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Chinese control Conference (CCC)

作者： Kun Gong Fang Deng Jie Chen School of Automation Beijing Institute of Technology Beijing Beijing CN Ministry of Education Key Laboratory of Complex System Intelligent Control and Decision Beijing Beijing CN Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing Beijing CN

Based on the comparison of several common methods of electronic compass error compensation, this paper presents a new error compensation method based on Adaptive Differential Evolution-Fourier Neural Networks (ADE-FNN) to improve the measurement accuracy of electronic compass. This method uses Fourier neural network to model electronic compass error, and adopts Adaptive Differential Evolution to optimize the weights of neural network, and get more exact error model to compensate measured values. The compensation object is the common electronic compass composed by two-dimensional magnetic resistance sensor. Compared with the compensation effect of Least-square method, BP neural network and Fourier neural networks, It proves that the mode of this method can realize the high precision in the sample space mapping and high non-linear approximation ability, and this method has faster convergence rate, can avoid falling into local minima, reduces the training error, and improves error compensation accuracy. This method decreases the error range from -3.4° ~ 25.2° before compensation to -0.20° ~ 0.72°, and the average of the absolute error is 0.30°. Repeatability tests also proved the compensation plan have a good consistency.

关键词： Compass Error compensation Adaptive systems Electronic mail Neural networks Training Measurement uncertainty

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Adaptive robust controller for electric cylinder with fast parameter estimation based on modified lugre model

Adaptive robust controller for electric cylinder with fast p...

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第36届中国控制会议

作者： Duo yang Li Jun Zheng Wang Ren Jian Hao Shou Kun Wang Wei Shen Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

In this paper,the adaptive robust control（ARC） algorithm is proposed for tracking control of the electric cylinder *** to the analysis of the working principle of the electric cylinder servo system,the model of the system is established on the modified Lugre friction model,and single observer is designed for calculating friction as *** the ARC controller and adaptive law are designed for on-line parameters estimation,frictional compensation and the suppression external *** the Lyapunov approach,the stability of this system can be obtained in this *** simulation result shows that ARC can suppresses external disturbances effectively and realize real-time online parameter estimation.

关键词： adaptive robust control Lugre model parameter estimation

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Multi-robot distributed cooperative monitoring of mobile targets 3rd

Multi-robot distributed cooperative monitoring of mobile tar...

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3rd International Conference on Intelligence Science, ICIS 2018

作者： Jiang, J.Q. Xin, B. Dou, L.H. Ding, Y.L. School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9783030013127

Cooperative monitoring targets of mobile robots is of great importance in military, civil, and medical applications. In order to achieve multi-robot coordinated monitoring, this paper proposes a new distributed path planning algorithm. In this algorithm, a method of dynamic sequential decision making is used firstly to determine which robots should be moved to the neighbourhoods of the target path. A distributed cooperative path planning algorithm is then proposed in which the robots can only utilize their local information to make the joint monitoring areas of robots cover the target path. To reduce the chance of detection and ensure enough time to implement monitoring handover, our distributed algorithm can guarantee enough overlapped monitoring areas between adjacent robots. The effectiveness of the algorithm was verified by simulation experiments and a comparison experiment in typical scenarios. © IFIP International Federation for Information Processing 2018.

关键词： Monitoring

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Distributed Tracking for Multiple Lagrangian systems Using Only Position Measurements.

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IFAC Proceedings Volumes 2014年第3期47卷 287-292页

作者： Qingkai Yang Fengyi Zhou Jie Chen Xin Li Hao Fang School of Automation Beijing Institute of Technology Beijing 100081 Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 China

This paper investigates the distributed tracking control problem for multiple Lagrangian systems under a general directed graph where only a portion of the agents have access to the desired time-varying trajectory. To overcome the problem that only positions are measured, a observer is designed to estimate the velocity for each follower. By employing the estimated states, the distributed observer-based controller is proposed using only position measurements. Furthermore, the condition for the distributed tracking problem on the directed graph is derived, such that the tracking errors and observer errors semi-globally converge to zero. Finally, simulation examples are provided to show the effectiveness of the proposed control algorithms.

关键词： Multi-agent systems Distributed control Coordinated tracking Lagrangian system Velocity observer

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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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Differentiator-Based Adaptive H8 Tracking control of Fully Actuated systems 3

Differentiator-Based Adaptive H8 Tracking Control of Fully A...

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3rd Conference on Fully Actuated system Theory and Applications, FASTA 2024

作者： Feng, Yuxin Li, Zhiqiang Liu, Yang He, Zhaoshui Li, Hongyi Guangdong University of Technology School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao266061 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China

ISBN: (纸本)9798350373691

This article addresses an adaptive tracking control problem for uncertain high-order fully actuated (HOFA) systems with unknown parameters and disturbances. Under the framework of backstepping, the unknown parameter is estimated and the external disturbance is handled by using adaptive control and H8 technique, respectively. Moreover, the Levant differentiator is employed to reduce the computation burden. Additionally, the boundedness of signals in the closed-loop system is proven using the Lyapunov theory. Finally, the effectiveness of the proposed scheme is validated through the simulation study. © 2024 IEEE.

