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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6509-6523页

作者： Meng, Yizhen Liu, Chun Wang, Qiang Tan, Longyu Shanghai Aerospace Control Technology Institute Shanghai201109 China Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai201109 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China Shanghai University Institute of Artificial Intelligence Shanghai200444 China Northwestern Polytechnical University College of Astronautics Xi'an710072 China

This article investigates the distributed optimal strategy problem in multiagent pursuit-evasion (MPE) games, striving for Nash equilibrium through the optimization of individual benefit matrices based on observations. To this end, a novel collaborative control scheme for MPE games using communication graphs is proposed. This scheme employs cooperative advantage actor-critic (A2C) reinforcement learning to facilitate collaborative capture by pursuers in a distributed manner while maintaining bounded system signals. The strategy orchestrates the actions of pursuers through adaptive neural network learning, ensuring proximity-based collaboration for effective captures. Meanwhile, evaders aim to evade collectively by converging toward each other. Through extensive simulations involving five pursuers and two evaders, the efficacy of the proposed approach is demonstrated, and pursuers seamlessly organize into pursuit units and capture evaders, validating the collaborative capture objective. This article represents a promising step toward effective and cooperative control strategies in MPE game scenarios. © 2024 IEEE.

关键词： Reinforcement learning

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Data-driven finite-time adaptive attitude tracking control for spacecraft

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Advances in Space Research 2025年第12期75卷 8780-8791页

作者： Wang, Jianhui He, Guangping Bian, Guibin Geng, Shixiong College of Mechanical & Energy Engineering Beijing University Of Technology Beijing100124 China Department of Mechanical and Electrical Engineering North China University of Technology Beijing100144 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

This paper proposes a novel data-driven finite-time adaptive control method for the spacecraft attitude tracking control problem with inertial uncertainty. Based on the dynamic regression extension technique, the disturbance unit matrix is introduced, and the parameter estimation law in the form of algebraic equations is presented, which realizes the arbitrarily small convergence of parameter estimation error. The convergence of parameter estimation errors is verified through stability analysis. Globally non-singular fast terminal sliding mode control is designed to achieve finite-time arrival of the sliding surface and convergence of the system state error. The stability analysis of the closed-loop system demonstrates the effectiveness of the proposed method. Furthermore, the effectiveness and superiority of the proposed method are verified through numerical simulations of spacecraft attitude tracking control. © 2025 COSPAR

关键词： Attitude control

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Software Detection and Warning Method for Pitch Angle Deviation of Road Traffic Radar 9

Software Detection and Warning Method for Pitch Angle Deviat...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Sun, Haokai Zhang, Zheng Jari Technology Lianyungang Jari Electronics Co. Ltd Lianyungang China Jiangsu Automation Research Institute Control Science and Engineering Lianyungang China

ISBN: (纸本)9798350376548

Introduced a software detection and warning method for the installation angle of road traffic radar in the pitch direction. Establish a mathematical model M1 based on the directional pattern of the transmitting antenna in the elevation direction. According to the RCS definition, the normalized curve shape of the received target echo signal strength is the same as model M1. The normalization coefficient is determined by on-site calibration methods or empirical value methods, and the model M1 is inversely normalized to obtain the model M2. Substitute the statistical analysis result A of the received signal strength result P ' calculated after real-time and cumulative sampling into model M2 to obtain the current elevation angle information of the radar. If the change in radar pitch angle exceeds the threshold, an alarm message will be output. This method does not require the addition of hardware sensors, and only estimates the current elevation angle of the radar through software calculations based on mathematical models and on-site sampled target echo signal strength, which has good engineering application value. © 2024 IEEE.

