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Chinese control Conference (CCC)

作者： Kun Gong Fang Deng Jie Chen School of Automation Beijing Institute of Technology Beijing Beijing CN Ministry of Education Key Laboratory of Complex System Intelligent Control and Decision Beijing Beijing CN Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing Beijing CN

Based on the comparison of several common methods of electronic compass error compensation, this paper presents a new error compensation method based on Adaptive Differential Evolution-Fourier Neural Networks (ADE-FNN) to improve the measurement accuracy of electronic compass. This method uses Fourier neural network to model electronic compass error, and adopts Adaptive Differential Evolution to optimize the weights of neural network, and get more exact error model to compensate measured values. The compensation object is the common electronic compass composed by two-dimensional magnetic resistance sensor. Compared with the compensation effect of Least-square method, BP neural network and Fourier neural networks, It proves that the mode of this method can realize the high precision in the sample space mapping and high non-linear approximation ability, and this method has faster convergence rate, can avoid falling into local minima, reduces the training error, and improves error compensation accuracy. This method decreases the error range from -3.4° ~ 25.2° before compensation to -0.20° ~ 0.72°, and the average of the absolute error is 0.30°. Repeatability tests also proved the compensation plan have a good consistency.

关键词： Compass Error compensation Adaptive systems Electronic mail Neural networks Training Measurement uncertainty

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Adaptive sliding mode control of electromechanical actuator with improved parameter estimation

Adaptive sliding mode control of electromechanical actuator ...

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作者： Li, Hao Bai, Yongqiang Su, Zhong School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Ministry of Education Beijing 100081 China Key Laboratory of Modern Measurement and Control Technology Beijing Information Science and Technology University Ministry of Education Beijing 100101 China

ISBN: (纸本)9788995605646

An adaptive sliding mode control scheme for electromechanical actuator has been presented. The adaptive control strategy can estimate the uncertain parameters and adaptively compensate the modeled dynamical uncertainties, while the sliding mode control method overcomes the unmodelled dynamics. In the adaptive law an equivalent output injection of the sliding mode observer which contains the parameter estimation error is used, and estimates of parameters can approximate the true values without prediction-error that is typically used in compositive adaptive law. Due to the improved estimation of uncertain parameters, the sliding mode law can robustifies the design against model uncertainties with a small swithcing gain. Stability of the system with the proposed approach has been proved and it has also been shown that the system states can reach the sliding mode in finite time. Finally, the effectiveness of the proposed control scheme has been exhibited via simulation examples. © 2011 Asian control Association.

关键词： Parameter estimation

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Real-Time Wide-Field Monitoring System Based on Image Mosaicing Technique and Its Application

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IFAC Proceedings Volumes 2011年第1期44卷 14928-14933页

作者： Li-hui Zou Jie Chen Juan Zhang Jing-hua Lu School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision (Beijing Institute of Technology) Ministry of Education Beijing 100081 China

Abstract Common surveillance system is usually limited by the scope of observation, which may increase the probability of missing or misjudging suspicious targets. A wide-field monitoring system based on efficient image mosaicing algorithm is proposed in this paper. It can monitor a wide field for both short and long distance in real time and highlight the detected moving objects simultaneously. The image mosaicing is not only aimed at obtaining the wide field of view, but also utilized in motion detection for forming a wide-field motion mask. A simulation platform is developed to verify the performance of the system. The experiment results show that it satisfies the demands of high resolution and real-time implementation. The system is of high practical value to transportation surveillance or other applications.

关键词： safety analysis image processing transportation

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A reinforcement learning based tag recommendation

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作者： Ge, Feng He, Yi Liu, Jin Lv, Xiaoming Zhang, Wensheng Li, Yiqun State Key Laboratory of Software Engineering Computer School Wuhan University 430072 China Logistics Group Wuhan University 430072 China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Baidu Inc. 100085 China

ISBN: (纸本)9783642256578

This paper proposes a reinforcement learning based tag recommendation algorithm to deal with the data sparseness that affects the performance stability of collaborative filtering algorithms. Our algorithm integrates user tags into traditional collaborative filtering algorithms and attaching importance to user interest shift in the process of user interest learning process. Empirical Cases of comparing with traditional collaborative filtering algorithms indicate that our recommend algorithm exhibits better performance competition. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Reinforcement learning

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3D Multi-Constraint Route Planning for UAV Low-altitude Penetration Based on Multi-Agent Genetic Algorithm

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IFAC Proceedings Volumes 2011年第1期44卷 11821-11826页

作者： Jin-Ping Wu Zhi-Hong Peng Jie Chen School of Automation Beijing Institute of Technology Beijing 100081 Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Ministry of Education P. R. China

Abstract Low-altitude penetration is one of the most important military applications of Unmanned Aerial Vehicles (UAVs). 3D route planning, which is a complex multi-objective optimization problem with multiple constraints, is the key technology of UAV low altitude-penetration. A 3D route planning method based on Multi-Agent Genetic Algorithm (MAGA) is proposed in this paper. Specifically, a dynamic route representation form is proposed to improve the flight route accuracy. An efficient constraint handling method is used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation. More importantly, details are presented to show how to apply MAGA to 3D route planning while some necessary improvements of MAGA are proposed to make it more efficient. Simulation and analysis illustrate the effectiveness of this method.

