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42nd Chinese control Conference, CCC 2023

作者： Lu, Huangyuwei Wang, Zuo Sun, Jiankun Xiao, Xi Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Shenzhen Research Institute Southeast University Shenzhen518063 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration Nanjing211106 China

ISBN: (纸本)9789887581543

To improve the disturbance rejection ability and repetitive tracking accuracy of manipulators, a composite iterative learning control (ILC) scheme via generalized proportional integral observer (GPIO) is proposed. A high-order polynomial is first employed to model the time-varying disturbances that include unmodeled dynamics, parameter uncertainties and load fluctuations. A GPIO is then designed to estimate the disturbances and high-order time derivatives. By introducing estimations into a PD-type ILC at each iteration, a composite control scheme is obtained, such that the undesirable influence of time-varying disturbances can be effectively attenuated in the repetitive tracking process of manipulators. In this paper, the rigorous stability analysis of the closed-loop system is presented to guarantee that the tracking error exponentially converges to zero as the iteration number tends to infinity. The effectiveness of the proposed control scheme is finally verified by simulation results. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Disturbance rejection

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Parallel systems Framework: A Safe and Efficient Testbed for Unmanned Ships 4

Parallel Systems Framework: A Safe and Efficient Testbed for...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Fu, Long Shen, Zhen Dong, Xisong Han, Yunjun Yang, Shuguang Xiong, Gang Wang, Fei-Yue University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing100190 China Chinese Academy of Sciences Guangdong Engineering Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Shandong Jiaotong University Schoool of Rail Transportation Jinan250399 China Chinese Academy of Sciences State Key Laboratory for Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

In this study, unmanned ships test platform based on the parallel systems framework is proposed to improve the test efficiency and accuracy of unmanned ships in complex ocean environment. The parallel intelligence theory and ACP approach, the efficient interaction between virtual and real environment is achieved, which provides more comprehensive and accurate data for unmanned ships testing. Future research will further expand the scope of virtual environment simulation and improve systems accuracy and response speed to a wider range of unmanned ships test requirements. © 2024 IEEE.

关键词： Virtual environments

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Smart Plant Application of Autonomous Decentralized systems with the Introduction of Edge Nodes 15

Smart Plant Application of Autonomous Decentralized Systems ...

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15th International Conference on Advanced Computer Theory and Engineering, ICACTE 2022

作者： Wang, Jingyang Zou, Tao Yang, Zhijia Wang, Hongrui Key Laboratory Of Networked Control System Institutes For Robotics And Intelligent Manufacturing Shenyang Institute Of Automation Chinese Academy Of Sciences Shenyang China Guangzhou University College Of Mechanical And Electrical Engineering Guangzhou China State Key Laboratory Of Robotics Key Laboratory Of Networked Control System Institutes For Robotics And Intelligent Manufacturing Shenyang Institute Of Automation Chinese Academy Of Sciences Shenyang China University Of Chinese Academy Of Sciences Beijing China

ISBN: (数字)9781665481809

ISBN: (纸本)9781665481809

This paper illustrates the application of an autonomous decentralized system introducing edge nodes in a smart factory. The nodes include atomic nodes, the most fundamental autonomic units constituting an autonomous decentralized system that are capable of independently extracting information from the data field and processing that information internally and simultaneously transmitting the processing results and other internal information to the data field proactively in a broadcast mode, where the transmitted information circulates in the data field. The group-based data field enables the sharing of information by atomic nodes. Grouped management nodes are the managers of the whole group, and edge nodes are the terminal intelligent control units of an intelligent autonomous decentralized system. Improvements in system decentralization, fault tolerance characteristics and flexibility of the system can lead to homogenization, autonomous control and autonomous coordination in complex manufacturing environments, as well as excellent online fault tolerance, online expansion and online maintenance of the system. In addition, it enhances the ever-changing and evolving control requirements by deconstructing the complexity in the system. © 2022 IEEE.

关键词： Fault tolerance

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A Method of Touchable 3d Model Reconstruction based on Mixed Reality -A Case Study of Medical Training Applications 23

A Method of Touchable 3d Model Reconstruction based on Mixed...

