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Consensus Tracking of Switched Heterogeneous Nonlinear Systems With Uncertain Target

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第12期71卷 5560-5570页

作者： Sun, Yaping Yang, Xinsong Shi, Peng Su, Housheng Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China Univ Adelaide Sch Elect & Mech Engn Adelaide SA 5005 Australia Obuda Univ Res & Innovat Ctr H-1034 Budapest Hungary Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

This paper proposes a new dual-design framework to investigate the consensus tracking problem of general switched heterogeneous nonlinear multi-agent systems (MASs) with an uncertain target. A distributed observer that only uses the neighbors' information is constructed to estimate the target signal by designing a time-varying positive-define matrix function and mode-dependent observer gain matrices. After that, a distributed tracking algorithm is developed to solve the three consensus issues among the observer states, tracker states, and target states at the same time by utilizing the neighbors' observer states and designing additional mode-dependent feedback gain matrices. The distinct merit of the research is that the designed Lyapunov function is strict monotone decreasing at switching instants, discovering the positive effect of the switching on the consensus. As an application, heterogeneous switched Chua's circuits are provided to demonstrate the distributed observers and tracking algorithms.

关键词： Target tracking Switches Observers Vehicle dynamics Task analysis Integrated circuit modeling Heuristic algorithms Consensus cooperative tracking multi-agent systems observer uncertain target

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Reaching Distributed Interval State Estimation on Discrete-Time LTI Systems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第12期71卷 5983-5996页

作者： Wang, Xiaoling Chang, Tingting Yang, Wen Su, Housheng Nanjing Univ Posts & Telecommun Coll Automat & AI Nanjing 210023 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

The distributed state estimation problem of a discrete-time linear time-invariant system is considered in the presence of external disturbances and measurement noise. In this scenario, where only the bounding information of the external disturbance and measurement noise is known, an initial design of a distributed interval observer is implemented to provide a set of intervals within which the state of the system locates, subject to certain requirements on relevant matrices. Subsequently, the Internal Positive Representation technique is introduced to eliminate the aforementioned requirements, so that a distributed observer can be accomplished, with the only prerequisite being collectively detectable of the output measurements of the sensor network. Finally, two examples are proposed to illustrate the effectiveness of the theoretical results.

关键词： Observers Transmission line measurements Uncertainty Noise measurement Noise Current measurement Transmission line matrix methods Distributed interval observer discrete-time system external disturbance internal positive representation measurement noise

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Data-Driven Modeling for Pneumatic Muscle Using Koopman-Based Kalman Filter

Data-Driven Modeling for Pneumatic Muscle Using Koopman-Base...

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IEEE International Conference on Development and Learning, ICDL

作者： Yu Caol Mengshi Zhang Bo Yang Jian Huang Key Laboratory for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

This paper presents a data-driven modeling approach for enhancing the understanding of pneumatic muscles (PMs) through Koopman kalman filter (KKF). The proposed method involves the construction of a comprehensive pneumatic muscle motion data-set, obtained by capturing extensive input-state-output data through the application of random inputs. Subsequently, the states of pneumatic muscle are lifted through a kernel-based technique. The nonlinear pneumatic muscle model is then transformed into a linear counterpart using the extended dynamic mode decomposition (EDMD). Then, a kalman filter is further used to improve the model accuracy. The effectiveness of this approach is demonstrated through simulation analysis, highlighting its capacity to yield superior pneumatic muscle modeling and state estimation results when compared to conventional Koopman operator methodologies.

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Signal Enhancement Method for Mechanical Fault Diagnosis in Flexible Drive-Train

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第3期68卷 2554-2563页

作者： Yao, Yuan Xie, Bin Lei, Li Li, Yesong Yin, Quan Huazhong Univ Sci & Technol Key Lab Minist Educ Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Nowadays, mechanical fault detection techniques based on signals from motor drive are becoming popular. However, in electromechanical system with flexible drive-train, mechanical fault signature reflected in motor drive is suppressed by the transmission path. This signal attenuation reduces the effectiveness of fault detection. Therefore, research on the law of fault signature transmission from load to motor is necessary. In this article, a two-mass electromechanical system with a long shaft is concerned. First, the transmission path from load torque to electromagnetic torque is analyzed, and the concept of torque transmission efficiency is proposed. Then, the frequency characteristic method and transfer function method are presented to establish the signal transmission model. Finally, the model-based signal enhancement method is proposed to compensate the attenuation. Simulation and experimental results show that the proposed method can enhance the quality of fault signature transmission and improve the performance of fault classification.

