a long way to go in exploring a balance between public security and data protection.展开更多
In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching systems.The system state is forced to track the reference signal by minimizing the performance func...In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching systems.The system state is forced to track the reference signal by minimizing the performance function.First,the problem is transformed to solve the corresponding Bellman optimality equation in terms of the Q-function(also named as action value function).Then,an iterative algorithm based on adaptive dynamic programming(ADP)is developed to find the optimal solution which is totally based on sampled data.The linear-in-parameter(LIP)neural network is taken as the value function approximator.Considering the presence of approximation error at each iteration step,the generated approximated value function sequence is proved to be boundedness around the exact optimal solution under some verifiable assumptions.Moreover,the effect that the learning process will be terminated after a finite number of iterations is investigated in this paper.A sufficient condition for asymptotically stability of the tracking error is derived.Finally,the effectiveness of the algorithm is demonstrated with three simulation examples.展开更多
This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown cova...This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.展开更多
This paper focuses on developing a system that allows presentation authors to effectively retrieve presentation slides for reuse from a large volume of existing presentation materials. We assume episodic memories of t...This paper focuses on developing a system that allows presentation authors to effectively retrieve presentation slides for reuse from a large volume of existing presentation materials. We assume episodic memories of the authors can be used as contextual keywords in query expressions to efficiently dig out the expected slides for reuse rather than using only the part-of-slide-descriptions-based keyword queries. As a system, a new slide repository is proposed, composed of slide material collections, slide content data and pieces of information from authors' episodic memories related to each slide and presentation together with a slide retrieval application enabling authors to use the episodic memories as part of queries. The result of our experiment shows that the episodic memory-used queries can give more discoverability than the keyword-based queries. Additionally, an improvement model is discussed on the slide retrieval for further slide-finding efficiency by expanding the episodic memories model in the repository taking in the links with the author-and-slide-related data and events having been post on the private and social media sites.展开更多
Inverse problem-solving methods have found applications in various fields,such as structural mechanics,acoustics,and non-destructive testing.However,accurately solving inverse problems becomes challenging when observe...Inverse problem-solving methods have found applications in various fields,such as structural mechanics,acoustics,and non-destructive testing.However,accurately solving inverse problems becomes challenging when observed data are incomplete.Fortunately,advancements in computer science have paved the way for data-based methods,enabling the discovery of nonlinear relationships within diverse data sets.In this paper,a step-by-step completion method of displacement information is introduced and a data-driven approach for predicting structural parameters is proposed.The accuracy of the proposed approach is 23.83%higher than that of the Genetic Algorithm,demonstrating the outstanding accuracy and efficiency of the data-driven approach.This work establishes a framework for solving mechanical inverse problems by leveraging a data-based method,and proposes a promising avenue for extending the application of the data-driven approach to structural health monitoring.展开更多
In this paper, the data-based control problem is investigated for a class of networked nonlinear systems with measurement noise as well as packet dropouts in the feedback and forward channels. The measurement noise an...In this paper, the data-based control problem is investigated for a class of networked nonlinear systems with measurement noise as well as packet dropouts in the feedback and forward channels. The measurement noise and the number of consecutive packet dropouts in both channels are assumed to be random but bounded. A data-based networked predictive control method is proposed, in which a sequence of control increment predictions are calculated in the controller based on the measured output error, and based on the control increment predictions received by the actuator, a proper control action is obtained and applied to the plant according to the real-time number of consecutive packet dropouts at each sampling instant. Then the stability analysis is performed for the networked closedloop system. Finally, the effectiveness of the proposed method is illustrated by a numerical example.展开更多
With the ever increasing complexity of industrial systems,model-based control has encountered difficulties and is facing problems,while the interest in data-based control has been booming.This paper gives an overview ...With the ever increasing complexity of industrial systems,model-based control has encountered difficulties and is facing problems,while the interest in data-based control has been booming.This paper gives an overview of data-based control,which divides it into two subfields,intelligent modeling and direct controller design.In the two subfields,some important methods concerning data-based control are intensively investigated.Within the framework of data-based modeling,main modeling technologies and control strategies are discussed,and then fundamental concepts and various algorithms are presented for the design of a data-based controller.Finally,some remaining challenges are suggested.展开更多
