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第34届中国控制与决策会议

作者： Pengcheng Hu Chaochen Gu Kaijie Wu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information ProcessingMinistry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

Aiming at the sudden change of load torque and uncertain external disturbance that multi-axis serial manipulators may face during operation,an improved Active Disturbance Rejection controller(ADRC) assisted by a Nonlinear Disturbance Observer(NLDOB) is designed and implemented in this *** realizes the estimation and compensation of known model parameter perturbation,and NLDOB is introduced to estimate and compensate the load torque and other unmodeled dynamic factors of the ***,the lumped control object model of three-bar mechanism and permanent magnet DC motor is established,and the control response curves of improved ADRC,traditional ADRC,and PID under the same conditions are compared and analyzed through *** experimental results show that compared with the traditional ADRC and PID,the improved ADRC has faster response speed,smaller overshoot and stronger robustness.

关键词： ADRC Multi-axis serial manipulator Permanent magnet DC motor NLDOB Load estimation

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Discrete particle swarm algorithm with Q-Learning for solving flexible job shop scheduling problem with parallel batch processing machine 2

Discrete particle swarm algorithm with Q-Learning for solvin...

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2022 2nd International Conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2022

作者： Song, Libo Liu, Chang Shi, Haibo Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

For the flexible job shop scheduling problem with parallel batch processing machines (FJSP-PBPM), the mathematical model of the problem is defined, and an improved enhanced discrete particle swarm algorithm is proposed to solve this problem. In terms of global search, an improved discrete particle swarm algorithm is adopted, and effective particle velocity and position update formulas are designed;in the local search, in order to speed up the convergence speed and avoid local convergence, it is integrated into the q-learning method in reinforcement learning. The state space, action space, reward function, and Q-table update methods that meet the scheduling problem are designed, and the proposed hybrid algorithm achieves an effective balance between global search and local search capabilities. At the same time, according to the characteristics of FJSP-PBPM, effective process sequence scheduling rules and batch machine selection rules are designed respectively to improve the search ability of the algorithm. Through the standard FJSP benchmark problem and the production data of a transformer manufacturer that meets the characteristics of the FJSP-PBPM problem, the validity and feasibility of the proposed algorithm are verified. © Published under licence by IOP Publishing Ltd.

关键词： Reinforcement learning

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Digital Twin-Based Service System Platform

Digital Twin-Based Service System Platform

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Ma, Zeyu Wang, Hongjun Zhang, Mingliang School of Mechanical Electrical Engineering Beijing University of Information Science and Technology Beijing100192 China Beijing International Science Cooperation Base of High-End Equipment Intelligent Perception Beijing100192 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100192 China Beijing Institute of Radio Measurement Beijing100854 China School of Information Management Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9783031702341

This paper discusses the integration and application of digital twin technology within the manufacturing sector, underscoring its pivotal role in advancing the Industry 4.0 revolution. Digital twin technology, which creates dynamic virtual replicas of physical systems, facilitates enhanced simulation, analysis, and optimization of real-world processes. The implementation of this technology, illustrated through examples from NASA and the U.S. Air Force Research laboratory, demonstrates its significant contributions across the entire product lifecycle, including design, manufacturing, service operation, and maintenance. The paper introduces a comprehensive framework for a digital twin-based production line service system, leveraging the Internet of Things (IoT) technology and flexible simulation to enable real-time user-application interaction, front-end operation responsiveness, and efficient data management. This system enhances operational efficiency, reduces time-to-market, and improves issue prediction and mitigation through virtual-real mapping and debugging. An application case using MongoDB highlights the technology's adaptability and effectiveness in managing complex data structures. The study concludes that digital twin technology is instrumental in driving smart manufacturing, innovation, and maintaining a competitive advantage in the evolving industrial landscape. key references provide a foundation for understanding the technology's development and application, reinforcing the digital twin's role as a transformative element in manufacturing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： NASA

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Health prognostics framework of lithium-ion batteries for real-world electric vehicles based on charging data

Health prognostics framework of lithium-ion batteries for re...