关键词： Flexible drive-train mechanical fault diagnosis motor drive signal enhancement torque transmission

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Thermal Safety Analysis and Dynamic control Strategy for Mode Switching Process of Reversible Solid Oxide Cell System

Thermal Safety Analysis and Dynamic Control Strategy for Mod...

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第42届中国控制会议

作者： Wei Zhao Guoqiang Liu Zexin Li Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and Technology

Reversible solid oxide cells(rSOC) can operate in both electrolysis mode and in fuel cell mode with high efficiency and reduced cost using the same device for both *** used in dynamic operation with intermittent electrical power sources,rSOC system switches from the fuel cell(SOFC) to electrolyzer(SOEC) and vice versa depending on load and grid *** can lead to temperature profiles within the stack that can potentially lead to the failure of the stack and eventually the *** the present work,a system-level dynamic model of r SOC is established and validated against experimental ***,detailed dynamic thermal behavior of stack during the switching between the two modes is analyzed,and a temperature management controller based on Model Predict control method(MPC) is *** flow and temperature at the stack inlet are controlled by air flow and bypass valves,avoiding problems associated with temperature overshoot during transient *** simulation results show that the temperature controller has the ability to follow fast thermal changes while maintaining thermal safety,which indicates the competitiveness of the controller.

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3-D temperature field reconstruction of lithium-ion battery pack using real-time distributed moving horizon estimation

3-D temperature field reconstruction of lithium-ion battery ...

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第35届中国控制与决策会议

作者： Jiayu Yan Yiming Wan School of Artificial Intelligence and Automation Huazhong University of Science and Technologyand Key Laboratory of Image Processing and Intelligent ControlMinistry of Education

As lithium-ion batteries are widely used in different fields,the thermal effect is of serious *** to achieve accurate temperature estimation in real time is the main challenge of current *** address this problem,we propose a realtime distributed moving horizon estimation(RT-DMHE) based on partial differential equations describing thermal dynamics of a lithium-ion battery *** decomposes the real-time centralized moving horizon estimation(RT-CMHE) into multiple local estimators that run in parallel with information exchange from adjacent *** validation shows that the root mean square error of temperature estimates from the proposed RT-DMHE is smaller than that of an existing distributed Kalman filter in ***,compared to the RT-CMHE,the proposed RT-DMHE achieves comparable estimation accuracy while vastly reducing the average computation time per sample.

关键词： lithium-ion battery pack distributed moving horizon estimation real-time computation state-of-temperature estimation

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MoLo: Motion-Augmented Long-Short Contrastive Learning for Few-Shot Action Recognition

MoLo: Motion-Augmented Long-Short Contrastive Learning for F...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiang Wang Shiwei Zhang Zhiwu Qing Changxin Gao Yingya Zhang Deli Zhao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Alibaba Group

Current state-of-the-art approaches for few-shot action recognition achieve promising performance by conducting frame-level matching on learned visual features. However, they generally suffer from two limitations: i) the matching procedure between local frames tends to be inaccurate due to the lack of guidance to force long-range temporal perception; ii) explicit motion learning is usually ignored, leading to partial information loss. To address these issues, we develop a Motion-augmented Long-short Contrastive Learning (MoLo) method that contains two crucial components, including a long-short contrastive objective and a motion autodecoder. Specifically, the long-short contrastive objective is to endow local frame features with long-form temporal awareness by maximizing their agreement with the global token of videos belonging to the same class. The motion autodecoder is a lightweight architecture to reconstruct pixel motions from the differential features, which explicitly embeds the network with motion dynamics. By this means, MoLo can simultaneously learn long-range temporal context and motion cues for comprehensive few-shot matching. To demonstrate the effectiveness, we evaluate MoLo on five standard benchmarks, and the results show that MoLo favorably outperforms recent advanced methods. The source code is available at https://***/alibaba-mmai-research/MoLo.

