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Path planning for solar-powered UAV in urban environment

Neurocomputing 2018年 275卷 2055-2065页

作者： Jianfa Wu Honglun Wang Na Li Peng Yao Yu Huang Hemeng Yang School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Shenyuan Honors College of Beihang University Beijing 100191 China Science and Technology on Aircraft Control Laboratory Beihang University Beijing 100191 China Unmanned System Research Institute Beihang University Beijing 100191 China College of Engineering Ocean University of China Qingdao 266100 China Institute of Public Safety Research Tsinghua University Beijing 100084 China Tianjin Zhongwei Aerospace Data System Technology Co. Ltd Tianjin 300301 China

Aiming at the complexity and particularity of urban environment, a solar-powered UAV (SUAV) path planning framework is proposed in this paper. The framework can be decomposed into three aspects to resolve. First, to make SUAV avoid the building obstacles, a nature-inspired path planning method called Interfered Fluid Dynamical system (IFDS) is introduced. Aiming at the defect that the traditional IFDS is not suitable for SUAV energy optimization calculation, the dynamic constraints and model are introduced to IFDS. The modified IFDS, called Restrained IFDS (RIFDS), is proposed. Second, to resolve the path planning issue efficiently, a novel intelligent optimization algorithm called Whale Optimization Algorithm (WOA) is selected as the basic framework solver. To further overcome the drawback of local minima, adaptive chaos-Gaussian switching solving strategy and coordinated decision-making mechanism are introduced to the basic WOA. The modified algorithm, called Improved WOA (IWOA), is proposed. Third, to solve the accurate modeling problem of solar energy in urban environment, two measures are adopted: (1) A practical judgment method for sunlight occlusions is proposed; (2) Aiming at some unreasonable aspects in the solar energy production model, the received solar energy is modified and recalculated by ASHRAE Clear Sky Model and the solar irradiance calculation principle for slant surfaces in this paper. Finally, the effectiveness of the proposed framework is tested by the simulations.

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Finite-time model-assisted active disturbance rejection control with a novel parameters optimizer for hypersonic reentry vehicle subject to multiple disturbances

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Aerospace Science and Technology 2018年 79卷 588-600页

作者： Yue Yu Honglun Wang Na Li Huiping Zhang Zikang Su Xingling Shao School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Science and Technology on Aircraft Control Laboratory Beihang University Beijing 100191 China Unmanned System Research Institute Beihang University Beijing 100191 China School of Automation Beijing Institute of Technology Beijing 100081 China Beijing Aerospace Automatic Control Institute Beijing 100854 China Key Laboratory of Instrumentation Science & Dynamic Measurement Ministry of Education North University of China Taiyuan 030051 China National Key Laboratory for Electronic Measurement Technology School of Instrument and Electronics North University of China Taiyuan 030051 China

In this paper, a scheme which combines finite-time model-assisted active disturbance rejection control and novel greedy criterion-based salp swarm algorithm is developed for attitude tracking problem of hypersonic reentry vehicle with multiple disturbances. To simplify control structure, the control scheme is designed within the framework of active disturbance rejection control completely. To lessen tuning parameters, a sigmoid function-based tracking differentiator is employed to generate a more realizable attitude transient profile instead of utilizing conventional nonlinear tracking differentiator. Taking known model information as model-assisted term, finite-time model-assisted extended state observers are constructed to estimate the lumped disturbance in attitude and angular rate loop with employment of function fal . To achieve rapid response and strong robustness, finite-time model-assisted control laws are derived by utilizing function fhan . Finally, a novel greedy criterion-based salp swarm algorithm is used to optimize control parameters in finite-time model-assisted control laws to achieve optimal solution that minimizes the tracking error and energy consumption. Comparative simulations are conducted to illustrate the effectiveness of the proposed scheme.

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Dynamical analysis of fractional-order financial system with delay 5

Dynamical analysis of fractional-order financial system with...

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5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2017

作者： Zhang, Zhe Liu, Feng Zhang, Jin Sun, Fenglan Ren, Jie School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China College of Electrical and Information Engineering Hunan University Hunan410082 China Research Center of System Theory and Application Chongqing University of Posts and Telecommunications Chongqing400065 China

This paper we investigates the problem of stability and bifurcation of fractional-order financial system time-delay via the Lyapunov stability judgment method and application of impulse control method. Finally, a numerical simulation example is provided to verify the effectiveness and the benefit of the proposed stability and bifurcation criterion.

关键词： Bifurcation (mathematics)

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The Comparison of Different Visual Features for Visual Odometry

The Comparison of Different Visual Features for Visual Odome...

