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第41届中国控制会议

作者： Li Kai Li Zhaorui Gui Huazhan Lan Chunyun Chen Wei Yuan Feng School of Instrumentation Science and Engineering Harbin Institute of Technology Beijing Aerospace Automatic Control Institute

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Induction motors usually use soft starters with three-phase six anti-parallel thyristors as the driving device to achieve motor soft start and reduce energy *** to the half-wave rectification effect of the thyristor,the stator voltage and current are very likely to generate many harmonics at the moment of commutation,which is the main source of motor failure,will bring losses to the motor,and even affect the reliable operation of the *** at this problem,taking the thyristordriven induction motor system as the research object,the circuit mathematical model of the object is established,the typical fault data set is constructed in the soft-start process,and the system operating state is evaluated by the spectral characteristics.A novel fault prediction model based on convolution neural network is proposed,the calculation results are verified by computer *** the final experiment proves that this method is effective to predict fault type in most situation.

关键词： Fault diagnosis Condition evaluation Soft start Induction Motor

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Virtual flight deck crew assistance utilizing artificial intelligence methods to interpret NOTAMs: a user acceptance study

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CEAS Aeronautical Journal 2024年第4期15卷 1137-1144页

作者： Dieter, Michelle Sprenger, Eric Pasnicu, Otilia Staudt, Josefine Ellenrieder, Nils Institute of Flight Systems and Automatic Control Technical University of Darmstadt Otto-Berndt-Str. 2 Darmstadt64287 Germany Work and Engineering Psychology Research Group Technical University of Darmstadt Alexanderstr. 10 Darmstadt64283 Germany

Over the past few years, artificial intelligence (AI) and machine learning (ML) methods have become deeply embedded in everyday applications. Great progress has been made, which is why these methods are now being introduced to other areas including aviation. The use of such methods in safety-critical areas like aviation, medicine or jurisdiction, entails not only benefits but also challenges, such as regulatory and ethical aspects and the necessary acceptance of users and other stakeholders. This research paper presents the findings of a qualitative user study conducted to investigate the potential benefits of integrating AI on the flight deck to improve the usability of Notices to Airmen (NOTAMs). Currently, pilots receive a large amount of NOTAMs for each flight. This overload of information can lead to important information being overlooked. Semi-structured interviews with ten pilots were conducted to gather their perspectives on current challenges in their daily work with NOTAMs, acceptance of AI-based methods in aviation with focus on the flight deck and possible fields of applications. Recommendations are derived for future developments. The findings suggest that methods based on AI have the potential to improve the usability of NOTAMs, making them more efficient and user-friendly for pilots. Additionally, the study highlights the importance of addressing pilots’ concerns and taking into account human factors, safety considerations, and the need for effective human–machine collaboration. © Deutsches Zentrum für Luft- und Raumfahrt e.V. 2024.

关键词： Aviation medicine

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CONVERGENCE GUARANTEES OF MODEL-FREE POLICY GRADIENT METHODS FOR LQR WITH STOCHASTIC DATA

arXiv

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arXiv 2025年

作者： Song, Bowen Iannelli, Andrea Institute for Systems Theory and Automatic Control University of Stuttgart Germany

Policy gradient (PG) methods are the backbone of many reinforcement learning algorithms due to their good performance in policy optimization problems. As a gradient-based approach, PG methods typically rely on knowledge of the system dynamics. However, this information is not always available, and in such cases, trajectory data can be utilized to approximate first-order information. When the data are noisy, gradient estimates become inaccurate and a formal investigation that encompasses uncertainty estimation and the analysis of its propagation through the algorithm is currently missing. To address this, our work focuses on the Linear Quadratic Regulator (LQR) problem for systems subject to additive stochastic noise. After briefly summarizing the state of the art for cases with a known model, we focus on scenarios where the system dynamics are unknown, and approximate gradient information is obtained using zeroth-order optimization techniques. We analyze the theoretical properties by computing the error in the estimated gradient and examining how this error affects the convergence of PG algorithms. Additionally, we provide global convergence guarantees for various versions of PG methods, including those employing adaptive step sizes and variance reduction techniques, which help increase the convergence rate and reduce sample complexity. One contribution of this work is the study of the robustness of model-free PG methods, aiming to identify their limitations in the presence of noise and propose improvements to enhance their applicability. Numerical simulations show that these theoretical analyses provide valuable guidance in tuning the algorithm parameters, thereby making these methods more reliable in practically relevant scenarios. © 2025, CC BY.

