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Deep Reinforcement Learning or Lyapunov Analysis?A Preliminary Comparative Study on Event-Triggered Optimal Control

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1702-1704页

作者： Jingwei Lu Lefei Li Qinglai Wei Fei-Yue Wang IEEE the Department of industrial Engineering Tsinghua UniversityBeijing 100084China The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter develops a novel method to implement event-triggered optimal control(ETOC) for discrete-time nonlinear systems using parallel control and deep reinforcement learning(DRL), referred to as Deep-ETOC. The developed Deep-ETOC method introduces the communication cost into the performance index through parallel control, so that the developed method enables control systems to learn ETOC policies directly without triggering conditions.

关键词： Deep letter enable

Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk

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Chinese Physics B 2024年第2期33卷 545-550页

作者：马铭磷熊康灵李志军贺少波 School of automation and Electronic Information Xiangtan UniversityXiangtan 411105China

Synaptic crosstalk is a prevalent phenomenon among neuronal synapses,playing a crucial role in the transmission of neural ***,considering synaptic crosstalk behavior and investigating the dynamical behavior of discrete neural networks are highly *** this paper,we propose a heterogeneous discrete neural network(HDNN)consisting of a three-dimensional KTz discrete neuron and a Chialvo discrete *** two neurons are coupled mutually by two discrete memristors and the synaptic crosstalk is *** impact of crosstalk strength on the firing behavior of the HDNN is explored through bifurcation diagrams and Lyapunov *** is observed that the HDNN exhibits different coexisting attractors under varying crosstalk ***,the influence of different crosstalk strengths on the synchronized firing of the HDNN is investigated,revealing a gradual attainment of phase synchronization between the two discrete neurons as the crosstalk strength decreases.

关键词： discrete memristor synaptic crosstalk coexisting attractor phase synchronization

Distributed Fault Estimation for Nonlinear Systems With Sensor Saturation and Deception Attacks Using Stochastic Communication Protocols

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1865-1876页

作者： Weiwei Sun Xinci Gao Lusong Ding Xiangyu Chen Institute of automation Qufu Normal UniversityQufu 273165China

This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor *** the distributed estimation structure under consideration,an estimation center is not necessary,and the estimator derives its information from itself and neighboring nodes,which fuses the state vector and the measurement *** an effort to cut down data conflicts in communication networks,the stochastic communication protocol(SCP)is employed so that the output signals from sensors can be ***,a recursive security estimator scheme is created since attackers randomly inject malicious signals into the selected *** this basis,sufficient conditions for a fault estimator with less conservatism are presented which ensure an upper bound of the estimation error covariance and the mean-square exponential boundedness of the estimating ***,a numerical example is used to show the reliability and effectiveness of the considered distributed estimation algorithm.

关键词： Actuator fault deception attacks distributed estimation sensor saturation stochastic communication protocol(SCP).

Quantum gate-assisted teleportation in noisy environments:robustness and fidelity improvement

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communications in Theoretical Physics 2024年第1期76卷 36-48页

作者： Sajede Harraz Jiaoyang Zhang Shuang Cong Department of automation University of Science and Technology of ChinaHefei 230027China

Quantum teleportation as the key strategy for quantum communication requires pure maximally shared entangled states among quantum *** practice,quantum decoherence drastically degrades the shared entanglement during entanglement distribution,which is a serious challenge for the development of quantum ***,most of the decoherence control strategies proposed thus far are either resource-intensive or *** overcome this obstacle,we enable noise-resistant teleportation through a noisy channel with a limited number of qubits and without applying time-consuming weak *** apply a quantum gate control unit consisting of a controlled NOT gate and a rotation gate after the original teleportation protocol is ***,we demonstrate that a teleportation fidelity of unity is attainable when environment-assisted measurement is added to the proposed teleportation protocol via quantum ***,we present an entanglement distribution process by employing the designed quantum gate control unit followed by the deterministic standard teleportation protocol to improve teleportation fidelity by establishing improved shared *** performance analysis indicates that the proposed teleportation schemes offer a competitive fidelity and success probability compared with the conventional schemes and a recent weak measurement-based teleportation protocol.

关键词： quantum communication quantum teleportation decoherence control quantum gate environment-assisted measurement

How is Germany approaching Industrie 4.0 And what influence is it having on other regions?

