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第三十九届中国控制会议

作者： Siquan Zhou Jie Ren Yong Heng Xiwang Dong Qingdong Li School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing Aerospace System Engineering Research Institute Beijing Institute of Electronic System Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Distributed cooperative guidance problems for multi-missile system with time-varying delays and switching topologies are investigated. Firstly, considering the complex communication environment, a two-step cooperative guidance strategy is proposed to achieve the simultaneous attack under time-varying delays and switching topologies. In the first step, a distributed cooperative guidance law using neighboring information is designed to coordinate range-to-go and leading angle based on feedback linearization and consensus theory. The second step starts while multi missiles disconnect from each other and hit the target using PNG（Proportional Navigation Guidance）. Then, the algorithm with three steps is presented to select the parameters in the cooperative guidance law. And the stability of the multi-missile system is proven using Lyapunov-Krasovskii theory. Finally, a numerical simulation is given to demonstrate the effectiveness of the proposed cooperative guidance strategy.

关键词： Cooperative guidance Distributed strategy Consensus theory Time-varying delays Switching topologies

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Adaptive Remaining Useful Life Prediction Based on a system-Level Variable Structure Degradation Model

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wang, Xiangyu Si, Xiaosheng Xi, Xiaopeng Zhou, Donghua Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao 266590 China Xi’an Research Institute of High-Tech Department of Automation Xi’an 710025 China Xi’an Jiaotong University Key Laboratory of Education Ministry for Modern Design and RotorBearing System Xi’an 710049 China Universidad Técnica Federico Santa María Advanced Center for Electrical and Electronic Engineering Valparaíso Chile Southeast University School of Automation Nanjing 211189 China

Accurate prediction of remaining useful life (RUL) for complex systems is often challenged by dynamic and non-stationary degradation behaviors, as well as model mismatches arising from fixed-structure assumptions. To address these challenges, this study proposes an adaptive RUL prediction method based on a system-level variable structure degradation model (SVSDM). In terms of model construction, the initial SVSDM is developed using historical data from multiple sensors, with calibration moments determined by the proposed triggering mechanism. When the calibration is triggered, the model structure is updated accordingly;otherwise, only the parameters of the SVSDM are updated. For RUL prediction, the probability density function (PDF) based on the SVSDM is analytically derived under the first hitting time (FHT) concept. To implement the proposed method, parameter estimation for the SVSDM is performed using maximum likelihood estimation (MLE) algorithm, with dynamic parameter updates facilitated by the Kalman filter (KF). Finally, a case study on furnace wall degradation demonstrates the effectiveness and practical applicability of the proposed method. © 1963-2012 IEEE.

关键词： Kalman filter maximum likelihood estimation model calibration Remaining useful life system-level variable structure degradation model triggering mechanism

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Fuzzy Comprehensive Evaluation of Performance on Urban Water Supply system

Fuzzy Comprehensive Evaluation of Performance on Urban Water...

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第40届中国控制会议

作者： Mei Meng Pingwei Zhao Miaoshun Bai Jingcheng Wang Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Chengtou Water Group Co. LTD. Shanghai Municipal Engineering Design Institute(Group)Co. Ltd.

A long-term comprehensive evaluation of the water supply system is helpful to improve the level of enterprise refined management and the whole water supply serviceability. This paper selects the water pressure, pressure fluctuation and energy consumption as indexes, and establishes a two-layer evaluation framework based on fuzzy comprehensive evaluation *** top layer only has a comprehensive evaluation objective. The second layer has three indexes, in which the SCADA data is used as the data source for evaluation. The evaluation indexes are standardized according to the industry standard and the probability distribution of normal historical data. The real SCADA data of the pipe network is used for the evaluation and the application results prove that the evaluation method is effective and has certain practical value.

关键词： Water Supply system Fuzzy Comprehensive Evaluation Pearson Correlation Coefficient Pressure Monitoring Point Team Assessment

