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IEEE Conference on Decision and control

作者： Xiaoyu Luo Chongrong Fang Chengcheng Zhao Jianping He The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China The State Key Laboratory of Industrial Control Technology and Institute of Cyberspace Research Zhejiang University China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The data-driven attack strategies recently have received much attention when the full knowledge of the system model is unknown or difficult to be obtained for the adversary. Note that despite the critical parameters of the system model being unavailable for the adversary, the existing data-driven attack methods still depend on the linearity of the unknown system model. In this paper, we design a completely model-free attack strategy where the adversary with limited capability aims to compromise state variables such that the output value follows the expected trajectory. Specifically, we first construct a zeroth-order feedback optimization framework and uninterruptedly use probing signals for real-time measurements. Then, we iteratively update the attack signals along the composite direction of the gradient estimates of the objective function evaluations and the projected gradients. These objective function evaluations can be obtained only by real-time measurements. Furthermore, we characterize the optimality of these solutions via the optimality gap, which is affected by the dimensions of the attack signal, the iterations of solutions, and the convergence rate of the system. Extensive simulations are conducted to show the effectiveness of the proposed attack strategy.

关键词： Networked control systems Computational modeling Current measurement Linear programming Real-time systems Data models Time measurement

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RHNet: A Real-Time High-Resolution Network for Surface Defect Detection

RHNet: A Real-Time High-Resolution Network for Surface Defec...

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第43届中国控制会议

作者： Yijie Wang Zhigang Ren Jianpu Cai Zongze Wu Shengli Xie School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology College of Mechatronics and Control Engineering Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)Shenzhen University Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing (GDUT) Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the industrial domain, surface defect detection after multiple processing steps is crucial for improving the outgoing quality of products. However, due to the characteristics of surface defects, such as low contrast, irregular shapes, small and elongated features, and significant noise. To address these issues, this paper proposes a novel real-time high-resolution network(RHNet). Unlike previous models, RHNet emphasizes the importance of modeling spatial information to capture small ***, the output feature maps of each module are maintained at 1/4 resolution of the original image. We introduce a short-term dual-branch module(SDBM) to handle real-time processing of high-resolution feature maps. Additionally, FPAPPM is proposed for swift extraction of deep information and multi-scale context. Through extensive experiments, we demonstrate that our proposed model exhibits robust defect modeling capabilities and detection performance while still meeting real-time requirements.

关键词： Surface defect inspection Anomaly detection Semantic segmentation Deep learning Pattern recognition

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Spatio-Temporal Transformer-Based Reinforcement Learning for Robot Crowd Navigation

Spatio-Temporal Transformer-Based Reinforcement Learning for...

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IEEE International Conference on Robotics and Biomimetics

作者： Haodong He Hao Fu Qiang Wang Shuai Zhou Wei Liu Yang Chen School of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan China

Ensuring robots can move safely and adhere to social norms in dynamic human environments is a crucial step towards robot autonomous decision-making. In existing work, double serial separate modules are generally used to capture spatial and temporal interactions, respectively. However, such methods lead to extra difficulties in improving the utilization of spatio-temporal features and reducing the conservatism of navigation policy. In light of this, this paper proposes a spatiotemporal transformer-based policy optimization algorithm to more effectively preserve the human-robot interactions. Specifically, a gated embedding mechanism is introduced to effectively fuses the spatial and temporal representations by integrating both modalities at the feature level. Then Transformer is leveraged to encode the spatio-temporal semantic information, with the hope of finding the optimal navigation policy. Finally, a combination of spatio-temporal Transformer and self-adjusting policy entropy significantly reduce the conservatism of navigation policies. Experimental results demonstrate the priority of the proposed algorithm over the state-of-the-art methods.

