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Parallel-Data-Based Social Evolution Modeling

Tsinghua Science and Technology 2021年第6期26卷 878-885页

作者： Weishan Zhang Zhaoxiang Hou Xiao Wang Zhidong Xu Xin Liu Fei-Yue Wang China University of Petroleum Qingdao 266580China Qingdao Academy of Intelligent Industry Qingdao 266111China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100049China Institute of National Security National Defense UniversityBeijing 100081China

Abnormal or drastic changes in the natural environment may lead to unexpected events,such as tsunamis and earthquakes,which are becoming a major threat to national ***,no effective assessment approach can deduce a situation and determine the optimal response strategy when a natural disaster *** this study,we propose a social evolution modeling approach and construct a deduction model for self-playing,self-learning,and self-upgrading on the basis of the idea of parallel data and reinforcement *** proposed approach can evaluate the impact of an event,deduce the situation,and provide optimal strategies for *** the breakage of a submarine cable caused by earthquake as an example,we find that the proposed modeling approach can obtain a higher reward compared with other existing methods.

关键词： paral el data reinforcement learning decision-making

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controlling the depth of a gliding robotic dolphin using dual motion control modes

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Science China(Information Sciences) 2020年第9期63卷 217-230页

作者： Jian WANG Zhengxing WU Min TAN Junzhi YU State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering ScienceBIC-ESAT College of Engineering Peking University

This paper investigates the performance of the dual mode, namely flipper mode and central pattern generator(CPG) mode, for controlling the depth of a gliding robotic dolphin. Subsequent to considering the errors in dynamic models, we propose a depth control system that combines the line-of-sight(LOS)method with an adaptive control approach(ACA) to deal with uncertainties in the model parameters. First,we establish a full-state dynamic model to conduct simulations and optimize the parameters used in later aquatic experiments. Then, we use the LOS method to transform the control target from the depth to the pitch angle and employ the ACA to calculate the control signal. In particular, we optimize the ACA’s control parameters using simulations based on our dynamic model. Finally, our simulated and experimental results demonstrate not only that we can successfully control the robotic dolphin’s depth, but also that its performance was better than that of the CPG-based control, thus indicating that we can achieve three-dimensional motion by combining flipper-based and CPG-based control. The results of this study suggest valuable ideas for practical applications of gliding robotic dolphins.

关键词： gliding robotic dolphin depth control dual motion adaptive control approach

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Real-time In-situ Defect Detection in 3D Printing of Continuous Fiber-Reinforced Composites 4

Real-time In-situ Defect Detection in 3D Printing of Continu...

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

作者： Yue, Zhijian Shen, Zhen Dong, Xiangyang Fang, Qihang Wang, Weixing Dong, Xisong 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 Qingdao Academy of Intelligent Industries Intelligent Manufacturing Center Qingdao266109 China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China Chinese Academy of Sciences State Key Laboratory for Management and Control for Complex Systems Institute of Automation Beijing100190 China

ISBN: (纸本)9798350349252

With the development of continuous fiber-reinforced composites (CFRCs) 3D printing technology, timely, efficient and accurate detection of fiber path defects is essential for ensuring product quality and performance. However, challenges still need to be addressed in this field, such as inadequate training data coverage and the complexity of fiber path structures. These factors hinder the capability of traditional methods to accurately assess the status of printed products. Therefore, developing accurate and robust fiber path defect detection techniques is crucial for CFRCs 3D printing technology. In this paper, we propose a precise detection method for fiber path defects in CFRCs 3D printing using the YOLOv7 object detection model combined with a Squeeze-and-Excitation (SE) attention mechanism. By introducing the SE attention mechanism, we enhance the perception capability of the YOLOv7 model for crucial features, thereby improving the accuracy of fiber path defect detection. The experimental results show that the YOLOv7 model, when augmented with an attention mechanism, achieves an average accuracy of 93.9% in identifying fiber path defects during the printing process. Compared to models without this attention mechanism, the results show that there is 10.7% increase, with a notably enhanced learning capacity during the training phase, which substantiates the efficacy and practicality of the proposed approach. © 2024 IEEE.

关键词： Fiber reinforced plastics

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Anomaly detection in digital twin model 1

Anomaly detection in digital twin model

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

作者： Wang, Yutong Cao, Yansong Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China Macau Institute of Systems Engineering Macau University of Science and Technology China

ISBN: (纸本)9781665433372

As the simulation model of a physical system, digital twin has been widely used in many complicated control systems. Providing an effective way to perform simulation, digital twin makes the evaluation, prediction and optimization process cheaper and easier than on physical systems. For smart manufacturing, digital twin achieves high productivity with less operation and maintenance cost. However, the advantages of digital twin in anomaly detection during manufacturing are always neglected. Taking a two-speed transmission system as an example, we generate three kinds of faults on this digital twin model. After extracting features from the system output, we train an anomaly detection model on these features to classify each type of fault. By this digital twin model, we can find the fault in the whole system at the very beginning, and reduce the time and cost of debugging and diagnosing. © 2021 IEEE.

关键词： Anomaly detection

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Mechanical design paradigm based on ACP method in parallel manufacturing 1

Mechanical design paradigm based on ACP method in parallel m...

