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Chinese control Conference (CCC)

作者： Fang Wang Xin Chen Chang Tan Jinbin Li Yaodong Zhang Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems State Grid Hubei Electric Power Research Institute Wuhan P. R. China

Traditional artificial live work asks for a high standard with operator's mental state and technical level in high-voltage and strong-magnetic environment, which not only reduces the efficiency of modern production and living but also causes potential threats to the safety of operators. Therefore, developing robot live work has great significance for both academic research and industrial applications. A key problem of the live work for an industrial robot is recognition and positioning of the hexagon-shaped screw in substation with high accuracy. To solve this problem, this article designs a hexagon-shaped screw recognition and positioning system based on binocular vision in transformer substation. A method for stereo matching including 3 steps is proposed: edge noise of the target is reduced by median filter at the beginning; then the target can be obtained through the image segmentation method of adjusting threshold; further moments are used to express the centers of hexagons in 2 images which work as stereo matching points. Finally depth information can be obtained due to visual geometric relationships based on stereo model and camera calibration parameters based on camera model. The experiment results show that the binocular system designed in this article can accurately identify the target with good stability, and the measurement error is controlled in 4%.

关键词： Cameras Fasteners Calibration Image segmentation Mathematical model

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Optimal Constrained Self-learning Battery Sequential management in Microgrid Via Adaptive Dynamic Programming

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IEEE/CAA Journal of Automatica Sinica 2017年第2期4卷 168-176页

作者： Qinglai Wei Derong Liu Yu Liu Ruizhuo Song IEEE State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing Institute of Automation Chinese Academy of Sciences

This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems. The main idea is to use the adaptive dynamic programming U+0028 ADP U+0029 technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery. Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally, simulation and comparison results are given to illustrate the performance of the presented method. © 2017 Chinese Association of Automation.

关键词： Adaptive control systems Automation Battery management systems control theory Electric batteries Energy management Energy management systems intelligent buildings Iterative methods Number theory Secondary batteries

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IEEE Transactions on Evolutionary Computation Special Issue on Parallel Evolution for Large Scale Optimization

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IEEE Transactions on Evolutionary Computation 2018年第4期22卷 633-633页

作者： Wang, Fei-Yue Wei, Qinglai Tang, Ke Coello Coello, Carlos A. State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China Qingdao Academy of Intelligent Industries China Department of Computer Science and Engineering Southern University of Science and Technology China Department of Computer Science CINVESTAV-IPN Mexico

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Decentralized Guaranteed Cost control of Modular and Reconfigurable Robots Based on Adaptive Dynamic Programming

Decentralized Guaranteed Cost Control of Modular and Reconfi...

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第30届中国控制与决策会议

作者： Yi An Bo Dong Fan Zhou Fu Liu Yuanchun Li College of Communications Engineering Jilin University Department of Control Science and Engineering Changchun University of Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

This paper investigates a decentralized guaranteed cost control method of modular and reconfigurable robots(MRRs) based on adaptive dynamic programming(ADP) ***,we formulate the dynamic model of systems and the interconnected couplings are ***,we design a decentralized guaranteed cost *** establishing a critic neural network,a learning-based adaptive dynamic programming algorithm is proposed to solve the Hamilton-Jacobi-Bellman(HJB) equation,and then the approximate optimal control policy can be *** asymptotic stability of the closed-loop system is proved based on the Lyapunov ***,simulations are performed for the 2-DOF MRRs to verify the effectiveness of the proposed method.

关键词： Modular and reconfigurable robots(MRRs) decentralized guaranteed cost control adaptive dynamic programming(ADP) critic neural network

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Generalizing Integer Projected Graph Matching Algorithm for Outlier Problem

Generalizing Integer Projected Graph Matching Algorithm for ...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Lei He Xu Yang Zhi-Yong Liu Field Engineering College Army Engineering University of PLA Nanjing China State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781538649923

Graph matching plays an important role in computer vision and pattern recognition. Recent graph matching algorithms usually formulate graph matching by a discrete optimization problem, and have designed various types of optimization techniques to find a local optimum in reasonable time. Among them some algorithms utilizing the graduated projection to the discrete domain exhibit superior performance, but these algorithms are limited to specific applications. From the outlier perspective, they are applicable to subgraph matching in which outliers exist in at most one graph. However, in real tasks there are usually outliers in both graphs. Previously we have proposed a method directly targeting at finding the most similar subgraphs in two weighted graphs. In this paper we show that the idea can be generalized to other algorithms, and the IPFP is chosen as a representative algorithm for generalization. Experiments witness the effectiveness of the generalization.

关键词： Optimization Task analysis Electronic mail Computer vision Kernel Linear programming Pattern matching

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Advances in Vision-Based Lane Detection：Algorithms,Integration,Assessment,and Perspectives on ACP-Based Parallel Vision

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IEEE/CAA Journal of Automatica Sinica 2018年第3期5卷 645-661页

作者： Yang Xing Chen Lv Long Chen Huaji Wang Hong Wang Dongpu Cao Efstathios Velenis Fei-Yue Wang Advanced Vehicle Engineering Centre Cranfield University Bedford MK43 0AL U.K Vehicle Intelligence Pioneers Ltd Qingdao 266000 China School of Data and Computer Science Sun Yat-Sen University Guangzhou 510275 China Mechanical and Mechatronics Engineering with the University of Waterloo 200 University Avenue West Waterloo ON N2L 3G1 Canada State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China ieee

Lane detection is a fundamental aspect of most current advanced driver assistance systems(ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system,and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.

