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Super-twisting Finite Time control for Third Order Nonlinear Systems

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Super-twisting Finite Time Control for Third Order Nonlinear...

第40届中国控制会议

作者： Xinjian Liu Jianxiong Li Zhi Li Yiming Fang Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment The Key Lab of Industrial Computer Control Engineering of Hebei Province

A novel super-twisting control method is proposed for a class of third-order systems with matched Lipschitz disturbance in this paper. The proposed method ensures finite-time convergence to the fourth-order sliding set, by using a homogeneous, continuously differentiable and strict Lyapunov function. Finally, numerical simulation results are shown to illustrate the effectiveness of the proposed method.

关键词： Third-order sliding mode Super-twisting algorithm Finite time control

Transcriptomic insights unveil the crucial roles of cytochromes, NADH, and pili in Ag(I) reduction by Geobacter sulfurreducens

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Chemosphere 2024年 358卷 142174页

作者： Liu, Chunmao Guo, Dongchao Wen, Su Dang, Yan Sun, Dezhi Li, Pengsong Beijing Key Lab for Source Control Technology of Water Pollution College of Environmental Science and Engineering Beijing Forestry University Beijing100083 China Engineering Research Center for Water Pollution Source Control & Eco-remediation College of Environmental Science and Engineering Beijing Forestry University Beijing100083 China School of Computer Science Beijing Information Science and Technology University Beijing100101 China

Silver (Ag) is a pivotal transition metal with applications in multiple industries, necessitating efficient recovery techniques. Despite various proposed methods for silver recovery from wastewaters, challenges persist especially for low concentrations. In this context, bioreduction by bacteria like Geobacter sulfurreducens, offers a promising approach by converting Ag(I) to Ag nanoparticles. To reveal the mechanisms driving microbial Ag(I) reduction, we conducted transcriptional profiling of G. sulfurreducens under Ag(I)-reducing condition. Integrated transcriptomic and protein-protein interaction network analyses identified significant transcriptional shifts, predominantly linked to c-type cytochromes, NADH, and pili. When compared to a pilus-deficient strain, the wild-type strain exhibited distinct cytochrome gene expressions, implying specialized functional roles. Additionally, despite a down-regulation in NADH dehydrogenase genes, we observed up-regulation of specific downstream cytochrome genes, highlighting NADH's potential role as an electron donor in the Ag(I) reduction process. Intriguingly, our findings also highlight the significant influence of pili on the morphology of the resulting Ag nanoparticles. The presence of pili led to the formation of smaller and more crystallized Ag nanoparticles. Overall, our findings underscore the intricate interplay of cytochromes, NADH, and pili in Ag(I) reduction. Such insights suggest potential strategies for further enhancing microbial Ag(I) reduction. © 2024 Elsevier Ltd

关键词： Proteins

WING: Wheel-Inertial Neural Odometry with Ground Manifold Constraints

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arXiv 2024年

作者： Jiang, Chenxing Zhang, Kunyi Yang, Sheng Shen, Shaojie Xu, Chao Gao, Fei The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Alibaba DAMO Academy Hangzhou China Huzhou Institute Zhejiang University Huzhou China

In this paper, we propose an interoceptive-only odometry system for ground robots with neural network processing and soft constraints based on the assumption of a globally continuous ground manifold. Exteroceptive sensors such as cameras, GPS and LiDAR may encounter difficulties in scenarios with poor illumination, indoor environments, dusty areas and straight tunnels. Therefore, improving the pose estimation accuracy only using interoceptive sensors is important to enhance the reliability of navigation system even in degrading scenarios mentioned above. However, interoceptive sensors like IMU and wheel encoders suffer from large drift due to noisy measurements. To overcome these challenges, the proposed system trains deep neural networks to correct the measurements from IMU and wheel encoders, while considering their uncertainty. Moreover, because ground robots can only travel on the ground, we model the ground surface as a globally continuous manifold using a dual cubic B-spline manifold to further improve the estimation accuracy by this soft constraint. A novel space-based sliding-window filtering framework is proposed to fully exploit the C2 continuity of ground manifold soft constraints and fuse all the information from raw measurements and neural networks in a yaw-independent attitude convention. Extensive experiments demonstrate that our proposed approach can outperform state-of-the-art learning-based interoceptive-only odometry methods. © 2024, CC BY.

关键词： Navigation systems

Process-Model-Free Design of MPC via a Reference-Model-Based PI controller for the Gas Turbine

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Process-Model-Free Design of MPC via a Reference-Model-Based...