高压共轨系统多次喷射下,预喷引发的压力波使得主喷油量产生波动,导致缸内燃烧效率降低、排放污染物增加.为实现多次喷射下喷油量的精确控制,本文提出一种基于高斯过程回归(Gaussian process regression,GPR)的多次喷射主喷油量数据驱...高压共轨系统多次喷射下,预喷引发的压力波使得主喷油量产生波动,导致缸内燃烧效率降低、排放污染物增加.为实现多次喷射下喷油量的精确控制,本文提出一种基于高斯过程回归(Gaussian process regression,GPR)的多次喷射主喷油量数据驱动预测模型.首先,采用D最优设计和二阶响应面方法,以轨压、预喷脉宽、预-主喷间隔和主喷脉宽为因素建立主喷油量响应面模型,通过方差分析揭示4个工况参数均属于极显著影响因素;然后,基于自主开发的多物理场耦合数字仿真平台,建立涵盖528组工况的主喷油量样本集并进行训练模型;最后,系统对比零均值、常数、线性和二次多项式等不同均值函数以及SEiso、RQard和Matérn等不同核函数的组合形式,确定线性均值函数与二次有理核函数为最优配置.结果表明:在测试工况下,GPR模型所预测主喷油量的平均绝对百分比误差为0.347%,决定系数R2为0.999 6,不同主喷脉宽和预-主喷间隔下的预测结果均紧密分布于回归线附近;在非测试工况下,该模型仍能准确再现主喷油量随预-主喷间隔变化的波动规律,与前馈式神经网络(BP)、广义回归神经网络(GR)和支持向量机回归(SVR)模型相比具有更低的误差与更高的一致性.研究证明基于GPR的多次喷射主喷油量数据驱动模型兼具较高预测精度与良好泛化能力,可为高压共轨系统多次喷射下的精确控制提供模型支撑.展开更多
基金supported in part by the National Natural ScienceFoundation of China(61533017,61973330,61773075,61603387)the Early Career Development Award of SKLMCCS(20180201)the State Key Laboratory of Synthetical Automation for Process Industries(2019-KF-23-03)。
摘要In this paper, a data-based fault tolerant control(FTC) scheme is investigated for unknown continuous-time(CT)affine nonlinear systems with actuator faults. First, a neural network(NN) identifier based on particle swarm optimization(PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network(PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation(HJBE) more efficiently.Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.
基金in part by the National Natural ScienceFoundation of China(61525302,61590922)in part by the Proj-ects of Liaoning Province(2014020021,LR2015021).
摘要In the globalized market environment,increasingly significant economic and environmental factors withincomplex industrial plants impose importance on the optimization of global production indices;such opti-mization includes improvements in production efficiency,product quality,and yield,along with reductionsof energy and resource usage.This paper briefly overviews recent progress in data-driven hybrid intelli-gence optimization methods and technologies in improving the performance of global production indicesin mineral processing.First,we provide the problem description.Next,we summarize recent progress indata-based optimization for mineral processing plants.This optimization consists of four layers:optimiza-tion of the target values for monthly global production indices,optimization of the target values for dailyglobal production indices,optimization of the target values for operational indices,and automation systemsfor unit processes.We briefly overview recent progress in each of the different layers.Finally,we point outopportunities for future works in data-based optimization for mineral processing plants.
基金the phased achievement of the major research project of the National Social Science Fund of China(Project Approval No.21VGQ010)supported by the 2021 Central University Basic Scientific Research Project of Lanzhou University(Project Approval No.21lzujbkyjd002).
摘要To cope with the challenges of CoViD-19,europe has adopted relevant measures of a data-based approach to governance,on which scholars have huge differences,and the related researches are conducive to further discussion on the differences.By sorting out the challenges posed by the pandemic to public security and data protection in europe,we can summarize the“european Solution”of the data-based approach to governance,including legislation,instruments,supervision,international cooperation,and continuity.The“Solution”has curbed the spread of the pandemic to a certain extent.However,due to the influence of the traditional values of the EU,the“Solution”is too idealistic in the balance between public security and data protection,which intensifies the dilemma and causes many problems,such as ambiguous legislation,inadequate effectiveness and security of instruments,an arduous endeavor in inter national cooperation,and imperfect regulations on digital green certificates.Therefore,in a major public health crisis,there is still a long way to go in exploring a balance between public security and data protection.
基金supported by the National Natural Science Foundation of China(61921004,U1713209,61803085,and 62041301)。
摘要In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching systems.The system state is forced to track the reference signal by minimizing the performance function.First,the problem is transformed to solve the corresponding Bellman optimality equation in terms of the Q-function(also named as action value function).Then,an iterative algorithm based on adaptive dynamic programming(ADP)is developed to find the optimal solution which is totally based on sampled data.The linear-in-parameter(LIP)neural network is taken as the value function approximator.Considering the presence of approximation error at each iteration step,the generated approximated value function sequence is proved to be boundedness around the exact optimal solution under some verifiable assumptions.Moreover,the effect that the learning process will be terminated after a finite number of iterations is investigated in this paper.A sufficient condition for asymptotically stability of the tracking error is derived.Finally,the effectiveness of the algorithm is demonstrated with three simulation examples.
摘要This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.
