期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到675,466篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Survey on data-driven control and its application in cyber-physical energy systems 认领 引用
1
作者 Wenjie Liu Mengfan Zhang +1 位作者 Qianwen Xu Lihua Xie 《Cyber-Physical Energy Systems》 2025年第1期28-48,共21页
Data-driven control has rapidly matured into a powerful complement to classical model-based paradigms.Among the most successful approaches are nonparametric,trajectory-centric methods built on Willems et al.’s fundam... Data-driven control has rapidly matured into a powerful complement to classical model-based paradigms.Among the most successful approaches are nonparametric,trajectory-centric methods built on Willems et al.’s fundamen-tal lemma and model-free reinforcement learning(RL)schemes.In this survey,we reverse the usual presentation order to reflect their differing assumptions and scopes.First,this paper provides a unified treatment of fundamen-tal lemma-based methods,which require knowledge of the system structure(LTI or suitably lifted nonlinear)but offer rigorous guarantees by replacing explicit model identification with input-output trajectory representations.This paper covers state-feedback synthesis for both linear and nonlinear systems,extensions to model predic-tive control that directly leverage data,and recent advances in data-driven state estimation under noise-free and noisy measurements.This paper then turns to RL-based control,which lifts the structural constraints entirely-trading formal stability proofs for broad applicability to complex and uncertain cyber-physical energy systems(CPESs)dynamics.This paper reviews key RL architectures,policy-and value-based algorithms,and their prac-tical challenges in energy applications.Recognizing the network-induced vulnerabilities of modern grids,this paper also surveys data-driven defense strategies against Denial-of-Service and false-data-injection attacks,from anomaly detection to resilient control.To illustrate practical impact,this paper steps through case studies ranging from canonical generator benchmarks to high-fidelity,grid-connected wind-turbine simulations,demonstrating both trajectory-based controllers and RL agents.This paper concludes by outlining future directions:integrating learning-augmented control with fundamental lemma frameworks,enhancing robustness to network effects,and embedding data-driven models in large-scale optimization for next-generation CPESs. 展开更多
关键词 Reinforcement learning Data-driven control State-estimation Cyber-security Cyber-physical energy systems
暂未订购 下载PDF
Data-Driven Control of Distributed Event-Triggered Network Systems 认领 引用 被引量：24
2
作者 Xin Wang Jian Sun +2 位作者 Gang Wang Frank Allgower Jie Chen 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第2期351-364,共14页
The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(a.k.a.network systems).To this end,we start by putting forth a novel distributed event-trigge... The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(a.k.a.network systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional approach.Marrying the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is established.Meanwhile,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input data.Finally,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure. 展开更多
关键词 Data-driven control distributed event-triggered network system(ETS) linear matrix inequalitie(LMI) looped-functional stability
暂未订购 下载PDF
Nonlinear direct data-driven control for UAV formation flight system 认领 引用 被引量：3
3
作者 WANG Jianhong Ricardo A.RAMIREZ-MENDOZA XU Yang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2023年第6期1409-1418,共10页
This paper proposes the nonlinear direct data-driven control from theoretical analysis and practical engineering,i.e.,unmanned aerial vehicle(UAV)formation flight system.Firstly,from the theoretical point of view,cons... This paper proposes the nonlinear direct data-driven control from theoretical analysis and practical engineering,i.e.,unmanned aerial vehicle(UAV)formation flight system.Firstly,from the theoretical point of view,consider one nonlinear closedloop system with a nonlinear plant and nonlinear feed-forward controller simultaneously.To avoid the complex identification process for that nonlinear plant,a nonlinear direct data-driven control strategy is proposed to design that nonlinear feed-forward controller only through the input-output measured data sequence directly,whose detailed explicit forms are model inverse method and approximated analysis method.Secondly,from the practical point of view,after reviewing the UAV formation flight system,nonlinear direct data-driven control is applied in designing the formation controller,so that the followers can track the leader’s desired trajectory during one small time instant only through solving one data fitting problem.Since most natural phenomena have nonlinear properties,the direct method must be the better one.Corresponding system identification and control algorithms are required to be proposed for those nonlinear systems,and the direct nonlinear controller design is the purpose of this paper. 展开更多
关键词 nonlinear system nonlinear direct data-driven control model inverse control unmanned aerial vehicle(UAV)formation flight.
