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arXiv 2024年

作者： Zhu, Zhengqiu Zhao, Yong Chen, Bin Qiu, Sihang Xu, Kai Yin, Quanjun Huang, Jincai Liu, Zhong Wang, Fei-Yue College of Systems Engineering National University of Defense Technology Hunan Province Changsha410073 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

The transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in Chatbots and Large Language Models (LLMs). Therefore, the advancement of parallel intelligence-powered Crowdsensing Intelligence (CSI) is witnessed, which is currently advancing towards linguistic intelligence. In this paper, we propose a novel sensing paradigm, namely conversational crowdsensing, for Industry 5.0. It can alleviate workload and professional requirements of individuals and promote the organization and operation of diverse workforce, thereby facilitating faster response and wider popularization of crowdsensing systems. Specifically, we design the architecture of conversational crowdsensing to effectively organize three types of participants (biological, robotic, and digital) from diverse communities. Through three levels of effective conversation (i.e., inter-human, human-AI, and inter-AI), complex interactions and service functionalities of different workers can be achieved to accomplish various tasks across three sensing phases (i.e., requesting, scheduling, and executing). Moreover, we explore the foundational technologies for realizing conversational crowdsensing, encompassing LLM-based multi-agent systems, scenarios engineering and conversational human-AI cooperation. Finally, we present potential industrial applications of conversational crowdsensing and discuss its implications. We envision that conversations in natural language will become the primary communication channel during crowdsensing process, enabling richer information exchange and cooperative problem-solving among humans, robots, and AI. © 2024, CC BY.

关键词： Multi agent systems

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Data-Driven Learning and control with Event-Triggered Measurements

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IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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Stability Analysis for Amplidyne Electrical systems with Time-Varying Delay via a Negative-Determination Quadratic Function Lemma

Stability Analysis for Amplidyne Electrical Systems with Tim...

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第42届中国控制会议

作者： Hong-Jian Huang Hong-Zhang Wang Yu-Long Fan Ke-You Xie Chuan-Ke Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

In this paper, the stability of Amplidyne Electrical systems(AESs) with a time-varying delay is studied. Firstly,the model of AESs with a time-varying delay is established. Secondly, an augmented Lyapunov-Krasovskii functional(LKF)is constructed. Then, a less conservative delay-dependent stability criterion for AESs with a time-varying delay is obtained by utilizing the generalized reciprocally convex combination and an advanced negative-determination quadratic function ***, the superiority and effectiveness of the proposed criterion is verified by a numerical example.

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State Estimation for Markov Jump Genetic Oscillator Networks Based on Hidden Markov Model

State Estimation for Markov Jump Genetic Oscillator Networks...

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第42届中国控制会议

作者： Banming Ruan Xiongbo Wan School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

This paper addresses the problem of state estimation for Markov jump genetic oscillator networks with time-varying delays based on hidden Markov model. Two non-identical types of time-varying delays, that is, the intercellular coupling delay,and the regulatory delay are considered in consideration in genetic oscillator networks. Then a state estimator is designed by solving a set of linear matrix inequalities that can be solved with existing software. Finally, The effectiveness of state estimation approach can then be demonstrated through a numerical example.

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Fault diagnosis for industrial alarm floods based on Discrete Hidden Markov Model

Fault diagnosis for industrial alarm floods based on Discret...

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第42届中国控制会议

作者： Xiwen Huang Wenkai Hu Weihua Cao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

A fault diagnosis method based on Discrete Hidden Markov Models is proposed in this paper to identify the fault causing alarm flood sequences. The proposed method consists of the following steps: First, the alarm flood data is pre-processed to ensure that all sequences are of uniform length, and a separate Discrete Hidden Markov model is trained for each fault to capture the relationship between the fault and the alarm sequences. Second, given an observation sequence, the log-likelihood probability values under different Discrete Hidden Markov models are calculated and the maximum probability is selected to determine the type of corresponding fault. Last, the feasibility of the proposed method is verified by simulation data obtained from a public industrial model. The results show that the method can effectively identify the faults that trigger alarm floods.

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Event-Triggered control of Networked System Considering Denial of Service Attack

Event-Triggered Control of Networked System Considering Deni...

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第42届中国控制会议

作者： Ting-Wei Zhang Yu-Long Fan Chuan-Ke Zhang School of Automation Chinese University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology of Geo-Exploration Ministry of Education

This paper presents an improved stability criterion and controller design scheme condition for a networked control system under denial of service(DoS) attack. Firstly, the DoS attack interval is divided into attack interval and no attack interval, therefore, a switching-like event-triggered control can be established to reduce the waste of network resources and improve network efficiency. Then, the studied system is transformed into a time-delay system, and an improved stability criterion and controller design method are established by using Lyapunov-Krasovskii functional(LKF). Finally, the effectiveness of the proposed method is verified by a simulation example.

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Displacement prediction of landslide using SARIMA model based on seasonal characteristic

Displacement prediction of landslide using SARIMA model base...