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Adaptive Learning-Based Distributed control of Cooperative Robot Arm Manipulation for Unknown Objects

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第2期53卷 1298-1307页

作者： Zhang, Hai-Tao Xu, Hao Xu, Bowen Wu, Yue Huang, Jian Han, Qing-Long Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc Wuhan 430074 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia

This article proposes a distributed cooperative manipulation control scheme for multirobot systems to track reference trajectories with unknown payload dynamics, grasp positions, and external disturbances. An online learning module is established to estimate the payload dynamics. Then a wrench-synthetic trajectory tracking control protocol is thereby developed to manipulate an object under unknown external disturbances no matter where the grasping points are. Moreover, sufficient conditions are derived to guarantee the uniform boundedness of the tracking errors of the closed-loop cooperative manipulation system. Finally, numerical simulations are conducted to substantiate the effectiveness of the proposed cooperative manipulation control scheme.

关键词： Robots Robot kinematics Manipulator dynamics Payloads Dynamics Trajectory tracking Uncertainty control systems cooperative systems manipulators neural network applications robot dynamics

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Disentangling Spatial and Temporal Learning for Efficient image-to-Video Transfer Learning

Disentangling Spatial and Temporal Learning for Efficient Im...

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IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Qing, Zhiwu Zhang, Shiwei Huang, Ziyuan Zhang, Yingya Gao, Changxin Zhao, Deli Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Alibaba Grp Hangzhou Zhejiang Peoples R China Natl Univ Singapore ARC Singapore Singapore

ISBN: (纸本)9798350307184

Recently, large-scale pre-trained language-image models like CLIP have shown extraordinary capabilities for understanding spatial contents, but naively transferring such models to video recognition still suffers from unsatisfactory temporal modeling capabilities. Existing methods insert tunable structures into or in parallel with the pre-trained model, which either requires back-propagation through the whole pre-trained model and is thus resource-demanding, or is limited by the temporal reasoning capability of the pre-trained structure. In this work, we present DiST, which disentangles the learning of spatial and temporal aspects of videos. Specifically, DiST uses a dual-encoder structure, where a pre-trained foundation model acts as the spatial encoder, and a lightweight network is introduced as the temporal encoder. An integration branch is inserted between the encoders to fuse spatio-temporal information. The disentangled spatial and temporal learning in DiST is highly efficient because it avoids the back-propagation of massive pre-trained parameters. Meanwhile, we empirically show that disentangled learning with an extra network for integration benefits both spatial and temporal understanding. Extensive experiments on five benchmarks show that DiST delivers better performance than existing state-of-the-art methods by convincing gaps. When pre-training on the large-scale Kinetics-710, we achieve 89.7% on Kinetics-400 with a frozen ViT-L model, which verifies the scalability of DiST. Codes and models can be found in https://***/alibaba-mmai-research/DiST.

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Fault diagnosis of key components in the rotating machinery based on Fourier transform multi-filter decomposition and optimized LightGBM

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MEASUREMENT SCIENCE AND TECHNOLOGY 2021年第1期32卷 015004-015004页

作者： Zhang, Changhe Kong, Li Xu, Qi Zhou, Kaibo Pan, Hao Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

Rotating machinery is a primary element of mechanical equipment, and thus fault diagnosis of its key components is very important to improve the reliability and safety of modern industrial systems. The key point to diagnose the faults of these components is to extract effectively the hidden fault information. However, the actual vibration signals of rotating machinery have nonlinear and non-stationary characteristics, so traditional signal decomposition methods are unable to extract the frequency components accurately, leading to spectrum overlap of the decomposed sub-signals. Therefore, a rotating machinery fault diagnosis approach based on Fourier transform multi-filter decomposition (FTMFD), fuzzy entropy (FE), joint mutual information maximization (JMIM), and a light gradient boosting machine (LightGBM), is proposed in this paper. FTMFD is used to extract the frequency domain information of the raw vibration signals, whereas FE is used to calculate and extract the fault information of the decomposed sub-signals. Then feature selection is carried out by using JMIM to reduce the influence of redundant features on data analysis and classification accuracy. Furthermore, LightGBM is used to rank the candidate features and outputs the fault diagnosis result. Experimental results from two real datasets show that the proposed method achieves higher accuracy with fewer features than some existing methods for fault recognition. Various working conditions are also considered and verified.

关键词： Fourier transform multifilter decomposition fuzzy entropy joint mutual information maximization LightGBM classifier rotating machinery fault diagnosis

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