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Robotics, control and automation (ICRCA), International Conference on

作者： Jian Sun Yuanjin Wang Wen Shi Ningyun Lu College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

With the increasing complexity of equipment systems, the requirements for testability design are becoming more demanding. This paper proposes using an integer-encoded fault dictionary as the testability model, integrating fault propagation and test detectability for sensor allocation. The proposed approach is carried out in three main steps. First, an integer-encoded fault dictionary model is developed to represent the relationship between faults and tests. Second, the model’s ability to diagnose faults is evaluated by defining the performance index, which considers fault propagation and the detectability of tests. Third, the Discrete Cuckoo Search (DCS) algorithm is applied to optimize the sensor allocation based on the defined performance index. Experimental results demonstrate that this approach can achieve more accurate testability models under imperfect conditions, thereby enhancing fault diagnosis performance.

关键词： Fault diagnosis Dictionaries Costs Fault detection Robot sensing systems Birds Performance analysis Complexity theory Resource management Complex systems

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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A Enhanced Feature Extraction for 3D Object Detection

A Enhanced Feature Extraction for 3D Object Detection

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System Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Qingao Meng Jigang Tong Sen Yang Tian Xie Xin Jin The School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China National Key Laboratory of Science and Technology on Aircraft Control Avic Xi’an Flight Automatic Control Research Institute Xi’an China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

3D object detection is crucial for autopilot and augmented reality applications. However, accurate feature extraction is the key to achieve high-precision detection, but traditional methods face the challenges of information loss and computational inefficiency. To address this, we propose an enhanced feature extraction method. We first divide the 3D space into voxel grids of varying scales. Then, using 3D sparse convolution, we extract features which are compressed along the Z axis to form BEV (Bird’s Eye View) feature images. An efficient multi-scale attention mechanism is then employed to enhance these BEV features. The refined feature map is subsequently located and classified through a CT-stacking module and RPN (Region Proposal Network). Voxel features from the proposal and 3D backbone are passed through a voxel RoI pool to directly extract RoI features from the 3D features for further refinement. Finally, a cascaded attention network refines the proposal and strengthens confidence prediction. Experiments on test set validate our method effectiveness, showing improved accuracy over traditional voxel methods. Overall, this enhanced feature extraction technique offers a promising solution for 3D target detection, particularly in scenarios demanding high real-time performance and precision.

关键词： Three-dimensional displays Accuracy Convolution Object detection Detectors Feature extraction Transformers Systems simulation Proposals Data mining

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An Optimization Method for the Swing of a Quadrotor Flight Suspension System

An Optimization Method for the Swing of a Quadrotor Flight S...

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System Simulation Technology and its Application (CCSSTA), IEEE China Conference on

作者： Sen Yang Xinlian Zang Jigang Tong Tian Xie Xin Jin The School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China National key laboratory of science and technology on aircraft control AVIC Xi’an Flight Automatic Control Research Institute Xi‘an China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

The aim of this study is to explore optimization control methods for suspension systems during the flight of quadcopters. The traditional control method of suspension system has some problems such as oscillation and instability, so an optimization scheme based on Gaussian pseudo-general algorithm is proposed in this paper. Through numerical simulation and actual flight test, the effectiveness of the optimization algorithm in improving system stability and reducing oscillation amplitude is verified. At the same time, this study conducted a comparative analysis of the suspension system of the quadrotor vehicle and the free fall body, and the optimization algorithm is found to be feasible and can well improve the control accuracy and stability of the system, and provide strong support for the safety of the quadrotor vehicle in various application scenarios.

关键词： Suspensions (mechanical systems) Accuracy Stability analysis Robustness Systems simulation Safety Vehicle dynamics Oscillators Numerical stability Quadrotors

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Research on Path Planning Algorithm Based on D∗ Search

Research on Path Planning Algorithm Based on D∗ Search

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2023 International Conference on Ocean Studies, ICOS 2023

作者： Leng, Jiajun Fu, Li Wang, Lingling Tang, Ning Laboratory of Science and Technology on Marine Navigation and Control China State Shipbuilding Corporation Beijing100036 China Tianjin Navigation Instruments Research Institute Tianjin300131 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Bauman Moscow State Technical University Moscow Department of Automatic Control Systems 105005 Russia

ISBN: (纸本)9798350382457

Path planning algorithms are current research hotspots. Heuristic algorithms that can solve dynamic environment problems are gradually becoming the mainstream research direction. The D∗ algorithm, as one of the new heuristic algorithms, has the advantages of being more computationally efficient than the A ∗ algorithm and being able to solve dynamic problems, and has played an important role in two-dimensional path planning projects such as the NASA Mars rover. But its theoretical research and practical application in 3D environment are very rare. In this context, this paper carries out an in-depth study of the path planning problem based on the D∗ algorithm, and completes the theoretical derivation and simulation process in 2D and 3D environments. In this paper, a complex random 2D map is established by random generation, and the simulation of D∗ algorithm is realized on the map with the help of Python language, which realizes the finding of the relative optimal path. Then, in order to study the feasibility of D∗ solving in 3D environment, the simulation of D∗ algorithm under 3D map is realized with the help of Python and Unity3d. The research results show that the simulation results are good, which is valuable for the theoretical research and practical application of heuristic algorithms, and plays a role in the application of robot path planning algorithms. © 2023 IEEE.

关键词： Python

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Intelligent Flight control for Cruise Vehicle Based on Backstepping Design and Reinforcement Learning

Intelligent Flight Control for Cruise Vehicle Based on Backs...