Electrical engineering, control and Robotics (EECR), International Conference on

作者： Gengjin Shi Chengbo Dai Miaomiao Ma Donghai Li Yanjun Ding Dept. of Energy & Power Engineering State Key Lab of Power Systems Tsinghua University Beijing China School of Control & Computer Engineering North China Electric Power University Beijing China

Model predictive control (MPC) is a modern advanced control strategy which has great reputation because of its excellent reference tracking performance and the ability to deal with process constraints, time delay and multivariable problems. However, the gas turbine is difficult to model accurately and unmeasurable disturbances are unavoidable, which limits the application of MPC to the gas turbine. To solve these problems, a process-model-free design method for MPC via a reference-model (RM)-based proportional-integral (PI) controller – desired dynamic equational (DDE)-PI controller (MPC-DDE) is proposed in this paper. The DDE-PI provides the design model for the MPC and handle with unmeasurable disturbances while MPC provides a rolling optimized set point for DDE-PI. Simulation results illustrate the superiorities of the proposed hierarchical control strategy in reference tracking, disturbance rejection and robustness, which indicates the promising prospect of practical application of MPC-DDE to the gas turbine.

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Multi-service collaboration and composition of cloud manufacturing customized production based on problem decomposition

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arXiv 2024年

作者： Yue, Hao Wu, Yingtao Wang, Min Hu, Hesuan Wu, Weimin Zhang, Jihui Shandong Qingdao266580 China College of Information Engineering Yangzhou University Jiangsu Yangzhou225127 China School of Electro-Mechanical Engineering Xidian University Shanxi Xi’an710071 China School of Computer Science and Engineering College of Engineering Nanyang Technological University 639798 Singapore State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang Hangzhou310027 China Institute of Cyber-Systems and Control Zhejiang University Zhejiang Hangzhou310027 China Institute of Complexity Science School of Automation Qingdao University Shandong Qingdao266071 China Shandong Key Laboratory of Industrial Control Technology Shandong Qingdao266071 China

Cloud manufacturing system is a service-oriented and knowledge-based one, which can provide solutions for the large-scale customized production. The service resource allocation is the primary factor that restricts the production time and cost in the cloud manufacturing customized production (CMCP). In order to improve the efficiency and reduce the cost in CMCP, we propose a new framework which considers the collaboration among services with the same functionality. A mathematical evaluation formulation for the service composition and service usage scheme is constructed with the following critical indexes: completion time, cost, and number of selected services. Subsequently, a problem decomposition based genetic algorithm is designed to obtain the optimal service compositions with service usage schemes. A smart clothing customization case is illustrated so as to show the effectiveness and efficiency of the method proposed in this paper. Finally, the results of simulation experiments and comparisons show that these solutions obtained by our method are with the minimum time, a lower cost, and the fewer selected services. © 2024, CC BY-NC-ND.

关键词： Genetic algorithms

Current Challenges and Advancements of Aerial Thermography for Outdoor Structural Health Monitoring: A Review

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IEEE Sensors Journal 2025年第12期25卷 21000-21016页

作者： Ma, Yue Zeng, Zhi Luo, Zipei Tao, Ning Deng, Lei Tian, Yibin Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Chongqing Normal University College of Computer and Information Science Chongqing401331 China Capital Normal University Department of Physics Key Lab of Terahertz Optoelectronics of the Ministry of Education Beijing100048 China

In the rapid development of outdoor structural health monitoring, infrared thermal imaging and unmanned aerial vehicles (UAVs) have emerged as transformative tools, significantly enhancing maintenance efficiency in expansive outdoor structures. This review highlights the advancements in UAV-assisted thermography, focusing on its impact on the monitoring capabilities of outdoor engineering structures. It introduces UAV-assisted thermographic methods tailored for outdoor environments, explorees current challenges influencing their efficacy, and thoroughly examines their integration with machine learning techniques, demonstrating their applications in identifying structural flaws and deformations. Furthermore, the review emphasizes the transformative potential of 3D thermographic modelling to localize and characterize defects effectively. It contributes actionable insights to optimize UAV-assisted thermographic practices. The findings underscore the importance of integrating UAVs and thermography to overcome existing limitations in outdoor structural health monitoring inspections, setting the stage for a paradigm shift in large-scale outdoor structural condition assessments. © 2001-2012 IEEE.

