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A differential-cascaded paradigm for control of nonlinear systems

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arXiv 2020年

作者： Wang, Hanlei Ren, Wei Cheah, Chien Chern Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing100094 China Department of Electrical and Computer Engineering University of California at Riverside RiversideCA92521 United States School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore

This paper focuses on developing a new paradigm motivated by investigating the consensus problem of networked Lagrangian systems with time-varying delay and switching topologies. We present adaptive controllers with piecewise continuous or arbitrary times differentiable control torques for realizing consensus of Lagrangian systems, extending the results in the literature. This specific study motivates the formulation of a new paradigm referred to as forwardstepping, which is shown to be a systematic tool for solving various nonlinear control problems. One distinctive point associated with forwardstepping is that the order of the reference dynamics is typically specified to be equal to or higher than that of the original nonlinear system, and the reference dynamics and the nonlinear system are governed by a differential/dynamic-cascaded structure. The order invariance or increment of the specified reference dynamics with respect to the nonlinear system and their differential/dynamic-cascaded structure expands significantly the design freedom and thus facilitates the seeking of solutions to many nonlinear control problems which would otherwise often be intractable. Copyright © 2020, The Authors. All rights reserved.

关键词： Lagrange multipliers

Regret and cumulative constraint violation analysis for online convex optimization with long term constraints

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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Institute for Advanced Study Tongji University Shanghai China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The cumulative constraint violation is used as the metric to measure constraint violations, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. A novel algorithm is first proposed and it achieves an O(Tmax{c,1-c}) bound for static regret and an O(T (1-c)/2) bound for cumulative constraint violation, where c ϵ (0, 1) is a user-defined trade-off parameter, and thus has improved performance compared with existing results. Both static regret and cumulative constraint violation bounds are reduced to O(log(T )) when the loss functions are strongly convex, which also improves existing results. In order to achieve the optimal regret with respect to any comparator sequence, another algorithm is then proposed and it achieves the optimal O( p T (1 + PT )) regret and an O(√T) cumulative constraint violation, where PT is the path-length of the comparator sequence. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Comparators (optical)

One-Dimensional EEG Artifact Removal Network Based on Convolutional Neural Networks

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Journal of Network Intelligence

Journal of Network Intelligence 2024年第1期9卷 142-159页

作者： Xiong, Jun Meng, Xiang-Long Chen, Zhao-Qi Wang, Chuan-Sheng Zhang, Fu-Quan Grau, Antoni Chen, Yang Huang, Jing-Wei School of Computer and Data Science Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulongjiang North Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 1 Campus New Village Longjiang Street Fuqing China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fuzhou China

The electroencephalogram (EEG) serves as a significant tool in the realms of clinical medicine, cerebral investigation, and neurological disorders research. However, the EEG records we obtain are often easily contaminated by various artifacts, which can blur or distort the underlying EEG signals and make data interpretation difficult. Generally speaking, removing EEG artifacts is considered an essential step in brain signal analysis. Therefore, removing artifacts is crucial for obtaining accurate and reliable EEG signals for subsequent analysis. Recently, deep learning techniques have found widespread application across various domains for denoising tasks, including image denoising and EEG denoising. Many advanced algorithms have been developed in image denoising, which has achieved good results in enhancing low-quality images. Moreover, it has shown superior performance in EEG denoising. In contrast, few people have devoted themselves to studying EEG denoising, and existing convolutional neural network EEG denoising methods still have problems of overfitting and poor denoising effect in Electromyograph(EMG) and ElectroOculoGram(EOG) artifact removal. Therefore, this paper proposes a method called DWINet (De-artifacting with Image-based Network for EEG Signals) based on an image dehazing network DRHNet for removing artifacts from EEG signals. Specifically, our approach DWINet, addresses the de-artifacting issue in EEG signals by converting it as an image dehazing problem and utilizes the image dehazing capability of DRHNet to enhance the denoising performance of EEG signals. Experimental results demonstrate that the proposed method outperforms the compared algorithms in removing the ocular artifact in EEG signals and exhibits higher accuracy and robustness. © 2024, J. Network Intell. All rights reserved.

