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第37届中国控制会议

作者： Xiaofei Zhang Hongbin Ma Zhuang Li Wenchao Zao School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

With the development of aeronautics and astronautics, the response speed of servo system need be faster. First, In order to improve the dynamic quality of servo system, the exponential and power reaching law, which combines the advantages of the exponential reaching law and the power reaching law, is introduced. Second, the chattering of the sliding mode controller（SMC） with the exponential and power reaching law for discrete systems is investigated. Finally, the adaptive sliding mode controller（ASMC） with the exponential and power reaching law is introduced. The stability of the ASMC with the exponential and power reaching law for discrete systems is analyzed, and the simulation of this approach on one joint of a six degrees of freedom robot is carried out. The experimental results indicate that the ASMC with the exponential and power reaching law is effective in reducing the time of arriving the sliding mode surface. The experimental results also indicate that the ASMC with the exponential and power reaching law may make output error reach zero in a shorter time.

关键词： adaptive sliding mode controller reaching law

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Study and Generalization on an End-to-End Spatial-temporal Driving Model

Study and Generalization on an End-to-End Spatial-temporal D...

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Chinese Automation Congress (CAC)

作者： Tingting Yao Xin Chen Sheng Yuan Huaying Wang Lili Guo Bin Tian Yunfeng Ai Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China Artificial Intelligence Department Beijing Automotive Technology Center Co. Ltd. China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Autonomous driving has attracted increasing attentions from both academia and industry, among which end-to-end driving model is a nascent research topic. In this work a deep learning based model is studied and generalized, which directly maps raw videos to steering angles without any manual feature extraction procedure. The model is learned and evaluated from human driving videos from both public dataset and self-recorded dataset under real environment, which is more practical and application oriented. Besides, the model combines both the spatial and temporal information by applying 3D convolutional neural network and recurrent unit. For the dataset studied, ablation experiment is performed to further verify the unity of recurrent block. Finally, transfer learning is applied to transfer the domain knowledge learned from the large-scale public dataset to two different small-scale dataset in unseen scenes. Comprehensive quantitative and qualitative evaluations demonstrate that the RMSE on pretrained model is 0.12603, while the transfer learning for the two unseen scenes achieve 0.13079 and 0.13198, respectively, which shows the effectiveness and generalization ability of the model even under different scenarios and abrupt turns.

关键词： 5G mobile communication Integrated circuits

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Algebraic Connectivity Estimation Based on Decentralized Inverse Power Iteration

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Asian Journal of control 2017年第2期19卷 805-812页

作者： Wei, Yue Fang, Hao Chen, Jie Xin, Bin School of Automation Beijing Institute of Technology Beijing 100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 China

In this work, we propose a new scheme to estimate the algebraic connectivity of the graph describing the network topology of a multi-agent system. We consider network topologies modeled by undirected graphs. The main idea is to propose a new decentralized conjugate gradient algorithm and a decentralized compound inverse power iteration scheme. The matrix inversion computation in this scheme is replaced by solving the non-homogeneous linear equations relying on the proposed decentralized conjugate gradient algorithm. With this scheme, we can achieve a fast convergence rate in estimating the algebraic connectivity by setting the parameter μ properly. Simulation results demonstrate the effectiveness of the proposed scheme. © 2016 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Network topology

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Flexible Robotic Grasping Strategy with Constrained Region in Environment

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International Journal of Automation and computing 2017年第5期14卷 552-563页

作者： Chao Ma Hong Qiao Rui Li Xiao-Qing Li School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

Grasping is a significant yet challenging task for the robots. In this paper, the grasping problem for a class of dexterous robotic hands is investigated based on the novel concept of constrained region in environment, which is inspired by the grasping operations of the human beings. More precisely, constrained region in environment is formed by the environment, which integrates a bio-inspired co-sensing framework. By utilizing the concept of constrained region in environment, the grasping by robots can be effectively accomplished with relatively low-precision sensors. For the grasping of dexterous robotic hands, the attractive region in environment is first established by model primitives in the configuration space to generate offline grasping planning. Then, online dynamic adjustment is implemented by integrating the visual sensory and force sensory information, such that the uncertainty can be further eliminated and certain compliance can be obtained. In the end, an experimental example of BarrettHand is provided to show the effectiveness of our proposed grasping strategy based on constrained region in environment.

