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第33届中国控制与决策会议

作者： Huijun Guo Jintao Liang School of Automation and Information Engineering Xi'an University of Technology Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing Guilin University of Electronic Technology

This paper studies the problem of distributed consensus tracking control for multi-robot systems with different friction coefficients and external disturbances based on the multi-agent *** the case where the communication topology is a weighted directed graph and leader node is the neighbor of a small portion of the follower *** follower nodes are modeled as two second-order nonlinear dynamical system with coupling parts.A distributed robust adaptive control law based on the neural networks(NN) is designed for each follower node,where the controller requires only relative state information from its adjacent *** such control scheme,the states of all follower nodes ultimately synchronize to the leader node with bounded residual *** results are provided to validate the effectiveness of the algorithm.

关键词： Consensus control Multi-robot systems Agent systems Neural networks Sliding mode control

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A 3D Point Attacker for LiDAR-Based Localization

A 3D Point Attacker for LiDAR-Based Localization

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International Conference on control and Automation (ICCA)

作者： Shiquan Yi Jiakai Gao Yang Lyu Lin Hua Xinkai Liang Quan Pan School of Automation Northwestern Polytechnical University Xi'an Shaanxi PR China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing PR China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

The safety and security issues of autonomous navigation function become the main obstacles that hinder the widespread applications of self-driving cars and unmanned systems. In this paper, we investigate the vulnerability of LiDAR-based localization methods to adversarial attacks. Specifically, we developed a feature-based spoofing attack strategy to degrade the localization performance of LiDAR-based localization algorithms. Reflecting on the vulnerability, we additionally provide a resilient strategy to defend existing LiDAR-based localization methods against this attack. The proposed attack strategy is tested on the KITTI dataset to illustrate its effectiveness.

关键词： Location awareness Simultaneous localization and mapping Laser radar Accuracy Three-dimensional displays Perturbation methods Feature extraction

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Target Recognition Inference Based on Knowledge Graph and Graph Neural Network

Target Recognition Inference Based on Knowledge Graph and Gr...

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Chinese Automation Congress (CAC)

作者： Wei Li Hongfeng Xu Yuan Li Beijing Institute of Technology Beijing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In complex environments with vast and diverse information sources, target recognition serves as a crucial precursor to situational analysis, providing the foundation for further decision-making and analysis. Traditional algorithms for target recognition often rely on human experience and rules, necessitating expert knowledge and subjective judgments, which introduces uncertainties. This paper introduces a method for target recognition inference, leveraging Situation Knowledge Graph Convolutional Neural Network (SKGCN). By employing active and passive radar sensor data in complex scenarios, the algorithm processes inputs within a self-constructed situational knowledge graph. It derives vector representations of target entities through aggregation operations among neighboring nodes and extracts perceptual data vectors using the embedding layer. The algorithm calculates the alignment between sensor data and target entities, facilitating target recognition and inference in complex scenarios. Simulation results confirm the effectiveness of the proposed approach.

关键词： Machine learning algorithms Target recognition Soft sensors Weapons Knowledge graphs Feature extraction Inference algorithms Vectors Data mining Convolutional neural networks

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Investigation of Best Robot Kicking

Investigation of Best Robot Kicking

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第三十二届中国控制会议

作者： Theophile Boue Chenguang Yang Hongbin Ma Phil Culverhouse School of Computing and Mathematics University of PlymouthPlymouth PL4 8AAUnited Kingdom Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Robot football competitions have been popular currently accross the *** to challenge for the FIRA (Federation of International Robot-soccer Association) Robot World Cup,we are considering an

ISBN: (纸本)9781479900305

Robot football competitions have been popular currently accross the *** to challenge for the FIRA (Federation of International Robot-soccer Association) Robot World Cup,we are considering an

关键词： Football Robot Robot World Cup Best Kick Motion control Compliance control Inverse Dynamics

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Adaptive Back-stepping Based control for Active Suspension systems with Prescribed Performance

Adaptive Back-stepping Based Control for Active Suspension S...

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第40届中国控制会议

This paper proposes an adaptive back-stepping tracking control with prescribed performance function for vehicle suspension systems with uncertain parameters. The prescribed performance function can lead the tracking error from a large set to an arbitrarily small residual set. Firstly, the constrained tracking error system incorporating the prescribed performance functions is transformed into unconstrained form. Then an adaptive updating scheme based on the projection algorithm is designed to estimate the uncertain parameter. Following that an adaptive nonlinear controller is designed using the back-stepping technique, and the stability of whole controlled systems is proved by the Lyapunov theory. Meanwhile, the state convergence of the internal dynamical system is also guaranteed. Finally, simulation results are provided to verify the proposed approach.

关键词： Active Suspension system Adaptive control Back-stepping technique prescribed performance

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A Multi-Kernel Principal Component Analysis Method for Quality-Related Fault Detection

A Multi-Kernel Principal Component Analysis Method for Quali...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Lingxia Mu Biyu Lei Ding Liu School of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

ISBN: (纸本)9781665465373

In this paper, a multi-kernel principal component analysis (MKPCA) method for quality-related fault detection is proposed. The initial space is firstly mapped to a new space. The correlated information between the new space and output quality is then obtained by the kernel function. Meanwhile, with consideration of the advantage of global function and local function, a weight factor which combines them together is introduced to construct a multi-kernel function. In this way, the algorithm achieves better learning ability. The new space is projected to two mutually orthogonal subspaces, i.e., quality-related part and quality-unrelated part. In each subspace, fault information is expressed by different statistical indicators. The numerical example is presented to evaluate the performance of the MKPCA. The results show better reliability and high fault detection rate through proper spatial decomposition and kernel function construction.