关键词： Radar warning systems

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Robot-Based Auto-labeling System for 6D Pose Estimation 8th

Robot-Based Auto-labeling System for 6D Pose Estimation

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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Lin, Hsien-I. Chang, Jun-Shiang Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate Institute of Automation Technology National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9789819930425

6D object pose estimation is an ongoing research area in the field of computer vision. Many existing methods rely on supervised deep learning models which require multiple accurate 6D pose annotations to predict object poses. However, labeling the 6D pose is complex and time-consuming in traditional methods. In this study, we propose a robotic-arm-based 6D object pose auto-labeling approach which has limited human interaction involved. Translations and rotations of the object in the camera coordinate system can be calculated using a sequence of known robot poses and the transformation between the camera and the robot. We also implemented our custom dataset generated by the auto-labeling system in the existing 6D object pose estimation approach. Evaluation results show that the model can recognize our own test dataset and attempted 90% accuracy using ADD metric with 0.05 threshold. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Robotic arms

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Safety-Critical Randomized Event-Triggered Learning of Gaussian Process With Applications to Data-Driven Predictive control

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IEEE Transactions on Automatic control 2024年第6期70卷 3920-3935页

作者： Zheng, Kaikai Shi, Dawei Shi, Yang Beijing Institute of Technology MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Victoria Department of Mechanical Engineering Faculty of Engineering VictoriaBCV8N 3P6 Canada

Safety and data efficiency are important concerns in data-driven control, especially for nonlinear systems with unknown dynamics and subject to disturbances. In this work, we consider a class of control-affine nonlinear systems with partially unknown dynamics and aim to introduce an event-triggered learning-based control approach with guaranteed safety and improved data utilization efficiency. Specifically, a randomized learning approach is employed to evaluate the safety of state trajectories by defining and estimating its confidence interval, with data from a multisample of randomly generated state trajectories. Using the proposed randomized learning algorithm, a nominal trajectory with a high probability safety guarantee is designed, thus ensuring the disturbed system states to remain within a pre-specified range around the nominal trajectory with a sufficiently high probability. Through removing irrelevant data, a local prediction model around the nominal trajectory is learned with satisfactory precision, and is updated online using an event-triggered learning strategy. Based on the learned model, an efficient data-driven predictive controller is designed to force the system states to evolve within the vicinity of the designed safety nominal trajectory. The effectiveness of the proposed event-triggered learning and data-driven control approaches is validated through comprehensive simulation studies. © 1963-2012 IEEE.

关键词： Predictive control systems

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A Multi-party Asymmetric Self-play Algorithm and Its Application in Multi-USV Adversarial Game Simulations 24

A Multi-party Asymmetric Self-play Algorithm and Its Applica...

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2024 International Conference on Machine Learning, Pattern Recognition and automation engineering, MLPRAE 2024

作者： Rao, Jinjun Wang, Cong Liu, Mei Lei, Jingtao Giernacki, Wojciech Department of Precision Mechanical Engineering School of Mechatronic Engineering and Automation Shanghai University China Institute of Robotics and Machine Intelligence Faculty of Automatic Control Robotics and Electrical Engineering Poznan University of Technology Poland

ISBN: (纸本)9798400709876

Aiming at the problem that the combination of self-play (SP) and deep reinforcement learning (DRL) only involves two-party games and the policy learning of each party is limited, a multi-party asymmetric self-play algorithm (MASP) is proposed. Firstly, by improving the ELO scoring system, the ELO scoring takes into account each party of the unmanned surface vehicle (USV) clusters, and the imbalance of the number of USV clusters, so that the frequency of exchanging strategies of all USV clusters in the confrontation process is equal. Secondly, it ensures that USV clusters have a balanced combat ability, and at the same time ensures that the combat ability of all parties is strong and weak and the gap is not too wide. In addition, the parameters are dynamically set to reduce the update frequency of the policy of the stronger party. The experimental results show that the MASP can make the USV clusters learn more effective policies, have a shorter game time, and obtain higher rewards and ELO scores in the simple 2v2 adversarial game scenario and the three-party game scenario of a warship escort mission. © 2024 ACM.