关键词： Grasping strategy compliant grasping dexterous robotic hands attractive region in environment constrained region inenvironment.

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Robot assisted rehabilitation of the arm after stroke:prototype design and clinical evaluation

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Science China(Information Sciences) 2017年第7期60卷 252-258页

作者： Liang PENG Zeng-Guang HOU Long PENG Lincong LUO Weiqun WANG State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Robot assisted rehabilitation training is a promising tool for post-stroke patients’ recovery, and some new challenges are imposed on robot design, control, and clinical evaluation. This paper presents a novel upper limb rehabilitation robot that can provide safe and compliant force feedbacks to the patient for the benefits of its stiff and low-inertia parallel structure, highly backdrivable capstan-cable transmission, and impedance control method in the workspace. The "assist-as-needed"(AAN) clinical training principle is implemented through the "virtual tunnel" force field design, the "assistance threshold" strategy, as well as the virtual environment training games, and preliminary clinical results show its effectiveness for motor relearning for both acute and chronic stroke patients, especially for coordinated movements of shoulder and elbow.

关键词： rehabilitation robot passive training active training force feedback impedance control

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PDP: Parallel Dynamic Programming

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IEEE/CAA Journal of Automatica Sinica 2017年第1期4卷 1-5页

作者： Fei-Yue Wang Jie Zhang Qinglai Wei Xinhu Zheng Li Li IEEE State Key Laboratory of Management and Control for Complex Systems(SKL-MCCS) Institute of AutomationChinese Academy of Sciences(CASIA) School of Computer and Control Engineering University of Chinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences(SKL-MCCSCASIA) Qingdao Academy of Intelligent Industries Department of Computer Science and Engineering University of Minnesota Department of Automation Tsinghua University

Deep reinforcement learning is a focus research area in artificial intelligence. The principle of optimality in dynamic programming is a key to the success of reinforcement learning methods. The principle of adaptive dynamic programming ADP is first presented instead of direct dynamic programming DP , and the inherent relationship between ADP and deep reinforcement learning is developed. Next, analytics intelligence, as the necessary requirement, for the real reinforcement learning, is discussed. Finally, the principle of the parallel dynamic programming, which integrates dynamic programming and analytics intelligence, is presented as the future computational intelligence. © 2014 Chinese Association of Automation.

关键词： Artificial intelligence Neural networks Reinforcement learning

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Comparison of control Methods Based on Imitation Learning for Autonomous Driving

Comparison of Control Methods Based on Imitation Learning fo...

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International Conference on intelligent control and Information Processing (ICICIP)

作者： Yinfeng Gao Yuqi Liu Qichao Zhang Yu Wang Dongbin Zhao Dawei Ding Zhonghua Pang Yueming Zhang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China The state Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China China Automotive Technology & Research Center Co. Ltd Tianjin China Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology China

ISBN: (数字)9781728100159

ISBN: (纸本)9781728100166

Recently, some learning-based methods such as reinforcement learning and imitation learning have been used to address the control problem for autonomous driving. Note that reinforcement learning has strong reliance on the simulation environment and requires a handcraft design of the reward function. Considering different factors in autonomous driving, a general evaluation method is still being explored. The purpose of imitation learning is to learn the control policy through human demonstrations. It is meaningful to compare the control performances of current main imitation learning methods based on the provided dataset. In this paper, we compare three typical imitation learning algorithms: Behavior cloning, Dataset Aggregation (DAgger) and Information maximizing Generative Adversarial Imitation Learning (InfoGAIL) in the The Open Racing Car Simulator (TORCS) and Car Learning to Act (CARLA) simulators, respectively. The performance of algorithms is evaluated on lane-keeping task in racing and urban environment. The experiment results show DAgger performs best in simple lane keeping problem, and InfoGAIL has the unique advantage of distinguishing different driving styles from expert demonstrations.