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第42届中国控制会议

作者： Ruoxi Wu Chengda Lu Wanke Yu Min Wu Xuzhi Lai School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Landslide displacement prediction is an important and indispensable part of landslide monitoring and *** change of the displacement is always considered being related to inducing factors,which are aimed at improving accuracy of the predicted ***,the seasonal characteristic of the displacement,which has not been carefully analyzed,reveals the law of inducing *** order to gain a deeper understanding of characteristics,the Baijiabao landslide is taken as an *** variational mode decomposition(VMD) method,which can extract effective information well,is introduced to decompose the *** the seasonal parameters,the seasonal autoregressive integrated moving average(SARIMA) model is established to predict the displacement ***,accumulative displacement prediction values are obtained by superimposing the predicted *** higher accuracy and lower error,the VMD-SARIMA model proves a better option in application compared with VMD-ARIMA,SARIMA and ARIMA models.

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Deep Learning control for Autonomous Robot

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Computers, Materials & Continua 2022年第8期72卷 2811-2824页

作者： Rihem Farkh Saad Alhuwaimel Sultan Alzahrani Khaled Al Jaloud Mohammad Tabrez Quasim College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsLR-11-ES20Department of Electrical Engineering National Engineering School of TunisTunis El Manar University1002Tunisia King Abdulaziz City for Science and Technology Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these *** cars are one such *** is expected to have a significant and revolutionary influence on *** with smart cities,new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving *** autonomous automobile,often known as selfdriving systems or driverless vehicles,is a vehicle that can perceive its surroundings and navigate predetermined routes without human *** are on the verge of evolving into autonomous robots,thanks to significant breakthroughs in artificial intelligence and related technologies,and this will have a wide range of socio-economic ***,in order for these automobiles to become a reality,they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate *** majority of self-driving car technologies are based on computer systems that automate vehicle control *** forward-collision warning and antilock brakes to lane-keeping and adaptive drive control,to fully automated driving,these technological components have a wide range of capabilities.A self-driving car combines a wide range of sensors,actuators,and *** researches on computer vision and deep learning are used to control autonomous driving *** self-driving automobiles,lane-keeping is *** study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the *** propose an advanced control for a selfdriving robot by using two controllers *** neural networks(CNNs)are employed,to predict the car’and a proportionalintegral-derivative(PID)controller is designed for speed and steering *** stu

关键词： Autonomous car cascade PID control deep learning convolutional neural network differential drive system raspberry PI road lane detector

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A Generalised Approach on Kerf Geometry Prediction during CO2 Laser cut of PMMA Thin Plates using Neural Networks

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Lasers in Manufacturing and Materials Processing 2021年第3期8卷 372-393页

作者： Kechagias, John D. Ninikas, Konstantinos Stavropoulos, Panagiotis Salonitis, Konstantinos Design and Manufacturing Laboratory School of Technology University of Thessaly Karditsa43100 Greece Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras Patras Greece Sustainable Manufacturing Systems Centre Manufacturing Department Cranfield University Cranfield United Kingdom

This study presents an application of feedforward and backpropagation neural network (FFBP-NN) for predicting the kerf characteristics, i.e. the kerf width in three different distances from the surface (upper, middle and down) and kerf angle during laser cutting of 4 mm PMMA (polymethyl methacrylate) thin plates. Stand-off distance (SoD: 7, 8 and 9 mm), cutting speed (CS: 8, 13 and 18 mm/sec) and laser power (LP: 82.5, 90 and 97.5 W) are the studied parameters for low power CO2 laser cutting. A three-parameter three-level full factorial array has been used, and twenty-seven (33) cuts are performed. Subsequently, the upper, middle and down kerf widths (Wu, Wm and Wd) and the kerf angle (KA) were measured and analysed through ANOM (analysis of means), ANOVA (analysis of variances) and interaction plots. The statistical analysis highlighted that linear modelling is insufficient for the precise prediction of kerf characteristics. An FFBP-NN was developed, trained, validated and generalised for the accurate prediction of the kerf geometry. The FFBP-NN achieved an R-all value of 0.98, in contrast to the ANOVA linear models, which achieved Rsq values of about 0.86. According to the ANOM plots, the parameter values which optimize the KA resulting in positive values close to zero degrees were the 7 mm SoD, 8 mm/s CS and 97.5 W LP. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Kerf Laser cutting Modelling Neural networks PMMA

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Dynamic Event-triggered Synchronization control for Complex Dynamical Networks

Dynamic Event-triggered Synchronization Control for Complex ...

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第42届中国控制会议

作者： Fuxiang Yang Xiongbo Wan School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

This paper explores the finite-time synchronization of a class of discrete-time nonlinear singularly perturbed complex networks using a dynamic event-triggered mechanism(DETM). The DETM is designed to optimize packet transmission, aiming to conserve network resources. By constructing a Lyapunov function considering singularly perturbed parameters(SPPs) and DETM information, a sufficient condition for the dynamics of synchronization error system to be finite-time stable is given. The parameters of the synchronization controller can be determined by solving a set of matrix inequalities. The effectiveness of the proposed controller is demonstrated through a numerical example.

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