暂未订购 下载PDF
A data-driven control method for ground locomotion on sloped terrain of a hybrid aerial-ground robot 认领 引用 被引量：1
4
作者 Xinhang Xu Yizhuo Yang +3 位作者 Muqing Cao Thien-Minh Nguyen Kun Cao Lihua Xie 《Journal of Automation and Intelligence》 2024年第4期219-229,共11页
In this work,we present a data-driven solution for the attitude control of DoubleBee on slopes.DoubleBee is a novel hybrid aerial-ground robot with two rotors and two active wheels.Inspired by the physics modeling of ... In this work,we present a data-driven solution for the attitude control of DoubleBee on slopes.DoubleBee is a novel hybrid aerial-ground robot with two rotors and two active wheels.Inspired by the physics modeling of the system,we add a channel-separated attention head to a deep ReLU neural network to predict disturbances from ground effects,motor torques and rotation axis shift.The proposed neural network is Lipschitz continuous,has fewer parameters and performs better for disturbance estimation than the baseline deep ReLU neural network.Then,we design a sliding mode controller using these predictions and establish its input-to-state stability and error bounds.Experiments show improvements of the proposed neural network in training speed and robustness over a baseline ReLU network,and a 40%reduction in tracking error compared to a baseline PID controller. 展开更多
关键词 Data-driven control Hybrid aerial-ground robot Adaptive control Machine learning Robotics Nonlinear control systems
暂未订购 下载PDF
Data-Driven Event-Triggered Control Dealing With Noisy Data 认领 引用
5
作者 Xian-Ming Zhang Qing-Long Han Xiaohua Ge 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期543-554,共12页
This paper is concerned with event-triggered control that deals with noisy data for both discrete-time and continuoustime linear systems with unknown system matrices.First,based on a sufficiently rich finite set of no... This paper is concerned with event-triggered control that deals with noisy data for both discrete-time and continuoustime linear systems with unknown system matrices.First,based on a sufficiently rich finite set of noisy data collected in an experiment,the pair of system matrices is represented as a data-based nominal matrix plus an uncertain matrix with a bounded norm.This formulation enables classical robust control techniques to be applied to tackle the robust control problem.Second,for discretetime systems,a novel event-triggering condition is proposed,by which an event is triggered if the sum of the squares of the weighted error exceeds the square of the weighted state from the previous event.For continuous-time systems,the event-triggering condition is devised as a monotonically increasing function that starts with a negative value and triggers an event when it reaches zero.This condition can exclude the so-called Zeno behaviour due to its monotonic increase property.Third,by employing a looped functional method,several criteria are derived to co-design suitable state feedback controllers and event-triggering parameters for the systems under study.Finally,the effectiveness of the proposed method is demonstrated through a case study involving a batch reactor system. 展开更多
关键词 Data-driven control event-triggered control looped functional method noisy data
暂未订购 下载PDF
Adaptive-length data-driven predictive control for post-operation of space robot non-cooperative target capture with disturbances 认领 引用 被引量：1
6
作者 Peiji WANG Bicheng CAI +2 位作者 Chengfei YUE Yong ZHAO Weiren WU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期485-498,共14页
This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi... This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering. 展开更多
关键词 Combined control Data-driven predictive control Post operation Predictive control systems Space non-cooperative target capture
暂未订购 下载PDF
Distributed robust data-driven event-triggered control for QUAVs under stochastic disturbances 认领 引用
7
作者 Chao Song Hao Li +2 位作者 Bo Li Jiacun Wang Chunwei Tian 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第1期155-171,共17页
