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An Improved Reinforcement Learning for Security-Constrained Economic Dispatch of Battery Energy Storage in Microgrids 1

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2nd International Conference on Neural Computing for Advanced Applications, NCAA 2021

作者： Zha, Zhongyi Wang, Bo Fan, Huijin Liu, Lei Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Artificial Intelligence and Automation School Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789811651885

ISBN: (纸本)9789811651878

Battery energy storage systems are widely used in microgrids integrated with volatile energy resources for their ability in peak load shifting. Security constrained economic dispatch over the system’s lifecycle is a constrained multi-period stochastic optimization problem, which is intractable. We propose an improved actor-critic-based reinforcement learning combined with a protection layer security control method for this issue, where the distributional critic net is applied to estimate the expected total reward value of a period more accurately, and the policy net with a mask action layer is used to make secure and real-time decision. Additionally, we propose a protection layer to assist the policy net as a secondary control to prevent the unsafe state of microgrids due to the trial-and-error learning of reinforcement learning. Numerical test results show the proposed algorithm can perform better than the conventional economic dispatch and other reinforcement learning algorithms while guaranteeing safe operation. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Renewable energy resources

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Pose detection and docking control for autonomous dynamic docking mechanism with non-cooperative targets

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INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION 2024年第5期51卷 715-728页

作者： Zhan, Gan Chen, Zhihua Zhang, Zhenyu Zhan, Jigang Yu, Wentao Li, Jiehao Beijing Inst Technol Sch Mechatron Beijing Peoples R China Nanchang Hangkong Univ Key Lab Jiangxi Prov Image Proc & Pattern Recogni Nanchang Peoples R China Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing Peoples R China Beijing Inst Technol Sch Mech Engn Beijing Peoples R China Beijing Zhongxin Hengyuan Technol Co Ltd Beijing Peoples R China China Acad Space Technol Beijing Spacecraf Beijing Peoples R China South China Agr Univ Coll Engn Guangzhou Peoples R China

PurposeThis study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion ***/methodology/approachFirstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned *** simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic ***/valueThis research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

关键词： Planning Perception Motion control Dynamic docking Pose detection

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Predefined-time Secondary control for DC Microgrids with Reduced Computational Cost 17

Predefined-time Secondary Control for DC Microgrids with Red...

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IEEE 17th International Conference on control and Automation (ICCA)

作者： Wang, Siqing Liu, Xiao-Kang Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

ISBN: (纸本)9781665495721

This paper proposes a novel predefined-time secondary control framework for an islanded DC microgrid to achieve the current sharing and voltage regulation within a tunable settling time. An integrated error, namely, a positive linear combination of current sharing error and DC bus voltage error is constructed and then used in the design of finite/fixed-time feedback loop. Using the integrated error, instead of the current error and the voltage error separately, a unified finite/fixed-time controller structure is proposed to save the computation resource. In addition, the framework enables us to provide rigorous theoretical analysis of the resulting system, which shows the upper bound of convergence time is dependent on both control and system parameters of DC microgrid and therefore is tunable by adjusting control parameters. Finally, simulation tests are carried out to demonstrate the effectiveness of the methods.

关键词： Feedback loop Upper bound Automation Computational modeling Microgrids Switches control systems

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Prescribed-Time Network-Based Deployment of Nonlinear Multiagent Systems: A Discrete-Space PDE Method

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第9期54卷 5543-5554页

作者： Wan, Peng Zeng, Zhigang Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Engn Res Ctr Met Automat & Measurement Technol Wuhan 430081 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

This article attempts to design the prescribed-time time-varying deployment schemes for first-order and second-order nonlinear multiagent systems (MASs). We assume that all agents can obtain the information of their current and final relative positions with their neighbors, and the final absolute velocities (as well as their current and final relative velocities, the final absolute accelerations for the second-order MASs) through a communication network, whereas two boundary agents are able to obtain their current and final absolute positions (as well as their current and final absolute velocities for the second-order MASs). The neighbor relationship of all agents is described by a spatial variable and two static-feedback controllers are introduced, which can be expressed as a second-order space difference of the spatial variable. Then, the deployment of MASs can be transformed into the stabilization of discrete-space partial differential equation (PDE) systems. Three virtual agents are introduced to constitute the Dirchlet and Neumann boundary conditions. Several algebraic inequality criteria are derived to guarantee that the prescribed-time time-varying deployment can be achieved within a prescribed time under the Dirchlet and mixed boundary conditions. Unlike the published results, our results are derived based on the discrete-space PDE systems instead of continuous-space PDE systems, which is consistent with the discrete spatial distribution of agents. Finally, two numerical examples are given to illustrate the effectiveness of our results.

