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arXiv 2022年

作者： Lu, Xiangri Ma, Hongbin Wang, Zhanqing State Key Laboratory of Intelligent Decision Control for Complex Systems Beijing Institute of Technology Beijing China Automated Institute Beijing Institute of Technology Beijing China

To address the problem of imperfect confrontation strategy caused by the lack of information of game environment in the simulation of non-complete information dynamic countermeasure modeling for intelligent game, the hierarchical analysis game strategy of confrontation model based on OODA ring (Observation, Orientation, decision, Action) theory is proposed. At the same time, taking into account the trend of unmanned future warfare, NetLogo software simulation is used to construct a dynamic derivation of the confrontation between two tanks. In the validation process, the OODA loop theory is used to describe the operation process of the complex system between red and blue sides, and the four-step cycle of observation, judgment, decision and execution is carried out according to the number of armor of both sides, and then the OODA loop system adjusts the judgment and decision time coefficients for the next confrontation cycle according to the results of the first cycle. Compared with traditional simulation methods that consider objective factors such as loss rate and support rate, the OODA-loop-based hierarchical game analysis can analyze the confrontation situation more comprehensively. © 2022, CC BY.

关键词： decision theory

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Data-Driven Learning and control with Event-Triggered Measurements

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IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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A Real-Time Visual-Inertial Monocular Odometry by Fusing Point and Line Features

A Real-Time Visual-Inertial Monocular Odometry by Fusing Poi...

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第40届中国控制会议

作者： Chengwei Li Liping Yan Yuanqing Xia Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

In this paper, a monocular visual-inertial odometry that utilize both point and line features is deduced. Compared with point features, line features provide more geometric information of the environment, which are more reliable in textureless scenes. However, extracting line segment features from the image are very time consuming, which will affect the real-time performance of the system. To deal with this problem, EDLines line segment detector is introduced to replace the LSD *** properties of lines are utilized to reject the mismatching of line segment feature. Pl ¨ucker coordinates and orthonormal representation of lines are used to represent 3 D lines. Afterwards, we optimize the state by minimizing a cost function consists of pre-integrated IMU residuals and visual feature re-projection residuals in a sliding window optimization framework. The proposed odometry was tested on the public datasets. The results demonstrate that the presented system can operate in real time with high accuracy.

关键词： visual-inertial odometry(VIO) point and line feature visual simultaneous localization and mapping(SLAM)

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Online adaptive Q-learning method for fully cooperative linear quadratic dynamic games

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Science China(Information Sciences) 2019年第12期62卷 164-177页

作者： Xinxing LI Zhihong PENG Lei JIAO Lele XI Junqi CAI School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems

A model-based offline policy iteration(PI) algorithm and a model-free online Q-learning algorithm are proposed for solving fully cooperative linear quadratic dynamic games. The PI-based adaptive Q-learning method can learn the feedback Nash equilibrium online using the state samples generated by behavior policies, without sending inquiries to the system model. Unlike the existing Q-learning methods, this novel Q-learning algorithm executes both policy evaluation and policy improvement in an adaptive *** prove the convergence of the offline PI algorithm by proving its equivalence to Newton's method while solving the game algebraic Riccati equation(GARE). Furthermore, we prove that the proposed Q-learning method will converge to the Nash equilibrium under a small learning rate if the method satisfies certain persistence of excitation conditions, which can be easily met by suitable behavior policies. Our simulation results demonstrate the good performance of the proposed online adaptive Q-learning algorithm.

关键词： adaptive dynamic programming reinforcement learning Q-learning fully cooperative linear quadratic dynamic games policy iteration off-policy

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Information Fusion of Topological Structure and Node Features in Graph Neural Network

Information Fusion of Topological Structure and Node Feature...

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第40届中国控制会议

作者： Hongwei Zhang Can Wang Yuanqing Xia Tijin Yan Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

Graph neural networks(GNNs) have shown great popularity and achieved promising performance on various graph-based tasks in the past years. However, there is little work that explores the information fusion mechanism, which plays an import role in GNNs. Besides, datasets in the real world often have noises, which make the information fusion difficult. In this paper, we give an information-theoretic explanation. Specifically, we focus on how the information from topological structures and node features fuses and how different information contributes to the downstream task. Furthermore, we propose a general framework named M-GCN to express the fusion process in GNNs. Graph embeddings and feature graph are introduced to extract the information from topological structure and node features separately in M-GCN. Extensive experiments are conducted on several benchmark datasets and experimental results show that our proposed models are more robust and outperform state-of-the-art methods.

关键词： Information Fusion Graph Neural Network Graph embedding

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The Separate Meter in Separate Meter Out control System Using Dual Servo Valves Based on Indirect Adaptive Robust Dynamic Surface control

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Journal of systems Science & complexity 2019年第2期32卷 557-576页

作者： CHEN Guangrong WANG Junzheng WANG Shoukun ZHAO Jiangbo SHEN Wei The Key Laboratory of Drive and Control of Servo Motion Systems Beijing Institute of Technology The Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Due to the demand for energy efficiency in electro-hydraulic systems, the separate meter in and separate meter out(SMISMO) control system attracts vast attention. In this paper, the SMISMO control system was configured with two servo valves to control the meter in and meter out *** designing two of the proposed indirect adaptive robust dynamic surface controllers(IARDSC)for the working-side and off-side system and setting the coupled items as estimated parameters, the SMISMO control system was decoupled into two subsystems completely. Here, indirect adaptive robust control(IARC) was employed to address the internal parameter uncertainties and external *** surface control(DSC) was utilized in the backstepping design procedure of IARC to deal with the inherent ‘explosion of terms' problem. As thus, the proposed IARDSC could simplify the design procedure, decrease the computational cost, and achieve an improved control performance in practical use. Finally, experimental results validated the effectiveness of proposed IARDSC and showed that the proposed SMISMO control system could provide a possibility to save more energy.