To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance dat... To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance data-driven event-triggered fusion control method,which achieves efficient fault diagnosis while suppressing random disturbances and mitigating communication conflicts within the QUAV swarm.First,the impact of random disturbances on the UAV swarm is analyzed,and a model for orientation and attitude control of QUAVs under stochastic perturbations is established,with the disturbance gain threshold determined.Second,a fault diagnosis system based on a high-gain observer is designed,constructing a fault gain criterion by integrating orientation and attitude information from QUAVs.Subsequently,a model-free dynamic linearization-based data modeling(MFDLDM)framework is developed using model-free adaptive control,which efficiently fits the nonlinear control model of the QUAV swarm while reducing temporal constraints on control data.On this basis,this paper constructs a distributed data-driven event-triggered controller based on the staggered communication mechanism,which consists of an equivalent QUAV controller and an event-triggered controller,and is able to reduce the communication conflicts while suppressing the influence of random interference.Finally,by incorporating random disturbances into the controller,comparative experiments and physical validations are conducted on the QUAV platforms,fully demonstrating the strong adaptability and robustness of the proposed distributed event-triggered fault-tolerant control system. 展开更多
关键词 Data-driven QUAV control Fault diagnosis Event-triggered Non-conflicting communication
暂未订购 下载PDF
Data-Driven Algorithms for Finite-Horizon and Infinite-Horizon Indefinite Linear Quadratic Stochastic Optimal Control Problems 认领 引用
8
作者 Guangchen Wang Heng Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1459-1469,共11页
This paper is devoted to devising data-driven algorithms for finite-horizon and infinite-horizon linear quadratic stochastic optimal control(LQSOC)problems.In our study,the diffusion terms of system dynamics are permi... This paper is devoted to devising data-driven algorithms for finite-horizon and infinite-horizon linear quadratic stochastic optimal control(LQSOC)problems.In our study,the diffusion terms of system dynamics are permitted to hinge upon both control and state variables,and the weighting matrices of cost functionals are allowed to be indefinite.It is acknowledged that the optimal controls of finite-horizon and infinite-horizon indefinite LQSOC problems are correlated with a generalized differential Riccati equation(GDRE)and a generalized algebraic Riccati equation(GARE).Herein,we propose two data-driven algorithms to approximate the solutions of these Riccati equations,and thereby determine optimal controls,without leveraging the information of all system parameters.Additionally,we prove the convergence of these algorithms and examine the impact of computational errors.Finally,we validate the performance of these data-driven algorithms via three simulation examples. 展开更多
关键词 Data-driven algorithm generalized algebraic Riccati equation (GARE) generalized differential Riccati equation (GDRE) indefinite problem linear quadratic stochastic optimal control (LQSOC)
暂未订购 下载PDF
Data-Driven Predictive Control for Continuous-Time Nonlinear Systems:A Nonzero-Sum Game Approach 认领 引用
9
作者 Juan Liu Hao Zhang +1 位作者 Yifan Xie Frank Allgöwer 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期495-497,共3页
Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over p... Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over predictive control input sequences,deriving multiple optimal predictive control input sequences from its solution. 展开更多
关键词 predictive control nonzero sum game observation loss predictive control input sequencesderiving continuous time nonlinear systems optimal predictive control input sequences reinforcement learning
暂未订购 下载PDF
Dual-Mode Data-Driven Iterative Learning Control:Applications in Precision Manufacturing and Intelligent Transportation Systems 认领 引用
10
作者 Lei Wang Menghan Wei +3 位作者 Ziwei Huangfu Shunjie Zhu Xuejian Ge Zhengquan Li 《Computers, Materials & Continua》 SCIE EI 2026年第2期153-184,共32页
Iterative Learning Control(ILC)provides an effective framework for optimizing repetitive tasks,making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transporta... Iterative Learning Control(ILC)provides an effective framework for optimizing repetitive tasks,making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems(ITS).This paper presents a systematic review of ILC's developmental progress,current methodologies,and practical implementations across these two critical domains.The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows.Through case studies,it evaluates demonstrated improvements in positioning accuracy,surface finish quality,and production throughput.Furthermore,the study examines ILC’s applications in ITS,with particular focus on vehicular motion control applications including autonomous vehicle trajectory tracking,platoon coordination,and traffic signal timing optimization,where its data-driven characteristics enhance adaptability to dynamic environments.Finally,the paper proposes targeted future research directions that are essential for fully realizing ILC’s potential in advancing these interconnected yet distinct fields. 展开更多