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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Liu, Mingyang Guo, Dalei Zhang, Zhicheng The School of Artificial Intelligence University of Chinese Academy of Sciences China The State Key Lab. of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Department of Orthopedic The 7th Medical Center of PLA General Hospital China

ISBN: (纸本)9781450398572

The application of trauma and hemorrhage medical treatment training has generated higher and higher requirements for the authenticity of the medical model. With the increasing integration of mixed reality technology and the medical field, multi-view-based 3D reconstruction technology can generate trauma medical object models not limited to specific targets, with low cost and high precision. However, the current reconstruction methods have weak points such as the inability to build the object surface beyond the direct view of humans, the uneven distribution of reconstructed point clouds, many holes, and reconstructed triangles. And it is time-consuming and laborious to fill and modify manually and requires highly skilled modeling expertise. Therefore, the models generated by traditional 3D reconstruction methods cannot meet the needs of interaction and touch in mixed reality applications. In order to solve these problems, we propose a new model generation method based on multi-view and point cloud processing, which enables the model to reduce the computational cost under the premise of meeting the need for direct interaction and touch in mixed reality applications. Finally, based on the reconstruction method proposed in this paper, a new medical trauma hemorrhage control and rescue training system is designed and implemented, which proves the effectiveness of the method proposed in this paper. © 2023 Owner/Author.

关键词： Mixed reality

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Zero-Sum Differential Game-Based Fault-Tolerant Tracking control for Nonlinear systems With Multiplicative Failures

Zero-Sum Differential Game-Based Fault-Tolerant Tracking Con...

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2022 Chinese automation Congress, CAC 2022

作者： Tan, Cong Lin, Mingduo Xia, Hongbing Zhao, Bo Xu, Yancai School of Systems Science Beijing Normal University Beijing100875 China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781665465335

A novel fault-tolerant tracking control (FTTC) approach for affine nonlinear systems is developed from the perspective of zero-sum differential games (ZSDG) to deal with unknown multiplicative actuator failures in this paper. By regarding the control input and the multiplicative actuator failure as two players of a zero-sum game, a performance index function reflecting the trajectory tracking error, the control policy and the unknown failure is established. To handle the tracking Hamilton-Jacobi-Isaacs (HJI) equation associated with the established performance index function, a critic neural network is utilized. Then, the Nash equilibrium solution can be obtained, and the FTTC is achieved. The stability of the faulty nonlinear system is examined through Lyapunov-based approach. Finally, simulation results of a spring system is provided to ensure the utility and the realizability of the ZSDG-based FTTC approach. © 2022 IEEE.

关键词： Nonlinear systems

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Learning impedance control of robots with enhanced transient and steady-state control performances

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Science China(Information Sciences) 2020年第9期63卷 204-216页

作者： Tairen SUN Long CHENG Liang PENG Zengguang HOU Yongping PAN State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Data and Computer Science Sun Yat-sen University

This study proposes a learning impedance controller comprising a proportional feedback control term, a composite-learning-based uncertainty estimation term, and a robot-environment interaction control term. The impedance control problem is converted into a particular reference-trajectory tracking problem based on a generated reference trajectory. The proposed controller ensures the exponential convergence of the auxiliary tracking error and the uncertainty estimation error. The interaction control term improves the transient control performance through suppression/encouragement of the incorrect/correct robot *** composite-learning update law enhances the transient and steady-state control performances based on the exponential convergence of the uncertainty estimation error and auxiliary tracking error. Finally, the effectiveness and advantages of the proposed impedance controller are validated by theoretical analysis and simulations on a parallel robot.

关键词： robot adaptive control neural network impedance control parameter convergence

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The Prospects of Multi-modal Pre-trained Models in Epidemic Forecasting 9th

The Prospects of Multi-modal Pre-trained Models in Epidemi...

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9th China National Conference on Big Data and Social Computing, BDSC 2024

作者： Fei, Jiaqiang Zhao, Pengfei Luo, Tianyi Wang, Jiaojiao Cao, Zhidong The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9789819758029