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IEEE Chinese Guidance, Navigation and control Conference (CGNCC)

作者： Yuehan Jiang Qing Wang Chaoyang Dong Min Zhou Kewei Zhong School of Automation Science and Electrical Engineering Beihang University Beijing People’s Republic of China School of Aeronautic Science and Engineering Beihang University Beijing People’s Republic of China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing People’s Republic of China Shanghai Aerospace Control Technology Institute Shanghai People’s Republic of China

ISBN: (数字)9781538611715

ISBN: (纸本)9781538611722

For robots to work effectively, the availability of a map with detailed information surrounding the workspace is an important requirement for indoor and outdoor tasks. This is usually achieved with using visual odometry techniques with feature-based methods. In this paper, we compare the performance of three different feature extraction methods: Scale Invariant Feature Transform (SIFT), Speed Up Robust Features (SURF) and Oriented FAST Rotated BRIEF (ORB). This paper presents experimental results on standard evaluation datasets and all experiments use measurement of the number of image correspondences as well as the ratio of good matched for the evaluation purpose. The results of experiments demonstrate that the performances of three methods in processing time, matching capability and accuracy. SIFT presents its stability in most scenarios although it is very slow. SURF is faster than SIFT and outperform SIFT on some scenarios. ORB is the most efficient feature and shows strong performance.

关键词： Feature extraction Detectors Robustness Visualization Transform coding Real-time systems Lighting

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Breast Cancer Diagnosis Using an Unsupervised Feature Extraction Algorithm Based on Deep Learning

Breast Cancer Diagnosis Using an Unsupervised Feature Extrac...

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Chinese control Conference

作者： Yawen Xiao Jun Wu Zongli Lin Xiaodong Zhao Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai China The Center for Bioinformatics and Computational Biology East China Normal University Shanghai China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA U.S.A School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Breast cancer is the most often detected cancer in women. At the same time, it is one of the most curable types of cancer if diagnosed early. With the development of the detection technology, a growing amount of clinical data and high-dimensional features can be used for breast cancer diagnosis. The high-dimensional data contributes to advances in the diagnostic technology, but also incurs a large amount of computational redundancy. Thus, extracting important information and reducing the feature dimension is critical to effective prediction and an accurate treatment decision. However, the previous works for breast cancer diagnosis are mainly based on labeled data that is difficult to obtain. To address this issue, in this paper, we demonstrate a new scheme, which integrates a deep learning based unsupervised feature extraction algorithm, the stacked auto-encoders, with a support vector machine model (SAE-SVM), for breast cancer diagnosis. The stacked auto-encoders with the greedy layer-wise pre-training and an improved momentum update algorithm is applied to capture essential information and extract necessary features of the original data. Then, a support vector machine model is employed to classify the samples with new features into malignant or benign tumors. The proposed method was tested on the Wisconsin Diagnostic Breast Cancer data set. The performance is evaluated using various measures and compared with the previously published results. The comparison results show that the proposed SAE-SVM method improves the accuracy to 98.25% and outperforms the other methods. The deep learning based unsupervised feature extraction significantly improves the performance of classification and provides a promising approach to breast cancer diagnosis.

关键词： Breast cancer Feature extraction Machine learning Support vector machines Electronic mail Principal component analysis

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Finite-Time decoupling direct control for hypersonic reentry vehicle with multiple disturbances via second-order ADRC

Finite-Time decoupling direct control for hypersonic reentry...

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2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017

作者： Yu, Yue Wang, Honglun Li, Na Zhang, Huiping Liu, Chang School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing 100191 China Unmanned System Research Institute Beihang University Beijing 100191 China School of Automation Beijing Institute of Technology and Beijing Aerospace Automatic Control Institute Beijing 100081 China

ISBN: (纸本)9781538637432;9781538637425

In this paper, a novel finite-Time decoupling direct control (FDDC) scheme is proposed for attitude tracking problem of hypersonic reentry vehicle (HRV) based on second-order active disturbance rejection control (SADRC). Unlike traditional control method which separates control system into outer-loop attitude control and inner-loop angular rate control, attitude is controlled by control actuator straightforward with single loop. Firstly, the attitude tracking problem is converted to the decoupling control problem of a three-input and three-output cross coupling system. In each channel, a SADRC controller, which is composed of tracking differentiator (TD), extended state observer (ESO) and nonlinear state error feedback (NLSEF), is designed to track attitude command of the corresponding channel in a finite-Time sense. Finally, the obtained virtual control input is transformed into actual control input, which acts on the HRV model. Simulations are conducted to verify the effectiveness of the proposed control scheme. © 2017 IEEE.

关键词： decoupling control extended state observer (ESO) finite-Time second-order active disturbance rejection control (SADRC)

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Cascaded PI controller Tuning for Power Plant Superheated Steam Temperature based on Multi-Objective Optimization

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IFAC-PapersOnLine 2017年第1期50卷 3227-3231页

作者： Fu H. Pan L. Xue Y.L. Sun L. Li D.H. Lee K.Y. Wu Z.L. He T. Zheng S. Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education Southeast University Nanjing 210096 China State Key Lab of Power System Department of Thermal Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Baylor University Waco TX 76798 United States Department of Automation Fuzhou University Fuzhou 350000 China

To improve the superheated steam temperature (SST) control performance, this paper considers the conflicting objectives in SST control, multi-objective optimization (MOO) algorithm is employed in this paper. Firstly, the inner-loop PI controller is optimized by considering the objectives of set-point tracking and disturbance rejection, with and without constraints on robustness level. It is found that robustness constraint is essential in the optimization. Secondly, the regulation performance of the outer-loop PI controller is addressed by optimizing the disturbance rejection and robustness simultaneously. The parameter are determined based on the simulation comparison between the non-dominated solutions. The simulation results demonstrate a promising prospect of promoting the proposed strategy to more industrial practices. © 2017

关键词： Disturbance rejection Pareto optimization Power plant control Robustness Superheated steam temperature

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Breast Cancer Diagnosis Using an Unsupervised Feature Extraction Algorithm Based on Deep Learning

Breast Cancer Diagnosis Using an Unsupervised Feature Extrac...