关键词： Advanced driver assistance systems(ADASs) ACP theory benchmark lane detection parallel vision performance evaluation

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Reinforcement Learning-Based Predictive control for Autonomous Electrified Vehicles

Reinforcement Learning-Based Predictive Control for Autonomo...

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2018 IEEE intelligent Vehicles Symposium, IV 2018

作者： Liu, Teng Yang, Chao Hu, Chuanzheng Wang, Hong Li, Li Cao, Dongpu Wang, Fei-Yue State Key Laboratory of Automotive Simulation and Control Jilin University China Vehicle Intelligence Pioneers Inc. Qingdao Shandong266109 China Jiangsu XCMG Construction Machinery Research Institute Ltd. Xuzhou Jiangsu221004 China Mechanical and Mechatronics Engineering Department Waterloo University N2L 3G1 Canada Faculty of Department of Automation Tsinghua University China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538644522

This paper proposes a learning-based predictive control technique for self-driving hybrid electric vehicle (HEV). This approach is a hierarchical framework. The higher-level is a human-like driver model, which is applied to predict accelerations in the car following situation to replicate a human driver's demonstrations. The lower-level is a reinforcement learning (RL)-based controller, which enforces the battery and fuel consumption constraints to improve energy efficiency of HEV. In addition, we present induced matrix norm (IMN) to handle cases that the training data cannot provide sufficient information on how to operate in current driving situation. Simulation results illustrate that the proposed method can reproduce human driver's driving style and promote fuel economy. © 2018 IEEE.

关键词： Reinforcement learning

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Augmented Marker Tracking for Peri-acetabular Osteotomy Surgery 39

Augmented Marker Tracking for Peri-acetabular Osteotomy Surg...

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39th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Pflugi, Silvio Vasireddy, Rakesh Lerch, Till Ecker, Timo M. Tannast, Moritz Boemke, Nane Siebenrock, Klaus Zheng, Guoyan State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institution of Technology Beijing P. R. China Universitat Bern Bern BE CH

ISBN: (纸本)9781509028092

We developed and validated a small, easy to use and cost-effective augmented marker-based hybrid navigation system for peri-acetabular osteotomy (PAO) surgery. The hybrid system consists of a tracking unit directly placed on the patient's pelvis, an augmented marker with an integrated inertial measurement unit (IMU) attached to the patient's acetabular fragment and the host computer. The tracking unit sends a live video stream of the marker to the host computer where the marker's pose is estimated. The augmented marker with the IMU sends its pose estimate to the host computer where we apply sensor fusion to compute the final marker pose estimate. The host computer then tracks the orientation of the acetabular fragment during peri-acetabular osteotomy surgery. Anatomy registration is done using a previously developed registration device. A Kalman filter-based sensor fusion was added to complete the system. A plastic bone study was performed for validation between an optical tracking-based navigation system and our proposed system. Mean absolute difference for inclination and anteversion was 1.63 degrees and 1.55 degrees, respectively. The results show that our system is able to accurately measure the orientation of the acetabular fragment.

关键词： Surgery Navigation Sensor fusion Cameras Pelvis Calibration Streaming media

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LoRa on the Move: Performance Evaluation of LoRa in V2X Communications

LoRa on the Move: Performance Evaluation of LoRa in V2X Comm...

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2018 IEEE intelligent Vehicles Symposium, IV 2018

作者： Li, Yuke Han, Shuangshuang Yang, Linyao Wang, Fei-Yue Zhang, Hui State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100049 China Qingdao Huituo Intelligent Machine Company Qingdao China Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China Computer Science Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Conviva Inc. Silicon ValleyCA94404 United States

ISBN: (纸本)9781538644522

Recent years have witnessed much interest in Low Power Wide Area (LPWA) technologies, which are gaining unprecedented momentum and commercial interest towards the realisation of the Internet of Things (IoT). Long Range (LoRa), as a representative LPWA technology, has the potential to satisfy the growing demand for the longer range and larger amount connectivities in vehicular communication networks. In this paper, LoRa is firstly applied into two typical vehicular networks, namely Vehicle-to-Infrastructure (V2I)and Vehicle-to-Vehicle (V2V), and performance of LoRa schemes with different parameter configurations are evaluated and compared. Further, Monte Carlo simulations indicate that the schemes equipped with higher bandwidth or lower spreading factor exhibit significant advantages in combating the fast fading caused by Doppler effect in networks. © 2018 IEEE.

关键词： Monte Carlo methods

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Current controller Based on Recurrent Neural Network for Grid-Connected Converter

Current Controller Based on Recurrent Neural Network for Gri...

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第37届中国控制会议

作者： Xuefen Wang Hongbin Wang Qingyi Wang Min Ding Quan Yin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems State Grid Corporation of China School of Automation Huazhong University of science and technology

Three-phase grid-connected converters are widely used in renewable and electric power system applications. Due to system nonlinearity and time-variant characteristic, there are limitations in standard decoupled d-q vector control mechanism. To mitigate these limitations, a RNN vector controller trained with Levenberg-Marquardt and FATT（Forward accumulation through time） algorithm is designed. The simulation is researched by using MATLAB software, and the results show that training neuralnetwork algorithm is effective and the system using RNN vector control method outperforms the system using conventional PI control method under low sampling rate conditions.

关键词： Grid-connected converter RNN controller LM plus FATT algorithm Simulation

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