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arXiv 2022年

作者： Li, Yuanzheng He, Shangyang Li, Yang Shi, Yang Zeng, Zhigang School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China China-Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China The Department of Mechanical Engineering University of Victoria VictoriaBC V8P 5C2 Canada

The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this paper tackles this practical yet challenging issue by proposing a federated multi-agent deep reinforcement learning (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy-sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data is not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge national laboratory distributed energy control communication lab microgrid (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Spatial non-locality induced non-markovian EIT in a single giant atom

arXiv

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arXiv 2021年

作者： Zhu, Y.T. Wu, R.B. Xue, S. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Department of Automation Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China

In recent experiments, electromagnetically induced transparency (EIT) were observed with giant atoms, but nothing unconventional were found from the transmission spectra. In this letter, we show that unconventional EIT does exist in giant atoms, and indicate why it has not been observed so far. Different from these existing works, this letter presents a consistent theory including a real space method and a time delayed master equation for observing unconventional EIT. We discover that this phenomenon is a quantum effect which cannot be correctly described in a semi-classical way as those in recent works. Our theory shows that it can be observed when the time delay between two neighboring coupling points is comparable to the relaxation time of the atom, which is crucial for a future experimental observation. This new phenomenon results from inherent non-locality of the giant atom, which physically forces propagating fields to be standing waves in space and the atom exhibiting retardations in time. Our theory establishes a framework for application of nonlocal systems to quantum information processing. Copyright © 2021, The Authors. All rights reserved.

关键词： Atoms

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Observer-like Model Reference Adaptive Augmenting based fixed-wing UAV control

Observer-like Model Reference Adaptive Augmenting based fixe...

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第三十九届中国控制会议

作者： Jizhang Xiong Ye Yang Zhongtao Cheng Lei Liu Yongji Wang Huijin Fan National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Science and Technology on Aerospace Intelligent Control Laboratory Beijing Aerospace Automatic Control Institute

This paper proposed an observer-like model reference adaptive augmenting control(OMRAAC) design method for a class of multi-input multi-output systems with uncertainty, which can improve the dynamic performance of the system. This control system design method can perform adaptive augmenting control design without changing the original baseline controller of the system(such as PID control, LQR control, etc.), to cope with possible uncertainty in the system and improve the tracking performance of the system. At the same time, in order to suppress the transient oscillation of the system caused by the large adaptive rates, the state tracking error is introduced into the reference model, and an observer-like reference model is designed to improve the dynamic process of the system. Finally, in order to verify the effectiveness of the method, simulation and analysis are performed on the longitudinal short-period model of the fixed-wing UAV, to verify the effectiveness of the method. The simulation compares the tracking performance of the control system based on the LQR controller, the traditional Model Reference Adaptive augmented Augmented controller, and the proposed OMRAAC method. The simulation results show that the observer-like adaptive augmenting control can effectively suppress the system uncertainties, and improve the dynamic response performance of the system.

关键词： adaptive augmenting control fixed-wing UAV control oscillation suppression

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Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting

arXiv

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arXiv 2022年

作者： Gao, Jiaqi Huang, Zhizhong Lei, Yiming Shan, Hongming Wang, James Z. Wang, Fei-Yue Zhang, Junping The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai201210 China The College of Information Sciences and Technology The Pennsylvania State University University ParkPA16802 United States The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Institute of Systems Engineering Macau University of Science and Technology China Qingdao Academy of Intelligent Industries Qingdao266109 China

Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images. This is attributed to the inherent self-structural information and rank consistency within a single image, offering additional qualitative relation supervision during training. Contrary to earlier methods that utilized the rank relations at the original image level, we explore such rank-consistency relation within the latent feature spaces. This approach enables the incorporation of numerous pyramid partial orders, strengthening the model representation capability. A notable advantage is that it can also increase the utilization ratio of unlabeled samples. Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (DREAM), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images. In addition, we have collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4,000 images for training purposes. Extensive experiments on four benchmark datasets, namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the effectiveness of our method compared with previous semi-supervised methods. The codes are available at https://***/bridgeqiqi/DREAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Research on Attitude control of Quadrotor UAV Based on Active Disturbance Rejection control

Research on Attitude Control of Quadrotor UAV Based on Activ...