关键词 Iterative learning control systematic review precisionmanufacturing intelligent transportation systems
暂未订购 下载PDF
Data-driven non-Gaussian stochastic dynamical system identification meets knowledge-based model predictive control 认领 引用
11
作者 Yicheng Mao Xianbin Liu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第3期71-85,共15页
Randomness and nonlinearity are essential properties of the real world,and their interaction gives rise to highly complex phenomena.With the advancement of technology,merely observing data of the current system state ... Randomness and nonlinearity are essential properties of the real world,and their interaction gives rise to highly complex phenomena.With the advancement of technology,merely observing data of the current system state is no longer sufficient for prediction and application in various fields.Consequently,extracting the nonlinear evolution nature of the system from noisy data has become a prominent and challenging issue.To address this,we propose an integrated approach that combines data-driven stochastic model identification with a knowledge-based model predictive control strategy.By leveraging high-precision model identification,our data-driven control design is particularly effective for continuous target tracking problems that are difficult to address using traditional precise-model-based control theory.Furthermore,the central challenge in data science lies in maximizing the informational value of datasets while minimizing the effects of observation noise.In this study,we propose and rigorously demonstrate the stochastic Occam’s razor principle,a stochastic error estimation theory that evaluates and enhances the design of data-driven schemes to mitigate the effect of observation noise.Notably,our approach offers valuable insights for contemporary data-driven,end-to-end control challenges,particularly those involving uncertain governing equations and substantial non-Gaussian observation noise. 展开更多
关键词 Nonlinear stochastic dynamical system Data-driven system identification Non-Gaussian Lévy noise Data-driven model predictive control
暂未订购 下载PDF
A data-driven modeling approach for predictive control of mixed traffic flow 认领 引用
12
作者 Yue Zuo Xudong Qi +2 位作者 Huifeng Hu Tao Zou Ping Wang 《Control Theory and Technology》 EI CSCD 2026年第2期293-306,共14页
Effective control of mixed traffic flow remains challenging due to vehicle behavior uncertainty and complex interactions.This paper proposes a data-driven control strategy for connected and autonomous vehicles(CAVs)in... Effective control of mixed traffic flow remains challenging due to vehicle behavior uncertainty and complex interactions.This paper proposes a data-driven control strategy for connected and autonomous vehicles(CAVs)in mixed traffic flow,implemented through variable speed limits and lane-changing guidance.First,a cellular automata model of mixed traffic flow is developed,with adjustable CAVs'maximum speed limit and lane-changing probability,thereby linking microscopic CAV operating rules to macroscopic traffic flow dynamics.Second,a recurrent neural network(RNN)is employed to capture the temporal dynamics of the traffic system and predict the evolution of traffic flow states.The RNN is then linearized via the Koopman operator,transforming the complex nonlinear model into a linear representation for the design of a computationally efficient model predictive controller.Finally,simulation results demonstrate that the strategy increases the average traffic speed by 14.2% across 12 traffic scenarios.Specifically,under the challenging conditions of high traffic density with low CAV penetration,it achieves a 5.22% improvement and promotes a more uniform vehicle distribution.These findings highlight the potential of the proposed approach for mixed traffic flow regulation. 展开更多
关键词 Mixed traffic flow Model predictive control Cellular automata model Koopman operator
Data-Driven Bipartite Consensus Control for Large Workpieces Rotation of Nonlinear Multi-Robot Systems 认领 引用 被引量：1
13
作者 Haoran Tan Xueming Zhang +3 位作者 Yaonan Wang You Wu Yun Feng Zhongsheng Hou 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第6期1144-1158,共15页