关键词： Thermography (imaging)

Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation

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arXiv 2023年

作者： Zhang, Yulong Chen, Shuhao Jiang, Weisen Zhang, Yu Lu, Jiangang Kwok, James T. The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering Southern University of Science and Technology Hong Kong University of Science and Technology Hong Kong The Zhejiang Laboratory Hangzhou China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Limited transferability hinders the performance of deep learning models when applied to new application scenarios. Recently, Unsupervised Domain Adaptation (UDA) has achieved significant progress in addressing this issue via learning domain-invariant features. However, the performance of existing UDA methods is constrained by the large domain shift and limited target domain data. To alleviate these issues, we propose DomAin-guided Conditional Diffusion Model (DACDM) to generate high-fidelity and diversity samples for the target domain. In the proposed DACDM, by introducing class information, the labels of generated samples can be controlled, and a domain classifier is further introduced in DACDM to guide the generated samples for the target domain. The generated samples help existing UDA methods transfer from the source domain to the target domain more easily, thus improving the transfer performance. Extensive experiments on various benchmarks demonstrate that DACDM brings a large improvement to the performance of existing UDA methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

FREPA: An Automated and Formal Approach to Requirement Modeling and Analysis in Aircraft control Domain

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arXiv 2023年

作者： Feng, Jincao Miao, Weikai Zheng, Hanyue Huang, Yihao Li, Jianwen Wang, Zheng Su, Ting Gu, Bin Pu, Geguang Yang, Mengfei He, Jifeng East China Normal University China Shanghai Key Lab of Trustworthy Computing China Beijing Institute of Control Engineering China Shanghai Trusted Industrial Control Platform Co. Ltd China China Academy of Space Technology China

Formal methods are promising for modeling and analyzing system requirements. However, applying formal methods to large-scale industrial projects is a remaining challenge. The industrial engineers are suffering from the lack of automated engineering methodologies to effectively conduct precise requirement models, and rigorously validate and verify (V&V) the generated models. To tackle this challenge, in this paper, we present a systematic engineering approach, named Formal Requirement engineering Platform in Aircraft (FREPA), for formal requirement modeling and V&V in the aerospace and aviation control domains. FREPA is an outcome of the seamless collaboration between the academy and industry over the last eight years. The main contributions of this paper include 1) an automated and systematic engineering approach FREPA to construct requirement models, validate and verify systems in the aerospace and aviation control domain, 2) a domain-specific modeling language AASRDL to describe the formal specification, and 3) a practical FREPA-based tool AeroReq which has been used by our industry partners. We have successfully adopted FREPA to seven real aerospace gesture control and two aviation engine control systems. The experimental results show that FREPA and the corresponding tool AeroReq significantly facilitate formal modeling and V&V in the industry. Moreover, we also discuss the experiences and lessons gained from using FREPA in aerospace and aviation projects. Copyright © 2023, The Authors. All rights reserved.

关键词： Modeling languages

Adaptive Sparse Deep Neural Network Inference on Resource-Constrained Cost-Efficient GPUs

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Adaptive Sparse Deep Neural Network Inference on Resource-Co...

IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Ming Dun Xu Zhang Huawei Cao Yuan Zhang Junying Huang Xiaochun Ye State Key Lab of Processors Institute of Computing Technology CAS Beijing China School of Computer and Control Engineering UCAS Beijing China Nanjing Institute of InforSuperbahn Nanjing China

Sparse Deep Neural Networks (SpDNNs) has gained great popularity and been widely applied in various machine learning area. Compared to traditional dense DNNs, the unpre-dictable irregularity and sparsity in the sparse weight matrices of SpDNNs make them difficult to be efficiently parallelled. Moreover, most of the recent advanced efforts to optimize SpDNNs are based on high-end GPUs like NVIDIA V100, which may not be affordable to individuals and smaller re-search groups. However, migrating the SpDNNs to those cost-efficient but resource-constrained GPUs confronts enormous challenges, including limitations in both memory and computing resources, as well as the tiresome hyper-parameter tuning in batch parallelism. In this paper, we accelerate SpDNNs on GPUs with more restricted resources through exploiting the memory and computing resources. On one hand, we design the adaptive memory-aware data partition scheme to reduce memory consumption automatically. On the other hand, we propose the Tensor core/CUDA core fusion mechanism to efficiently utilize the hetergeneous computing resources on modern GPU architecture. To the best of our knowledge, we are the first to improve the performance on SpDNNs through adaptive memory tuning and utilizing hetergeneous computing core concurrency. We compare our implementation with the state-of-art previous champions, and the results demonstrate that our work achieves the highest speedup of 1.35 x and 1.39x compared to 2022 champion S&Z on single and multiple NVIDIA Tesla T4 GPUs, respectively. What's more, our work can reach similar or even better throughput compared to 2020 champion H&P on 6 V100 GPUs with only 4 T4 GPUs.

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