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Adaptive Decentralized Asymptotic Tracking control for Large-Scale Nonlinear systems With Unknown Strong Interconnections

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 173-186页

作者： Ben Niu Jidong Liu Ding Wang Xudong Zhao Huanqing Wang College of Information Science and Engineering Shandong Normal UniversityJinan 250014China School of Artificial Intelligence Nankai UniversityTianjin 300071China Faculty of Information Technology Beijing University of TechnologyBeijing 100124 State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Control Science and Engineering Dalian University of TechnologyDalian 116023China Department of Mathematical Science and Automatization Institute Bohai UniversityJinzhou 121000China

An adaptive decentralized asymptotic tracking control scheme is developed in this paper for a class of large-scale nonlinear systems with unknown strong interconnections,unknown time-varying parameters,and ***,by employing the intrinsic properties of Gaussian functions for the interconnection terms for the first time,all extra signals in the framework of decentralized control are filtered out,thereby removing all additional assumptions imposed on the interconnec-tions,such as upper bounding functions and matching ***,by introducing two integral bounded functions,asymptotic tracking control is ***,the nonlinear filters with the compensation terms are introduced to circumvent the issue of“explosion of complexity”.It is shown that all the closed-loop signals are bounded and the tracking errors converge to zero *** the end,a simulation example is carried out to demonstrate the effectiveness of the proposed approach.

关键词： Adaptive decentralized control asymptotic tracking control large-scale nonlinear systems unknown strong interconnections

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Development and Optimization of a WebGL-Based Mechanical Model Simulation Platform 4th

Development and Optimization of a WebGL-Based Mechanical Mod...

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4th International Conference on Mechanical Design and Simulation, MDS 2024

作者： Wang, Xintong Tian, Hao Fang, Jingzhe Zhang, Hualiang Zhang, Tao Software College Shenyang University of Technology Liaoning Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Electrical Engineering Shenyang University of Technology Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9789819778867

The need for mechanical model simulation platforms is growing as a result of the ongoing advancements in digital technology and the mechanical manufacturing sector. The application scope and efficacy of simulation platforms are severely limited by the issue of sluggish rendering performance, which frequently arises when obtaining high-quality rendering. When drawing high demand mechanical models, a lot of processing power is frequently needed, which makes real-time rendering speed unsatisfactory for practical purposes. In order to achieve real-time rendering and simulation of 3D robots, this experiment built and implemented a WebGL-based 3D simulation platform. Additionally, the simulation platform can be optimized by increasing frame rate and rendering speed without sacrificing rendering effect by building a mixed bounding box. © The Author(s) 2025.

关键词： Digital elevation model

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AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion

AdvDiffuser: Generating Adversarial Safety-Critical Driving ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yuting Xie Xianda Guo Cong Wang Kunhua Liu Long Chen School of Computer Science and Engineering Sun Yat-Sen University School of Computer Science Wuhan University State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of Sciences School of Mechanical and Automotive Engineering Qingdao University of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance.

关键词： Training Diffusion models Intelligent robots Autonomous vehicles Testing

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Improved control of Grid-Connected Photovoltaic and Energy Storage System Based on Virtual Resistance 2

Improved Control of Grid-Connected Photovoltaic and Energy S...

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2nd IEEE International Power Electronics and Application Symposium, PEAS 2023

作者： Kang, Jiahua Dong, Wei Xu, Jinming Diao, Hanxu Lv, Zhenhua Xie, Shaojun College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China China Electric Power Research Institute State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems Beijing China State Grid Jiangsu Electric Power Co. LTD. Research Institute Nanjing China

ISBN: (纸本)9798350313765

In practical applications, the photovoltaic and energy storage (PV&ES) system can reduce grid-connected power fluctuations and help solve the problems caused by the large amount of distributed photovoltaic generation connected to the grid. However, the presence of non-ideal factors such as rich voltage harmonics and large grid impedance in the grid may lead to the destabilization of the PV&ES system, so analyzing the stability of the PV&ES system under complex grid is needed. The emphasis of this paper is on the modeling and stability analysis of the PV&ES system. The accurate model is established by using the harmonic linearization, and stability analysis under different grid impedances is carried out using impedance analysis. As proved, the PV&ES system has better stability under weak grid, but poor stability under strong grid with lower phase margin. Based on this, a virtual resistance-based method for improving the PV&ES system output impedance characteristics is proposed to achieve better stability under different grid impedance conditions. The validity and feasibility of the above analysis are confirmed through simulation of three-phase grid-connected inverters prototype. © 2023 IEEE.

关键词： frequency coupling impedance modeling photovoltaic and energy storage system (PV&ES) weak grid

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Aeroservoelastic induced drag modelling and minimization for the T-FLEX demonstrator

Aeroservoelastic induced drag modelling and minimization for...

