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IEEE International Conference on Industrial Technology (ICIT)

作者： Baichao Zhang Xin Chen Jinge Chen Jinbin Li Xiaokai Wang Xu Jian School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Wuhan P. R. China Hubei Fangyuan Dongli Electric Power Science Research Co. Ltd Wuhan P. R. China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

In this paper, we propose a hybrid algorithm that combines an improved Artificial Potential Field (APF) method with the Simulated Annealing (SA) algorithm for path planning of an electric power operation robot manipulator in complex distribution grid environments. To address the unreachable target issue inherent in traditional APF, we introduce a distance regulation factor to optimize the repulsive function. This modification allows the manipulator to smoothly approach the target point as it nears, while the repulsion from obstacles gradually decreases. Additionally, to overcome the limitations of the traditional SA algorithm, such as its tendency to get trapped in local minimum solutions and its inefficiency in complex environments, we propose an adaptive temperature rise strategy. This strategy increases the temperature, enhancing the probability of escaping local optimal solutions. When the APF algorithm becomes trapped in a local optimum, the improved SA algorithm is applied to escape the local minimum. Once the local optimum is avoided, the algorithm switches back to APF to continue the path planning process. Simulation results demonstrate that the proposed improved APF-SA algorithm adapts effectively to various complex environments, achieving shorter planning times and higher success rates compared to traditional APF and SA algorithms. It successfully resolves the unreachable target and local minimum problems associated with APF. Finally, the feasibility of the proposed APF-SA fusion algorithm is validated through experiments conducted on an electric power operation robot experimental platform for distribution grid applications.

关键词： Electric potential Adaptation models Force Distribution networks Simulated annealing Manipulators Path planning Planning Collision avoidance Robots

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Coordinated Motion control for a Six-wheel-drive Skid-steering Mobile Robot

Coordinated Motion Control for a Six-wheel-drive Skid-steeri...

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Chinese automation Congress (CAC)

作者： Yongkang Xu Shoukun Wang Hongqiang Zhao Zhihua Chen State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China State Key Laboratory of High Performance and Complex Manufacturing Central South University Changsha China

ISBN: (纸本)9781665426480

A speed consensus control strategy for a six-wheel-drive skid-steering mobile robot was proposed based on a hierarchical controller in a complex environment with unknown disturbances. The effective method of reliable speed consensus control strategy should consider some disturbances, such as the internal-robot assembly error, which including instability of motor control system and external physical interference in robot dynamic system. In this paper, a driving anti-slip control system with PID and the stability control system with sliding mode by variable structure is designed and integrated to elevate the stability of the robot. And, a G-vector control algorithm based on sliding mode control is proposed to reduce the yaw angle as much as possible during the process of differential steering of robots. The effectiveness of the proposed control strategy is verified by an example of a robot. The results show that the method has good control performance in control accuracy and stability.

关键词： Uncertainty Robot kinematics Heuristic algorithms Velocity control Torque control Wheels Consensus control

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Continuous-Time Linear Parallel control

Continuous-Time Linear Parallel Control

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Chinese automation Congress (CAC)

作者： Qinglai Wei Hongyang Li Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This paper presents a continuous-time linear parallel control by modeling the derivative of system control with respect to time. The framework and properties of continuous-time linear parallel control are first presented. Then, considering the controllability, the pole assignment theorems will be analyzed. Finally, two examples show the correctness of continuous-time linear parallel control.

关键词： control systems controllability control theory complex systems Adaptive control Numerical analysis Linear systems

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Research on Attention-Based Predictive Model in Multi-Agent Reinforcement Learning

Research on Attention-Based Predictive Model in Multi-Agent ...

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Chinese automation Congress (CAC)

作者： Weixin Hong Huaiyu Wu Zhen Shen Gang Xiong Bin Hu Xisong Dong Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

Agent actions planning is a challenging problem in multi-agent reinforcement learning. Recent methods typically build their predictive models by full connection layers, but the shortage of utilizing action and observation information in complex multi-agent environment limits the performance of those methods. To address this issue, we propose a novel predictive model based on self-attention mechanism which can effectively use information in the environment. Specifically, we use the MADDPG algorithm to make a dataset where the input is the observations and actions and the output is the predicted observations and rewards. We also introduce the multi-head attention mechanism into the predictive model so that the predictive model can better combine different information for prediction. We perform extensive experiments to assess the proposed method and compare it with the representative baseline approach. Results demonstrate that our method outperforms competitors with higher accuracy in different tasks. Our implementation will be publicly available.

关键词： automation Reinforcement learning Predictive models Prediction algorithms Planning Task analysis

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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Chinese control and Decision Conference, CCDC

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and structured. However, with robots gradually applied in more domains, robots often encounter working scenes which are complicated, unpredictable, and unstructured. To overcome the limitation of traditional programming method, in this paper, we apply deep reinforcement learning (DRL) method to train robot agent to obtain skill policy. As policy trained with DRL on real-world robot is time-consuming and costly, we propose a novel and simple learning paradigm with the aim of training physical robot efficiently. Firstly, our method train a virtual agent in an simulated environment to reach random target position from random initial position. Secondly, virtual agent trajectory sequence obtained with the trained policy, is transformed to real-world robot command with coordinate transformation to control robot performing reaching tasks. Experiments show that the proposed method can obtain self-adaptive reaching policy with low training cost, which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot kinematics Training Task analysis Machine learning Trajectory Neural networks

