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Chinese automation Congress (CAC)

作者： Junhao Ren Xiaofeng Zong Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In this paper, tolerance and detection problems for the edge disconnection in consensus issues of discrete-time multi-agent systems with the time-delay are investigated. We first discuss the tolerance of the edge disconnection in consensus problems. For the case with the strongly connected communication topology, the faulty system can still achieve consensus if the connectedness of the multi-agent system is not changed as the edge disconnection, but the consensus value of the faulty system might be different from the original one. Especially, the consensus value of the system with the edge disconnection will be consistent with the original one if the communication topology of the faulty system is undirected and connected. In addition, the detection problem of the edge disconnection in multi-agent systems is considered by transforming the detection problem into the discernibility problem between the faulty system and the original system. By the state augmentation method and observability properties of the system, some equivalent conditions for the discernibility of the edge disconnection are established.

关键词： Topology Image edge detection Delays Multi-agent systems Laplace equations Matrix decomposition Directed graphs

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Quadrotor UAV Trajectory Tracking control based on ASMC and Improved ARDC

Quadrotor UAV Trajectory Tracking Control based on ASMC and ...

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Chinese automation Congress (CAC)

作者： Ruru Jia Xiaofeng Zong 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 Wuhan China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In this paper, a new double closed-loop control method for trajectory tracking of quadrotor unmanned aerial vehicles (UAV) is proposed to attenuate wind gusts disturbance and effects of parameter uncertainties. The dynamics and kinematics equations of the quadrotor UAV are established by the Euler and Newton theorem. In the outer loop subsystem, the adaptive sliding mode control (ASMC) algorithm is proposed to estimate the payload variation and air disturbance, and it can realize the stable tracking of the target position. For the inner loop subsystem, this paper designs an improved active disturbance rejection control (ADRC), which replaces the extended state observer with a higher-order sliding modes observer, and improves nonlinear state error feedback by a new nonlinear functions. Simulation experiments are given to evaluate the robustness and effectiveness of the designed controller algorithm.

关键词： Unmanned aerial vehicles Mathematical model Rotors Payloads Observers Attitude control Heuristic algorithms

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Improving Rate-of-Penetration and Reducing State Fluctuation of Drill-String System Based on Multiobjective Optimization

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IEEE Transactions on Industrial Informatics 2025年

作者： Yang, Xiao Wu, Min Lu, Chengda Huang, Hengyu Wu, Xiao Li, Wangnian Zhang, Youzhen China University of Geosciences School of Future Technology Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China China University of Geosciences School of Automation Wuhan430074 China Company Ltd. Xi'an710077 China

This article is concerned with the balance between improving rate-of-penetration and suppressing drill-string vibrations by optimizing weight-on-bit and rotational speed. The main contribution of this article lies in three points. First, due to the lack of downhole data, the problem of suppressing drill-string vibrations is transformed into reducing state fluctuation of drill-string system. Meanwhile, a multiobjective optimization scheme is established. Second, the temporal relations are revealed, including modeling time, optimization time, resampling time, controller response time, buffer time, and optimization interval. They act as constraints for subsequent modeling and optimization. Third, the improved extreme learning machine methods are developed to build the calculation models of rate-of-penetration and state fluctuation of drill-string system, separately. The nondominated sorting genetic algorithm II, technique for order preference by similarity to an ideal solution method, and moving window are used to obtain the optimal weight-on-bit and rotational speed. The effectiveness of the approach is demonstrated through an industrial case study based on the actual drilling data. © 2005-2012 IEEE.