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Hierarchical Learning for IRS-Assisted MEC systems with Rate-Splitting Multiple Access

arXiv

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arXiv 2024年

作者： Wu, Yinyu Zhang, Xuhui Ren, Jinke Shen, Yanyan Yang, Bo Wang, Shuqiang Guan, Xinping Niyato, Dusit The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China The University of Chinese Academy of Sciences Beijing100049 China The Shenzhen Future Network of Intelligence Institute The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen University of Advanced Technology Guangdong518055 China The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency. However, the resource competition among users will lead to uneven allocation, increased latency, and lower throughput. Fortunately, the rate-splitting multiple access (RSMA) technique has emerged as a promising solution for managing interference and optimizing resource allocation in MEC systems. This paper studies an IRS-assisted MEC system with RSMA, aiming to jointly optimize the passive beamforming of the IRS, the active beamforming of the base station, the task offloading allocation, the transmit power of users, the ratios of public and private information allocation, and the decoding order of the RSMA to minimize the average delay from a novel uplink transmission perspective. Since the formulated problem is non-convex and the optimization variables are highly coupled, we propose a hierarchical deep reinforcement learning-based algorithm to optimize both continuous and discrete variables of the problem. Additionally, to better extract channel features, we design a novel network architecture within the policy and evaluation networks of the proposed algorithm, combining convolutional neural networks and densely connected convolutional network for feature extraction. Simulation results indicate that the proposed algorithm not only exhibits excellent convergence performance but also outperforms various benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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A New Islanding Detection Scheme Based on Combination of Slantlet Transform and Probabilistic Neural Network for Grid-Tied Photovoltaic system 1

A New Islanding Detection Scheme Based on Combination of Sla...

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1st IEEE International Youth Conference on Radio electronics, Electrical and Power engineering, REEPE 2019

作者： Ahmadipour, Masoud Giyoev Borbad, M. Hizam, Hashim Department of Electrical and Electronic Engineering University Putra Malaysia Serdang Selangor43400 Malaysia Department of Relay Protection and Power System Automation Moscow Power Engineering Institute Moscow Russia

ISBN: (纸本)9781538693346

According to find islanding in a grid tied photovoltaic system, a combination of Slantlet Transform (SLT) and Probabilistic Neural Network (PNN) has been proposed in this paper. The SLT is used to extract the unique features from three phase voltage signals at the Point of Common Coupling (PCC). The produced features vector is fed to a probabilistic neural network classifier as input pattern which is used to predict islanding condition. The proposed technique is validated through comparing with islanding response time in different situations with the offered response time through Rate of Change of Frequency (ROCOF) method. © 2019 IEEE.

关键词： Neural networks

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Cloud-assisted nonlinear model predictive control for finite-duration tasks

arXiv

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arXiv 2021年

作者： Li, Nan Zhang, Kaixiang Li, Zhaojian Srivastava, Vaibhav Yin, Xiang The Department of Aerospace Engineering University of Michigan Ann ArborMI48109 United States The Department of Mechanical Engineering Michigan State University East LansingMI48824 United States The Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States The Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai 200240 China

Cloud computing creates new possibilities for control applications by offering powerful computation and storage capabilities. In this paper, we propose a novel cloud-assisted model predictive control (MPC) framework in which we systematically fuse a cloud MPC that uses a high-fidelity nonlinear model but is subject to communication delays with a local MPC that exploits simplified dynamics (due to limited computation) but has timely feedback. Unlike traditional cloud-based control that treats the cloud as powerful, remote, and sole controller in a networked-system control setting, the proposed framework aims at seamlessly integrating the two controllers for enhanced performance. In particular, we formalize the fusion problem for finite-duration tasks by explicitly considering model mismatches and errors due to request-response communication delays. We analyze stability-like properties of the proposed cloud-assisted MPC framework and establish approaches to robustly handling constraints within this framework in spite of plant-model mismatch and disturbances. A fusion scheme is then developed to enhance control performance while satisfying stability-like conditions, the efficacy of which is demonstrated with multiple simulation examples, including an automotive control example to show its industrial application potentials. Copyright © 2021, The Authors. All rights reserved.

关键词： Model predictive control

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Semantic-Based Road Segmentation for High-Definition Map Construction 5th

Semantic-Based Road Segmentation for High-Definition Map Con...

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5th International Conference on Cognitive systems and Signal Processing, ICCSIP 2020

作者： Zhuang, Hanyang Wang, Chunxiang Qian, Yuhan Yang, Ming University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China Guangxi Key Laboratory of Automobile Components and Vehicle Technology Guangxi University of Science and Technology Liuzhou545006 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789811623356

The development of autonomous driving technology proposes higher requirements of the fidelity of the high-definition maps (HD maps). The construction of HD map based on orthophotos generated from panoramic images is the state-of-the-art approach. However, in this process, the dynamic obstacles and shadows on the road captured by the panoramic camera has significant impact on reducing the map quality. Moreover, the GNSS signal may inevitably unavailable or have jitter error, leading to the unsatisfactory of the orthophoto mosaic. Therefore, an approach is proposed to tackle these problems of HD map construction. The semantic segmentation of the panoramic images is firstly implemented to extract the dynamic obstacles such as vehicles and the road segments. Then the shadows on the road segments are removed through GAN networks to generate clean orthophotos. Afterwards, the clean orthophotos are used for feature extraction and image registration based on the road segmentation to provide finer pose estimations. Finally, the GNSS data, odometer data, and estimated poses are combined to optimize the vehicle pose for orthophoto mosaic. The experimental results illustrate that the proposed approach can improve the HD map construction accuracy under the congested environment. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image registration

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Robotic Electrospinning Actuated by Non-Circular Joint Continuum Manipulator for Endoluminal Therapy

Robotic Electrospinning Actuated by Non-Circular Joint Conti...