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

作者： Li, Shimeng Wang, Yutong Wang, Xiao Wang, Feiyue Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781665433372

Parallel Manufacturing is a new manufacturing paradigm in industry, deeply integrating informalization, automation, and artificial intelligence. In this paper we propose a new mechanical design paradigm in Parallel Manufacturing based on ACP method. The key is to regard the design procedure based on artificial design and emulation method as two independent procedures, which can be modeled as a parallel system. The design procedure based on ACP method does not include a real system, which is an inventive extension of the traditional parallel system. This method can be implemented with social information by introducing the definition of SDV, SDM, and Intelligent Design Manager, making it highly adaptive for social manufacturing and Parallel Manufacturing. © 2021 IEEE.

关键词： Manufacture

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Speed control of a Biomimetic Robotic Fish Based on Linear Active Disturbance Rejection control

Speed Control of a Biomimetic Robotic Fish Based on Linear A...

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2021 China Automation Congress, CAC 2021

作者： Huang, Yupei Wu, Zhengxing Du, Sheng The State Key Laboratory of Management and Control for Complex Systems Insititute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426473

This paper proposes a speed control method for a biomimetic robotic fish based on linear active disturbance rejection control. Inspired by a bluefin tuna in nature, a robotic fish with a two-joint propulsive mechanism and a crescent-shaped caudal fin is first designed. Thereafter, for a steady forward swimming speed, a speed control framework based on the linear form of active disturbance rejection control method is proposed, where the oscillation frequency of the caudal fin is adopted to regulate the swimming speed. Meanwhile, a swimming speed estimation approach based on the oscillation period is provided to effectively reduce the phenomenon of swimming speed fluctuation. Finally, both simulation and experiment results illustrate that the proposed method is effective for the speed control of robotic fish. © 2021 IEEE

关键词： Speed control

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Backstepping-based parallel control for cascaded nonlinear systems 1

Backstepping-based parallel control for cascaded nonlinear s...

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

作者： Lu, Jingwei Wei, Qinglai Zhou, Tianmin Han, Liyuan Wang, Fei-Yue School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9781665433372

In this study, a novel nonlinear parallel control method is proposed for cascaded nonlinear systems using the backstepping technique. Unlike the existing state feedback control methods, the control input is taken into the feedback system. First, an augmented system is constructed to facilitate the constructing the Lyapunov function. Then, the backstepping technique can be applied to obtain the nonlinear parallel control law, and the stability analysis is shown using the Lyapunov theory. Finally, a simulation is conducted to demonstrate the effectiveness of the proposed parallel control method. © 2021 IEEE.

关键词： Backstepping

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Parallel control-Based Event-Triggered Optimal control for Constrained Discrete-Time Nonlinear systems

Parallel Control-Based Event-Triggered Optimal Control for C...

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2021 China Automation Congress, CAC 2021

作者： Lu, Jingwei Bai, Tianxiang Wei, Qinglai Zhou, Tianmin Wang, Fei-Yue School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9781665426473

This study proposes a new event-triggered optimal control (ETOC) method for discrete-time (DT) constrained nonlinear systems. First, a new triggering condition is proposed. We show the asymptotic stability of the closed-loop system using the proposed triggering condition and analyze the degeneration degree of the real performance index. Second, to perform the proposed ETOC method effectively, parallel control (PC) combined with adaptive dynamic programming (ADP) is applied. Finally, the validity of the ETOC method is validated by a simulation. © 2021 IEEE

关键词： Asymptotic stability

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Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 47-63页

作者： Yongliang Yang Zihao Ding Rui Wang Hamidreza Modares Donald C.Wunsch Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation&Electrical EngineeringUniversity of Science and Technology BeijingBeijing 100083China School of Automation Beijng Institute TechnologyBeijing 100081China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Mechanical Engineering Department Michigan State UniversityEast LansingMI 48824 USA Department of Electrical&Computer Engineering Missouri University of Science&TechnologyRollaMO 65401 USA

In this paper,we present a novel data-driven design method for the human-robot interaction(HRI)system,where a given task is achieved by cooperation between the human and the *** presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller *** task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop,while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the ***-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance *** the inner-loop,a velocity-free filter is designed to avoid the requirement of end-effector velocity *** this basis,an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task *** simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.

关键词： Adaptive impedance control data-driven method human-robot interaction(HRI) reinforcement learning velocity-free

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A Novel Heterogeneous Actor-critic Algorithm with Recent Emphasizing Replay Memory

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International Journal of Automation and computing 2021年第4期18卷 619-631页

作者： Bao Xi Rui Wang Ying-Hao Cai Tao Lu Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Reinforcement learning(RL) algorithms have been demonstrated to solve a variety of continuous control tasks. However,the training efficiency and performance of such methods limit further applications. In this paper, we propose an off-policy heterogeneous actor-critic(HAC) algorithm, which contains soft Q-function and ordinary Q-function. The soft Q-function encourages the exploration of a Gaussian policy, and the ordinary Q-function optimizes the mean of the Gaussian policy to improve the training efficiency. Experience replay memory is another vital component of off-policy RL methods. We propose a new sampling technique that emphasizes recently experienced transitions to boost the policy training. Besides, we integrate HAC with hindsight experience replay(HER) to deal with sparse reward tasks, which are common in the robotic manipulation domain. Finally, we evaluate our methods on a series of continuous control benchmark tasks and robotic manipulation tasks. The experimental results show that our method outperforms prior state-of-the-art methods in terms of training efficiency and performance, which validates the effectiveness of our method.

关键词： Reinforcement learning(RL) actor-critic experience replay training efficiency manipulation skill learning

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