关键词： Multiobjective optimization

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Learning Multiple Network Embeddings for Social Influence Prediction ⁎

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IFAC-PapersOnLine 2020年第2期53卷 2868-2873页

作者： Feng Wang Jinhua She Yasuhiro Ohyama Min Wu School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China School of Engineering Tokyo University of Technology Tokyo 192-0982 Japan

How to effectively predict social influence is an essential issue in social network analysis. Almost all reported methods for social influence prediction are mainly concerned with estimating influence probabilities for each linking edge. However, all of this past work cannot accurately predict influence probabilities for all edges due to the problem of data sparsity. Unlike conventional approaches, this work focuses on exploring a cross problem for multiple network embeddings and social influence prediction. This study developed a new end-to-end approach, Multi-Influor, that learns multiple influence vectors for each user in social networks, instead of estimating influence probabilities for each edge. The multiple network embeddings consider multi-dimensional influence factors that incorporate pairwise node interactions, network structures, and global similarity comparisons. Moreover, this study solves the problem of influence evaluation caused by sparse observations. Extensive comparisons based on large-scale datasets indicate that the Multi-Influor approach outperforms several state-of-the-art baselines, and the experimental results demonstrate that the Multi-Influor approach is more practical on real-world social networks.

关键词： Multiple Network Embeddings Representation Learning Online Social Networks Neural Network Social Influence Prediction

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Fault Diagnosis of Micro Grid Inverter Based on Wavelet Transform and Probabilistic Neural Network

Fault Diagnosis of Micro Grid Inverter Based on Wavelet Tran...

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第三十九届中国控制会议

作者： Xin Gong Nan Wang Yiqiong Zhang Shuai Yin Mingyang Wang Genping Wu School of Automation China University of Geosciences (Wuhan) Hubei Provincial Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Second Ship Design and Research Institute

This paper focuses on the fault diagnosis of IGBT switch for microgrid three-phase inverters. Based on the analysis of all kinds of fault mechanisms, firstly, the fault simulation model of microgrid inverter is built on MATLAB/Simulink to simulate the fault state of inverter switch under various faults. The load-side three-phase current fault data is collected. Then, wavelet transform is used to extract feature parameters, and normalize the process in the form of energy to construct the fault feature vector. Finally, the genetic algorithm-based probability neural network(PNN) is used for classification. The experimental results show that the method that combining wavelet transform and PNN can diagnose the faults of microgrid inverter accurately and effectively.

关键词： Inverter Fault diagnosis Wavelet transform Genetic algorithm Probabilistic neural network

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Model-free Output Consensus control for Nonlinear Multi-agent Systems with Random Disturbance

Model-free Output Consensus Control for Nonlinear Multi-agen...

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Industrial Cyber-Physical Systems (ICPS)

作者： Yipu Sun Xin Chen Hao Fu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9781728163895

ISBN: (纸本)9781728163901

The model-free output consensus control problem for the nonlinear multi-agent systems (MASs) with random disturbance is considered. In order to avoid the difficulty of designing a consensus protocol when the dynamic model is unknown, this paper proposes an online learning method for a consensus protocol by Gaussian regression based two-phase iteration. First, output of the leader is estimated by an adaptive distributed observer. On this basis, measurable input/output data is used to solve the consensus control problem of model-free systems. A two-phase iteration for Gaussian-kernel based adaptive critic design (GK-ACD) is proposed to approximate optimal Q-function and optimal control policies, which can simultaneously approximate and update value functions and hyper-parameters. This avoids the problem of artificial selection of improper hyper-parameters may mislead the kernel-based regression. Finally, the simulation results show that, without knowledge of dynamics, the distributed policies achieves the output consensus control of nonlinear MASs, and proves the effectiveness of the method.

关键词： Observers Adaptation models Vehicle dynamics System dynamics Synchronization Nonlinear dynamical systems Multi-agent systems

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MRAC Based Consensus control for Heterogeneous Discrete-Time Nonlinear Multi Agent Systems Under Switching Typology with Unknown Dynamics

MRAC Based Consensus Control for Heterogeneous Discrete-Time...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Wenpeng He Xin Chen Hao Fu Yipu Sun School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan P. R. China

ISBN: (纸本)9781665442084

This paper investigates the consensus problems of heterogeneous discrete-time (DT) nonlinear multi-agent systems (MASs) with unknown dynamics and switching typology. By adding a virtual model to each agent and using the model reference protocol to the actual model, the consensus problems of unknown nonlinear MASs are transformed into consensus problems of known virtual linear MASs, and a distributed control law is designed for the virtual model to make the virtual linear MASs achieve consensus under the switching typology. Two numerical simulations with nonidentical nonlinear dynamics and switching typology are given to prove the effectiveness of the proposed method.

