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第33届中国控制与决策会议

作者： Wenzhe Fu Wei Wang Lei Liu Bo Wang Zhongtao Cheng 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

Since the solid rocket boosters（SRB） can only be extinguished after fuel consumption,the energy management problem for the launch aircraft has received more and more attention *** paper comes up with a novel endoatmospheric energy management method based on the changeable launch plane（CLP） strategy for the SRB energy management *** transverse maneuvering energy management model is *** on the CLP theory,the height in the longitudinal plane and the displacement in the lateral plane are approximated by polynomial spline *** angle of attack（AOA） and the sideslip angle is calculated with the inverse of the dynamics ***,a simulation result for a three-stage solid rocket,called Vega,is given to demonstrate the effectiveness and efficiency of our proposed *** with the traditional energy management method,our proposed method has a 75.86% improvement in the capacity of energy consumption.

关键词： Solid rocket booster changeable launch plane strategy polynomial spline guidance method energy management

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Adaptive control of Hypersonic Vehicles with Parametric uncertainties and Angular Velocity Constraints

Adaptive Control of Hypersonic Vehicles with Parametric unce...

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第40届中国控制会议

作者： Xuan'an Ji Wei Wang Zhongtao Cheng Bo 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

In this paper,an backstepping control scheme with state constraint is proposed for a hypersonic flight vehicle model with the speed constraints and unknown *** Nonlinears Disturbance Observer（NBO） is used to approximate the unknown *** is commonly known that the stability and security of the HFV will be weakened when the constraints are ***,the control problem of the speed constraints for the HFV is a very important task because of the demand of the handing *** auxiliary system and thetime-varying barrier Lyapunov functions are used to guarantee the constraints of the angular velocity not violated,and it can prove the stability of the closed-loop ***,a simulation example for the GHV is employed to show the feasibility and rationality of the proposed approach.

关键词： Hypersonic Vehicle Barrier Lyapunov Function State Constraint Auxiliary System

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A Discrete Beetle Swarm Optimization Algorithm-based method for Multi-Targets Assignment for Multi-missiles Coordinated Attack

A Discrete Beetle Swarm Optimization Algorithm-based method ...

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第33届中国控制与决策会议

作者： Haojun Sun Wei Wang Gaoxiang Peng Zhongtao Cheng Bo 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

Due to the nonlinearity and difficulty in the process of the cooperative target assignment,a discrete beetle swarm optimization algorithm is ***,the target assignment model is established based on the damage probability and target value of missile attacking ***,according to the constraint characteristics of the model,a particle integer coding method is proposed to reduce the complexity of the ***,the discrete beetle swarm optimization algorithm is designed and used to solve the problem,which is an improved particle swarm optimization algorithm by introducing beetle antennae search *** simulation results show that the algorithm combines the advantages of particle swarm optimization algorithm and beetle antennae search algorithm,and has fast convergence speed and is not easy to fall into local *** with other algorithms,the effectiveness of the algorithm is verified.

关键词： Weapon Target Assignment Particle Coding Discrete Beetle Swarm Optimization Algorithm

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Fault Diagnosis Method for An Aircraft Attitude control System based on Deep Learning

Fault Diagnosis Method for An Aircraft Attitude Control Syst...

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第33届中国控制与决策会议

作者： Xiaoli Luo Wei Wang Hui Hu Zhongtao Cheng Maoqin Tang Bo 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

This article proposes a deep-learning-based fault diagnosis method for an aircraft attitude control *** core idea is to construct two deep neural networks to approximate the mapping between actuator fault information and the real-time attitude generated by the *** proposed fault-diagnosis network consists of an input layer,a convolutional neural network（CNN） layer,a two-layer long and short-term memory network（LSTM）,and a fully connected layer in *** attitude quaternion and the attitude angular velocity are taken as the inputs of the networks,and the estimations of the bias fault size of each actuator are the ***,numerical simulations are carried out to verify the proposed method and to make a comparison with the existing diagnose *** simulation results show the effectiveness of the proposed method and the superiority over the compared method.

关键词： Aircraft attitude control system Bias fault CNN LSTM Fault diagnosis

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A GM-TCN based Hybrid Prediction Method for Ash Fouling Prediction in Coal-fired Power Plant Boiler

A GM-TCN based Hybrid Prediction Method for Ash Fouling Pred...