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Medical Image Segmentation Using Mean Shift Algorithm and General Edge Detection

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IFAC Proceedings Volumes 2011年第1期44卷 9656-9661页

作者： Jinghua Lu Jie Chen Juan Zhang Lihui Zou School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing 100081 China (Tel: 86-010-68913795)

Abstract The segmentation of the medical image faces the challenges of the existence of large number of diverse structures of human anatomy and inevitable artifacts induced from the imaging procedure. In this paper we treat some structures and artifacts as general edge features, and introduce the edge information into the mean shift segmentation algorithm in both clustering and the fusion steps. Considering the medical images as two dimensional signals, the general edge can be detected and described with its local spatial frequency properties. The segmentation results show the improvement in preserving the completeness of details while sketching the overall structures.

关键词： image segmentation signal processing medical application

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Decentralized Loading Coordinations for Large-population Plug-in Electric Vehicles and a few controllable Bulk Loads

Decentralized Loading Coordinations for Large-population Plu...

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IEEE Conference on decision and control

作者： Xiaokun Yin Zhongjing Ma Lei Dong School of Automation Beijing Institute of Technology (BIT) Beijing China School of Automation BIT and the Key Laboratory of Complex System Intelligent Control and Decision (BIT) Ministry of Education Beijing China School of Automation BIT

ISBN: (纸本)9781612848006

This paper studies the loading coordinations for large-population autonomous individual (plug-in) electric vehicles (EVs) and a few controllable bulk loads, e.g. EV fleets, pumped storage hydro units, and so on. Due to the computational infeasibility of the centralized coordination methods to the underlying large-population systems, in this paper we develop a novel game-based decentralized coordination strategy. Following the proposed decentralized strategy update mechanism and under some mild conditions, the system may quickly converge to a nearly valley-fill Nash equilibrium. The results are illustrated with numerical examples.

关键词： (Plug-in) electric vehicles Loading coordination Nash equilibrium (NE) Valley-fill

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Online Path Planning for UAV Using an Improved Differential Evolution Algorithm

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IFAC Proceedings Volumes 2011年第1期44卷 6349-6354页

作者： Xing Zhang Jie Chen Bin Xin Hao Fang School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing 100081 China Tel: 86-10-68912464-22

Abstract This paper presents a 3D online path planning algorithm for unmanned aerial vehicle (UAV) flying in partially known hostile environment. In order to provide a smooth fight route for UAV, the algorithm adopts B-Spline curve to describe UAV's path whose control points are optimized by an improved differential evolution algorithm. The planner gradually produces a smooth path for UAV from starting location to its target. Regarding path planning performance, the proposed improved diferential evolution based planner is compared with its three competitors which are based on classical differential evolution, genetic algorithm and particle swarm optimization, respectively. Numerical simulation demonstrates that the proposed UAV path planner not only produces high-quality feasible flight routes for UAV, but also obviously outperforms its competitors.

关键词： UAV online path planning differential evolution B-Spline

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Improved differential evolution-based particle filter algorithm for target tracking

Improved differential evolution-based particle filter algori...

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作者： Wang, Yanan Chen, Jie Gan, Minggang School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex Systems Intelligent Control and Decision Ministry of Education BIT Beijing 100081 China

ISBN: (纸本)9789881725592

This paper presents an improved target tracking algorithm based on the differential evolution particle filter (DEPF) in order to solve the problem of particle degeneracy. In this method, the mutation, crossover and selection steps of the differential evolution are introduced into the particle filter, which optimizes the particle sampling process. Particles are moved towards regions where they are close to the value of the posterior density function through DEPF algorithm, and it improves the estimation precision, inhibits the particle degeneracy and enhances the diversity of the particles. The target observation model based on the color feature is constructed simultaneously in the algorithm, and the method of the target model update in different states is also studied. The experimental results show that the target even in attitude change and occlusion can be tracked accurately and real-timely with the DEPF algorithm. The proposed algorithm has higher tracking accuracy and is robust. © 2011 Chinese Assoc of automation.

关键词： Target tracking

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Impulsive control method based on improved differential evolution algorithm

Impulsive control method based on improved differential evol...

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作者： Zhao, Juan Cai, Tao Deng, Fang Yang, Hongwei School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex Systems Intelligent Control and Decision Ministry of Education BIT Beijing 100081 China

ISBN: (纸本)9789881725592

In this paper, a new strategy based on impulsive control model of high speed roller is proposed. To make the roller hit the specified target, the strategy is summarized as an optimal control model calculating required number of impulses, the igniting time and phase of each impulse, and also an improved Differential Evolution (DE) Algorithm is adopted to resolve. It is too difficult to solve the orderliness of igniting time by Basic DE, so a Mapping Differential Evolution Algorithm (MDE) is proposed, which maps the sorting time variables to random coefficient range [0,1]. In addition, a DE with self-adapting parameter is used to accelerate the convergence rate. Simulation results show the high accuracy of high speed roller with the method. © 2011 Chinese Assoc of Automati.

关键词： Mapping

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