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Optimal control in Both Steady State and Transient Process with Unknown Disturbances

arXiv

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arXiv 2024年

作者： Li, Ming Wang, Zhaojian Liu, Feng Cao, Ming Yang, Bo The Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai Jiao Tong University Shanghai200240 China The State Key Laboratory of Power System The Department of Electrical Engineering Tsinghua University Beijing100084 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands

The scheme of online optimization as a feedback controller is widely used to steer the states of a physical system to the optimal solution of a predefined optimization problem. Such methods focus on regulating the physical states to the optimal solution in the steady state, without considering the performance during the transient process. In this paper, we simultaneously consider the performance in both the steady state and the transient process of a linear time-invariant system with unknown disturbances. The performance of the transient process is illustrated by the concept of overtaking optimality. An overtaking optimal controller with known disturbances is derived to achieve the transient overtaking optimality while guaranteeing steady-state performance. Then, we propose a disturbance independent near-optimal controller, which can achieve optimal steady-state performance and approach the overtaking optimal performance in the transient process. The system performance gap between the overtaking optimal controller and the proposed controller proves to be inversely proportional to the control gains. A case study on a power system with four buses is used to validate the effectiveness of the two controllers. © 2024, CC BY.

关键词： Electromagnetic transients

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Additive manufacturing of silicon carbide microwave-absorbing metamaterials

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Additive Manufacturing Frontiers 2025年第1期4卷

作者： Zhao, Hanqing Liao, Qingwei Li, Yinghao Chu, Xiangcheng Yuan, Songmei Qin, Lei Key Laboratory of Sensors Beijing Information Science and Technology University Beijing 100192 China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing 100192 China State Key Laboratory of New Ceramics and Fine Processing School of Material Science and Engineering Tsinghua University Beijing 100084 China School of Mechanical Engineering and Automation Beihang University Beijing 100191 China

SiC is a wave-absorbing material with good dielectric properties, high-temperature resistance, and corrosion resistance, which has great potential for development in the field of high-temperature wave-absorbing. However, SiC is limited by its low impedance-matching performance and single wave-absorbing mechanism. Therefore, compatible metamaterial technologies are required to enhance its wave-absorbing performance further. The electromagnetic wave (EMW) absorbing metamaterials can realize perfect absorption of EMWs in specific frequency bands and precise regulation of EMW phase, propagation mode, and absorption frequency bands through structural changes. However, the traditional molding methods for manufacturing complex geometric shapes require expensive molds, involve process complexity, and have poor molding accuracy and other limitations. Therefore, additive manufacturing (AM) technology, through material layered stacking to achieve the processing of materials, is a comprehensive multidisciplinary advanced manufacturing technology and has become the core technology for manufacturing metamaterials. This review introduces the principles and applications of different AM technologies for SiC and related materials, discusses the current status and development trends of various AM technologies for fabricating silicon–carbon-based wave-absorbing metamaterials, summarizes the limitations and technological shortcomings of existing AM technologies for fabricating silicon–carbon-based wave-absorbing metamaterials, and provides an outlook for the future development of related AM technologies. © 2024 The Author(s)

关键词： Additive manufacturing Electromagnetic absorption Metamaterials SiC

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Surface Defect Detection Based on Deep Learning and Collaborative Cloud-edge Computation 7

Surface Defect Detection Based on Deep Learning and Collabor...

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7th International Conference on Computer and Communications, ICCC 2021

作者： He, Shunjie Yang, Bo Zhu, Dafeng Li, Cheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Industrial Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9781665409506

Surface defect detection is a fundamental step of quality management. Nowadays, surface defect detection system based on machine vision technology has made great progress which can gradually replace the human inspector. With the help of convolutional neural networks, the accuracy and stability of the surface defect detection algorithm are improving rapidly. However, it is difficult to process the images which have small defect areas and complex backgrounds. Furthermore, lacking defective samples and limited computation resources are also tough problems. To deal with these problems, a two-stage algorithm combined with object detection and classification networks is proposed. Transform learning and data augmentation are used to deal with the problem of lacking defect samples. Besides, a detection system based on collaborative cloud-edge computation is designed to improve the performance of the algorithm. The result shows that the proposed method has high performance and low requirements of computation resources. © 2021 IEEE.

关键词： Computer vision

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Space Target Surveillance Based on Non-linear Model Predictive control

Space Target Surveillance Based on Non-linear Model Predicti...