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Chinese automation Congress (CAC)

作者： Yuanhao Shi Tianxiang Han Fangshu Cui Jie Wen Gangzhu Qiao School of Electrical and Control Engineering North University of China Taiyuan China Department of Automation Ministry of Education of China Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Shanghai China School of Data Science and Technology North University of China Taiyuan China

In response to the problem that ash deposition in boilers has a great impact on production efficiency as well as production safety, a grey level theory prediction model based on time convolution networks (GM-TCN) is proposed to predict the degree of ash in boiler heated areas by using a cleaning factor to assess the ash deposition condition on the heated surfaces. The accuracy of the prediction model is improved by utilizing the features of grey level theory that can predict the developmental changes in system behavior, combined with the high-precision approximation capability of the time-convolutional network. The modelling prediction of the ash level in the heated area of boilers during the production of coal-fired power stations is investigated. Experiments demonstrate that the model can predict the cleanliness factor with high accuracy, and the predicted values are closest to the true values compared to the other two models, verifying the accuracy and reliability of the model.

关键词： Cogeneration Time series analysis Ash Production Predictive models Boilers Data models

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Prediction of Drug-Disease Associations Based on Long Short-Term Memory Network and Gaussian Interaction Profile Kernel 15th

Prediction of Drug-Disease Associations Based on Long Short-...

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15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Jiang, Han-Jing Wang, Yan-Bin Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Computer Science and Technology Zhejiang University Hangzhou315000 China Department of Pathology Renmin Hospital of Wuhan University Wuhan430060 China

ISBN: (纸本)9789811613531

Drug-disease association depicts the accessibility landscape of the association at the molecular level, thereby revealing the intermolecular reaction in the process of drug reposition. However, the scarcity of drug-disease association data often complicates the drug reposition. In this work, we present a method for predicting drug-disease association, which is called drug-disease association prediction via Gaussian interaction profile kernel and long short-term memory network (G-LSTM). G-LSTM combines a recurrent network framework with a Gaussian interaction profile kernel to learn underlying features that accurately characterize drug-disease-association data. We have verified G-LSTM models on the two benchmark datasets Fdataset and Cdataset, respectively. The experimental results show that the predictive performance was significantly improved, with AUC of 0.9527 and 0.9705, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Unsignalized Intersection Management Strategy for Mixed Autonomy Traffic Streams

arXiv

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arXiv 2022年

作者： Zhou, Junjie Shen, Zhaokun Wang, Xiaofan Wang, Lin The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The Department of Automation Shanghai Jiao Tong University The School of Mechatronic Engineering and Automation Shanghai University The School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai201418 China

With the rapid development of connected and automated vehicles (CAVs) and intelligent transportation infrastructure, CAVs, connected human-driven vehicles (CHVs), and un-connected human-driven vehicles (HVs) will coexist on the roads in the future for a long time. This paper comprehensively considers the different traffic characteristics of CHVs, CAVs, and HVs, and systemically investigates the unsignalized intersection management strategy from the upper decision-making level to the lower execution level. The unsignalized intersection management strategy consists of two parts: the heuristic priority queues based right of way allocation (HPQ) algorithm and the vehicle planning and control algorithm. In the HPQ algorithm, a vehicle priority management model considering the difference between CAVs, CHVs, and HVs, is built to design the right of way management for different types of vehicles. In the lower level for vehicle planning and control algorithm, different control modes of CAVs are designed according to the upper-level decision made by the HPQ algorithm. Moreover, the vehicle control execution is realized by the model predictive controller combined with the geographical environment constraints and the unsignalized intersection management strategy. The proposed strategy is evaluated by simulations, which show that the proposed intersection management strategy can effectively reduce travel time and improve traffic efficiency. Results show that the proposed method can decrease the average travel time by 5% to 65% for different traffic flows compared with the comparative methods. The intersection management strategy captures the real-world balance between efficiency and safety for future intelligent traffic systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Travel time