关键词： Stochastic systems

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Link Transmission Stability Detection Based on Deep Learning in Opportunistic Networks 5th

Link Transmission Stability Detection Based on Deep Learning...

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5th EAI International Conference on Multimedia technology and Enhanced Learning, ICMTEL 2023

作者： Ren, Jun Wang, Ruidong Jia, Huichen Li, Yingchen Pei, Pei North Automatic Control Technology Institute Taiyuan030000 China Department of Foreign Languages Changchun University of Finance and Economics Changchun130200 China

ISBN: (纸本)9783031505768

In order to solve the low throughput and high delay problems of traditional link transmission stability detection methods, a detection method of link transmission stability in opportunistic networks based on deep learning is proposed. Establish the network link blocking model. Considering the impact of path delay, analyze the network link information to adjust the hierarchical structure, divide the link data into data blocks, and complete the construction of the link model. According to the link transmission data, the ground point coordinates of the network links in the area are obtained. Under the constraint of link carrying capacity, obtain the barcode sent by network transmission. Calculate the number of packets sent by the network source during congestion, extract network level features using deep learning algorithm, select the number of network layers, set hidden layer nodes, implement network training according to the learning rate, achieve the construction of classification prediction model, and complete the link transmission stability detection. The experimental results show that the proposed link transmission stability detection method can effectively improve the throughput of opportunistic network links and reduce the communication delay of opportunistic networks. © ICST institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Stability

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Towards an Auxiliary Perception Conception for Improvement of the Situation Awareness in UAV Operation 3

Towards an Auxiliary Perception Conception for Improvement o...

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3rd IEEE International Conference on Human-Machine systems, ICHMS 2022

作者： Liu, Jie Sui, Boyu Institute of Flight Systems and Automatic Control Tu Darmstadt Darmstadt64287 Germany

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

With the development of the Human Autonomy Teaming concept in the field of UAV operations and considering the limitations of onboard sensors and perception algorithms, this paper proposes a new engineering conception of auxiliary perception. The principle is to enhance the perception capabilities of UAVs through perceiving, understanding and interpreting the environment from human. A basic structural framework of the UAV auxiliary perception system is developed, and the functions of each subsystem under this structure are analyzed in terms of information flow processing. Moreover. the information exchange system is implemented to a simulation environment through a relational database. Finally, through the experiment, the effectiveness of the auxiliary perception system is verified. Future work and shortcomings of the current approach are also pointed out. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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RoG-SAM: A Language-Driven Framework for Instance-Level Robotic Grasping Detection

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IEEE Transactions on Multimedia 2025年 27卷 3057-3068页

作者： Mei, Yunpeng Sun, Jian Peng, Zhihong Deng, Fang Wang, Gang Chen, Jie Beijing Institute of Technology State Key Lab of Autonomous Intelligent Unmanned Systems Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Tongji University Department of Control Science and Engineering Shanghai201804 China

Robotic grasping is a crucial topic in robotics and computer vision, with broad applications in industrial production and intelligent manufacturing. Although some methods have begun addressing instance-level grasping, most remain limited to predefined instances and categories, lacking flexibility for open-vocabulary grasp prediction based on user-specified instructions. To address this, we propose RoG-SAM, a language-driven, instance-level grasp detection framework built on Segment Anything Model (SAM). RoG-SAM utilizes open-vocabulary prompts for object localization and grasp pose prediction, adapting SAM through transfer learning with encoder adapters and multi-head decoders to extend its segmentation capabilities to grasp pose estimation. Experimental results show that RoG-SAM achieves competitive performance on single-object datasets (Cornell and Jacquard) and cluttered datasets (GraspNet-1Billion and OCID), with instance-level accuracies of 91.2% and 90.1%, respectively, while using only 28.3% of SAM's trainable parameters. The effectiveness of RoG-SAM was also validated in real-world environments. © 1999-2012 IEEE.

关键词： Robot vision

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Adaptive Unscented Kalman Filter Based Sensor Fusion for Aircraft Positioning Relative to Unknown Runway

Adaptive Unscented Kalman Filter Based Sensor Fusion for Air...