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An overview on GNSS carrier-phase time transfer research

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Science China(Technological Sciences) 2020年第4期63卷 589-596页

作者： ZHANG Ming Lü JinHu BAI ZhengDong JIANG ZhiQi CHEN BoBo The Key Laboratory of Systems and Control Institute o f Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Institute of Geomatics of Tsinghua University Beijing 100084China Management and Department of Energy and Resources Engineering College of EngineeringPeking UniversityBeijing 100871China

Global navigation satellite system(GNSS)carrier phase observations are two orders of higher accuracy than pseudo-range observations,and they are less affected by multipath *** a result,the time transfer accuracy can reach 0.1 ns,and the frequency transfer stability can reach 1×10^-15 with carrier phase(CP)method,therefore CP method is considered the most accurate and promising time transfer *** focus of this paper is to present a comprehensive summary of CP method,with specific attention directed toward day-boundary clock jump,ambiguity resolution(AR),multi-system time transfer and real-time time ***-boundary clock jump is essentially caused by pseudo-range *** approaches were proposed to solve the problem,such as continuously processing strategy,sliding batch and bidirectional filtering methods which were compared in this ***,researches on AR in CP method were *** scholars attempted to fix the single-difference ambiguities to improve the time transfer result,however,owing to the uncalibrated phase delay(UPD)was not considered,the current studies on AR in CP method were still ***,because four GNSS systems could be used for time-transfer currently,which was helpful to increase the accuracy and reliability,the researches on multi-system time transfer were ***’s more,real-time time transfer attracted more attention nowadays,the preliminary research results were presented.

关键词： carrier phase(CP) time transfer day-boundary clock jump ambiguity resolution(AR) multi-system time transfer real-time time transfer

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Parallel hospital: ACP-based hospital smart operating system 1

Parallel hospital: ACP-based hospital smart operating system

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

作者： Wang, Yongjun Wang, Fei-Yue Wang, Xiao Guan, Zhongjun Ouyang, Liwei Wang, Jiangong Yan, Lan Zheng, Wenbo Zhang, Wenwen Beijing Tiantan Hospital Capital Medical University Beijing Beijing100070 China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China School of Software Engineering Xi'An Jiaotong University Xi'an710049 China

ISBN: (纸本)9781665433372

This article aims to propose a parallel hospital based on parallel medicine. The parallel hospital takes the parallel idea of virtual and real interaction as its core concept and uses parallel idea's artificial scenarios, computational experiments, and parallel execution (ACP) theory as the guiding theory to construct a new generation of intelligent hospital management system with virtual and real interaction. We conducted a case study of the parallel hospital practice case of Tiantan Hospital-Tiantan Smart Brain, so as to confirm the feasibility and scientificity of the proposed parallel hospital. © 2021 IEEE.

关键词： Hospitals

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Editorial Special Issue on Adaptive Dynamic Programming and Reinforcement Learning

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IEEE Transactions on systems, Man, and Cybernetics: systems 2020年第11期50卷 3944-3947页

作者： Liu, Derong Lewis, Frank L. Wei, Qinglai School of Automation Guangdong University of Technology Guangzhou510006 China Uta Research Institute University of Texas at Arlington Fort WorthTX76118 United States State Key Laboratory of Management and Control for Complex Systems Istitute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

The past decade has witnessed a surge in research activities related to adaptive dynamic programming (ADP) and reinforcement learning (RL), particularly for control applications. Several books [item 1)–5) in the Appendix] and survey papers [item 6)–10) in the Appendix] have been published on the subject. Both ADP and RL provide approximate solutions to dynamic programming problems. In a 1995 article by Barto et al. [item 11) in the Appendix], they introduced the so-called “adaptive real-time dynamic programming,” which was specifically to apply ADP for real-time control. Later, in 2002, Murray et al. [item 12) in the Appendix] developed an ADP algorithm for optimal control of continuous-time affine nonlinear systems. On the other hand, the most famous algorithms in RL are the temporal difference algorithm [item 13) in the Appendix] and the Q-learning algorithm [item 14) and 15) in the Appendix].

关键词： Special issues and sections Reinforcement learning Learning systems control systems Dynamic programming Real-time systems Optimal control

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A Conceptual Framework of Subjective and Objective Fusion Assessment System for Surgical Robot Surgeons

A Conceptual Framework of Subjective and Objective Fusion As...

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control Conference (ANZCC), Australian and New Zealand

作者： Haoxin Cui Nan Zheng Xiaoyu Chang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of International Economics and Management Beijing Technology and Business University Beijing China

With the continuous advancement of technology, Robot-Assisted Minimally Invasive Surgery (RAMIS) has gained increasing recognition and is considered one of the important trends in the future of surgery. However, there is still a lack of standardized, systematic, and comprehensive assessment systems for assessing the skills of surgeons in RAMIS. In this paper, we propose a conceptual framework of assessment system that combines subjective and objective measures, which focuses on the design of the assessment system and the design of the experimental paradigm. It is expected that through a scientifically designed comprehensive assessment system, the training efficiency in the field of robot-assisted minimally invasive surgery can be improved, enabling the rapid development of highly skilled surgeons in hospitals, thus promoting the advancement of robot-assisted minimally invasive surgery.

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells

Application of Deep Learning Method to Estimate Bottomhole P...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

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