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3rd International Conference on Intelligent Mechanical and Human-Computer Interaction Technology, IHCIT 2024

作者： Li, Jiatong Tu, Chaofan Wang, Xingjian Wang, Zhaoyang Xiu, Wanbo School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Laboratory of Science and Technology on Marine Navigation and Control China State Shipbuilding Corporation Tianjin300131 China China Tianjin Navigation Instrument Research Institute Tianjin300131 China

ISBN: (纸本)9781510683105

Electro-hydraulic actuator (EHA) system is a key component of the ship's electro-hydraulic control system, but the internal failure mechanism of EHA is extremely complex, and it is limited by insufficient detection methods in practical systems, making fault diagnosis of EHA a great challenge. To address this issue, this paper proposes a fault diagnosis method based on Continuous Wavelet Transform (CWT), Convolutional Neural Network (CNN) and Transformer. Firstly, the collected one-dimensional time series signal of the sensor is converted into a two-dimensional time-frequency map through CWT. Then, a hybrid module of CNN-Transformer is used to extract local and global fault features, and finally the classification results are output through softmax. Conduct EHA fault simulation experiments to validate the proposed method. The research results indicate that the proposed method has a fault diagnosis accuracy of 96.33%, achieving high-precision diagnosis of EHA faults. © 2024 SPIE.

关键词： Wavelet transforms

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Two-Archive-Based Competitive Swarm Optimizer for Constrained Multiobjective Optimization 5

Two-Archive-Based Competitive Swarm Optimizer for Constraine...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Li, Lianghao He, Cheng Lin, Jianqing Pan, Linqiang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Wuhan China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350393521

Constrained multiobjective optimization problems (CMOPs) are prevalent in various real-world applications, presenting a formidable challenge to existing evolutionary algorithms when faced with intricate constraints. When solving CMOPs, a crucial concern is achieving a balance between convergence, diversity, and feasibility. To address these challenges, this paper proposes a two-archive-based constrained multiobjective competitive swarm optimizer. The algorithm preserves two collaborative and complementary archives to improve population convergence and feasibility. By implementing a competitive particle swarm mechanism, offspring solutions are generated by drawing upon solutions from both archives, thus capitalizing on their synergistic effect and exceptional information. An adaptive parameter is also used to adjust the bias in choosing the winner in the paired competition during the evolution. The experimental results demonstrate the effectiveness of the proposed algorithm in handling constrained multiobjective optimization problems. © 2023 IEEE.

关键词： Constraint handling

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Path Planning Method of Unmanned Surface Vessel Based on Strategy Integration 23rd

Path Planning Method of Unmanned Surface Vessel Based on Str...

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23rd IFToMM China International Conference on Mechanism and Machine science and engineering, IFToMM CCMMS 2022

作者： Yang, Caipei Zhao, Yingqi Liu, Jie Zhou, Kaibo MOE Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811993978

In the task of path planning, the unmanned surface vessels (USV) are required to reach the destination while avoiding obstacle. However, it is difficult for USV to prioritize the two sub-target tasks of destination arrival and obstacle avoidance in a complex dynamic environment using traditional path planning methods. Based on the above considerations, a path planning method for USVs based on strategy integration is proposed. Firstly, the path planning scenario containing dynamic obstacles and two sub-target scenarios are set up. In order to strengthen its ability to reach the destination and avoid dynamic obstacles, the dynamic obstacles and the destination are removed separately in the two sub-target scenarios. Furthermore, the model for strategy selection is trained on action decisions in sub-target scenarios through double deep Q-Network with prioritized experience replay. Finally, a framework for strategy integration is designed to optimize strategy selection for USV. The simulation results show that the proposed method achieves a good overall performance and a higher path planning success rate than the traditional path planning method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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Neural learning control for discrete-time nonlinear systems in pure-feedback form

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science China(Information sciences) 2022年第2期65卷 124-138页

作者： Min WANG Haotian SHI Cong WANG Jun FU School of Automation Science and Engineering South China University of Technology School of Control Science and Engineering Shandong University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

