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Distributed Containment control of Markov Jump Multi-agent systems under Asynchronous Communications

Distributed Containment Control of Markov Jump Multi-agent S...

引用

第40届中国控制会议

作者： Chao Ma Yidao Ji Hang Fu Wei Wu School of Mechanical Engineering University of Science and Technology Beijing School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

This paper studies the distributed containment control issue for general Markov jump multi-agent systems. More specifically, a novel asynchronous mode-dependent communication scheme is proposed, which is based on observed communication mode information. By means of mode-dependent Lyapunov method, sufficient containment conditions are derived and communication protocol gains are further designed by convex optimization. A simulation example is finally presented to validate the correctness of our developed control scheme.

关键词： Distributed containment Markov jump multi-agent systems asynchronous communications

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Finding Patterns in Adversarial Training

Finding Patterns in Adversarial Training

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Chinese Automation Congress (CAC)

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

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Adversarial training has become an universally accepted robust method to train networks defending against adversarial attacks. However, feeding networks with adversarial examples is a cumbersome process, and there is no clear explanation for the differences between networks with the same architecture that are learned by adversarial training and learned by natural training method. So in this paper, we focus on the patterns of network learned by adversarial training. For comparison, we visualize the weights and feature maps of networks learned by these two training methods on MNIST, and find some patterns of adversarially trained model that are important for defending against adversarial examples and that are unique to naturally trained model. First, we find that adversarially trained network denoises the noisy data caused by adversarial images and enhances the outlines of semantically informative content in feature maps. Second, we find that adversarial training performs model compression, making some of the channels of a convolutional layer have no activation on input images. Further observation shows that different from naturally trained model, which have different most activation channel corresponding to different input, adversarially trained model often has the same most activation channel regardless of input. This phenomenon also leads to a sparse network reducing 50% number of parameters and 87% computation costs, and the pruned network performs as good as original network after pruning the least activation channels without retraining. But the naturally trained counterpart loses more than 40% accuracy using the same pruning strategy.

关键词： Training Kernel Robustness Perturbation methods Visualization Computational modeling Noise reduction

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Geometry Problem Solving Based on Counter-Factual Evolutionary Reasoning

Geometry Problem Solving Based on Counter-Factual Evolutiona...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Bing Song Gang Xiong Zhen Shen Fenghua Zhu Yisheng Lv Peijun Ye The State Key Laboratory of Management and Control for 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 China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China

As a representative topic in natural language processing and automated theorem proving, geometry problem solving requires an abstract problem understanding and symbolic reasoning. A major challenge here is to find a feasible reasoning sequence that is consistent with given axioms and the theorems already proved. Most recent methods have exploited neural network-based techniques to automatically discover eligible solving steps. Such a kind of methods, however, is greatly impacted by the expert solutions for training. To improve the accuracy, this paper proposes a new method called counterfactual evolutionary reasoning, which uses a generative adversarial network to generate initial reasoning sequences and then introduces counterfactual reasoning to explore potential solutions. By directly exploring theorem candidates rather than the neural network selection, the new method can sufficiently extend the searching space to get a more appropriate reasoning step. Through comparative experiments on the recent proposed Geometry3k, the largest geometry problem solving dataset, our method generally achieves a higher accuracy than most previous methods, bringing an overall improvement about 4.4% compared with the transformer models.

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Network agenda setting and social cognition construction of the dengue fever epidemic event based on social media big data 2

Network agenda setting and social cognition construction of ...

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23rd Pacific Asia Conference on Information systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019

作者： Wang, Yuejiao Cao, Zhidong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Science Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This article collected dengue-related information in China from News, Apps, Forums, Microblog and WeChat. We extract the 100 highest frequency of agenda settings from News, and built agenda setting networks of the five platforms respectively. Social network analysis methods are used to reveal the characteristics of these five agenda setting networks and the QAP is used to test the correlation between them. The robustness analysis of the results is applied. We found that: the information dissemination in dengue can be roughly divided into three categories: epidemic situation and change, prevention and control measures, and epidemic outbreak area;The context of News has the highest similarity with Apps (correlation coefficient=0.9561, p © Proceedings of the 23rd Pacific Asia Conference on Information systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019.

