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第三十九届中国控制会议

作者： Jin-Cang Liu Li-Hui Geng Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical EngineeringTianjin University of Technology and Education

This paper studies attitude model identification of a quadrotor using a new subspace identification method. To begin with a mechanism model, a nonlinear attitude model of the quadrotor is first established, and its linearized model is then obtained under the assumption of an ideal mechanism configuration. The identification framework for the quadrotor is a closed-loop errorsin-variables（EIV） system. In order to estimate the attitude model, the reference attitude input is designed as an M-sequence and the new subspace identification method is applied to estimate a coprime factor（CF） model of the quadrotor from the available reference input and attitude output measurements. The three-axis attitude estimation model can be eventually extracted from the identified CF model. Finally, a numerical simulation is given to verify the accuracy of the estimated attitude models by a redesigned LQR controller.

关键词： Quadrotor Attitude model identification Subspace identification Closed-loop errors-in-variables

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Low-latency federated learning over wireless channels with differential privacy

arXiv

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arXiv 2021年

作者： Wei, Kang Li, Jun Ma, Chuan Ding, Ming Chen, Cailian Jin, Shi Han, Zhu Poor, H. Vincent School of Electrical and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Data61 CSIRO SydneyNSW2015 Australia The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul Korea Republic of The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. The performance of uploaded models in such situations can vary widely due to imbalanced data distributions, potential demands on privacy protections, and quality of transmissions. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement. We solve this problem in the framework of multi-agent multiarmed bandit (MAMAB) to deal with the situation where there are multiple clients confornting different unknown transmission environments, e.g., channel fading and interferences. Specifically, we first transform the long-term constraints on both training performance and each client's DP into a virtual queue based on the Lyapunov drift technique. Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach. More importantly, we propose two efficient solutions to this matching problem, i.e., modified Hungarian algorithm and greedy matching with a better alternative (GMBA), in which the first one can achieve the optimal solution with a high complexity while the second one approaches a better tradeoff by enabling a verified low-complexity with little performance loss. In addition, we develop an upper bound on the expected regret of this MAMAB based FL framework, which shows a linear growth over the logarithm of communication rounds, justifying its theoretical feasibility. Extensive experimental results are conducted to validate the effectiveness of our proposed algorithms, and the impacts of various parameters on the FL performance over wireless edge networks are also discussed. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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Comfort-aware Cooperative Cruise control of Multiple High-speed Trains: An Artificial Potential Field Approach ⁎

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IFAC-PapersOnLine 2020年第2期53卷 15223-15228页

作者： Pingping Wang Zhiwu Huang Feng Zhou Bin Chen Yue Wu Yongjie Liu Fei Li Jun Peng School of Automation Central South University Changsha China 410083 School of Electrical and Information Engineering Changsha University of Science and Technology Changsha China 410114 School of Computer Science and Engineering Central South University Changsha China 410083

In this work, the cruise control problem of multiple high-speed trains movements is investigated. Different from the classical PID control method applied in the practical highspeed train operation system, in this paper, a cooperative cruise control strategy considering both safety and passenger comfort based on layered potential function is proposed. First, the cyber-physical modeling of the high-speed trains system is presented, where the physical layer models the dynamic characteristic, and the cyber layer describes the communication topology. Second, a cooperative control algorithm based on layered potential function is designed. The underlying artificial potential function is introduced to keep a safe distance between adjacent high-speed trains, and the speed consensus rule is realized through the consensus algorithm. The hyperbolic tangent function is selected as the upper artificial potential function to ensure the acceleration of the high-speed train within a comfortable range. Finally, the stability of the control system is proved by the Lyapunov stability theorem, and simulations verify the effectiveness of the control strategy.

关键词： High-speed trains Ride comfort Cruise operation Cooperative control Artificial potential field

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Automatic Seizure Prediction from Scalp EEG with Optimal Feature and Minimum Channels

Automatic Seizure Prediction from Scalp EEG with Optimal Fea...

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第32届中国控制与决策会议

作者： Hailing Zheng Yingmei Qin Qing Qin Wei Chen Jingyu Zhang Jian Dong Tianjin Key Laboratory of Information Sensing & Intelligent Control School of Automation and Electrical EngineeringTianjin University of Technology and Education

The aim of this study is to develop an efficient,reliable and automatic epileptic seizure prediction system using scalp EEG measurements with optimal feature and minimum *** data in interictal and preictal periods from the CHB-MIT dataset are used for seizure ***,the original signals are decomposed into several frequency bands using a digital wavelet transform(DWT).Then,features including standard deviation(S),log of amplitude(L),quartile(Q) and coefficient of variation(CV) are ***,different combinations of feature vectors are fed into classifiers(support vector machine(SVM) and extreme learning machine(ELM))to classify the above two states(preictal and interictal states).Performance analysis shows that the optimal feature is CV,the optimal sub-band is 16-31 Hz and the optimal EEG channel can be chosen as FP1-F7,T7-P7,FP1-F3,P3-O1 or *** comparing the classification results,ELM provides a more robust and higher overall accuracy than SVM,and the best average accuracy of both ELM and SVM can reach as high as 100%.

关键词： CHB MIT ELM Automatic Seizure Prediction from Scalp EEG with Optimal Feature and Minimum Channels

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Robust Attitude control for Hypersonic Vehicles with Coupling Analysis

Robust Attitude Control for Hypersonic Vehicles with Couplin...

