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2017 IEEE International Conference on Robotics and Automation, ICRA 2017

作者： Wang, Rui Wang, Shuo Wang, Yu Tang, Chong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781509046331

This paper addresses the problem of path following for a biomimetic underwater vehicle (BUV) propelled by undulatory fins with uncertain model and unknown disturbance. The mechanical structure of the BUV is briefly described. Moreover, the general kinematics and dynamics models of the vehicle are presented and the path following problem is formulated. The controller combining line-of-sight (LOS) guidance system with active disturbance rejection control (ADRC) technique is designed to maneuver the BUV to follow a predefined parameterized curve. Specifically, a guidance system based on LOS principle is implemented to decouple the multi-variable system to steer the surge speed and the course respectively. Furthermore, in order to deal with model uncertainty, ADRC is used in development of the surge speed controller and the course controller. Finally, simulations and experimental results validated the performance of the proposed path following control scheme. © 2017 IEEE.

关键词： controllers

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Adaptive-Learning-Based H∞ Optimal Tracking control Design for a Class of Constrained Nonlinear systems

Adaptive-Learning-Based H∞ Optimal Tracking Control Design ...

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第37届中国控制会议

作者： Jiaxu Hou Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology Beijing The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences School of Automation Guangdong University of Technology

This paper, considering constrained inputs, focuses on designing the optimal controller for discrete-time H∞ tracking control problems. The neural-network-based iterative learning algorithm is deemed to be an excellent method to work. The iterative heuristic dynamic programming algorithm is employed for developing the formulated regulation of the tracking error. Strict convergence guarantees are given to support the optimality of the adaptive algorithm. A neural-network-based training scheme is adopted to implement the learning algorithm and stabilize the system in the optimal manner. Two numerical examples are provided to illustrate the applicability and good performance of the designed tracking control scheme.

关键词： Adaptive dynamic programming H∞ tracking control constraints convergence proof

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Regression convolutional network for vanishing point detection 32

Regression convolutional network for vanishing point detecti...

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32nd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2017

作者： Shuai, Yan Tiantian, Yan Guodong, Yang Zize, Liang University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538629017

This paper presents a detection method for estimation of vanishing point position with designed regression convolutional neural network. Due to the deep structures of convolutional networks, global high-level features are extracted from the whole image, which helps to locate the vanishing point. In this paper, we provide a new structure of regression neural network based on AlexNet. The structure consists of five convolutional layers, four fully connected layers, Tanh activation function and regression loss function. We feed the neural net with a small number of training dataset and the result proves that this method is adaptable. Compare to classical method, deep learning is more effective on blurred pictures and complex circumstances. © 2017 IEEE.

关键词： Convolution

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A new framework of science and technology innovation education for k-12 in Qingdao, China

A new framework of science and technology innovation educati...

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2017 ASEE International Forum

作者： Liu, Xiwei Gong, Xiaoyan Wang, Fei-Yue Sun, Rui Gao, Yanqing Zhang, Yu Zhou, Jiehan Deng, Xiaofei State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Intelligent Education Institution Qingdao Academy of Intelligent Industries China Qingdao Experimental High School China Oregon Institute of Technology United States University of Oulu Finland

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An Assist-as-Needed controller for Robotic Rehabilitation Therapy Based on RBF Network

An Assist-as-Needed Controller for Robotic Rehabilitation Th...

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International Joint Conference on Neural Networks

作者： Lincong Luo Liang Peng Chen Wang Zengguang Hou Weiqun Wang 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 Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China

The rehabilitation training with active involvement and efforts of impaired subjects can enhance the outcome of rehabilitation therapies. To promote patients' voluntary engagement, a variety of assist as needed (AAN) controllers have been proposed for robot-aided therapy. However, patients' impairment level is not taken into account in the implementation of those control schemes. In this study, a novel AAN controller is developed for upper limb rehabilitation therapy. The control paradigm uses Gaussian radial basis function (RBF) network to learn the model of subjects' motor capability in workspace. The update of weight vectors of RBF network is based on a greedy strategy, which has the potential to promote subjects' voluntary engagement by providing task challenge for them. Considering the difference in the impairment degree of patients, the assistance and impedance level of robot control is regulated based on the task performance of patients. The results of experiments at four healthy subjects verify the feasibility and adaptability of the proposed AAN controller.

关键词： Radial basis function networks Robot sensing systems Training Medical treatment Task analysis Mathematical model

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Passivity-based Fault Detection for semi-Markov Jump systems with Time-varying Delays

Passivity-based Fault Detection for semi-Markov Jump Systems...

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Chinese Automation Congress (CAC)

作者： Yidao Ji Wei Wu School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing P.R. China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing P.R. China

This paper investigates the passivity-based fault detection of semi-Markov jump systems with time-varying delays. By constructing the filter type residual generator and model transformation, the augmented error system is first established. Then, by means of Lyapunov-Krasovskii approach, fault detection conditions are given such that the augmented error systems can satisfy the passivity performance. The desired fault detection gains can be obtained with the help of matrix techniques. The simulation example is presented to show the validity of the design method.