This study focuses on state-feedback and output-feedback neural learning control problems for discrete-time nonlinear systems in the pure-feedback form. First, an extended result for the exponential stability for a class of discrete-time linear time-varying(LTV) systems with n-step delays is proposed to verify the exponential convergence of estimated weights. Subsequently, both state-feedback and outputfeedback adaptive neural network(NN) controllers are constructed by combining the classical n-step and n-step input-output predictors. After ensuring convergence of the system output to a recurrent reference signal, the radial basis function subvector of NN is verified to satisfy the persistent exciting condition using the system state equation and the implicit function theorem. By combining the extended stability corollary of an LTV system, the estimated weights are verified to exponentially converge to their optimal values. By constructing "learning rules" and using a "mod" function, the estimated weights with a convergent sequence are synthetically represented and stored as the experience knowledge, which is reused to construct neural learning controllers. The proposed neural learning controllers not only accomplish similar control tasks but also reduce the burden of online computation compared with the conventional adaptive NN ***, simulation results are presented to demonstrate the effectiveness of the presented schemes.

关键词： discrete-time nonlinear systems pure-feedback systems learning control neural networks persistent excitation

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Loop Closure Detection Based on Geometry of Semantic Point Cloud 18

Loop Closure Detection Based on Geometry of Semantic Point C...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Cao, Danyang Lyu, Zhanggang Liu, Zhong Yue, Haosong Wu, Xingming Chen, Weihai Beihang University School of Automation Science and Electrical Engineering Beijing China Beijing Aerospace Automatic Control Institute Beijing China Beihang University Hangzhou Innovation Institute Hangzhou China

ISBN: (纸本)9798350312201

The spatiotemporal constraints provided by loop closure detection play a crucial role in the simultaneous localization and mapping of robots. Traditional methods based on key points information are vulnerable to point-level feature instability. In this paper, a loop closure detection method based on robust geometry of the objects extracted through semantics of point cloud is proposed. Our method is based on the fact that point cloud can robustly describe geometric properties, and the extracted local features are extremely robust and invariant to rotation, thus achieving remarkable results in loop closure detection. We conduct experiments on the KITTI odometry dataset and confirm the effectiveness of our method. © 2023 IEEE.

关键词： Semantics

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Adaptive control for Spacecraft Proximity Operations Under Motion Constraints 22

Adaptive Control for Spacecraft Proximity Operations Under M...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Chen, Bin Li, Haoran Shao, Xiaodong Shi, Yongxia Hu, Qinglei Guo, Yabo The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Institute of Control Engineering Beijing100094 China Nanyang Technological University Singapore639798 Singapore The 60th Research Institute of CRTC Nanjing210000 China

ISBN: (纸本)9798331527471

This paper addresses the issue of relative position tracking control for spacecraft rendezvous and proximity operations with a freely tumbling target, considering spatial motion constraints, including path and relative velocity limits, as well as mass uncertainty and external disturbances. To handle these challenges, a two-stage operation is proposed to ensure compliance with path constraints. In each stage, the constrained zones are parameterized using geometric features of the space, and corresponding artificial potential functions (APFs) are designed. Based on these APFs, an adaptive tracking controller is developed inside a backstepping control frame that allows the pursuer to reach terminal docking position while respecting the spatial motion constraints. Lyapunov stability analysis shows that the proposed control scheme ensures asymptotic convergence of tracking errors, provided that the APFs avoid local minima. Finally, numerical simulations are performed to validate the efficacy of the proposed control scheme. © 2024 IEEE.

关键词： Backstepping

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Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy 63

Approximate state estimation with large-scale sensor network...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Liu, Xinhui Cheng, Meiqi Shi, Dawei Shi, Ling Beijing Institute of Technology Miit Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong

ISBN: (纸本)9798350316339

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of E(varepsilon)-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement. © 2024 IEEE.