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Chinese control Conference (CCC)

作者： Ruihao Cao Yong Xi Qinglei Hu Tuo Han School of Automation Science and Electrical Engineering Beihang University Beijing China Shanghai Aerospace Control Technology Institute China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

The attitude control issue for hypersonic vehicles (HSV) with coupling analysis is presented in this paper. An auxiliary variable is introduced to identify the effects of coupling terms that are negative or beneficial for the attitude stabilization. To handle the environmental uncertainties and aerodynamic perturbations involved in the hypersonic vehicles flight control system, a modified gain adaptation strategy is presented to tackle these terms that are limited but with unknow bounds. Then, a robust control scheme containing the coupling effect identified terms is constructed to deal with the robustness and coupling issues. It is proved that the utilized control scheme can enforce the tracking errors to a small vicinity of the origin in finite time. Finally, numerical simulations are conducted to demonstrate the provided controller.

关键词： Couplings Attitude control Aerodynamics Uncertainty Vehicle dynamics Robustness control systems

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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A Novel Multi-objective Evolutionary Algorithm Based on Space Partitioning 11th

A Novel Multi-objective Evolutionary Algorithm Based on Spac...

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11th International Symposium on Intelligence Computation and Applications, ISICA 2019

作者： Wu, Xiaofang Li, Changhe Zeng, Sanyou Yang, Shengxiang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9789811555763

To design an effective multi-objective optimization evolutionary algorithms (MOEA), we need to address the following issues: 1) the sensitivity to the shape of true Pareto front (PF) on decomposition-based MOEAs;2) the loss of diversity due to paying so much attention to the convergence on domination-based MOEAs;3) the curse of dimensionality for many-objective optimization problems on grid-based MOEAs. This paper proposes an MOEA based on space partitioning (MOEA-SP) to address the above issues. In MOEA-SP, subspaces, partitioned by a k-dimensional tree (kd-tree), are sorted according to a bi-indicator criterion defined in this paper. Subspace-oriented and Max-Min selection methods are introduced to increase selection pressure and maintain diversity, respectively. Experimental studies show that MOEA-SP outperforms several compared algorithms on a set of benchmarks. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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F-ResPoint: a multi-modal fusion object detection algorithm based on frustum culling

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Displays 2025年 90卷

作者： Jinjun Rao Kai Yang QinFei Zhao Zhenwei Li Jinbo Chen Jingtao Lei Mei Liu Wojciech Giernacki Department of Precision Mechanical Engineering School of Mechatronic Engineering and Automation Shanghai University Shanghai 200444 China Mechanical and Electrical Department Kashi Polytechnic Vocational and Technical College Xinjiang 844700 China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan 430074 China Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering Poznan University of Technology Piotrowo 3a 60-965 Poznan Poland

Accurate and lightweight 3D object detection is crucial for the perception ability of mobile intelligent robots. Aiming at the problem that current 3D object detection is difficult to balance lightweight and accuracy, this paper presents a multimodal fusion object detection method, F-ResPoint, based on point cloud culling in vision frustum. F-ResPoint is a lightweight two-stage object detection method, in which the frustum point cloud generation algorithm based on the improved YOLOv5 is used first to obtain the point cloud containing the object information in the view frustum from the point cloud in the original field. Then, the point cloud detection network based on the Residual-SA module is utilized for detecting the three-dimensional (3D) object from point cloud in the view frustum and obtaining its position and actual size of the object in 3D space. Experimental results demonstrate that this method achieves high detection accuracy and maintains good real-time performance while remaining lightweight.

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H∞ State Estimation for Stochastic Jumping Neural Networks with Fading Channels Over a Finite-Time Interval

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Neural Processing Letters 2019年第1期50卷 1-18页

作者： Shen, Liang Shen, Hao Gao, Mingming Liu, Yajuan Huang, Xia School of Electrical and Information Engineering Anhui University of Technology Ma’anshan243002 China School of Metallurgical Engineering Anhui University of Technology Ma’anshan243002 China The State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China

In the work, a class of Markov jump neural networks in the discrete-time domain with fading channels are taken into account. The main aim is to investigate the H∞ state estimation issue when the Rice fading occurs in measured networks. In the first place, the analyses of the finite-time boundedness and the H∞ performance for the estimation error system with the aid of finite-time stability theory are presented. Some conditions which guarantee the solvability of the addressed problem are established. Furthermore, by applying an unique decoupling method, the gains of the presented estimator are obtained under the feasible solutions of the conditions derived before. Finally, the validity of the presented approach is verified by a numerical example. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

关键词： State estimation

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Subspace Identification of Closed-loop Errors-in-variables Systems

Subspace Identification of Closed-loop Errors-in-variables S...

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第32届中国控制与决策会议

作者： Li-Hui Geng Terefe Bayisa Ayele Jin-Cang Liu Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical EngineeringTianjin University of Technology and Education

This paper studies an identification method for errors-in-variables(EIV) systems governed by an observer based state feedback control *** to the input and output measurement noises,traditional identification methods are typically inapplicable in most *** order to estimate such a closed-loop EIV system,a coprime factor(CF) model for the true plant is first identified from the available reference,input and output measurements by using the subspace identification method and then a plant estimation model is able to be extracted from the identified CF ***,a numerical simulation is given to show the effectiveness of the studied identification method.

关键词： Subspace identification Closed-loop Errors-in-variables system Coprime factor

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