关键词： Vectors Aerospace electronics Convergence Mathematical models Time-varying systems Multi-agent systems Boundary conditions Discrete-space system partial differential equation (PDE) prescribed-time control time-varying deployment

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GPC-LIO-SAM: Lidar inertial odometry and mapping method for mobile robot with ground plane constraint 43

GPC-LIO-SAM: Lidar inertial odometry and mapping method for ...

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43rd Chinese control Conference (CCC)

作者： Cao, Chong Peng, Geng He, Dingxin Cao, Yihao Xu, Yue Deng, Zhang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Autonomous Intelligent Unmanned Syst Wuhan 430074 Peoples R China Wuhan Lisai Technol Co Ltd Wuhan 430073 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

Due to the limitation of the vertical angle resolution of the lidar sensor, the observation information in the vertical direction is reduced, resulting in the elevation cumulative drift phenomenon of many lidar SLAM algorithms in large-scale environment, which reduces the accuracy of pose estimation and the quality of mapping. In order to solve this problem, based on LIO-SAM, this paper proposes a lidar SLAM algorithm with ground plane constraint. The method can effectively extract the ground point cloud and obtain the plane normal vector, and construct the constraint relationship with the prior plane normal vector. The optimal state estimation is solved by nonlinear optimization method, which effectively reduces the elevation drift caused by the long-term operation of the system in large-scale environment. This paper compares the improved algorithm with A-LOAM, LeGO-LOAM and original LIO-SAM algorithms in challenging datasets such as KITTI, M2DGR and UrbanNay. The results show that this method effectively improves the accuracy of pose estimation and the quality of mapping, and limit the elevation drift of mobile robot in ground operation, which has high application value.

关键词： Ground plane constraint elevation drift LIDAR simultaneous localization and mapping (SLAM)

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DiffMoCa: Diffusion Model Based Multi-modality Cut and Paste 18th

DiffMoCa: Diffusion Model Based Multi-modality Cut and Paste

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18th International Conference on Neural Networks (ISNN)

作者： Zhang, Junjie Wu, Shaojin Gao, Junbin Yu, Fusheng Xu, Hao Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Natl Univ Singapore 18 Sci Dr 4 Singapore Singapore Dongfeng Motor Corp Res & Dev Inst Wuhan Peoples R China

ISBN: (纸本)9789819743988;9789819743995

The Multi-mOdality Cut and pAste (MoCa) method cuts data from other frames and pastes it onto the current training data frame to increase the number of training object samples. However, the samples used by MoCa are all derived from the original dataset, which limits its ability to enhance object diversity. Recently, diffusion models have achieved remarkable results in the field of image generation, where simple prompts can enable the model to create entirely different paintings. In this paper, we propose DiffMoCa, which leverages the powerful creative capabilities of diffusion models to redraw the images cut by MoCa, thereby increasing the diversity of the objects and enhancing the generalization ability of the model. DiffMoCa demonstrates its capabilities in extensive experiments, wherein it surpasses MoCa by 2.2% in mAP on the KITTI dataset under moderate conditions.

关键词： 3D object detection Data augmentation Diffusion model

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Software-hardware co-design for accelerating large-scale graph convolutional network inference on FPGA

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NEUROCOMPUTING 2023年 532卷 129-140页

作者： Ran, Shaolin Zhao, Beizhen Dai, Xing Cheng, Cheng Zhang, Yong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Intelligent Control & Image Proc Wuhan 430074 Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China

Inspired by convolutional neural networks, graph convolutional networks (GCNs) have been proposed for processing non-Euclidean graph data and successfully been applied in recommendation systems, smart traffic, etc. However, subject to the sparsity and irregularity of GCN models, the complex execution pattern of large-scale GCN poses huge challenges to the efficient inference on general purpose CPUs and GPUs, such as workload imbalance and irregular memory access. Therefore, we propose a softwarehardware co-design framework for low-latency GCN inference on field programmable gate array. Specifically, at the algorithm level, we propose an attention-mechanism-based graph sparsification approach to reduce the redundant relation in the graph structure and alleviate irregularity without losing accuracy. Then, at the hardware design level, based on the sparsified graph, we propose a two-stage hardware architecture that supports the two phases with a distinct execution mode in the GCN. In order to achieve low-latency computation, edge-level and feature-level parallelism are exploited in the aggregation phase. In addition, a graph partition strategy is exploited to efficiently improve data reuse. The experimental results demonstrate that our proposed framework can achieve 739x speedup compared to CPU, 13.7x speedup compared to GPU on average and 6.8x speedup compared to state-of-the-art accelerators.