关键词： Protocols Conferences Switches Consensus control Numerical simulation Numerical models Nonlinear dynamical systems

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Stability of switched systems with admissible switching signals

Research Square

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Research Square 2021年

作者： Wang, Hui-Ting He, Yong Wang, Qing-Guo Zhang, Chuan-Ke Wu, Min School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Institute of Artificial Intelligence and Future Networks Beijing Normal University Beijing Normal University-Hong Kong Baptist University United International College Zhuhai519087 China

In this paper, stability of switched systems is investigated for a class of switching signals which meet some admissibility conditions. Firstly, the admissible edge-dependent divergence time is defined in terms of admissible transition edges and it will vary with the compensation bounds. Then the admissible edgedependent bounded maximum average dwell time (BM ADT) is imposed on switching signals. As a result, a sufficient condition is obtained for globally uniformly exponential stability of switched nonlinear systems with such switching signals. Secondly, by setting the equal compensation bounds for the same reaching subsystems, the mode-dependent divergence time is defined, and then the mode-dependent BMADT is proposed. A stability condition under the mode-dependent BMADT is established. These stability results are then applied to switched linear systems. The numerical example is presented to show that the proposed techniques are less restrictive and more flexible in application, compared with the BMADT. © 2021, CC BY.

关键词： Linear systems

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Research on path planning of robot based on deep reinforcement learning

Research on path planning of robot based on deep reinforceme...

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第三十九届中国控制会议

作者： Feng Liu Chang Chen Zhihua Li Zhi-Hong Guan Hua O Wang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems College of Automation Huazhong University of Science and Technology Department of Mechanical Engineering Boston University

In this paper, to avoid the problem of local optimization and slow convergence in complex environment, a reinforcement learning algorithm is proposed to solve the problem. A robot path planning model is built and its feasibility is verified by simulation. In addition, this paper proposes a deep environment to neural network for robot camera to establish a deep reinforcement learning path planning model, and establishes a deep recursive Q-network（DRQN） and Deep Dueling Q-network（DDQN）respectively. In the comparison of the final simulation results, DRQN needs to consume more computation time, but can achieve better results with higher accuracy.

关键词： Reinforcement learning path planning robots deep reinforcement learning

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Burden control Strategy Based on Reinforcement Learning for Gas Utilization Rate in Blast Furnace ⁎

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IFAC-PapersOnLine 2020年第2期53卷 11704-11709页

作者： Xiaoling Shen Jianqi An Min Wu Jinhua She School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China School of Engineering Tokyo University of Technology HachiojiTokyo 192-0982 Japan

Gas utilization rate (GUR) is an important state parameter to reflect the energy consumption, the quality and production of the pig iron, and the distribution of the gas flow in a blast furnace. The GUR is mainly adjusted by burden distribution and hot-blast supply. According to the analysis of mechanism and data, burden distribution and hot-blast supply affect the GUR on a long-time scale and short-time scale, respectively. However, few of the previous researches proposed the control method for the GUR and they did not consider multi-time-scale characteristics. Thus, it is necessary to design a control strategy or system for the GUR considering the multi-time-scale characteristics, which can make the GUR have a reasonable development trend. This paper presented a burden control strategy based on a reinforcement learning algorithm for the GUR. The method improved the development trend of the GUR on a long-time scale. The experimental results demonstrated that the sequence of the parameters of the burden distribution given by the presented method ensured a reasonable development trend of the GUR on a long-time scale.

关键词： Blast furnace gas utilization rate burden control strategy reinforcement learning algorithm long-time scale

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