关键词： Scheduling algorithms

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REDUNDANT DIGITAL BRAKE VALVE controlLER BIT DESIGN

REDUNDANT DIGITAL BRAKE VALVE CONTROLLER BIT DESIGN

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2024 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2024

作者： Jin, Hongtao Gao, Xiaolong Wu, Shuai Li, Chuandong School of Automation Science and Electrical Engineering Beihang University Beijing100191 China National Key Laboratory of Aircraft Integrated Flight Control Beijing100191 China Ningbo Institute of Technology Beihang University Ningbo315800 China

High-speed switching valves are widely used in aerospace and other fields due to their simple structure and high resistance to contamination. The digital brake valve controller is the driving device of the digital brake valve, which requires high reliability. In this paper, a redundant digital brake valve controller scheme is proposed, and the corresponding BIT circuit is designed based on the structural characteristics of the brake valve driving circuit. The reliability model of the controller is established based on Markov process theory and analysed by simulation, and the results show that the reliability of this redundant digital brake valve controller is mentioned to be greatly improved. © The Institution of engineering & Technology 2024.

关键词： Markov processes

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A Very-Short-Term Online PV Power Prediction Model Based on RAN With Secondary Dynamic Adjustment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1214-1224页

作者： Zhang, Tengfei Ma, Fumin Peng, Chen Yu, Yang Yue, Dong Dou, Chunxia O'Hare, Gregory M. P. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence The Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing210023 China Nanjing University of Finance and Economics College of Information Engineering Nanjing210023 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200072 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210023 China Trinity College Dublin School of Computer Science and Statistics Dublin 2 Ireland

Photovoltaic (PV) power is progressively being subsumed into power grids. As a consequence, reliable PV power forecasting has become essential in order to ensure the optimal functioning of the power grid. Neural networks remain the dominant prediction model utilized. Conventional neural network forecasting models are wholly dependent upon offline data. Subsequent to offline training, no further structural adjustments can be made during the forecasting process, which therefore they fail to cater for PV power supply fluctuations that are fundamentally dynamic. To address this failing, this article proposes a very-short-term online prediction model based on a resource-allocating network (RAN) incorporating a secondary dynamic adjustment. The RAN is initially trained offline to obtain a basic forecasting model. Thereafter, in an online prediction process, those samples with large prediction errors that exceed a preset value are recorded in a specific buffer. When the set conditions are triggered, the secondary dynamic adjustment strategy is employed, which enables the online prediction model to effectively relearn previously unmodeled samples while shielding external interference. Experimental results obtained from actual testing demonstrate the validity of the secondary dynamic adjustment strategy for online learning while also providing higher accuracy levels from the prediction model. © 2020 IEEE.

关键词： Forecasting

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High-performance and Multi-functional control of Transformerless Single-phase Smart Inverter for Grid-connected PV System

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Journal of Modern Power Systems and Clean Energy 2021年第6期9卷 1386-1394页

作者： Kamran Zeb Saif Ul Islam Waqar Uddin Imran Khan Muhammad Ishfaq Tiago Davi Curi Busarello H.J.Kim Department of Electrical Engineering Pusan National University School of Electrical Engineering and Computer Science National University of Sciences and Technology Department of Control Automation and Computing EngineeringFederal University of Santa Catarina Blumenau

Highly reliable and flexible control is required for distributed generation(DG) to efficiently connect to the *** inverters play a key role in the control and integration of DG into the power grid and provide advanced functionalities. In this paper, an energy-based single-phase voltage-source smart inverter(SPV-SSI) of 5 k VA is designed and analyzed in detail. SPV-SSI is capable of supplying the power to local load and the utility load up to the rated capacity of the inverter, injecting the power into the grid, storing the energy in lead-acid battery bank, controlling the voltage at the point of common coupling(PCC) during voltage sags or faults, and making decisions on real-time pricing information obtained from the utility grid through advanced metering. The complete design of smart inverter in dq frame, bi-directional DC-DC buck-boost converter, IEEE standard 1547 based islanding and recloser, and static synchronous compensator(STATCOM) functionalities is presented in this paper. Moreover, adaptive controllers, i. e., fuzzy proportional-integral(F-PI) controller and fuzzy-sliding mode controller(F-SMC) are designed. The performances of F-PI controller and F-SMC are superior, stable, and robust compared with those of conventionally tuned PI controllers for voltage control loop(islanded mode) and current control loop(grid-connected mode).

关键词： Distributed generation(DG) inverter convert-er fuzzy-sliding mode controller(F-SMC) fuzzy proportionalintegral(F-PI)controller

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