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AIAA Science and technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Ghosh, Nabarun Maity, Arnab Intelligent Systems and Control Lab Department of Aerospace Engineering Indian Institute of Technology Bombay Maharashtra Mumbai400076 India

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper proposes an estimator to provide precise position, velocity, and orientation for a landing aircraft relative to the runway based on image sensors, IMU sensor data, and barometric sensor measurements. This work excludes GPS data, as it is expected that the proposed estimator can be used for fault detection in GPS and instrument landing systems. The runway is unknown, and hence, its width is estimated online. An adaptive UKF-based approach is adopted to estimate the states. In order to verify the nearer-to-practical performance and accuracy of bias estimation, realistically simulated data that constitute noises and biases from the sensors are taken into consideration. The proposed approach provides a solution for nonlinear state estimation in the presence of measurement noises that are non-additive. In addition to this, it provides estimates of the unknown measurement noise covariance. A comparative study is conducted between the conventional UKF and the proposed AUKF to analyze the adopted approach. Results show that the proposed algorithm gives comparable estimation results and faster convergence than the conventional UKF. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Fault detection

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Adaptive fuzzy backstepping control for attitude stabilization of flexible spacecraft with signal quantization and actuator faults

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Science China(Information Sciences) 2021年第5期64卷 135-150页

作者： Qiuhong LIU Ming LIU Guangren DUAN Research Institute of Intelligent Control Systems Harbin Institute of Technology Research Center of Satellite Technology Harbin Institute of Technology Center for Control Theory and Guidance Technology Harbin Institute of Technology

In this study, the fault-tolerant attitude control of flexible spacecraft is investigated over digital communication channels, where a uniform quantizer is considered with respect to the sensor signals and controller indexes. Further, an adaptive fuzzy backstepping control strategy has been developed for the considered attitude stabilization issue, where the adaptive fuzzy logic method is used to approximate the rigid-flexible coupled nonlinearity of the spacecraft. In this design, the online adjusting quantizer parameters are injected into the controller gains to simultaneously compensate for the quantization errors and timevarying actuator faults. In the proposed control method, the attitude stabilization task is achieved in the presence of external disturbances, time-varying actuator faults, and signal quantization. Finally, the practical examples are compared to demonstrate the effectiveness of the proposed control strategy.

关键词： fault-tolerant control attitude stabilization signal quantization adaptive fuzzy control backstepping control

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Multi-Agent Cooperation Decision-Making by Reinforcement Learning with Encirclement Rewards 42

Multi-Agent Cooperation Decision-Making by Reinforcement Lea...

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42nd Chinese control Conference, CCC 2023

作者： Rubing, Ma Bo, Wang Jingyuan, Jia Changchun, Li Hao, Dong School of Automation Beijing Institute of Technology Beijing100081 China Norinco Group North Automatic Control Technology Institute Taiyuan030006 China

ISBN: (纸本)9789887581543

Multi-agent decision-making is increasingly applied on many situations especially on military applications, but their autonomous decision-making ability needs to be improved. Multi-agent Deep Deterministic Policy Gradient (MADDPG) adopts the method of centralized evaluation and decentralized execution, while updates each agent's network parameters based on the global state information rather than only its own state, which can make the entire agent's policy network update in the direction of the global optimum, rather than the individual optimum. In the process of multi-agent cooperation decision-making, the encirclement rewards is introduced to guide agents to make cooperative actions and alleviate the problem of sparse rewards. Firstly, we define the encirclement. By using Graham's algorithm, we find out the effective encirclement of N agents. We evaluate the encirclement quality from the area of the encirclement and the difficulty of breaking-through for the adversary, and then design the rewards function based on this. Simulation experiments show that the convergence speed and win rate of MADDPG algorithm based on encirclement rewards is significantly improved, and it also has strong adaptability to various task scenarios. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Decision making

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CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS BY DEEP SUBSPACE ENCODING 11

CONTINUOUS-TIME IDENTIFICATION OF DYNAMIC STATE-SPACE MODELS...

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11th International Conference on Learning Representations, ICLR 2023

作者： Beintema, Gerben I. Schoukens, Maarten Tóth, Roland Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. This paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. The proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Cost functions

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