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Chinese automation Congress (CAC)

作者： Jiancheng Zhang Yutao Chen Jie Huang College of Electrical Engineering and Automation Fuzhou University Fuzhou China Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

ISBN: (纸本)9781665426480

Space target surveillance plays an important role in maintaining space advantages. Trajectory planning and attitude control are the key technologies of space target surveillance. We propose a nonlinear model predictive control framework to plan the trajectory of the surveillance satellite, track the surveillance target and keep a certain distance. In particular, the orbit dynamics and environmental disturbance of surveillance satellite are taken into account. A MatLab based real-time nonlinear model predictive control toolbox is used to solve these problems. The toolbox uses an efficient numerical method to calculate the maneuvering solution of MPC, which means that it allows the prediction horizon length to be greatly extended and save calculation time. The developed controller is simulated in a simulation scene, including the interference of near ground environment. We show that the proposed MPC method can be successfully applied to the satellite orbit planning and attitude control and the MATMPC toolbox can greatly speed up the solution of MPC optimization functions.

关键词： Space vehicles Satellites Target tracking Surveillance Aerospace electronics Predictive models Orbits

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Two-Archive-Based Competitive Swarm Optimizer for Constrained Multiobjective Optimization

Two-Archive-Based Competitive Swarm Optimizer for Constraine...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Lianghao Li Cheng He Jianqing Lin Linqiang Pan Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

Constrained multiobjective optimization problems (CMOPs) are prevalent in various real-world applications, presenting a formidable challenge to existing evolutionary algorithms when faced with intricate constraints. When solving CMOPs, a crucial concern is achieving a balance between convergence, diversity, and feasibility. To address these challenges, this paper proposes a two-archive-based constrained multiobjective competitive swarm optimizer. The algorithm preserves two collaborative and complementary archives to improve population convergence and feasibility. By implementing a competitive particle swarm mechanism, offspring solutions are generated by drawing upon solutions from both archives, thus capitalizing on their synergistic effect and exceptional information. An adaptive parameter is also used to adjust the bias in choosing the winner in the paired competition during the evolution. The experimental results demonstrate the effectiveness of the proposed algorithm in handling constrained multiobjective optimization problems.

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Optimized Formation control for a Class of Second-order Multi-agent Systems based on Single Critic Reinforcement Learning Method

Optimized Formation Control for a Class of Second-order Mult...

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IEEE International Conference on Networking, Sensing and control

作者： Wentai Shao Yutao Chen Jie Huang College of Electrical Engineering and Automation Fuzhou University Fuzhou China Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

ISBN: (纸本)9781665440493

In this paper, an optimized formation control based on single critic reinforcement learning is developed for a class of second-order multi-agent systems. Unlike first-order systems, both position and velocity variables need to be considered in second-order system control. Therefore, the control of second-order systems is more challenging. In the control design, single critic reinforcement learning method combined with fuzzy logic systems is used. Fuzzy logic systems approximator is used to compensate the nonlinearity of the systems. Compared with the actor-critic reinforcement learning method, single critic reinforcement learning requires only one network iterative training such that the training errors are smaller, and the calculation time caused by the iterative loop between actor and critic can be reduced. According to the analysis of Lyapunov stability theory, the proposed control design can achieve the control objective. Finally, the effectiveness of the proposed method is verified by simulation.

关键词： Training Fuzzy logic control design Simulation Reinforcement learning Stability analysis Sensors

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Impulsive Formation control of Nonlinear Leader-Following Multi-agent Systems with Input Saturation*

Impulsive Formation Control of Nonlinear Leader-Following Mu...

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industrial Cyber-Physical Systems (ICPS)

作者： Ni Zhang Xiaowei Jiang Xianhe Zhang Le You School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

Leader-following formation analysis problem for a second-order nonlinear multi-agent system(MAS) with input saturation is investigated in this paper. And the impulsive formation control algorithm is introduced in the designed protocol which only works at the impulse times. Owing to the real-world limited communication channels, input saturation is considered in the impulsive controller. Furthermore, based on Lyapunov stability theories, Kronecker properties, eigenvalue and so on, some sufficient conditions that guarantee the leader-following consensus of MAS are obtained. Lastly, several simulations are worked out to verify the correctness and effectiveness of the theoretical results.

关键词： Sufficient conditions Protocols Simulation Communication channels Cyber-physical systems control systems Formation control

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