关键词： Image enhancement

Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization

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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden The Digital Futures Stockholm10044 Sweden The Department of Control Science and Engineering College of Electronics and Information Engineering The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

This paper considers the distributed online convex optimization problem with time-varying constraints over a network of agents. This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions. At each round, each agent selects a decision from the decision set, and then only a portion of the loss function and a coordinate block of the constraint function at this round are privately revealed to this agent. The goal of the network is to minimize the network-wide loss accumulated over time. Two distributed online algorithms with full-information and bandit feedback are proposed. Both dynamic and static network regret bounds are analyzed for the proposed algorithms, and network cumulative constraint violation is used to measure constraint violation, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. In particular, we show that the proposed algorithms achieve O(T max{κ,1-κ}) static network regret and O(T 1-κ/2) network cumulative constraint violation, where T is the time horizon and κ ∈ (0, 1) is a user-defined trade-off parameter. Moreover, if the loss functions are strongly convex, then the static network regret bound can be reduced to O(T κ). Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Economic and social effects

DOA Estimation for Coexistence of Circular and Non-Circular Signals Based on Atomic Norm Minimization

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DOA Estimation for Coexistence of Circular and Non-Circular ...

Sensor Array and Multichannel Signal Processing Workshop

作者： Liping Teng Qing Wang Hua Chen Wei-Ping Zhu Wei Liu Jingjing Cai Schcol of Electrical and Information Engineering Tianjin University Tianjin China Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs Commission Dalian China Department of Electrical and Computer Engineering Concordia University Montreal Canada Department of Electronic and Electrical Engineering University of Sheffield Sheffield United Kingdom Department of Electronic Engineering Xidian University Xian China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

In this paper, a gridless DOA estimation method with coexistence of non-circular and circular signals is proposed by employing an enhanced sparse nested array, whose virtual array has no holes. The virtual signals derived from both sum and difference co-arrays are constructed based on atomic norm minimization. Simulation results are provided to demonstrate the performance of the proposed method.

关键词： Direction-of-arrival estimation Array signal processing Conferences Computer simulation Estimation Apertures Minimization

A Smartphone-Based Networked control Platform: Design and Implementation

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A Smartphone-Based Networked Control Platform: Design and Im...

第三十九届中国控制会议

作者： Dong-Liang Chen Guo-Ping Liu College of Mechanical and Electronic Engineering Dalian Minzu University Center for Control Theory and Guidance Technology Harbin Institute of Technology Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs Commission Department of Artificial Intelligence and Automation Wuhan University

The rapid development of embedded systems and IT makes electrical devices functional and tiny. This progress is especially reflected in the smartphone industry. In this paper, a novel applicable wireless control platform based on smartphones is proposed. The system is of high value due to the advantages of smartphones such as mobility, resourcefulness, tiny and *** are designed in this paper to guarantee the real-timeness of the controller. To ease the controller implementation,code auto-generation technique is integrated to the platform, which is able to convert Simulink block diagrams to executable files for Android smartphones. In addition, protocols are designed to transfer data to remote workstations such that the control system can be supervised and monitored on-line. At last, the control platform is tested on a spacecraft simulator. The experimental results validate the effectiveness and reliability of the designed platform.

关键词： Networked control Platform Smartphones Real Time control

General-purpose machine-learned potential for 16 elemental metals and their alloys

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arXiv 2023年

作者： Song, Keke Zhao, Rui Liu, Jiahui Wang, Yanzhou Lindgren, Eric Wang, Yong Chen, Shunda Xu, Ke Liang, Ting Ying, Penghua Xu, Nan Zhao, Zhiqiang Shi, Jiuyang Wang, Junjie Lyu, Shuang Zeng, Zezhu Liang, Shirong Dong, Haikuan Sun, Ligang Chen, Yue Zhang, Zhuhua Guo, Wanlin Qian, Ping Sun, Jian Erhart, Paul Ala-Nissila, Tapio Su, Yanjing Fan, Zheyong Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China School of Materials Science and Engineering Hunan University Changsha410082 China MSP group QTF Centre of Excellence Department of Applied Physics Aalto University P.O. Box 15600 Aalto EspooFI-00076 Finland Chalmers University of Technology Department of Physics Gothenburg41926 Sweden National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Department of Civil and Environmental Engineering George Washington University WashingtonDC20052 United States Department of Electronic Engineering and Materials Science and Technology Research Center The Chinese University of Hong Kong N.T. Shatin999077 Hong Kong Department of Physical Chemistry School of Chemistry Tel Aviv University Tel Aviv6997801 Israel Institute of Zhejiang University-Quzhou Quzhou324000 China College of Chemical and Biological Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Mechanics and Control of Mechanical Structures Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education Institute for Frontier Science Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Mechanical Engineering The University of Hong Kong Pokfulam Road Hong Kong School of Science Harbin Institute of Technology Shenzhen518055 China College of Physical Science and Technology Bohai University Jinzhou121013 China Interdisciplinary Centre for Mathematical Modelling Department of Mathematical Sciences Loughborough University Leicestershire LoughboroughLE11 3TU United Kingdom

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a feasible approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach's effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys. This work represents a significant leap towards a unified general-purpose MLP encompassing the periodic table, with profound implications for materials science. Copyright © 2023, The Authors. All rights reserved.