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2021 IEEE International Conference on Robotics and automation, ICRA 2021

作者： Wu, Zicong Lou, Chuqian Jin, Zhu Huang, Shaoping Liu, Ning Zou, Yun Kovac, Mirko Gao, Anzhu Yang, Guang-Zhong The Institute of Medical Robotics Department of Bioengineering Shanghai Jiao Tong University Shanghai200240 China Imperial College London Empa-Swiss Federal Laboratories for Materials Science and Technology United Kingdom Limited 999077 Hong Kong Aerial Robotics Laboratory Imperial College London LondonSW7 2AZ United Kingdom The Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781728190778

Electrospinning has exhibited excellent benefits to treat the trauma for tissue engineering due to its produced micro/nano fibrous structure. It can effectively adhere to the tissue surface for long-term continuous therapy. This paper develops a robotic electrospinning platform for endoluminal therapy. The platform consists of a continuum manipulator, the electrospinning device, and the actuation unit. The continuum manipulator has two bending sections to facilitate the steering of the tip needle for a controllable spinning direction. Non-circular joint profile is carefully designed to enable a constant length of the centreline of a continuum manipulator for stable fluid transmission inside it. Experiments are performed on a bronchus phantom, and the steering ability and bending limitation in each direction are also investigated. The endoluminal electrospinning is also fulfilled by a trajectory following and points targeting experiments. The effective adhesive area of the produced fibre is also illustrated. The proposed robotic electrospinning shows its feasibility to precisely spread more therapeutic drug to construct fibrous structure for potential endoluminal treatments. © 2021 IEEE

关键词： Electrospinning

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A Safe Hierarchical control Scheme Incorporated with Constrained Deep Neural Networks

A Safe Hierarchical Control Scheme Incorporated with Constra...

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第三十九届中国控制会议

作者： Hongyuan Wang Jingcheng Wang Haotian Xu Shangwei Zhao Xiaocheng Li Yuanhao Shi Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai School of Electrical and Control Engineering North University of China

A methodology is proposed for the control design of hierarchical systems. The high layer makes a decision without knowing the overall systems dynamics precisely. This procedure is implemented by a deep neural network constructed ***, to handle the safe learning issue, an additional technique is developed to guarantee constraints satisfaction of the trained neural network. At the underlying layer, local model predictive control（MPC） controllers are designed to regulate the subsystems to follow the optimal trajectories derived from high layer. The sample data from lower layer is utilized as the training set of neural network in high layer. The efficacy of the presented algorithm is illustrated in a simulation example.

关键词： Hierarchical multilevel and multilayer control Energy Management Model Predictive control Deep Neural Network Safe Learning

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Predicting the Robustness of Undirected Network controllability

Predicting the Robustness of Undirected Network Controllabil...

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第三十九届中国控制会议

作者： Yang Lou Yaodong He Lin Wang Kim Fung Tsang Guanrong Chen Department of Electrical Engineering City University of Hong Kong Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education

Robustness of the network controllability reflects how well a networked system can maintain its controllability against destructive attacks. The measure of the network controllability robustness is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the controllability robustness is determined by attack simulations, which is computationally time consuming. In this paper, an improved method for predicting the controllability robustness of undirected networks is developed based on machine learning using a convolutional neural network. This approach is motivated by the following observations: 1) there is no clear correlation between the topological features and the controllability robustness of a general undirected network, 2) the adjacency matrix of a network can be represented as a gray-scale image, 3) the convolutional neural network technique has proved successful in image processing without human intervention. In the new framework, preprocessing and filtering are embedded, and a sufficiently large number of training datasets generated by simulations are used to train several convolutional neural networks for classification and prediction,respectively. Extensive experimental studies were carried out, which demonstrate that the proposed framework for predicting the controllability robustness of undirected networks is more accurate and reliable than the conventional single convolutional neural network predictor.

关键词： Complex network Convolutional neural network controllability Robustness Performance prediction

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