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International Conference on Power Electronics and Motion control (IPEMC)

作者： Yi Yang Xiaohua Wu Gang Yang Ji Yan Vehicle Measurement Control and Safety Key Laboratory of Sichuan Province School of Automobile and Transportation Xihua University Chengdu China Sichuan Engineering Research Center of Intelligent Control and Simulation Test Technology for New Energy Vehicles Xihua University Chengdu China

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

Lithium-ion batteries’ state of health (SOH) is critical to safely managing electric vehicles (EVs). This paper proposes a battery health prognostic framework based on charging data for real-world EVs. First, the capacity of the battery is calculated using the ampere-hour integration method based on historical charging data. The capacity trend is fitted using the locally weighted linear regression (LWLR) algorithm and the effect of charging depth on the capacity calculation results is eliminated. Then, the features strongly correlated with battery capacity are extracted using Pearson correlation analysis (PCA). Finally, Gaussian process regression (GPR) is employed to establish the capacity estimation model of lithium-ion batteries. The effectiveness and accuracy of the proposed method are validated on EVs from different regions. The results indicate that the mean absolute error (MAE) and the root mean square error (RMSE) of the estimated capacity on the test set are both less than 0.5.

关键词： Lithium-ion batteries Accuracy Estimation Feature extraction Market research Electric vehicles State of charge

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Multi-target Cooperative Path Planning for Air-Sea Heterogeneous Unmanned System

Multi-target Cooperative Path Planning for Air-Sea Heterogen...

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International Symposium on Autonomous Systems (ISAS)

作者： Shaoxin Qin Xingwang Yang Wangcheng Zhang Mingyang Sun Xingyu Liu Ziyang Zhen State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China CASIC Research Institute of Intelligent Decision Engineering Wuhan China

This paper focuses on the cooperative path planning of multi-target heterogeneous unmanned systems in air-sea. In the face of multiple targets, Unmanned Aerial Vehicles (UAV) flies fast, but its range and load capacity are limited; Unmanned Surface Vehicles (USV) has strong load capacity and long endurance, but its navigation speed is slow. This paper studies the path planning problem of the air-sea heterogeneous unmanned system composed of UAVs and USVs for a large range and multiple targets, taking the time for the unmanned system to complete this task as the optimization objective. Firstly, distinguish targets with ReConDensity-Ratio based clustering algorithm, and find appropriate refueling points for UAVs and USVs in each classification. Secondly, the Double-deck Travel Modified Particle Swarm Optimization algorithm (DT-MPSO) is used to plan the path of targets and find the shortest route. Finally, the effectiveness of the proposed method is verified by simulation experiments.

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Achieving Network Resilience through Graph Neural Network-enabled Deep Reinforcement Learning

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IEEE Network 2025年

作者： Li, Xuzeng Zhang, Tao Wang, Jian Han, Zhen Liu, Jiqiang Kang, Jiawen Niyato, Dusit Jamalipour, Abbas Beijing Jiaotong University School of Cyberspace Science and Technology China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Guangdong University of Technology Technology School of Automation China Nanyang Technological University College of Computing and Data Science Singapore University of Sydney SydneyNSW2006 Australia

Deep reinforcement learning (DRL) has been widely used in many important tasks of communication networks. In order to improve the perception ability of DRL on the network, some studies have combined graph neural networks (GNNs) with DRL, which use the GNNs to extract unstructured features of the network. However, as networks continue to evolve and become increasingly complex, existing GNN-DRL methods still face challenges in terms of scalability and robustness. Moreover, these methods are inadequate for addressing network security issues. From the perspective of security and robustness, this paper explores the solution of combining GNNs with DRL to build a resilient network. This article starts with a brief tutorial of GNNs and DRL, and introduces their existing applications in networks. Furthermore, we introduce the network security methods that can be strengthened by GNN-DRL approaches. Then, we designed a framework based on GNN-DRL to defend against attacks and enhance network resilience. Additionally, we conduct a case study using an encrypted traffic dataset collected from real IoT environments, and the results demonstrated the effectiveness and superiority of our framework. Finally, we highlight key open challenges and opportunities for enhancing network resilience with GNN-DRL. © 2025 IEEE. All rights reserved,

关键词： Deep reinforcement learning

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A Self-Adaptive Trust Region Method to Solve the Tensor Eigenvalue Complementarity Problems 23

A Self-Adaptive Trust Region Method to Solve the Tensor Eige...

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Proceedings of the 3rd International Conference on Electronic Information Technology and Smart Agriculture

作者： Ping Wang Jianguo Liu Yong Zhang Yueming Xiang School of Mathematics and Computational Science Huaihua University China School of Mathematics and Computational Science Huaihua University China and Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Huaihua University China

ISBN: (纸本)9798400716775

In this paper, according to utilizing a self-adaptive technique, we present a self-adaptive trust region method for effectively solving tensor eigenvalue complementarity problems of various structures. Our proposed method demonstrates global convergence under suitable conditions. The numerical examples conducted in this study highlight the superior efficiency of our approach in comparison to the inexact Levenberg-Marquardt method and modified spectral PRP conjugate gradient method for computing tensor eigenvalue complementarity problems.