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第32届中国控制与决策会议

作者： Zuling Song Yongji Wang Lei Liu Zhongtao Cheng Ye Yang National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Science and Technology on Aerospace Intelligent Control Laboratory Beijing Aerospace Automatic Control Institute

Aiming at enhancing the disturbance suppression ability of attitude control of quadrotor UAV（Unmanned Aerial Vehicle）,design an ADRC（Active Disturbance Rejection control） based on quadrotor *** from the traditional trial adjustment of the parameter b0 in ADRC design,it can be derived according to the characteristics of the quadrotor model in this ***,a non-linear kinematic model of a quadrotor UAV is established,and then each module of the ADRC is designed and debugged by ***,the system is simulated and *** experimental results show that ADRC has a stronger disturbance suppression capability than PID control,which greatly improves the stability of the system and enhances the drone’s flight control ability in complex environments.

关键词： Quadrotor UAV ADRC Attitude control Disturbance observation Disturbance Rejection

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Simultaneous Precision Assembly of Multiple Objects through Coordinated Micro-robot Manipulation

Simultaneous Precision Assembly of Multiple Objects through ...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Song Liu Yuyu Jia You-Fu Li Yao Guo Haojian Lu ShanghaiTech Automation and Robotics Center School of Information Science and Technology ShanghaiTech University Shanghai China City University of Hong Kong Kowloon Hong Kong Institute of Medical Robotics Shanghai Jiaotong University Shanghai China State Key Laboratory of Industrial Control and Technology and Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Simultaneous assembly of multiple objects is a key technology to form solid connections among objects to get compact structures in precision assembly and micro-assembly. Dramatically different from traditional assembly of two objects, the interaction among multiple objects is more complicated on analysis and control. During simultaneous assembly of multiple objects, there are multiple mutually effected contact surfaces, and multiple force sensors are needed to perceive the interaction status. In this paper, a coordinated micro-robot manipulation strategy is proposed for simultaneous assembly problem, which is based on microscopic vision and force information. Taking simultaneous assembly of three objects as an instance, the proposed method is well articulated, including calibration of assembly system, force analysis for each contacting surface, and insertion control strategy for assembly process. The proposed method is applicable also to case with more objects. Experiment results demonstrate effectiveness of the proposed method.

关键词： automation Shape Microscopy Conferences Force Process control Robot sensing systems

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Parameter training methods for convolutional neural networks with adaptive adjustment method based on Caputo fractional-order differences

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Chaos, Solitons & Fractals 2025年 199卷

作者： Haiming Zhao Honggang Yang Jiejie Chen Ping Jiang Zhigang Zeng School of Computer and Information Engineering Hubei Normal University Huangshi 435000 China School of Automation Wuhan University of Technology Wuhan 430000 China School of Computer Hubei Polytechnic University Huangshi 435000 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430000 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan 430000 China

As deep learning technologies continue to permeate various sectors, optimization algorithms have become increasingly crucial in neural network training. This paper introduces two adaptive momentum algorithms based on Grünwald–Letnikov and Caputo fractional-order differences—Fractional Order Adagrad (FAdagrad) and Fractional Order Adam (FAdam)—to update parameters more flexibly by adjusting momentum information. Commencing from the definitions of fractional derivatives, we propose integrating fractional-order differences with gradient algorithms in convolutional neural networks (CNNs). These adaptive momentum algorithms, leveraging Grünwald–Letnikov and Caputo fractional-order differences, offer enhanced flexibility, thereby accelerating convergence. Our nonlinear parameter tuning method for CNNs demonstrates superior performance compared to traditional integer-order momentum algorithms and the standard Adam algorithm. Experimental results on the BraTS2021 dataset and CIFAR-100 dataset reveal that the proposed fractional-order optimization algorithms significantly outperform their integer-order counterparts in model optimization. They not only expedite convergence but also improve the accuracy of image recognition and segmentation.

