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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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Early Forest Fire Segmentation Based on Deep Learning

Early Forest Fire Segmentation Based on Deep Learning

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Mengna Li Youmin Zhang Jing Xin Lingxia Mu Ziquan Yu Han Liu Guo Xie Shangbin Jiao Yingmin Yi Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an Shaanxi China Concordia University Montreal Quebec Canada College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106 China

ISBN: (纸本)9781665401166

Fire segmentation is very important for fire rescue. It can make firefighters get the information on fire area, spread direction and so on, and then help them make quick and effective fire-fighting plan. Therefore, this paper proposes an early forest fire segmentation algorithm based on a deep learning model, named F-Unet, which mainly uses the architecture idea of Unet for reference. F-Unet consists of contraction path, feature fusion layer and expansion path. The contraction path is composed of the first 13 layers of VGG16, which is used to obtain feature maps with different scales. In order to improve the segmentation accuracy of the model, the feature fusion network proposed in this paper is added to the Unet architecture to fuse these feature maps with different scales. The expansion path is used for upsampling these feature maps to restore the size of the original input image and obtain the fire segmentation results. The experimental testing results on the FLAME dataset show that F-Unet can significantly improve the fire segmentation precision, and also prove that the proposed feature fusion network is effective to improve the performance of fire segmentation.

关键词： Deep learning Image segmentation Fuses Fault detection Fires Forestry Safety

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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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Precedence-constrained path planning of messenger UAV for air-ground coordination

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control Theory and Technology 2019年第1期17卷 13-23页

作者： Yulong DING Bin XIN Jie CHEN School of Automation Beijing Institute of Technology Beijing 700087 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing 100081 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing 100081 China

This paper addresses an unmanned aerial vehicle (UAV) path planning problem for a team of cooperating heterogeneous vehicles composed of one UAV and multiple unmanned ground vehicles (UGVs). The UGVs are used as mobile actuators and scattered in a large area. To achieve multi-UGV communication and collaboration, the UAV serves as a messenger to fly over all task points to collect the task information and then flies all UGVs to transmit the information about tasks and UGVs. The path planning of messenger UAV is formulated as a precedence-constrained dynamic Dubins traveling salesman problem with neighborhood (PDDTSPN). The goal of this problem is to find the shortest route enabling the UAV to fly over all task points and deliver information to all requested UGVs. When solving this path planning problem, a decoupling strategy is proposed to sequentially and rapidly determine the access sequence in which the UAV visits task points and UGVs as well as the access location of UAV in the com mu nication n eighborhood of each task point and each UGV. The effectiveness of the proposed approach is corroborated through computational experiments on randomly generated instances. The computational results on both small and large in stances dem on strate that the proposed approach can generate high-quality solutions in a reasonable time as compared with two other heuristic algorithms.

关键词： Air-ground coordination curvature-constrained path planningz precedence constraints Dubins traveling salesman problem

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Disturbance rejection of two-dimensional repetitive control system based on T-S fuzzy model

Disturbance rejection of two-dimensional repetitive control ...

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IEEE Conference on Decision and control

作者： Shengnan Tian Min Wu Manli Zhang Chengda Lu Luefeng Chen Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Engineering Tokyo University of Technology Hachioji Japan

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

In this paper, the issue of disturbance rejection of nonlinear repetitive control system (RCS) based on a continuous-discrete two-dimensional (2D) model is studied. Since a RCS involves continuous control and discrete learning, higher tracking performance may be realizable by taking into account these two different actions in system design. This paper construct a fuzzy RCS based on T-S model at first. Then, an improved fuzzy-equivalent-input-disturbance (IFEID)-based 2D RCS is presented to achieve both high tracking precision of periodic input and satisfactory rejection of aperiodic disturbances. Stability conditions of two subsystems are derived by exploiting separation theorem to ease the solution of state-feedback and observer gains. A design algorithm is presented via the concept of parallel distributed compensation. A simulation and a comparison demonstrate the availability and advantage of the IFEID-based 2D RCS approach.

关键词： Two dimensional displays Observers control systems Estimation State feedback Stability analysis Time-frequency analysis

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Graph Convolutional Extreme Learning Machine

Graph Convolutional Extreme Learning Machine

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International Joint Conference on Neural Networks (IJCNN)

作者： Zijia Zhang Yaoming Cai Wenyin Gong Xiaobo Liu Zhihua Cai School of Computer Science China University of Geosciences Wuhan China School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Extreme Learning Machine (ELM) has gained lots of research interest due to its universal approximation capability and fast learning speed. However, traditional ELMs are devised for regular Euclidean data, such as 2D grid and 1D sequence, and thus don't apply to non-Euclidean data, e.g., graph-structured data. To overcome this shortcoming, this paper presents a Graph Convolutional Extreme Learning Machine (termed as GCELM) for semi-supervised classification. Technically, a random graph convolutional layer is introduced to replace the random projection of original ELM, which endues ELM with the capability of dealing with graph-structured data directly. To generate a robust graph from the raw dataset, a self-representation model is adopted to construct a weighted graph. Extensive experiments on 27 UCI datasets demonstrate that GCELM outperforms many popular semi-supervised methods, and with faster learning speed. To the best of our knowledge, this is the first work that combines graph convolution with ELM.