摘要This paper focuses on developing a system that allows presentation authors to effectively retrieve presentation slides for reuse from a large volume of existing presentation materials. We assume episodic memories of the authors can be used as contextual keywords in query expressions to efficiently dig out the expected slides for reuse rather than using only the part-of-slide-descriptions-based keyword queries. As a system, a new slide repository is proposed, composed of slide material collections, slide content data and pieces of information from authors' episodic memories related to each slide and presentation together with a slide retrieval application enabling authors to use the episodic memories as part of queries. The result of our experiment shows that the episodic memory-used queries can give more discoverability than the keyword-based queries. Additionally, an improvement model is discussed on the slide retrieval for further slide-finding efficiency by expanding the episodic memories model in the repository taking in the links with the author-and-slide-related data and events having been post on the private and social media sites.
基金supported by the National Natural Science Foundation of China(Grant Nos.11991030,11991031,and 11972205).
摘要Inverse problem-solving methods have found applications in various fields,such as structural mechanics,acoustics,and non-destructive testing.However,accurately solving inverse problems becomes challenging when observed data are incomplete.Fortunately,advancements in computer science have paved the way for data-based methods,enabling the discovery of nonlinear relationships within diverse data sets.In this paper,a step-by-step completion method of displacement information is introduced and a data-driven approach for predicting structural parameters is proposed.The accuracy of the proposed approach is 23.83%higher than that of the Genetic Algorithm,demonstrating the outstanding accuracy and efficiency of the data-driven approach.This work establishes a framework for solving mechanical inverse problems by leveraging a data-based method,and proposes a promising avenue for extending the application of the data-driven approach to structural health monitoring.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61673023,61203230,61273104,61333003,61210012,and 61490701the Beijing Municipal Natural Science Foundation under Grant No.4152014+3 种基金the Great Wall Scholar Candidate Training Program of North China University of Technology(NCUT)the Excellent Youth Scholar Nurturing Program of NCUTthe Outstanding Young Scientist Award Foundation of Shandong Province of China under Grant No.BS2013DX015the Research Fund for the Taishan Scholar Project of Shandong Province of China
摘要In this paper, the data-based control problem is investigated for a class of networked nonlinear systems with measurement noise as well as packet dropouts in the feedback and forward channels. The measurement noise and the number of consecutive packet dropouts in both channels are assumed to be random but bounded. A data-based networked predictive control method is proposed, in which a sequence of control increment predictions are calculated in the controller based on the measured output error, and based on the control increment predictions received by the actuator, a proper control action is obtained and applied to the plant according to the real-time number of consecutive packet dropouts at each sampling instant. Then the stability analysis is performed for the networked closedloop system. Finally, the effectiveness of the proposed method is illustrated by a numerical example.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.60874013,60953001 and 61034002).
摘要With the ever increasing complexity of industrial systems,model-based control has encountered difficulties and is facing problems,while the interest in data-based control has been booming.This paper gives an overview of data-based control,which divides it into two subfields,intelligent modeling and direct controller design.In the two subfields,some important methods concerning data-based control are intensively investigated.Within the framework of data-based modeling,main modeling technologies and control strategies are discussed,and then fundamental concepts and various algorithms are presented for the design of a data-based controller.Finally,some remaining challenges are suggested.
摘要高压共轨系统多次喷射下,预喷引发的压力波使得主喷油量产生波动,导致缸内燃烧效率降低、排放污染物增加.为实现多次喷射下喷油量的精确控制,本文提出一种基于高斯过程回归(Gaussian process regression,GPR)的多次喷射主喷油量数据驱动预测模型.首先,采用D最优设计和二阶响应面方法,以轨压、预喷脉宽、预-主喷间隔和主喷脉宽为因素建立主喷油量响应面模型,通过方差分析揭示4个工况参数均属于极显著影响因素;然后,基于自主开发的多物理场耦合数字仿真平台,建立涵盖528组工况的主喷油量样本集并进行训练模型;最后,系统对比零均值、常数、线性和二次多项式等不同均值函数以及SEiso、RQard和Matérn等不同核函数的组合形式,确定线性均值函数与二次有理核函数为最优配置.结果表明:在测试工况下,GPR模型所预测主喷油量的平均绝对百分比误差为0.347%,决定系数R2为0.999 6,不同主喷脉宽和预-主喷间隔下的预测结果均紧密分布于回归线附近;在非测试工况下,该模型仍能准确再现主喷油量随预-主喷间隔变化的波动规律,与前馈式神经网络(BP)、广义回归神经网络(GR)和支持向量机回归(SVR)模型相比具有更低的误差与更高的一致性.研究证明基于GPR的多次喷射主喷油量数据驱动模型兼具较高预测精度与良好泛化能力,可为高压共轨系统多次喷射下的精确控制提供模型支撑.