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MG-SLAM: Structure Gaussian Splatting SLAM with Manhattan World Hypothesis

arXiv

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arXiv 2024年

作者： Liu, Shuhong Deng, Tianchen Zhou, Heng Li, Liuzhuozheng Wang, Hongyu Wang, Danwei Li, Mingrui Department of Information Science and Technology Department of Complexity Science and Engineering The University of Tokyo Tokyo113-8654 Japan Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Department of Mechanical Engineering Columbia University New York10027 United States School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science Dalian University of Technology Dalian116024 China

Gaussian Splatting SLAMs have made significant advancements in improving the efficiency and fidelity of real-time reconstructions. However, these systems often encounter incomplete reconstructions in complex indoor environments, characterized by substantial holes due to unobserved geometry caused by obstacles or limited view angles. To address this challenge, we present Manhattan Gaussian SLAM, an RGB-D system that leverages the Manhattan World hypothesis to enhance geometric accuracy and completeness. By seamlessly integrating fused line segments derived from structured scenes, our method ensures robust tracking in textureless indoor areas. Moreover, The extracted lines and planar surface assumption allow strategic interpolation of new Gaussians in regions of missing geometry, enabling efficient scene completion. Extensive experiments conducted on both synthetic and real-world scenes demonstrate that these advancements enable our method to achieve state-of-the-art performance, marking a substantial improvement in the capabilities of Gaussian SLAM systems. © 2024, CC BY.

关键词： Gaussian distribution

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Construction of Minecraft Virtual Reality Scene Based on Voxel Point Cloud 8th

Construction of Minecraft Virtual Reality Scene Based on Vox...

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8th International Conference on Cognitive Systems and information processing, ICCSIP 2023

作者： Ji, Nuocheng Zhuang, Hanyang Yang, Ming The University of Michigan – Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 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 Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9789819980208

Virtual Reality is an important development direction in recent years. Minecraft, a popular sandbox game, has a great potential to become a virtual reality platform for building immersive environments. Traditional methods of building virtual reality scenes rely heavily on massive human labors and require high levels of proficiency. To address these limitations, we propose an automatic, voxelated 3D modeling method with foreground and background environment segmented that can reduce modeling costs and achieve effective application in Minecraft virtual reality scenes such as virtual campuses. The proposed method leverages the use of point cloud reconstruction using a voxelization approach. A pipeline of converting point cloud to Minecraft scene is developed. Through preprocessing, segmenting, coloring, and exporting, a Minecraft virtual reality scene can be automatically generated based on voxel point cloud. Due to the noise in point cloud may cause defect during voxelization, a denoising and smoothing method using slice scanning and voxel connectivity segmentation has been implemented. It can generate building structures with clear outlines and uniform colors. And the entire process requires low computational complexity and achieves fast generation. We conducted experiments based on this method in indoor scenes, outdoor building landmarks, and complex underground scenes, and obtained results with clear structures and smooth surfaces, saving a lot of time for subsequent detailed modeling. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： 3D modeling

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Joint Association, Beamforming, and Resource Allocation for Multi-IRS Enabled MU-MISO Systems With RSMA

arXiv

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arXiv 2024年

作者： Wang, Chunjie Zhang, Xuhui Xing, Huijun Xue, Liang Wang, Shuqiang Shen, Yanyan Yang, Bo Guan, Xinping Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Information and Electrical Engineering Hebei University of Engineering Handan056038 China Hebei Key Laboratory of Security and Protection Information Sensing and Processing Handan056038 China 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 Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Shenzhen Future Network of Intelligence Institute The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

Intelligent reflecting surface (IRS) and rate-splitting multiple access (RSMA) technologies are at the forefront of enhancing spectrum and energy efficiency in the next generation multi-antenna communication systems. This paper explores a RSMA system with multiple IRSs, and proposes two purpose-driven scheduling schemes, i.e., the exhaustive IRS-aided (EIA) and opportunistic IRS-aided (OIA) schemes. The aim is to optimize the system weighted energy efficiency (EE) under the above two schemes, respectively. Specifically, the Dinkelbach, branch and bound, successive convex approximation, and the semidefinite relaxation methods are exploited within the alternating optimization framework to obtain effective solutions to the considered problems. The numerical findings indicate that the EIA scheme exhibits better performance compared to the OIA scheme in diverse scenarios when considering the weighted EE, and the proposed algorithm demonstrates superior performance in comparison to the baseline algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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