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第35届中国控制会议

作者： Cheng Long Wang Yunpeng Hou Zeng-Guang Tan Min State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences Beijing Institute of Control Engineering

ISBN: (纸本)9781509009107

A mean square leader-following consensus protocol is proposed for discrete-time linear multi-agent systems with communication *** attenuate the noise's effect,a specific class of time-varying consensus gains are applied to the noisecorrupted relative states between *** distinguished feature of the proposed protocol is that each agent can have its own noise-attenuation *** the steady-state performance and the transient performance of the closed-loop multi-agent system are *** convergence rates of the mathematical expectation and the second-moment of the leader-following consensus error are explicitly presented.

关键词： Multi-agent systems convergence rate mean square noises leader-following consensus

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Training and testing object detectors with virtual images

arXiv

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arXiv 2017年

作者： Tian, Yonglin Li, Xuan Wang, Kunfeng Wang, Fei-Yue Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world has a great demand for labor and money investments and is usually too passive to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations. A virtual dataset named ParallelEye is built, which can be used for several computer vision tasks. Then, by training the DPM (Deformable Parts Model) and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining ParallelEye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors. Copyright © 2017, The Authors. All rights reserved.

关键词： Object detection

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Robust frequency control design on micro-grid with unknown dead-zone nonlinearity

Robust frequency control design on micro-grid with unknown d...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Chaoxu Mu Ding Wang Changyin Sun School of Automation Southeast University Nanjing China Tianjin Key Laboratory of Process Measurement and Control Tianjin University Tianjin China The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781538629024

The frequency stability is very critical for the safe operation of power system. Meanwhile, distributed energy storages and unknown governor dead band (GDB) will cause system frequency deterioration when the load-frequency control (LFC) is not able to tackle these uncertainties. The stability of an island smart grid is a challenging topic because the less power sources can be regulated to handle power unbalance. In this paper, a neural network-based adaptive sliding mode controller is designed to be as the load frequency controller for an island smart grid with electrical vehicles (EVs), load disturbances and unknown governor dead band. The on-line neural compensation technology is employed to enable the sliding mode frequency control adaptive, thus the frequency stability of power system is improved. Simulation results on a benchmark island smart grid with governors, micro-turbine, EVs, load changes are provided to illustrate the competitive performance.

关键词： Smart grids Frequency control Power system stability Stability analysis Sliding mode control Biological neural networks Benchmark testing

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Optimal management of office energy consumption via Q-learning algorithm

Optimal management of office energy consumption via Q-learni...

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American control Conference

作者： Guang Shi Derong Liu Qinglai Wei The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Automation and Electrical Engineering University of Science and Technology Beijing

ISBN: (纸本)9781509045839

In this paper, a Q-learning based algorithm is developed to optimize energy consumption in an office, where solar energy is introduced as the renewable source and a battery is installed as the control unit. The energy consumption in the office, regarded as the energy demand, is divided into those from sockets, lights and air-conditioners. First, the time series of real-time electricity rate, renewable energy, and energy demand are modeled by echo state networks as periodic functions. Second, given these periodic functions, a Q-learning based algorithm is developed for optimal control of the battery in the office, so that the total cost on energy from the grid is reduced. Finally, numerical analysis is conducted to show the performance of the developed algorithm.

关键词： office energy consumption Energy requirements Batteries Periodic Function control unit algorithms Total Costs Solar Energy Optimal control

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A Real-time and Unsupervised Advancement Scheme for Underwater Machine Vision

A Real-time and Unsupervised Advancement Scheme for Underwat...

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IEEE International Conference on Cyber Technology in Automation, control, and intelligent systems

作者： Xingyu Chen Zhengxing Wu Junzhi Yu Li Wen State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China School of Mechanical Engineering and Automation Beihang university Beijing China

This paper presents a real-time and unsupervised advancement scheme (RUAS) for underwater machine vision in the natural light condition. RUAS consists of three steps, pre-searching, restoration, and post-enhancing. In pre-searching, we provide a Protected and Greedy Artificial Fish School Algorithm (PGAFSA) to optimize the key parameters of the underwater images, and design an evaluating indicator for the PGAFSA based on the features of underwater images. During the restoration, an image degeneration model is built and the Wiener Filter is employed for noise suppression. Moreover, a filtering-aided color correlation method (FCCM) is then presented against color absorption caused by water. The contrast limited adaptive histogram equalization is employed for the contrast stretch in post-enhancing. Finally, we validated the effectiveness and feasibility of the proposed RUAS with deep-sea environmental videos and practical underwater environments.

关键词： Image color analysis Histograms Image restoration Wiener filters Colored noise Absorption Automation

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Adaptive Dynamic Programming and Optimal Stabilization for Linear systems with Time-varying Uncertainty

Adaptive Dynamic Programming and Optimal Stabilization for L...