关键词： Deep reinforcement learning

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Sustainable Mining in the Era of Artificial Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 1-4页

作者： Long Chen Yuting Xie Yutong Wang Shirong Ge Fei-Yue Wang IEEE the State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of SciencesBeijing 100190China the National Laboratory of Pattern Recognition at the Institute of Automation and Waytous Ltd.China the School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275GuangdongChina the School of Mechanical Electronic and Information Engineering China University of Mining and Technology BeijingBejing 100083China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China

The mining sector historically drove the global economy but at the expense of severe environmental and health repercussions,posing sustainability challenges[1]-[3].Recent advancements on artificial intelligence(AI)are revolutionizing mining through robotic and data-driven innovations[4]-[7].While AI offers mining industry advantages,it is crucial to acknowledge the potential risks associated with its widespread ***-reliance on AI may lead to a loss of human control over mining operations in the future,resulting in unpredictable consequences.

关键词： Sustainable mining consequences

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CNS/INS Integrated Navigation Method Based on Improved Adaptive CKF Algorithm 49

CNS/INS Integrated Navigation Method Based on Improved Adapt...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Wang, Dong Yang, Jing Xiong, Kai Li, Wenling School of Automation Science and Electrical Engineering Beijing University of Aeronautics and Astronautics Beijing China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9798350331820

Aiming at the problem that measurement model uncertainty restrains navigation accuracy in the celestial/ inertial (CNS/INS) integrated navigation system, a kind of CNS/INS integrated navigation method based on improved adaptive cubature Kalman filter (CKF) is proposed. In order to mitigate the influence of installation error and axis disturbance error of star camera, angular distance between starlight vector of stars and line of sight vector of space targets is employed to establish the measurement model;the measurement noise covariance matrix (MNCM) is estimated online to lower the model uncertainty;adaptive adjustment coefficient is constructed based on squared Mahalanobis distance (SMD) of measurement innovation to adjust forgetting factor online to further improve robustness and adaptability of the method. Simulation results demonstrate that the proposed method can achieve higher navigation accuracy compared to standard and classical method under measurement model mismatch. © 2023 IEEE.

关键词： Uncertainty analysis

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An Optimization Method for the Swing of a Quadrotor Flight Suspension System 25

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

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25th IEEE China Conference on System Simulation Technology and its Application, CCSSTA 2024

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

ISBN: (纸本)9798350366600

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. © 2024 IEEE.

关键词： Flight testing

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Optimal Multi-agent Collision-Free Path Planning with Temporal Logic Constraints 5th

Optimal Multi-agent Collision-Free Path Planning with Tempo...

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5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Zheng, Yiwei Liu, Zhaocong Lai, Aiwen Yu, Xiao Lan, Weiyao Department of Automation Xiamen University Fujian Xiamen361005 China The Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing100094 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9789811939976

In this paper, we present a novel optimal multi-agent collision-free path planning algorithm (OMPATL) for the problem of multi-robot path planning with temporal logic constraints. Given a high-level mission specification in the form of linear temporal logic (LTL), OMPATL can automatically synthesize a set of trajectories for the multi-agent system (MAS), which achieves the collision avoidance among agents and minimizes the cost of the maximum traveling time between successive satisfactions. To synthesize the optimal trajectories, the motion of all agents, together with the mission area, is modeled as a weighted team transition system (TS) with a set of atomic propositions (APs). If the collision avoidance were not required, the optimal trajectories would be easily synthesized by an algorithm such as shortest paths. However, once the multi-agent systems are required to be safety-critical and collision-free, these trajectories may become infeasible. In OMPATL, a strategy is proposed for adding a set of transitions including "wait" and "go-back" to each individual TS based on the optimal run which does not consider collision avoidance. Then, the states and transitions with collisions can be removed such that the optimal collision-free trajectories are founded by the synthesis algorithm. The synthesized run balances collision avoidance, mission fulfillment, and optimality. The optimal run is synthesized offline and online adjustments are not needed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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