In this paper,a novel data-driven bipartite consensus control scheme is proposed for the rotation problem of large workpieces with multi-robot systems(MRSs)under a directed communication topology.The rotation of a lar... In this paper,a novel data-driven bipartite consensus control scheme is proposed for the rotation problem of large workpieces with multi-robot systems(MRSs)under a directed communication topology.The rotation of a large workpiece is described as the MRSs with cooperation and antagonism interaction.By the signed graph theory,it is further transformed into a bipartite consensus control problem,where all followers are uniformly degenerated into the general nonlinear systems based on the lateral error model.To augment the flexibility of control protocol and improve control performance,a higher-dimensional full form dynamic linearization(FFDL)technique is committed to the MRSs.The control input criterion function consists of the data model based on FFDL and the bipartite consensus error based on the signed graph theory,and the proposed control protocol is given by optimizing this criterion function.In this way,this scheme has a higher degree of freedom and better adaptive adjustment capability while not excessively increasing the control method complexity,and it can also be compatible with other forms of dynamic linearization techniques in MRSs.Further,three matrix norm lemmas are introduced to deal with the challenges of stability analysis caused by higher matrix dimensions and more robots.Finally,the effectiveness of the proposed method is verified by numerical simulations. 展开更多
关键词 Bipartite consensus control data-driven control(DDC) full form dynamic linearization(FFDL)technique multirobot systems(MRSs)
暂未订购 下载PDF
Conflict-Free Planning and Data-Driven Control of Large-Scale Nonlinear Multi-Robot Systems 认领 引用
14
作者 You Wu Yi Lei +2 位作者 Haoran Tan Jin Guo Yaonan Wang 《IET Cyber-Systems and Robotics》 EI 2025年第3期11-23,共13页
This paper addresses a crucial challenge in the domain of smart factories and intelligent warehouse logistics,focusing on conflict-free planning and the smooth operation of large-scale nonlinear mobile robots.To tackl... This paper addresses a crucial challenge in the domain of smart factories and intelligent warehouse logistics,focusing on conflict-free planning and the smooth operation of large-scale nonlinear mobile robots.To tackle the challenges associated with scheduling large-scale mobile robots,an improved space-time multi-robot planning algorithm is proposed.The cloud servers are adopted in this algorithm for computation,which enables faster response to the planning requirements of large-scale mobile robots.Furthermore,enhancements to a model-free adaptive predictive control method are proposed to enhance the networked control effectiveness of the nonlinear robots.The algorithm's capability to accommodate conflict-free path planning for large-scale mobile robots is demonstrated through simulation results.Experimental findings further validate the effectiveness of the cloud-based large-scale mobile robot planning and control system in achieving both conflict-free path planning and accurate path tracking.This research holds substantial implications for enhancing logistics transportation efficiency and driving ad-vancements in the field of smart factories and intelligent warehouse logistics. 展开更多
关键词 conflict-freepath planning data-driven control model-free adaptive predictive control spatio-temporal path planning
A Novel Model Free Adaptive Fuzzy Control for Discrete-Time T-S Fuzzy Systems With Local Nonlinear Models 认领 引用
15
作者 Xiaodong Bu Shangtai Jin +1 位作者 Xisheng Dai Zhongsheng Hou 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第5期1207-1216,共10页
This paper presents a model-free adaptive fuzzy control(MFAFC)scheme for discrete-time Takagi-Sugeno(T-S)fuzzy systems with local nonlinear models.First,the T-S fuzzy system is transformed into a linearized model usin... This paper presents a model-free adaptive fuzzy control(MFAFC)scheme for discrete-time Takagi-Sugeno(T-S)fuzzy systems with local nonlinear models.First,the T-S fuzzy system is transformed into a linearized model using a dynamic linearization technique.Then,a model-free adaptive control scheme is developed for T-S fuzzy systems.Next,a rigorous convergence analysis of the tracking error is carried out using the contraction mapping theory.Finally,to validate the theoretical results,the scheme is tested by two numerical simulations and a mass-spring damper mechanical system.The results show that the proposed MFAFC strategy is effective in ensuring that the system output tracks the desired trajectory. 展开更多