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Chinese automation Congress (CAC)

作者： Xiaodong Liu Chenhui Jia Qingjun Yang Tiancai Wu Yu Zhang National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China Beijing Aerospace Automatic Control Institute Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

Aiming at velocity/height tracking control and attitude stabilization for the longitudinal flight of cruise vehicle, the research of intelligent flight control method is carried out by combining the backstepping control theory and the deep reinforcement learning method. Firstly, the longitudinal flight control system of the vehicle is decomposed into velocity and height subsystems, and the control law is designed based on backstepping control theory. Then, aiming at the problems that the selection of the multiple control gains will directly affect the control performance of the closed-loop system and the adjustment process of control gains is cumbersome, the twin delayed deep deterministic policy gradient algorithm (TD3) will be adopted to train the cruise vehicle, so that it can determine the control gains online according to the current flight states. The numerical simulation results show that, the proposed intelligent flight control method can guarantee the high-precision tracking with respect to height and velocity commands, and also realize the attitude stability of cruise vehicle.

关键词：

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Dynamic Fault Detection Based on Variational Bayesian Mixture Probabilistic Principal Component Analysis 11

Dynamic Fault Detection Based on Variational Bayesian Mixtur...

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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Huang, Chenghong Chai, Yi Wei, Chihang Zhu, Zheren Chongqing University College of automation Chongqing400044 China Hangzhou Normal University School of Information Science and Technology Hangzhou311121 China Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

It is essential to achieve real-time fault detection of the industrial process to reduce the occurrence of accidents during the industrial process. However, there are some problems in the actual monitoring process, such as the instability of sensor measurement, losing packet while transferring data, which causes problems like missing data and outliers while sampling from actual monitoring data. In addition, the monitoring data are interrelated in time series. Thus, a fault detection method based on DMPPCA model is proposed in this study. DMPPCA can not only consider the above problems at the same time, but also determine the dimension of latent variables automatically. Finally, the effect of proposed DMPPCA model was validated by a three-phase flow facility. © 2022 IEEE.

关键词： Fault detection

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Time-Attention Graph Convolutional Network Soft Sensor in Biochemical processes 4

Time-Attention Graph Convolutional Network Soft Sensor in Bi...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Jia, Mingwei Xu, Danya Yang, Tao Yao, Yuan Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering 30013 Taiwan

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Most data-driven soft sensor methods can model nonlinear time-varying characteristics of biochemical processes. However, the intrinsic relationship between variables, which is helpful for understanding model behavior, has rarely been investigated in existing data-driven methods. In this work, a novel soft sensor model of time-attention graph convolutional network (TA-GCN) is proposed, which jointly leverages variable relationships and long-term temporal dependencies to improve interpretability and prediction accuracy. This model first uses the maximum information coefficient to construct a topology graph and trains edge strengths end-to-end. The data are then encoded in the spatial-temporal dimension based on GCN and attention mechanism. Finally, the empirical knowledge that analyzes the operating state of the process and graph are combined to explain the model behavior. In comparison to existing soft sensors, TA-GCN enables efficient and scalable training for long-term spatial-temporal dependencies. Experimental results on InPenSim dataset demonstrate that TA-GCN is competitive with state-of-the-art methods. © 2022 IEEE.

关键词： Convolution

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Topology-Informed Graph Convolutional Network for Fault Diagnosis 11

Topology-Informed Graph Convolutional Network for Fault Diag...

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11th IEEE Data Driven control and Learning Systems Conference, DDCLS 2022

作者： Jia, Mingwei Liu, Yi Xu, Danya Yang, Tao Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering 30013 Taiwan

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

The development of an accurate data-driven fault diagnosis model faces several obstacles. One significant challenge is how to combine the model with the process mechanism rationally and improve the model interpretability. In this work, a fault diagnosis method based on a topology-informed graph convolutional network (TIGCN) is proposed. This model first uses the empirical knowledge that analyzes the operating state of the process and combines the operating data to construct a signed directed graph. Then the operating data is divided into three modalities in the time dimension. The model utilizes a well-designed spatial-temporal convolutional layer and signed directed graph to construct different modalities separately to effectively detect fault types. The superior fault diagnosis performance of the proposed TIGCN model is demonstrated on the Tennessee-Eastman process as compared to traditional approaches. © 2022 IEEE.

关键词： Failure analysis

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H∞ Filter Based Cooperative Localization for Multi-Robot System

H∞ Filter Based Cooperative Localization for Multi-Robot Sy...

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第43届中国控制会议

作者： Guoliang Yang Jin Zeng Dengfeng Wei Yiyang Liu Heng Wang Key Laboratory of Aeronautical Science and Technology of Inertial AVIC XI'AN Flight Automatic Control Research Institute School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper addresses the problem of multi-robot cooperative localization in non-Gaussian noise environments. Traditionally, the Kalman filter（KF） based algorithms are used as solutions to cooperative localization（CL） problems. However,the KF algorithms cannot obtain accurate state estimation when noises are non-Gaussian. To deal with this issue, an H filter based cooperative localization algorithm is proposed. Unlike the KF algorithm, the H filter algorithm does not require the assumption of Gaussian noise, thus achieving better localization accuracy for non-Gaussian scenarios. The effectiveness of the proposed algorithm is demonstrated through simulation examples.

关键词： Cooperative Localization H_∞ filter Multi-Robot Systems

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