关键词： Correlation Automation Fault detection Feature extraction Eigenvalues and eigenfunctions Reliability Matrix decomposition

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Optimized Dispatching of Seawater Pumping-Storing/Wind/Solar Hybrid Power Generating system

Optimized Dispatching of Seawater Pumping-Storing/Wind/Solar...

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第40届中国控制会议

作者： Yuyu Liu Ding Liu Lu Kang National & Local Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Different power sources in Seawater Pumping-Storing/Wind/Solar Hybrid Power Generating system supply power to the island *** order to ensure the reliability of the power supplying and improve the economical efficiency of the island micro-grid system at the same time,the output of each power source in the island micro-grid system needs to be optimized and dispatched according to historical wind power and photo-voltaic *** this paper,the mathematical model of Seawater Pumping-Storing/Wind/Solar Hybrid Power Generating system is established,and the one-time cost,*** the joint system are taken as the objective *** immune particle swarm algorithm is used to figure out the optimal output of each power source in the system after considering the restrictions including power balance of the system,the power constraints of each generator set and the online power of wind power and *** example shows that the optimization result of improved PSO algorithm is better than the original one,and it can also achieve the optimized dispatching of the joint system economically and effectively.

关键词： Seawater pumped-storage power station IPSO Optimized Dispatching

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A Robust Multi-AGV Cooperative Path Planning Method Considering Spatiotemporal Distance 5

A Robust Multi-AGV Cooperative Path Planning Method Consider...

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5th International Conference on Computer Engineering and intelligent control, ICCEIC 2024

作者： Chen, Maolin Chen, Songhang Lian, Mingchang School of Advanced Manufacturing Fuzhou University Quanzhou China Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou China Fujian College University of Chinese Academy of Sciences Fuzhou China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China

ISBN: (纸本)9798331507992

With the widespread application of Automated Guided Vehicles (AGVs) in warehousing and logistics systems, the optimization of multi-AGV path planning has become a critical issue. Current methods primarily focus on minimizing operating time and energy consumption but often overlook spatiotemporal distances between paths, leading to frequent path conflicts and interferences in the system. This paper proposes a neural network based time window estimation model, designed to generate time windows that better align with dynamic environments. Based on this model, we introduce a collaborative multi-AGV path planning method that optimizes spatiotemporal distances between paths. Compared to the Conflict-Based Search (CBS) method, this approach significantly improves the spatiotemporal distance between AGVs during task execution, enhancing the robustness of AGV clusters while only marginally increasing time costs. © 2024 IEEE.

关键词： Automatic guided vehicles

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Scene Classification Using Deep Networks Combined with Visual Attention

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Journal of Sensors 2022年第0期2022卷

作者： Shi, Jing Zhu, Hong Li, Yuxing Li, Yanghui Du, Sen School of Automation and Information Engineering Xi'An University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'An University of Technology Xi'an710048 China

In view of the scene's complexity and diversity in scene classification, this paper makes full use of the contextual semantic relationships between the objects to describe the visual attention regions of the scenes and combines with the deep convolution neural networks, so that a scene classification model using visual attention and deep networks is constructed. Firstly, the visual attention regions in the scene image are marked by using the context-based saliency detection algorithm. Then, the original image and the visual attention region detection image are superimposed to obtain a visual attention region enhancement image. Furthermore, the deep convolution features of the original image, the visual attention region detection image, and the visual attention region enhancement image are extracted by using the deep convolution neural networks pretrained on the large-scale scene image dataset Places. Finally, the deep visual attention features are constructed by using the multilayer deep convolution features of the deep convolution networks, and a classification model is constructed. In order to verify the effectiveness of the proposed model, the experiments are carried out on four standard scene datasets LabelMe, UIUC-Sports, Scene-15, and MIT67. The results show that the proposed model improves the performance of the classification well and has good adaptability. © 2022 Jing Shi et al.

关键词： Convolution

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Neural network structure design based on N-gauss activation function1

arXiv

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arXiv 2021年

作者： Lu, Xiangri Ma, Hongbin Zhang, Jingcheng State Key Laboratory of Intelligent Decision and Control for Complex Systems Beijing Institute ofTechnology Beijing China Institute of Pattern Recognition and Intelligent Systems School of Automation Beijing Institute ofTechnology China Institute of Navigation Guidance and Control School of Automation Beijing Institute of

Recent work has shown that the activation function of the convolutional neural network can meet the Lipschitz condition, then the corresponding convolutional neural network structure can be constructed according to the scale of the data set, and the data set can be trained more deeply, more accurately and more effectively. In this article, we have accepted the experimental results and introduced the core block N − Gauss, N − Gauss, and Swish (Conv1, Conv2, FC1) neural network structure design to train MNIST, CIFAR10, and CIFAR100 respectively. Experiments show that N − Gauss gives full play to the main role of nonlinear modeling of activation functions, so that deep convolutional neural networks have hierarchical nonlinear mapping learning capabilities. At the same time, the training ability of N − Gauss on simple one-dimensional channel small data sets is equivalent to the performance of ReLU and Swish. Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian distribution

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