关键词 Data-driven control model-free adaptive fuzzy control(MFAFC) tracking control Takagi-Sugeno(T-S)fuzzy system
暂未订购 下载PDF
A Systematic Review of Learning-Based Control:Theory and Applications 认领 引用
16
作者 Weinan Gao Kaiyue He Zhong-Ping Jiang 《Artificial Intelligence Science and Engineering》 2026年第2期85-100,共16页
Learning-based control is an important advancement in intelligent control theory,integrating data-driven learning,feedback optimization,and complex system control.In modern industrial applications,it is widely deploye... Learning-based control is an important advancement in intelligent control theory,integrating data-driven learning,feedback optimization,and complex system control.In modern industrial applications,it is widely deployed in robotic manipulation,autonomous driving,power electronics,and process industries.Given that real-world systems are frequently subject to unknown dynamics,strong nonlinearities,and external disturbances,traditional model-based approaches often fail to ensure high precision,robustness,and adaptive optimization,leading to performance degradation or even instability.Learning-based control has received significant attention,as it can overcome the challenges faced by modelbased control.This paper presents a review of learning-based control techniques.First,learning-based control problems are formulated for nonlinear dynamic systems.Second,this paper overviews three typical categories of learning-based control methods including model-informed learning-based control,model-free learning-based control,and integrated learning-based control,with particular attention to their theoretical foundations and algorithmic implementations.Third,the applications of each learning-based control method across different engineering domains are discussed.Finally,this paper concludes by outlining future research directions. 展开更多
关键词 learning-based control reinforce-ment learning data-driven control optimal control adaptive control nonlinear systems robustness
暂未订购 下载PDF
Prescribed Performance Bipartite Consensus Control for MASs Under Data-Driven Strategy 认领 引用 被引量：6
17
作者 Qi Zhou Caiyun Yin +2 位作者 Hui Ma Hongru Ren Hongyi Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第5期937-946,共10页
This paper investigates the bipartite consensus control problem for discrete time nonlinear multiagent systems(MASs)based on data-driven adaptive method.To begin with,a dynamic linearization strategy is utilized to es... This paper investigates the bipartite consensus control problem for discrete time nonlinear multiagent systems(MASs)based on data-driven adaptive method.To begin with,a dynamic linearization strategy is utilized to establish the relationship between bipartite tracking error and control input for MASs.Secondly,the unknown parameter linearly associated with control input is acquired by the adaptive control approach,and a discrete time extended state observer is designed to estimate nonlinear uncertainties.Thirdly,in order to achieve the prescribed performance,the constrained bipartite consensus error is transformed through a strictly increasing function.Based on the converted equivalent unconstrained error function,a sliding mode controller using only the input and output data of the MASs is designed.Finally,the efficacy of the controller is confirmed by simulations. 展开更多
关键词 Data-driven nonlinear multiagent systems(MASs) prescribed performance sliding mode control
暂未订购 下载PDF
Big data-driven analysis of shale gas enrichment patterns:A case study of the Wufeng–Longmaxi Formation in the Sichuan Basin and its periphery 认领 引用 被引量：1
18
作者 Zongquan Hu Jin Meng +10 位作者 Wei Du Yitian Xiao Chuanxiang Sun Guanping Wang Baojian Shen Tianrui Ye Dongjun Feng Zengqin Liu Longfei Lu Ruyue Wang Qianru Wang 《Energy Geoscience》 EI CAS CSCD 2026年第1期166-178,共13页