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可编程控制器与工厂自动化（PLC FA） 2015年第8期 24-25页

作者： Mark Watson discrete automation industrial communications & switchgear

A number of associations,including DFKI,Bitkom,VDMA,and ZVEI,are leading the charge to smarter manufacturing in *** goal is to promote Germany’s expertise as an industrial automation technology,machine design and man... 详细信息

关键词：德国智能制造工业自动化技术发展现状

Multidomain Correlation-Based Multidimensional CSI Tensor Generation for Device-FreeWi-Fi Sensing

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1749-1767页

作者： Liufeng Du Shaoru Shang Linghua Zhang Chong Li JianingYang Xiyan Tian School of Mechanical and Electrical Engineering Henan Institute of Science and TechnologyXinxiang453003China School of communications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing210003China School of Mechatronic Engineering and automation Shanghai UniversityShanghai200444China

Due to the fine-grained communication scenarios characterization and stability,Wi-Fi channel state information(CSI)has been increasingly applied to indoor sensing tasks *** spatial variations are explicitlyreflected in CSI measurements,the representation differences caused by small contextual changes are easilysubmerged in the fluctuations of multipath effects,especially in device-free Wi-Fi *** existing datasolutions cannot fully exploit the temporal,spatial,and frequency information carried by CSI,which results ininsufficient sensing resolution for indoor scenario *** a result,the well-liked machine learning(ML)-based CSI sensing models still struggling with stable *** paper formulates a time-frequency matrixon the premise of demonstrating that the CSI has low-rank potential and then proposes a distributed factorizationalgorithm to effectively separate the stable structured information and context fluctuations in the CSI ***,a multidimensional tensor is generated by combining the time-frequency gradients of CSI,which containsrich and fine-grained real-time contextual *** evaluations and case studies highlight thesuperiority of the proposal.

关键词： Wi-Fi sensing device-free CSI low-rank matrix factorization

Emergence of machine language: towards symbolic intelligence with neural networks

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National Science Review 2024年第4期11卷 27-30页

作者： Yuqi Wang Xu-Yao Zhang Cheng-Lin Liu Tieniu Tan Zhaoxiang Zhang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences

Representation learning is a core issue in artificial intelligence (AI). Currently,there exists a disparity in the choice of representation between humans and machines. Humans rely on discrete language for communicati...

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Error-based adaptive optimal tracking control of nonlinear discrete-time systems

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Science China(Information Sciences) 2024年第1期67卷 154-167页

作者： Chun LI Jinliang DING Frank L.LEWIS Tianyou CHAI State Key Laboratory of Synthetical automation for Process Industries Northeastern University UTA Research Institute The University of Texas at Arlington

In this paper,for the output tracking problem of nonlinear discrete-time systems,a performance index is newly defined using the adaptive dynamic programming(ADP) technique to completely eliminate tracking errors in *** contrast to traditional definitions of performance indices in other ADP-based methods,the proposed performance index is not only designed from the perspective of output tracking errors but also introduced errors of system states and control inputs at adjacent stages,which is suitable for practical situations in many industrial applications,such as the alumina production,the flotation process,and the mineral grinding *** proved that the obtained controller can make the system output fully track the given reference trajectory by applying the iterative criteria of the ADP *** addition,the proposed algorithm was implemented using a data-driven technique and neural networks to avoid analyzing and deducing the complicated dynamics of actual industrial ***,using historical data for a forced-circulation evaporation system in the alumina production process,the effect of the proposed approach was verified through a numerical simulation and compared with that of the proportional-integral controller.

关键词： adaptive dynamic programming iterative criteria performance index tracking errors tracking problem

Deterministic learning-based neural output-feedback control for a class of nonlinear sampled-data systems

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Science China(Information Sciences) 2024年第9期67卷 164-183页

作者： Yu ZENG Fukai ZHANG Tianrui CHEN Cong WANG School of automation Science and Engineering South China University of Technology School of Control Science and Engineering Shandong University

This study investigates the deterministic learning(DL)-based output-feedback neural control for a class of nonlinear sampled-data systems with prescribed performance(PP). Specifically, first, a sampleddata observer is employed to estimate the unavailable system states for the Euler discretization model of the transformed system dynamics. Then, based on the observations and backstepping method, a discrete neural network(NN) controller is constructed to ensure system stability and achieve the desired tracking performance. The noncausal problem encountered during the controller deduction process is resolved using a command filter. Moreover, the regression characteristics of the NN input signals are demonstrated with the observed states. This ensures that the radial basis function NN, based on DL theory, meets the partial persistent excitation condition. Subsequently, a class of discrete linear time-varying systems is proven to be exponentially stable, achieving partial convergence of neural weights to their optimal/actual values. Consequently, accurate modeling of unknown closed-loop dynamics is achieved along the system trajectory from the output-feedback control. Finally, a knowledge-based controller is developed using the modeling *** controller not only enhances the control performance but also ensures the PP of the tracking error. The effectiveness of the scheme is illustrated through simulation results.

关键词： deterministic learning neural networks adaptive neural control predefined performance nonlinear sampled-data systems