关键词： Generalized tensor eigenvalue complementarity problem Global convergence Self-adaptive technique Trust region

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Barrier Offset Varying-Parameter Dynamic Learning Network for Solving Dual-Arms Human-Like Behavior Generation

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Zhang, Zhijun Zhang, Mingyang Guo, Jinjia He, Haotian School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Key Library of Autonomous Systems and Network Control Ministry of Education China Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China Guangzhou510335 China Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shanxi University of Technology Hanzhong723001 China School of Information Science and Engineering Changsha Normal University Changsha410100 China School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China School of Computer Science and Engineering Jishou University Jishou416000 China

Enabling robots to imitate human actions and perform tasks with high precision while avoiding potential obstacles in the environment can effectively enhance the interaction between social robots and humans. In this paper, to achieve higher precision trajectory tracking and obstacle avoidance for dual-arm humanoid robots, the barrier offset varying-parameter dynamic learning neural (BOVDL) network method is proposed and applied to dual-arm humanoid behavior generation scheme. To do so, a dual-arms humanoid robot model is set up, and transformed into a constrained time-varying quadratic programming (TVQP) problem. Secondly, by using lagrangian multiplier method and Karush-Kuhn-Tuchker condition, the inequality constrained TVQP is converted as a time-varying equation with a barrier parameter. Thirdly, a varying-parameter dynamic learning network is presented to solve the time-varying equation with a barrier parameter. Computer simulation experiments are conducted to verify the feasibility, accuracy, and safety of the proposed BOVDL network method. Experimental results show that all 14 joints of the humanoid robot's arms are within the motion range of each real human arm's physical constraints. The maximum position error and velocity error between the desired trajectory and the actual trajectory of the end effector are less than 10-6m magnitude and 10-7m magnitude, respectively, representing a reduction of 5 orders of magnitude compared to the traditional varying-parameter convergent-differential neural network. Furthermore, the proposed method also enables the dual-arm humanoid robot to avoid collisions with obstacles while performing tasks, demonstrating the superiority of the proposed BOVDL network scheme. © 2016 IEEE.

关键词： Anthropomorphic robots

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The improved approach for calculating residual error rates in SIS communication 12

The improved approach for calculating residual error rates i...

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12th International Conference on CYBER Technology in automation, control, and intelligent Systems, CYBER 2022

作者： Zhou, Xiufang Xu, Aidong Yan, Bingjun Sun, Yue Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The residual error rate is an essential parameter for evaluating the functional safety of a communication system, based on the requirements of safety communication in the safety instrumented system (SIS). The traditional approach of quantifying residual error rates is incomplete in some cases. The proposed improvements focus on the different cases addressing full and partial redundancy models, time expectations and different verification approaches for connection identification. A hypergeometric distribution approach is used when the partial message redundancy mode is considered. © 2022 IEEE.

关键词： Redundancy

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Protein-Folding Inspired Programmable control System for Robot Swarms

Protein-Folding Inspired Programmable Control System for Rob...

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IEEE International Conference on Robotics and Biomimetics

作者： Feng Zhang Yuping Gu Zebin Wang Shuai Yuan Yongliang Yang School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang Liaoning China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Liaoning China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang Liaoning China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In swarm robotics, most of the control algorithms are inspired by the behaviors of animal swarms. The capability gap between robots and animals, however, hinders the reliability of these algorithms. Toward developing reliable algorithms of robot swarms, a promising solution is learning from the collective behaviors of low-intelligent natural swarms, such as cell collectives and molecular assemblies. Here, we proposed a programmable control system inspired by protein folding. Using our homemade RoboFold platform, we demonstrated a protein-folding inspired programmable control method for robot swarms. By programming and designing the sequence of a thread of protein-like robots, the developed system can mimic the formation of hierarchical structures in robot swarms as the three-level of protein structures. We quantitatively characterized the specific features of self-organized morphologies. The experimental results together demonstrated the feasibility of this method. To summarize, this work successfully translated the principles of protein-folding into programming methods to control the morphology of robot swarms. Considering that the protein folding problem is essentially a physicochemical problem, this system is not only beneficial for the control of swarm robots but also has the potential to develop into a tool for studying the protein folding problem.

关键词： Proteins Vibrations Programmable control Animals Morphology Swarm robotics Programming control systems Robustness Robots

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