关键词： Convolution Robustness Closed-form solutions Neural networks Training Convolutional codes Optimization

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An Universal control Strategy for Planar 2-DoF Underactuated Manipulator with One Passive Joint

An Universal Control Strategy for Planar 2-DoF Underactuated...

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第40届中国控制会议

作者： Zixin Huang Yaosheng Zhou Zhen Chen Wei Wang Lejun Wang School of Electrical and Information Engineering Wuhan Institute of Technology Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University 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 Innovation and Entrepreneurship Wuhan Institute of Technology School of Information and Safety Engineering Zhongnan University of Economics and Law

This paper presents an unified control strategy based on the trajectory planning and tracking control for the planar 2-Do F underactuated manipulator. The trajectory of the active link is composed of two parts. The first part trajectories are designed according to the initial and target angles of the active link. The second part trajectories are designed based on the constraints between the underactuated link and the active link. Meanwhile, the parameters of the trajectories are optimized by the differential evolution algorithm(DEA) to ensure all links eventually reach to their target values by tracking the designed trajectories. Then,the sliding mode variable structure controller are designed to make the active link track their trajectories. The effectiveness of such strategy is demonstrated through simulation results.

关键词： Planar 2-DoF underactuated manipulator Trajectory planning Tracking control Differential evolution algorithm

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Graph convolutional subspace clustering: A robust subspace clustering framework for hyperspectral image

arXiv

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arXiv 2020年

作者： Cai, Yaoming Zhang, Zijia Cai, Zhihua Liu, Xiaobo Jiang, Xinwei Yan, Qin School of Computer Science China University of Geosciences Wuhan430074 China School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China

Hyperspectral image (HSI) clustering is a challenging task due to the high complexity of HSI data. Subspace clustering has been proven to be powerful for exploiting the intrinsic relationship between data points. Despite the impressive performance in the HSI clustering, traditional subspace clustering methods often ignore the inherent structural information among data. In this paper, we revisit the subspace clustering with graph convolution and present a novel subspace clustering framework called Graph Convolutional Subspace Clustering (GCSC) for robust HSI clustering. Specifically, the framework recasts the self-expressiveness property of the data into the non-Euclidean domain, which results in a more robust graph embedding dictionary. We show that traditional subspace clustering models are the special forms of our framework with the Euclidean data. Basing on the framework, we further propose two novel subspace clustering models by using the Frobenius norm, namely Efficient GCSC (EGCSC) and Efficient Kernel GCSC (EKGCSC). Both models have a globally optimal closed-form solution, which makes them easier to implement, train, and apply in practice. Extensive experiments on three popular HSI datasets demonstrate that EGCSC and EKGCSC can achieve state-of-the-art clustering performance and dramatically outperforms many existing methods with significant margins. Copyright © 2020, The Authors. All rights reserved.

关键词： Spectroscopy

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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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Design of audio to image cross-modal learning and generation based on single-layer CoPt spin-orbit torque devices

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Journal of Magnetism and Magnetic Materials 2025年 629卷

作者： Qian, Likun Yang, Liu Zuo, Chao Tao, Ying Li, Wendi Jin, Fang Li, Huihui Dong, Kaifeng School of Future Technology China University of Geosciences Wuhan430074 China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Wuhan Second Ship Design and Research Institute Wuhan430064 China Beijing Superstring Academy of Memory Technology Beijing100176 China

Despite remarkable achievements in neural network applications using spin–orbit torque (SOT) devices, current research predominantly focuses on unimodal classification tasks, while cross-modal learning tasks often suffer from suboptimal generation quality. To address this limitation, we employ generative adversarial networks (GANs) based on CoPt-SOT to achieve cross-modal learning and generation from speech to handwritten digit images. Initially, we explore the field-free switching characteristics of single-layer CoPt devices, developing spin-based implementations of Scaled Rectified Linear Unit (SReLu), Sigmoid, and Tanh neuronal functions. Simultaneously, we leverage the nonvolatile multistate characteristics of CoPt devices to realize excitatory-inhibitory synaptic plasticity. Subsequently, for the first time, we construct half-spin and full-spin GAN networks using these spin-based neurons and synapses, enabling cross-modal learning and generation between speech and image domains. Finally, we evaluate the performance of these networks through handwritten digit recognition tasks, achieving impressive recognition accuracies of 93.78 % and 88.61 % for half-spin and full-spin implementations, respectively. Notably, this work overcomes the existing bottleneck in SOT device applications, which have been largely confined to unimodal classification tasks, and demonstrates significant potential for expanding the scope of SOT-based technologies. © 2025 Elsevier B.V.

关键词： Generative adversarial networks

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