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AIAA SciTech Forum and Exposition, 2023

作者： Meddaikar, Yasser M. Krüger, Wolf R. Kier, Thiemo Bartasevicius, Julius Yu, Fanglin Vanek, Bálint Olgyay, Ábel Takarics, Béla Loads Analysis and Aeroelastic Design DLR - Institute of Aeroelasticity Bunsenstrasse 10 Goettingen37073 Germany Department of System Dynamics and Control DLR - Institute of System Dynamics and Control Weßling82234 Germany Institute of Aircraft Design Technical University of Munich Boltzmannstraße 15 Garching/München85748 Germany Systems and Control Lab SZTAKI Kende u. 13-17 Budapest1111 Hungary Computer and Automation Research Institute SZTAKI Kendeu. 13-17 Budapest1111 Hungary

ISBN: (数字)9781624106996

ISBN: (纸本)9781624106996

This work is part of the on-going EU-project FliPASED, wherein flight tests using a UAV demonstrator (T-FLEX) and a multi-disciplinary optimization (MDO) exercise involving the aeroservoelastic optimization of UAV demonstrator and commercial-scale aircraft wings are performed. In this paper, several potential flow methods have been applied to estimate induced drag of the T-FLEX aircraft, followed by solving an optimization problem to minimize it using the control surfaces on the wings. The results from this study were used in the retrofit design of a pair of manufactured wings - the number and position of the control surfaces and the wing selection. In a next step, the developed tools will form the basis for a controller that will minimize induced drag, which will be tested on the T-FLEX testbed. The developed tools and the validated results will also be used within a broader MDO task involving optimization studies on a commercial short/medium range aircraft. © 2023, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Wings

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Low-Rank Optimal Transport for Robust Domain Adaptation

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1667-1680页

作者： Bingrong Xu Jianhua Yin Cheng Lian Yixin Su Zhigang Zeng IEEE the School of Automation Wuhan University of TechnologyWuhan 430070China Intelligent Transportation Systems Research Center Wuhan University of TechnologyWuhan 430063 Chongqing Research Institute Wuhan University of TechnologyChongqingChina the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074China

When encountering the distribution shift between the source(training) and target(test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are welllabeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation:distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.

关键词： Domain adaptation low-rank constraint noise corruption optimal transport

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Visual Grasping with Spectral Clustering and Heuristic Searching for Robot in Cluttered Environments

Visual Grasping with Spectral Clustering and Heuristic Searc...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Geng, Wenjie Cao, Zhiqiang Tang, Yingbo Wang, Shuo Jing, Fengshui The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665481090

Grasping the target object is an essential requirement for the robot to provide better services. It becomes complicated especially in cluttered environments, which still remains challenging. This paper proposes a grasping chain generation solution that enables the robot to grasp the target after other obstruction objects are moved in a good order. SSD is firstly adopted to acquire the information of detectable objects and then Euclidean clustering is employed to obtain the untrained objects. After that, the minimum bounding box of each object is obtained, which is then projected on the plane and represented by a smooth differentiable minimum ellipse. On this basis, an information density kernel function is designed to express the interaction between objects. By abstracting each ellipse as a node of the graph whose edge weight is calculated by this kernel function, the whole scene is described in a form of graph. To simplify the complexity of the scene graph, we use spectral clustering algorithm to classify the objects, and the task-oriented objects graph is constructed according to the objects closely related to the target one. As a result, the searching space is reduced. With space division of task-oriented objects graph, each candidate grasping chain is iteratively extended by using the heuristic searching and the best chain with the shortest length is determined. The proposed method can deal with secondary obstruction and its effectiveness is testified by experiments. © 2022 IEEE.

关键词： Iterative methods

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AI4S Based on DeSci: Reference Model and Research Issues

AI4S Based on DeSci: Reference Model and Research Issues

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IEEE International Conference on systems, Man and Cybernetics

作者： Wenwen Ding Juanjuan Li Rui Qin Sangtian Guan Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macau China The State Key Laboratory for Management and Control of Complex Systems The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

The rise of Artificial Intelligence for Science (AI4S) has highlighted the importance and urgency of ensuring open-ness, fairness, impartiality, diversity, and sustainability in scientific systems. Existing scientific systems, referred to as Centralized Science (CeSci), are built on centralized organizational structures and top-down institutional frameworks, which are lagging behind the development and practical requirements of AI4S. To address these limitations, AI4S needs to embrace a new scientific organizational and operational paradigm, namely Decentralized Science (DeSci). It can provide strong support to AI4S via effectively addressing issues such as information silos, biases, unfair distribution, and monopolies and promoting multidisciplinary, interdisciplinary, and trans disciplinary cooperation in science. Based on these considerations, this paper presents the framework of AI4S based on DeSci and explores its potential application scenarios and research issues. The research can provide effective guidance for the development of scientific systems.

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Research on Engraving Recognition Method of Nodular Cast Iron Pipe Bearing End Based on Improved YOLOX

Research on Engraving Recognition Method of Nodular Cast Iro...

引用

Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Mingxuan Han Jun Wang Kecheng Zhou Jian Wang School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

This study proposes a method for recognizing the engravings on the spigot ends of ductile iron pipes based on an improved YOLOX model. Ductile iron pipes, known for their excellent mechanical properties and corrosion resistance, are widely used in critical fields such as municipal water supply, drainage, and gas transportation. To ensure product quality and achieve traceability, identification information is typically engraved on the spigot ends of ductile iron pipes during production. However, traditional manual recognition methods are inefficient and prone to human error. To address this issue, this paper proposes an automated engraving recognition system based on an improved YOLOX model. By integrating techniques such as Depthwise Separable Convolutions, a multi-scale feature extraction network, and an adaptive attention mechanism, the YOLOX model structure is further optimized to enhance recognition accuracy and robustness in complex industrial environments. Experimental results demonstrate that the improved YOLOX model surpasses traditional models in both recognition accuracy and computational efficiency, proving its effectiveness and reliability for industrial production applications.

关键词： Water Adaptation models Attention mechanisms Accuracy Computational modeling Transportation Production Feature extraction Robustness Computational efficiency

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Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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