关键词： Fault detection Generators Radio frequency Delays Stability criteria Symmetric matrices Time-varying systems

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An efficient deep reinforcement learning model for urban traffic control

arXiv

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arXiv 2018年

作者： Lin, Yilun Dai, Xingyuan Li, Li Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China University of Chinese Academy of Sciences Beijing100049 China Department of Automation TNList Tsinghua University Beijing100084 China

Urban Traffic control (UTC) plays an essential role in intelligent Transportation System (ITS) but remains difficult. Since model-based UTC methods may not accurately describe the complex nature of traffic dynamics in all situations, model-free data-driven UTC methods, especially reinforcement learning (RL) based UTC methods, received increasing interests in the last decade. However, existing DL approaches did not propose an efficient algorithm to solve the complicated multiple intersections control problems whose state-action spaces are vast. To solve this problem, we propose a Deep Reinforcement Learning (DRL) algorithm that combines several tricks to master an appropriate control strategy within an acceptable time. This new algorithm relaxes the fixed traffic demand pattern assumption and reduces human invention in parameter tuning. Simulation experiments have shown that our method outperforms traditional rule-based approaches and has the potential to handle more complex traffic problems in the real world. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Visual Place Recognition in Long-term and Large-scale Environment based on CNN Feature

Visual Place Recognition in Long-term and Large-scale Enviro...

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IEEE intelligent Vehicles Symposium

作者： Jianliang Zhu Yunfeng Ai Bin Tian Dongpu Cao Sebastian Scherer School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Department of Mechanical & Mechatronics Engineering University of Waterloo Robotics Institute Carnegie Mellon University

ISBN: (纸本)9781538644539

With the universal application of camera in intelligent vehicles, visual place recognition has become a major problem in intelligent vehicle localization. The traditional solution is to make visual description of place images using hand-crafted feature for matching places, but this description method is not very good for extreme variability, especially for seasonal transformation. In this paper, we propose a new method based on convolutional neural network (CNN), by putting images into the pre-trained network model to get automatically learned image descriptors, and through some operations of pooling, fusion and binarization to optimize them, then the similarity result of place recognition is presented with the Hamming distance of the place sequence. In the experimental part, we compare our method with some state-of-the-art algorithms, FABMAP, ABLE-M and SeqSLAM, to illustrate its advantages. The experimental results show that our method based on CNN achieves better performance than other methods on the representative public datasets.

关键词： Hamming distance intelligent vehicles binarization Recognition (Psychology) Visualization network model Cameras images

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An Improved Page Rank Algorithm Based on Fuzzy C-Means Clustering and Information Entropy 3

An Improved Page Rank Algorithm Based on Fuzzy C-Means Clust...

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2017 3rd IEEE International Conference on control Science and systems Engineering (ICCSSE)

作者： Wenbo Zheng Shaocong Mo Pengfei Duan Xiaotian Jin School of Software Engineering Xi’an Jiaotong University The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Computer Science and Technology Wuhan University of Technology Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology

ISBN: (纸本)9781538604854

This paper proposes an improvement to the Page Rank algorithm. Most existing Page Rank algorithms expect a strong correlation among consecutively accessed webpages, which in reality should be a fuzzy relationship when a user accesses pages on an arbitrarily basis. We mine data from search-behavior logs by analyzing chronological sequential patterns, and cluster all webpages using fuzzy C clustering. The weight of each cluster is identified with information entropy, which is then used to adjust the average weight. A sample of 1 million pages is used for testing. Compared with traditional Page Rank, the new algorithm decreases search time by 34.83% and increases search accuracy by 41.88%;when compared with HITS, the improvements are 31.82% and 64.04% respectively.

关键词： fuzzy c-means information entropy PageRank

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Seasonal Scheduling of Office Electricity Use via Adaptive Dynamic Programming

Seasonal Scheduling of Office Electricity Use via Adaptive D...

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Chinese control Conference (CCC)

作者： Guang Shi Bo Zhao Chao Li Xin Jin Derong Liu National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China School of Automation Guangdong University of Technology Guangzhou China

In this paper, an adaptive dynamic programming (ADP) based optimization method is proposed to schedule the electricity use of an office, where a battery is considered as the control variable, while solar and wind energies are included as additional energy supplies besides the grid. The electricity demand of an office generally contains socket, lighting and air-conditioning demands. Based on the periodic models of electricity price, electricity demand, and solar and wind energies, the optimal control strategies of the battery are determined by the proposed ADP based optimization method, so that the electricity cost from the grid can be saved. Simulation analysis demonstrates that the proposed method can achieve optimal real-time scheduling of office electricity use in different seasons of a year.

关键词： Batteries Optimization Scheduling Wind energy Performance analysis Optimal control Energy management

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