Accurate epidemic forecasting is imperative in light of the increasing global threat posed by epidemic diseases. Presently, researchers predominantly approach this challenge as a time-series prediction task and have proposed a variety of effective methodologies. However, two critical issues deserve thorough consideration. Firstly, the transmission of infectious diseases is a profoundly intricate process influenced by a multitude of factors, encompassing complex dynamics and propagation mechanisms. In contrast to the multifaceted nature of real-world scenarios, prevailing modeling approaches often exhibit undue simplification, resulting in imprecision and a deficit of interpretability. Secondly, existing methodologies excessively rely on data and require substantial adaptation for new infectious disease scenarios. The endeavor to conduct rapid and precise simulations and predictions during the early stages of transmission is challenging due to limited data availability. Thus, we aim to explore the feasibility and effective strategies of leveraging multi-modal pre-trained technology for epidemic forecasting. This approach offers the potential to comprehensively grasp the transmission process from multi-view perspectives and acquire richer representations by integrating multi-modal data. Moreover, through pre-trained, this methodology captures overarching patterns and principles of infectious disease transmission, facilitating swift and accurate predictions via fine-tuning, particularly in scenarios of sparse early-stage transmission data. We discuss the prospects of multi-modal pre-trained models in epidemic forecasting and provide feasible strategies for constructing the model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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GAP: Goal-Aware Prediction with Hierarchical Interactive Representation for Vehicle Trajectory 7th

GAP: Goal-Aware Prediction with Hierarchical Interactive Re...

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7th International Conference on Data Mining and Big Data, DMBD 2022

作者： Li, Ding Zhang, Qichao Lu, Shuai Pan, Yifeng Zhao, Dongbin School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9789811992964

Predicting the future trajectories of surrounding vehicles plays a vital role in ensuring the safety of autonomous driving. It is extremely challenging for the pure imitation method due to the high degree of multimodality and uncertainty in the future. In fact, when driving in most traffic scenarios, vehicles should obey some traffic rules such as "vehicles follow the lane and do not collide with each other". Inspired by this, this paper proposes a goal-aware prediction (GAP) framework to predict the multimodal trajectories, where goals are chosen in the lanes with hierarchical interactive representation and a multi-task loss. Based on the graph-based vectorized input, a novel hierarchical interactive representation module is first designed to obtain the fine-grained goal features, which progressively models interactions between goal-to-goal, goal-to-lane, and lane-to-agent, corresponding to the individual, local and global levels, respectively. Then, an auxiliary collision loss is developed to take into account learning from demonstration and injecting common sense of collision avoidance, and is served as a part of the multi-task loss to guide the generation of multimodal plausible trajectories. In the end, the proposed method is verified on the Baidu In-house Cut-in dataset, which includes more than 370K interactive scenarios collected in the real road testing. The comparative results demonstrate the superior performance of our proposed GAP model than the mainstream prediction methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Trajectories

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PI Tuning and Optimization for Speed control of PMSM Based on Multivariable Sliding-Mode Extremum Seeking

PI Tuning and Optimization for Speed Control of PMSM Based o...

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第34届中国控制与决策会议

作者： Fan Li Wei Shen Chuanyu Ge Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology

ISBN: (纸本)9781665478977

The PI controller is the most basic and widely used for the permanent magnet synchronous motor speed *** cumbersome parameter adjustment process makes it difficult for the PI controller to balance convenience and *** this end,this paper presents a new PI tuning and optimization scheme based on multivariable sliding-mode extremum ***,by analyzing the frequency domain model of permanent magnet synchronous motor,the analytical calculation formulas of controller parameters and motor parameters are derived as the initial value of PI parameter ***,a multivariable sliding-mode extremum seeking optimizer is designed to optimize the control parameters by minimizing the cost function based on the feedback error *** feasibility of the above scheme is proved by theoretical derivation,and the simulation and experimental results also verify the feasibility and effectiveness of the proposed method.

关键词： permanent-magnet synchronous motor(PMSM) multivariable sliding mode extremum seeking(MSME) self-tuning parameter optimization

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A parallel intelligent system for optimizing high-speed railway rescheduling by learning 1

A parallel intelligent system for optimizing high-speed rail...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Yang, Pengxin Cui, Dongliang Dai, Xuewu Lv, Yisheng Dong, Hairong Wang, Xinghao Liu, Ruiguang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety Beijing China

ISBN: (纸本)9781665433372

The railway system is a complex system because of many constraints, randomness and high security requirements, so it is difficult to establish an accurate mathematical model for it, which brings great challenges to railway scheduling. Parallel intelligence theory can effectively solve the modeling, analysis and control of complex systems, so it has a wide range of applications in the field of intelligent transportation. In this paper, we build an artificial system according to the actual system, and use the surrogate modelling to carry out the calculation experiments, and proposed a parallel scheduling system based on the parallel intelligence method. Select a case to design scenario and perform computational experiment, the scheduling strategy is given and the scheduling strategy is analyzed. © 2021 IEEE.

关键词： Intelligent systems

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