关键词： Dynamic surface control indirect adaptive robust control parameter estimation SMISMO stability analysis

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Distributed Cooperative Search Method for Multi-Uav with Unstable Communications

SSRN

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SSRN 2023年

作者： Zhang, Huaqing Ma, Hongbin Mersha, Bemnet Wondimagegnehu Zhang, Xiaofei Jin, Ying School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institution of Technology Beijing100081 China School of Vehicle and Mobility Tsinghua University Beijing100084 China

In search-attack and search-rescue tasks performed by multi-UAV, cooperative search plays an important part. The majority of approaches in use today assume that the UAV swarm's communication network is complete. However, these links are susceptible to environmental changes or adversary interference. Aiming at cooperative search in search-attack and search-rescue tasks, we propose a distributed cooperative search method for multi-UAV with unstable communications (DCS-UC), which is developed based on ant colony optimization (ACO). In this method, three algorithms are presented to enable the UAV swarm to conduct online cooperative search efficiently and safely. Firstly, an algorithm of pheromone matrix consensus update used in ACO is designed under switching and connected topology graphs, which can make the pheromone matrix maintained by each UAV achieve consensus, so that the cooperative efficiency of the UAV swarm is improved. Then, a position consensus update algorithm is presented where the position vector, including the positions of all the UAVs in each UAV, can achieve consensus under switching and connected topology graphs. Finally, to tackle the collision avoidance of all the UAVs, an algorithm of collision avoidance between UAVs is designed by using the obtained consistent positions of all the UAVs, which can make the UAV swarm conduct cooperative search safely. Extensive physical simulation results carried out in Gazebo verify the advantages of our multi-UAV cooperative search method. © 2023, The Authors. All rights reserved.

关键词： Ant colony optimization

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Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

arXiv

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arXiv 2024年

作者： Chen, Zhuo Ou, Ni Jiang, Jiaqi Luo, Shan Robot Perception Lab Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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TransForce: Transferable Force Prediction for Vision-based Tactile Sensors with Sequential Image Translation

arXiv

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arXiv 2024年

作者： Chen, Zhuo Ou, Ni Zhang, Xuyang Luo, Shan The Robot Perception Lab Centre for Robotics Research Department of Engineering King's College London LondonWC2R 2LS United Kingdom The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Vision-based tactile sensors (VBTSs) provide high-resolution tactile images crucial for robot in-hand manipulation. However, force sensing in VBTSs is underutilized due to the costly and time-intensive process of acquiring paired tactile images and force labels. In this study, we introduce a transferable force prediction model, TransForce, designed to leverage collected image-force paired data for new sensors under varying illumination colors and marker patterns while improving the accuracy of predicted forces, especially in the shear direction. Our model effectively achieves translation of tactile images from the source domain to the target domain, ensuring that the generated tactile images reflect the illumination colors and marker patterns of the new sensors while accurately aligning the elastomer deformation observed in existing sensors, which is beneficial to force prediction of new sensors. As such, a recurrent force prediction model trained with generated sequential tactile images and existing force labels is employed to estimate higher-accuracy forces for new sensors with lowest average errors of 0.69N (5.8% in full work range) in x-axis, 0.70N (5.8%) in y-axis, and 1.11N (6.9%) in z-axis compared with models trained with single images. The experimental results also reveal that pure marker modality is more helpful than the RGB modality in improving the accuracy of force in the shear direction, while the RGB modality show better performance in the normal direction. Copyright © 2024, The Authors. All rights reserved.

关键词： RGB color model

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Path Tracking control of Tracked Paver Based on Improved Pure Pursuit Algorithm

Path Tracking Control of Tracked Paver Based on Improved Pur...

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第40届中国控制会议

作者： Shuai Wang Shanshan Fu Bin Li Shoukun Wang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

This paper investigates the high-precision path tracking control of tracked paver combined with global satellite navigation *** the paver is performing paving operations,it requires high path tracking accuracy and good vehicle ***,considering the influence of road curvature on path tracking accuracy and vehicle stability,and the situation that the vehicle can not move quickly to the expectation path when the lateral position of the vehicle deviates from the expectation path,this paper proposes a lateral path tracking control method based on improved Pure Pursuit *** control method is verified through *** experimental results show that the maximum lateral tracking error of the improved algorithm is 0.04 m,which is 55.56% lower than that of the original algorithm,and the average lateral tracking error is 0.02 m,which is60% lower than that of the original *** purpose of high-precision path tracking of the paver is realized.

关键词： Tracked paver Path tracking Road curvature Pure Pursuit algorithm

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