关键词： Backstepping

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Time-Varying Formation control for Multiagent systems with Different Time Delays 9

Time-Varying Formation Control for Multiagent Systems with D...

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9th International Conference on Fuzzy systems and Data Mining, FSDM 2023

作者： Sun, Fenglan Liang, Qinbo Zhu, Wei Li, Qiuying Key Lab of Intelligent Analysis and Decision on Complex Systems School of Science Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781643684703

This article studies the time-varying formation (TVF) problem of multiagent systems (MASs) with different time delays. By designing the control protocol, the followers could achieve the desired TVF. Considering different communication delays, based on graph theory and stability theory, sufficient conditions for the system to realize TVF are obtained. Finally, the numerical example is employed to validate the effectiveness of theoretical results. © 2023 The authors and IOS Press.

关键词： Multi agent systems

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A review of cooperative path planning of an unmanned aerial vehicle group

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Frontiers of Information Technology & Electronic Engineering 2020年第12期21卷 1671-1694页

作者： Hao ZHANG Bin XIN Li-hua DOU Jie CHEN Kaoru HIROTA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081China Department of Computational Intelligence and Systems Science Tokyo Institute of TechnologyTokyo 1528550Japan

As a cutting-edge branch of unmanned aerial vehicle(UAV)technology,the cooperation of a group of UAVs has attracted increasing attention from both civil and military sectors,due to its remarkable merits in functionality and flexibility for accomplishing complex extensive tasks,e.g.,search and rescue,fire-fighting,reconnaissance,and *** path planning(CPP)is a key problem for a UAV group in executing tasks *** this paper,an attempt is made to perform a comprehensive review of the research on CPP for UAV ***,a generalized optimization framework of CPP problems is proposed from the viewpoint of three key elements,i.e.,task,UAV group,and environment,as a basis for a comprehensive classification of different types of CPP *** following the proposed framework,a taxonomy for the classification of existing CPP problems is proposed to describe different kinds of CPPs in a unified ***,a review and a statistical analysis are presented based on the taxonomy,emphasizing the coordinative elements in the existing CPP *** addition,a collection of challenging CPP problems are provided to highlight future research directions.

关键词： Unmanned aerial vehicle group Cooperation Path planning Optimization problem

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Graph-based Selection-Activation Reinforcement Learning for Heterogenous Multi-agent Collaboration

Graph-based Selection-Activation Reinforcement Learning for ...

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第43届中国控制会议

作者： Hao-Xiang Chen Xi-Wen Zhang Jun-Nan Shen State Key Laboratory of Intelligent Control and Decision of Complex System School of Automation Beijing Institute of Technology Chongqing Innovation Center Beijing Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In practical applications of multi-agent systems, agents are often heterogeneous, and each type of them typically has different task objectives. For heterogeneous multi-agent reinforcement learning(HMARL), the diversity of agent types and the unbalanced agent number of each type can lead to the curse of dimensionality and non-stationary. Moreover, the increase in the number of heterogeneous agents may result in slow convergence during training. This paper proposes a graph-based selection-activation reinforcement learning(GSARL) method for training heterogenous multi-agent collaboration strategies. It first constructs agents based on their types, then extracts the global adjacency matrices and the ally adjacency matrices from the agents' observations, and calculates the global feature matrices. Afterwards, GSARL utilizes hierarchical graph convolutional network to sequentially convolve the global information and ally information, obtaining action logits based on agent types. By using the neural topology graph and the selection-activation method, the optimal multi-agent collaboration configuration is obtained through combinatorial optimization. Experiments are conducted in an adversarial combat simulation environment involving collaborative Unmanned Aerial Vehicles(UAVs) and Unmanned Ground Vehicles(UGVs). Simulation results show that the proposed method can accelerate convergence while allowing that each type of heterogeneous agents can leverage its unique advantages.

关键词： Multi-Agent system Hierarchical Graph Convolutional Network Heterogenous Multi-Agent Collaboration

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Research on Weighted Model of Air Defense Weapon-Target Assignment Based on AHP and Entropy Method

Research on Weighted Model of Air Defense Weapon-Target Assi...

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Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), International Conference on

作者： Puyang Zhang School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex System Beijing P. R. China

ISBN: (数字)9781728181431

ISBN: (纸本)9781728181448

In many previous studies, the analytical model of weapon-target assignment (WTA) is only based on the maximum damage to hostile targets. In order to overcome the deficiency of single decision-making, three sub models are established, which not only take the maximum damage to hostile targets, but also minimum loss and shortest interception time as the optimization objectives. In the meantime, the established models combine the air defense operation pattern and the characteristics of multiple air defense platforms. Based on the sub models, a weighted WTA model is proposed in this study using analytic hierarchy process (AHP) and entropy method. Through optimization, the optimal air defense WTA scheme is obtained by the weighted WTA model, and the optimization results are analyzed. Comparative study demonstrates that it is more reasonable to optimize the WTA scheme with multi-objectives.

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