关键词： Adaptive momentum algorithms Convolutional neural networks Deep learning Fractional-order differences

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End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization

arXiv

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arXiv 2023年

作者： Wang, Guangming Zheng, Yu Guo, Yanfeng Liu, Zhe Zhu, Yixiang Burgard, Wolfram Wang, Hesheng Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China China Department of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Electrical and Computer Engineering University of California Los Angeles United States Institute of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Engineering University of Technology Nuremberg Germany

Robot localization using a previously built map is essential for a variety of tasks including highly accurate navigation and mobile manipulation. A popular approach to robot localization is based on image-to-point cloud registration, which combines illumination-invariant LiDAR-based mapping with economical image-based localization. However, the recent works for image-to-point cloud registration either divide the registration into separate modules or project the point cloud to the depth image to register the RGB and depth images. In this paper, we present I2PNet, a novel end-to-end 2D-3D registration network. I2PNet directly registers the raw 3D point cloud with the 2D RGB image using differential modules with a unique target. The 2D-3D cost volume module for differential 2D-3D association is proposed to bridge feature extraction and pose regression. 2D-3D cost volume module implicitly constructs the soft point-to-pixel correspondence on the intrinsic-independent normalized plane of the pinhole camera model. Moreover, we introduce an outlier mask prediction module to filter the outliers in the 2D-3D association before pose regression. Furthermore, we propose the coarse-to-fine 2D-3D registration architecture to increase localization accuracy. We conduct extensive localization experiments on the KITTI Odometry and nuScenes datasets. The results demonstrate that I2PNet outperforms the state-of-the-art by a large margin. In addition, I2PNet has a higher efficiency than the previous works and can perform the localization in real-time. Moreover, we extend the application of I2PNet to the camera-LiDAR online calibration and demonstrate that I2PNet outperforms recent approaches on the online calibration task. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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Attitude Coupling control and Lateral Stability Analysis of Hypersonic Vehicle

Attitude Coupling Control and Lateral Stability Analysis of ...

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第32届中国控制与决策会议

作者： Xu Li Ye Yang Qianwei He Zhongtao Cheng Yongji Wang Lei Liu National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Beijing Aerospace Automatic Control Institute

For the strong coupling characteristics of pitch,yaw and roll channels in the attitude control system of a hypersonic vehicle,a coupling controller is designed based on the state feedback and the dominant pole assignment of the Dutch roll mode to realize the system decoupling but improve the maneuverability of the *** new diagonal dominance matrix and row radius coefficient are introduced to analyze the coupling characteristics and degree of the *** based on this,the coupling PID controller is briefly designed as a comparison with the coupling *** addition,the lateral stability of the vehicle is analyzed,and the sensitivity of the coupling control system to runaway is measured based on the Weissman ***,the simulation results show that the designed coupling controller has more precise control effect and less rudder deflection than the decoupling controller,which has a certain practical engineering significance.

关键词： Hypersonic vehicle Attitude Coupling control Lateral stability

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Ascent Guidance for Launch Vehicle Based on Receding Horizon control

Ascent Guidance for Launch Vehicle Based on Receding Horizon...

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第三十九届中国控制会议

作者： Qianwei He Ye Yang Lei Liu Zhongtao Cheng National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Beijing Aerospace Automatic Control Institute

For the difficulties and challenges caused by the unknown disturbance during the ascent stage of the launch vehicle, a trajectory tracking strategy using particle swarm optimization(PSO) algorithm in a receding horizon control(RHC) framework is investigated in this paper. Since the RHC solves the online optimization problem at each step, the calculations can be time-consuming. To speed up the computation of RHC, the online optimization problem is transformed into a one-dimensional variable optimal problem and the control actions are obtained by PSO. Providing solve times in milliseconds, which guarantees the feasibility of the RHC controller. Simulation results are provided to illustrate that this strategy can not only satisfy the fast calculation speed, but also can realize better performance with the existence of aerodynamic uncertainty, thrust uncertainty, and process constraint.

关键词： Trajectory tracking Receding horizon control Particle swarm optimization Launch vehicle

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