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Asian control Conference

作者： Meng Zhang Ming-Gang Gan Jie Chen Zhong-Ping Jiang State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China Department of Electrical and Computer Engineering New York University Brooklyn NY

ISBN: (纸本)9781509015740;9781509015733

This paper focuses on the optimal control of continuous-time linear time-varying uncertain systems with completely unknown internal dynamics and proposes a novel approach which leads to an optimal controller with guaranteed stability. A model-free algorithm of adaptive dynamic programming is employed to deal with the uncertainty of system parameters, yielding an optimal feedback controller for the system subject to a predefined cost. Then the stability of the system in time-varying uncertain situation which may undergo parameter changes or jumps is analyzed from the perspective of finite-time stability. On the basis of these results, a switching control strategy is presented to ensure the stability of the time-varying uncertain system with desired optimality properties in the long run. The effectiveness of the strategy is verified by simulations on a DC torque motor servo system.

关键词： Time-varying systems Stability analysis Symmetric matrices Asymptotic stability Uncertainty Optimal control

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Design of Low-cost Position Differential Positioning System Based on STM32

Design of Low-cost Position Differential Positioning System ...

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Chinese Automation Congress

作者： Haijun Fu Ming Qiu Xisong Dong Gang Xiong Haonan Xiong Xiang Su Shichao Chen Zhen Shen Xiujiang Guo School of Artificial Intelligence Beijing Union University Beijing China The Cloud Computing Center Chinese Academy of Sciences Dongguan China The Qingdao Academy of Intelligent Industries Macau University of Science and Technology Qingdao China Beijing Union University Beijing China School of Electrical Engineering and Electronic Information Xihua University Chengdu China The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China Department of Computer Science University of Helsinki Fin Finland Measurement Center Station PetroChina Huabei Oilfield Company Renqiu China

There exist some obstacles to realize high-precision positioning with using the position differential technique due to the limiting factors. However, with the advancement of chip performance and the establishment of multiple satellite navigation systems, position differential positioning can be realized to meet the civilian high-precision positioning demand while significantly reducing costs. This paper is to explore the engineering application and propose a new method. Through the experiments and data analysis, we have found the error correlation between two receivers in a small area and designed a set of positioning system, and programmed in the Keil μVision5 in C language. The system consists of the current mainstream low-cost STM32, UBLOX NEO 6M chip, NRF24L01 chip, GPS antenna and power supply module. Each two receivers can form a differential positioning system. At present, the system in the low-speed status is, in the urban environment, basically reaching within 0.5 - 3 m of the positioning error.

关键词： Position differential positioning Low cost Error correlation GPS STM32

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Policy Gradient Methods with Gaussian Process Modelling Acceleration

Policy Gradient Methods with Gaussian Process Modelling Acce...

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International Joint Conference on Neural Networks

作者： Dong Li Dongbin Zhao Qichao Zhang Chaomin Luo The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences. Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509061839

Policy gradient algorithm is often used to deal with the continuous control problems. But as a model-free algorithm, it suffers from the low data efficiency and long learning phase. In this paper, a policy gradient with Gaussian process modelling (PGGPM) algorithm is proposed to accelerate learning process. The system model is approximated by Gaussian process in an incremental way, which is used to explore state action space virtually by generating imaginary samples. Both the real and imaginary samples are used to train the actor and critic networks. Finally, we apply our algorithm to two experiments to verify that Gaussian process can accurately fit system model and the supplementary imaginary samples can speed up the learning phase.

关键词： Gaussian processes Learning (artificial intelligence) Approximation algorithms Kernel Computational modeling System dynamics

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A Review of Quadruped Robots and Environment Perception 35

A Review of Quadruped Robots and Environment Perception

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第35届中国控制会议

作者： MENG Xiangrui WANG Shuo CAO Zhiqiang ZHANG Leijie State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences

ISBN: (纸本)9781509009107

As legged robots are suitable to be used in unstructured environments,it becomes a popular field of research *** this paper,the development of quadruped robots is *** several typical and recent robot systems are addressed in details,such as HyQ series,StarlETH,ANYmal,MIT Cheetah and BigDog,***,some key techniques of environment perception for quadruped robots,including sensors,feature extraction and identification,mapping and SLAM,are also ***,future researches of quadruped robots in environment perception are given.

关键词： Quadruped robot environment perception sensors feature extraction mapping and SLAM

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Long short-term memory based on a reward/punishment strategy for recurrent neural networks

Long short-term memory based on a reward/punishment strategy...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Jiangjiang Liu Biao Luo Pengfei Yan Ding Wang Derong Liu 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 Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781538629024

Recurrent neural networks and their variants have received huge success in many difficult tasks, such as handwriting recognition and generation, natural language processing, acoustic modeling of speech, and so on. As a kind of recurrent neural network architectures, the long short-term memory (LSTM) has attracted great attention. Most research works focus on its structures, training algorithms and topology structures. As an improvement to the structure of LSTM, a reward/punishment strategy is developed for LSTM in this paper, which we call RP-LSTM. In RP-LSTM, a reward/punishment (RP) strategy is proposed to evaluate its memory cells' memorization such that it improves its efficiency by forgetting more reasonably. Analysis of properties of the developed RP-LSTM is conducted from the neuroscience aspect. To test the performance of the developed RP-LSTM, comparative simulation studies are conducted on three structures, i.e., LSTM, LSTM with forget gate (LSTM-FG) and RP-LSTM. Simulation results on sentiment analysis model and sequence to sequence model demonstrate that RP-LSTM achieves better performance.

关键词： Logic gates Mathematical model Training Memory management Recurrent neural networks Speech Neuroscience

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