The Wufeng–Longmaxi Formation derives its name from the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation,found in sequence in the Sichuan Basin.This formation hosts rich shale gas reservoir... The Wufeng–Longmaxi Formation derives its name from the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation,found in sequence in the Sichuan Basin.This formation hosts rich shale gas reservoirs,and its shale gas enrichment patterns are examined in this study using data from 1197 shale samples collected from 14 wells.Five basic and three key parameters,eight in all,are assessed for each sample.The five basic parameters include burial depth and the contents of four mineral types—quartz,clay,carbonate,and other minerals;the three key parameters,representing shale gas enrichment,are total organic carbon(TOC)content,porosity,and gas content.The SHapley Additive exPlanations(SHAP)analysis originated in game theory is used here in an interpretable machine learning framework,to address issues of heterogeneous data structure,noisy relationships,and multi-objective optimization.An evaluation of the ranking,contribution values,and conditions of changes for these parameters offers new quantitative insights into shale gas enrichment patterns.A quantitative analysis of the relationship between data-sets identifies the primary factors controlling TOC,porosity,and gas content of shale gas reservoirs.The results show that TOC and porosity jointly influence gas content;mineral content has a significant impact on both,TOC and porosity;and the burial depth governs porosity which,in turn,affects the conditions under which shale gas is preserved.Input parameter thresholds are also determined and provide a basis for the establishment of quantitative criteria to evaluate shale gas enrichment.The predictive accuracy of the model used in this study is significantly improved by the step-wise addition of two input parameters,namely TOC and porosity,separately and together.Thus,the game theory method in big data-driven analysis uses a combination of TOC and porosity to evaluate the gas content with encouraging results—suggesting that these are the key parameters that indicate source rock and reservoir properties. 展开更多
关键词 Big data-driven analysis Primary controlling factor Shale gas enrichment pattern Wufeng–Longmaxi Formation Sichuan basin
暂未订购 下载PDF
Neural Network-Based Switching Output Regulation Control for High-Speed Nano-Positioning Stages 认领 引用 被引量：1
19
作者 Hongwei Sun Ning Xing +4 位作者 Jiayu Zou Yuqi Rong Yang Shi Han Ding Hai-Tao Zhang 《Engineering》 SCIE EI CSCD 2026年第2期227-235,共9页
This study establishes a high-speed nano-positioning stage composed of a symmetrically driven structure with multiple parallel-bonded thin piezoelectric ceramic layers capable of performing micro-or nanoscale manipula... This study establishes a high-speed nano-positioning stage composed of a symmetrically driven structure with multiple parallel-bonded thin piezoelectric ceramic layers capable of performing micro-or nanoscale manipulations.Accordingly,a neural-network-based switching output regulation controller(NNSORC)was developed to compensate for the associated hysteresis nonlinearity.To address the challenges of slow floating-point computation speeds and low compilation efficiency,a closed-loop control system with a field-programmable gate array–central processing unit(FPGA–CPU)dual-layer data-processing framework was developed.A feedback linearization method was designed to linearize the hysteresis nonlinearity of the framework,resulting in a switching-tracking error system.With the assistance of Lyapunov theory and an average dwell time technique,sufficient conditions were derived to ensure the asymptotic stability of the NN-SORC governing closed-loop system using the switching reference signals often encountered in realistic micro-ano-scale detection and manufacturing processes.Finally,extensive comparative experiments were conducted to verify the effectiveness and superiority of the proposed NN-SORC scheme. 展开更多
关键词 High-speed nano-positioning stage Switched system Intelligent control Output regulation control
暂未订购 下载PDF
Adaptive Data-Driven Coordinated Control of UUVs for Maritime Search and Rescue 认领 引用
20
作者 Hao-Liang Wang De-Zhi Yu +1 位作者 Li-Yu Lu Zhou-Hua Peng 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第9期1953-1955,共3页
Dear Editor,This letter is concerned with a coordinated path following control method for multiple unmanned underwater vehicles(UUVs)to carry out maritime search and rescue(MSR)missions.The kinetic model parameters of... Dear Editor,This letter is concerned with a coordinated path following control method for multiple unmanned underwater vehicles(UUVs)to carry out maritime search and rescue(MSR)missions.The kinetic model parameters of each UUV is totally unknown.Firstly,a kinematic control law is constructed by designing a vertical line-of-sight(LOS)guidance scheme. 展开更多
关键词 data driven control coordinated path following control method coordinated control kinetic model parameters adaptive control unmanned underwater vehicles maritime search rescue unmanned underwater vehicles uuvs
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