关键词： Gallium nitride Trajectory Data models Entropy Task analysis Linear programming Autonomous vehicles

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A Negotiation-based Right-of-way Assignment Strategy to Ensure Traffic Safety and Efficiency in Lane Changes

arXiv

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arXiv 2019年

作者： Zhao, Can Li, Zhiheng Li, Li Wu, Xiangbin Wang, Fei-Yue Department of Automation Tsinghua University Beijing China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Intel Labs China Intel Corporation Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

It is widely acknowledged that verifying the safety of autonomous driving strategies requires a substantial body of simulation testing and road testing. In recent years, the formal safety methods represented by Responsibility-Sensitive Safety (RSS) have encouraged low-cost autonomous driving safety research, benefitting from its accurate assessment of safety and clear division of responsibilities. However, how to maintain traffic efficiency while ensuring safety remains a challenge. To address this problem, this paper proposes a formulized negotiation-based lane-changing strategy that makes a trade-off between safety and efficiency. Both theoretical analysis and numerical experimental results shows that compared to RSS, our strategy can noticeably improve the success rate of changing lanes on the premise of safety. Copyright © 2019, The Authors. All rights reserved.

关键词： Autonomous vehicles

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An Analysis of Blockchain-based Bitcoin Mining Difficulty: Techniques and Principles

An Analysis of Blockchain-based Bitcoin Mining Difficulty: T...

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Chinese Automation Congress

作者： Xiaojing Zhang Rui Qin Yong Yuan Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China

Bitcoin is a decentralized peer-to-peer electronic cash system, and it realizes the circulation and distribution of cryptocurrency via miners contributing their computational power to solve a cryptographic puzzle. With more and more people participating in Bitcoin mining, the continuous growing of the computational power might greatly reduce the time for finding a new valid block. To keep the stability of the Bitcoin system, the difficulty of the Bitcoin network will be adjusted every 2016 blocks. This paper aims to study the principles of the difficulty adjustment of the whole Bitcoin blockchain network in Bitcoin mining. We first describe the computing process of the target and thus the difficulty of a block, and then study its influence factors and their effect on the difficulty. After that, we study the adjustment of the difficulty, and propose a framework for the adjustment of the difficulty that can be easily used in practice. Our work can help researchers better understand the principles of Bitcoin mining.

关键词： Bitcoin mining Blockchain Network difficulty Target Adjustment of difficulty

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A New Approach for Face Anti-Spoofing Using Handcrafted and Deep Network Features

A New Approach for Face Anti-Spoofing Using Handcrafted and ...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Polash Kumar Das Bin Hu Chang Liu Kaixin Cui Prabhat Ranjan Gang Xiong School of Electronic and Information Engineering South China University of Technology Guangzhou China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China College of Computer Science Sichuan University Chengdu China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Cloud Computing Center Chinese Academy of Science Dongguan China

In biometrics, face recognition methods are achieving momentum with recent progress in the computer vision(CV). Face recognition is widely used in the identification of an individual's identity. Unfortunately, in recent research work has revealed this face biometrics system is unprotected to spoofing attacks using by very low price instrument such as printed 2D photos attack, 3D masking attack and taking videos using smart devices (reply attack). Therefore, a Liveness Attack Detection (LAD) approach is needed to improve the high-quality security of face recognition system. Most of the earlier worked LAD methods for face anti-spoofing methods have highlight on using the handcrafted features, which are developed by expert knowledge of researcher. As example Gabor filter, Histogram of Oriented Gradients, local ternary pattern, and the Local Binary Pattern. Because of that, the extracted features consider limited factors of the problem, yielding a capture accuracy that is very low and changes with the point of presentation in attack face images. The deep learning method has developed in the computer vision research community, which is proven to be suitable for automatically training. In this article, we approach to mix or combine the handcrafted features and deep neural network features to design the discriminant face spoofing detection. The handcrafted features were based on LBP analysis. We examine the features information from the brightness and the chrominance channels using LBP descriptor. In deep features, we present an approach based on pre-trained convolutional neural network VGG-16 model using static features to recognize video and printed(2D) photo attacks. By attaching this two types of image features on our dataset and public databases, we get good results to identify real and attack images feature, called hybrid features, which has better discrimination ability to understand spoofing image feature.

关键词： Face Videos Feature extraction Databases Face recognition Password

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