关键词： Radiation damage

2-D GCANet Applied to Denoise 1-D EEG Signals in Online Remote Teaching Scenarios

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Journal of Network Intelligence

Journal of Network Intelligence 2023年第4期8卷 1289-1302页

作者： Huang, Ting-Qing Wang, Chuan-Sheng Chen, Zhao-Qi Zhang, Fu-Quan Meng, Xiang-Long Grau, Antoni Chen, Yang Huang, Jing-Wei College of Computer and Control Engineering Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulong Jiangbei Avenue Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music No. 2 Wannianchang Street Chenghua District Sichuan Province Chengdu City China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town NO. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 599 Quanxiu Road Juyuanzhou Ecological Civilization District Fujian Province Fuzhou City China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fujian Province Fuzhou City China

When EEG signals are used to assess the level of student engagement in online teaching tasks, they are often interfered by noise. It is a challenge to effectively remove these noises. Currently, deep learning methods have been applied to the field of EEG denoising. However, existing methods still have some problems. The denoised signals still have obvious noise residue, or the original EEG signals are damaged, and the model fitting speed is too slow. Image dehazing, as a typical denoising task in the field of image enhancement, has achieved great success in recent years. Therefore, inspired by advanced models in this field, we introduce CNN into EEG denoising tasks. In this paper, we take GCANet, an excellent image enhancement model, as an example. The dilated convolutions and gate fusion subnetworks included in GCANet enable more efficient EEG signal denoising. The results demonstrate that the proposed model effectively reduces noise while preserving essential features. Furthermore, in comparison to other state-of-the-art models, our proposed model exhibits enhanced robustness and faster convergence, as evidenced by achieving lower loss values after five epochs. Its good performance provides a new development idea for the field of EEG denoising. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Image enhancement

Adaptive finite-time synchronized control of multi-robotic fiber placement system with model uncertainties and disturbances

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ISA transactions 2025年

作者： Ronghua Zhang Yaonan Wang Wenfang Xie Pengcheng Li Haoran Tan Yiming Jiang College of Electrical and Information Engineering Hunan University Changsha 410082 Hunan China National Engineering Laboratory of Robot Visual Perception and Control Technology Hunan University Changsha 410082 Hunan China. Department of Mechanical Industrial and Aerospace Concordia University Montreal H3G2W1 Quebec Canada. College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 Jiangsu China. National Engineering Laboratory of Robot Visual Perception and Control Technology Hunan University Changsha 410082 Hunan China. Electronic address: tanhaoran@***. School of Robotics Hunan University Changsha 410082 Hunan China

The use of multiple robots to manufacture composite components represents a critical development direction for fiber placement systems (FPSs). In multi-robotic fiber placement systems (MRFPSs) with heterogeneous mechanical structures, robots collaborate to perform fiber placement tasks. Consequently, robot synchronization emerges as a primary factor in determining the performance of the fiber placement process. However, the difficulty in establishing accurate system models and the presence of disturbances are two significant challenges to achieving precise robot synchronization. Additionally, the system is expected to exhibit desirable dynamic characteristics, such as finite-time error convergence. To address these issues and requirements, we propose a novel adaptive finite-time synchronization control (AFSC) algorithm for the system. Specifically, a finite-time sliding mode observer is developed to handle kinematic uncertainty. A novel fast non-singular terminal sliding mode (FNTSM) manifold is constructed in the AFSC algorithm. Moreover, the control algorithm integrates an adaptive law to handle dynamic uncertainty and an adaptive term to counteract disturbances. Performance analysis demonstrates that the AFSC ensures that the coupled, synchronization, and tracking errors converge to zero within finite time. Furthermore, simulations and experiments are conducted to validate the effectiveness of the AFSC algorithm.

关键词： Disturbances Fiber placement Finite-time control Model uncertainties Multi-robots Synchronization control