关键词： Big data

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Nonlinear Model Predictive Clamping Force control for Electromechanical Brake System

Nonlinear Model Predictive Clamping Force Control for Electr...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Wenpeng Wei Tianyi He Yifan Men Jinxiang Wang Lu Xiong Guodong Yin Key Laboratory of Measurement and Control of Complex Systems of Engineering School of Mechanical Engineering Southeast University Nanjing China Department of Mechanical and Aerospace Engineering Utah State University Logan UT USA ZDrive.ai 7th Floor Block T2 Ali Center 1398 Shenchang Road Minhang District Shanghai China School of Mechanical Engineering Southeast University Nanjing China School of Automotive Studies Tongji University Shanghai China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

In this paper, the problem of clamping force control for the Electromechanical Brake (EMB) system in autonomous vehicles is addressed. A nonlinear model predictive control framework is proposed to solve for the optimal maneuver at each optimization step to realize accurate clamping force tracking. First, the EMB system is modeled to a state-space representation through the first-principle modeling; Second, a control framework that includes three blocks is proposed to achieve clamping force tracking. The first block designs a clamping force separation strategy and an unknown input observer based on Kalman filter is applied to estimate the clamping force to replace the force sensors; the second block develops an adaptive estimation of polynomial coefficients to deal with the time-varying coefficients to improve model adaptiveness; the last block conceives the nonlinear model predictive control to track the reference force, and the resulted nonlinear programming is solved by the multiple shooting method. Finally, the proposed framework is validated to achieve faster tracking speed and more accurate tracking in multiple simulation scenarios.

关键词： Adaptation models Force Predictive models Observers Prediction algorithms Polynomials Clamps Kalman filters Force control Predictive control

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Research on trajectory planning and motion control of 7-DoF sawyer manipulator 1

Research on trajectory planning and motion control of 7-DoF ...

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

作者： Luo, Kun Xiong, Gang Wang, Yuxiao Hu, Bin Liu, Sheng Chen, Shichao Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijng China School of Artificial Intelligence University of Chinese Academy of Sciences Beijng China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Beijng China Qingdao Academy of Intelligent Industries Chinese Academy of Sciences Beijng China

ISBN: (纸本)9781665433372

Regarding human-machine collaborative task, generated trajectories of a robot manipulator should be able to avoid joint limits and singularities, but the 6 Degrees of Freedom(DoF) or lower DoF manipulators encounter difficulties in many schemes. In this paper, research is implemented on the methods of trajectory planning and motion control on the newly human-machine collaborative 7-DoF Sawyer manipulator. We try to pay more attention to these approaches and find a better combination between trajectory planners and motion controllers for Sawyer manipulator. First of all, comparison is proposed between the methods about trajectory planning in task space and joint space, drawing a conclusion that the latter has the advantage of quickness and smoothness on trajectories in real time. Next, we choose to plan trajectory in the joint space by interpolating positions and orientations. Three approaches, i.e., trapezoidal velocity trajectory, cubic polynomial trajectory and basic spline trajectory, are applied to interpolate positions. Experimental results show that it results in the smoothest trajectory using basic spline trajectory planning. We also compare linear and nonlinear orientation interpolations to address the angle wrapping issue, which leads to difficulties in smooth orientation interpolation between several waypoints. Combining polynomial trajectory planning methods with the proportional-derivative (PD) motion controller and the computed-torque motion controller, we focus on the performance of the two different combinations. It is found that trajectories with computed-torque controller have shorter path with a congruous convergence on joint position, and more accurate trajectories with less overshoots in position than others. © 2021 IEEE.

关键词： Motion control

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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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Continuous-Time Linear Parallel control

Continuous-Time Linear Parallel Control

引用

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

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

引用

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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Spiking Neuron Networks based Energy-Efficient Object Detection for Mobile Robot

Spiking Neuron Networks based Energy-Efficient Object Detect...

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2021 China Automation Congress, CAC 2021

作者： Liu, Bo-Chao Yu, Qian Gao, Jing-Wen Zhao, Shuang Liu, Xiong-Chang Lu, Yan-Feng Beijing Information Science and Technology University Beijing China Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Instrument Science and Optoelectronic Engineering Beijing Information Science and Technology University Beijing China The State Key Laboratory of Management and Control for Complex Systems CASIA Beijing China Science and Technology on Underwater Vehicle Technology Laboratory Harbin Engineering University Harbin China

ISBN: (纸本)9781665426473

This paper focuses on the highly energy-efficient visual detection method, which first apples spiking neuron networks based object detection method in mobile robot. Visual perception is an important part of mobile robot, but the real-time visual perception is with high energy consumption that limits the battery running time of the robotic platform. In this paper, we bring in a Spiking-YOLO algorithm for the visual detection of the mobile robot, which combines the YOLO algorithm with the third generation artificial neural network-Spiking Neural Network (SNN). The Spiking-YOLO integrates the characteristics in real-time and high precision of the YOLO, and event-driven and low-powered nature of the SNN. We introduce the Spiking-YOLO model to the Robotnik's SUMMIT-XL mobile robot to perform the task of visual detection and the experimental verification is carried out on data sets. The experimental results show that this method has excellent energy-efficient and precision on the detection task of the robot. © 2021 IEEE

关键词： Neural networks

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