关键词： Graph convolutional network Hardware acceleration Graph sparsification Software -hardware co -design FPGA

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Self-Supervised Learning from Untrimmed Videos via Hierarchical Consistency

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第10期45卷 12408-12426页

作者： Qing, Zhiwu Zhang, Shiwei Huang, Ziyuan Xu, Yi Wang, Xiang Gao, Changxin Jin, Rong Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Alibaba Grp Hangzhou 311100 Zhejiang Peoples R China Natl Univ Singapore Adv Robot Ctr Singapore 119077 Singapore Dalian Univ Technol Dalian 116024 Liaoning Peoples R China Twitter San Francisco CA 94103 USA

Natural untrimmed videos provide rich visual content for self-supervised learning. Yet most previous efforts to learn spatio-temporal representations rely on manually trimmed videos, such as Kinetics dataset (Carreira and Zisserman 2017), resulting in limited diversity in visual patterns and limited performance gains. In this work, we aim to improve video representations by leveraging the rich information in natural untrimmed videos. For this purpose, we propose learning a hierarchy of temporal consistencies in videos, i.e., visual consistency and topical consistency, corresponding respectively to clip pairs that tend to be visually similar when separated by a short time span, and clip pairs that share similar topics when separated by a long time span. Specifically, we present a Hierarchical Consistency (HiCo++) learning framework, in which the visually consistent pairs are encouraged to share the same feature representations by contrastive learning, while topically consistent pairs are coupled through a topical classifier that distinguishes whether they are topic-related, i.e., from the same untrimmed video. Additionally, we impose a gradual sampling algorithm for the proposed hierarchical consistency learning, and demonstrate its theoretical superiority. Empirically, we show that HiCo++ can not only generate stronger representations on untrimmed videos, but also improve the representation quality when applied to trimmed videos. This contrasts with standard contrastive learning, which fails to learn powerful representations from untrimmed videos. Source code will be made available here.

关键词： Self-supervised learning video representation learning long video understanding contrastive learning

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Deployment of second-order networked mobile agents over a smooth curve

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AUTOMATICA 2022年 146卷

作者： Su, Housheng Xu, Qiuju Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Luoyu Rd 1037 Wuhan 430074 Peoples R China Chongqing Normal Univ Sch Math Sci Univ Town Middle Rd 37 Chongqing 401331 Peoples R China

In this paper, we investigate second-order deployment of multiple mobile agents over a C2 curve in 2D or 3D space. We assume that an informed agent in the group can measure its relative position with respect to the given curve and velocity, other agents have access to the local information about the given curve as well as the position and velocity in regard to their closest neighbors, where the agent dynamics is described by a strongly damped wave equation. Without delay, we establish a boundary control law for the informed agent. When taking into account time-varying delay, we assume that the informed agent transmits its measurements to its neighbors through a communication mechanism. Given appropriate controller gains, second-order deployment can be achieved with an exponential decay. Finally, some simulation results are presented to illustrate our theoretical results.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Wave equations

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Fog images Generation about Unmanned Surface Vessels with Improved Generative Adversarial Network

Fog Images Generation about Unmanned Surface Vessels with Im...

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第42届中国控制会议

作者： Yifeng Tang Zhihui Huang Chuancong Tang Chuanshang Luo Yifan Xu Bin Liu Hai-Tao Zhang School of Artificial Intelligence and Automation the Engineering Research Center of Autonomous Intelligent Unmanned Systems the Key Laboratory of Image Processing and Intelligent Control and the State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology

This paper proposes a fog weather data augmentation method for the unmanned surface vessels(USVs) via improved Generative Adversarial Network(GAN) model. First, a generator scheme for GAN is proposed with the guided generation of the atmospheric scattering model in this paper. A Laplacian Pyramid Based Depth Residuals model is added to the generator which reduces the difficulty of generating fog images caused by the degradation of water surface image and improves the quality of generated images. Finally, fog images are generated from sunny weather images collected with HUST-12C by LPBDR-GAN model and experiments show that generated images are very close to real fog images.

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