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第37届中国控制会议

作者： Yawen Xiao Jun Wu Zongli Lin Xiaodong Zhao Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of Ministry of Education The Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal University Charles L.Brown Department of Electrical and Computer Engineering University of Virginia School of Biomedical Engineering Shanghai Jiao Tong University

Breast cancer is the most often detected cancer in women. At the same time, it is one of the most curable types of cancer if diagnosed early. With the development of the detection technology, a growing amount of clinical data and highdimensional features can be used for breast cancer diagnosis. The high-dimensional data contributes to advances in the diagnostic technology, but also incurs a large amount of computational redundancy. Thus, extracting important information and reducing the feature dimension is critical to effective prediction and an accurate treatment ***, the previous works for breast cancer diagnosis are mainly based on labeled data that is difficult to obtain. To address this issue, in this paper, we demonstrate a new scheme, which integrates a deep learning based unsupervised feature extraction algorithm, the stacked auto-encoders, with a support vector machine model（SAE-SVM）, for breast cancer diagnosis. The stacked auto-encoders with the greedy layerwise pre-training and an improved momentum update algorithm is applied to capture essential information and extract necessary features of the original data. Then, a support vector machine model is employed to classify the samples with new features into malignant or benign tumors. The proposed method was tested on the Wisconsin Diagnostic Breast Cancer data set. The performance is evaluated using various measures and compared with the previously published results. The comparison results show that the proposed SAE-SVM method improves the accuracy to 98.25% and outperforms the other methods. The deep learning based unsupervised feature extraction significantly improves the performance of classification and provides a promising approach to breast cancer diagnosis.

关键词： Deep learning auto-encoder feature extraction breast cancer diagnosis unlabeled data

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Research on Weld Quality Detection Method Based on Machine Vision and Computer Image Processing

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IOP Conference Series: Materials Science and engineering 2019年第5期631卷

作者： Bin Xue Shufang Ma Huihui Chu Kang Liu Shuangben Jiao Qingsen Meng Engineering and Technology R&D center of Mechanical and Electrical in Colleges of Shandong Qingdao Binhai University Qingdao Shandong 266555 PR China School of Mechanical and Electrical Engineering Qingdao Binhai University Qingdao Shandong 266555 PR China School of Material Science and Engineering Taiyuan University of Science and Technology Taiyuan Shanxi 030024 PR China Qingdao Zhongzhuang Vision Automation System Control Co. Ltd. Shandong 266555 PR China

In the weld quality inspection system based on machine vision and computer image processing, according to the principle of laser triangulation, a line laser is used to project structured light on the weld surface, and the weldment moves uniformly in a straight line on the moving platform. At the same time, the CMOS camera captures the structured light image and transmits it to the industrial computer through Gigabit Ethernet. The image is denoised by the median filtering algorithm on the industrial computer, then the image is segmented by the maximum inter-class error (Otus) method, and the image is binarized according to the threshold value. Then the center of the stripe is extracted by constructing four direction templates, and the 3D model is reconstructed by curve fitting. After the completion of the image processing, the geometric size of the weld is measured by mathematical formula. After the weld seam measurement is completed, the weld seam is transversely cut off at the same position, measured by manual method, and then the data measured by the two methods are compared. Through repeated measurements and comparisons of weld width, residual height and back forming, the precision of the system meets the practical requirements. The weld forming varies with the welding parameters within a certain range, but changes little and has good stability.

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The Ellipsoidal Invariant Set of Fractional Order systems Subject to Actuator Saturation:The Convex Combination Form

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IEEE/CAA Journal of Automatica Sinica 2016年第3期3卷 311-319页

作者： Kai Chen Junguo Lu Chuang Li School of Mechanical and Electrical Engineering Hainan University Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

The domain of attraction of a class of fractional order systems subject to saturating actuators is investigated in this paper. We show the domain of attraction is the convex hull of a set of ellipsoids. In this paper, the Lyapunov direct approach and fractional order inequality are applied to estimating the domain of attraction for fractional order systems subject to actuator saturation. We demonstrate that the convex hull of ellipsoids can be made invariant for saturating actuators if each ellipsoid with a bounded control of the saturating actuators is invariant. The estimation on the contractively invariant ellipsoid and construction of the continuous feedback law are derived in terms of linear matrix inequalities (LMIs). Two numerical examples illustrate the effectiveness of the developed method. © 2014 Chinese Association of automation.

关键词： Algebra Computational geometry Linear matrix inequalities Numerical methods Saturation (materials composition)

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