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Chinese control Conference (CCC)

作者： Naizhou Wang Hailong Pei Lianwen Jin Tiancai Liang School of Electronic and Information Engineering South China University of Technology Guangzhou P. R. China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou P. R. China GRG Banking Intelligent Security Research Institute Guangzhou P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

This paper presents new techniques for construction of dynamic output feedback controllers with a static anti-windup compensator for a class of saturated systems with sector-bounded and slope-restricted nonlinearities. To handle the slope restriction, a so-called differential compensator is incorporated into the traditional nonlinear controller. Considering its sector-bounded property and convex-hull representation, respectively, two LMI-based synthesis methods are proposed to address the problems of the estimates of the region of attraction (ROA) and £ 2 performance analysis of the closed-loop system. A numerical example illustrates the effectiveness of the proposed approaches.

关键词： Linear matrix inequalities Closed loop systems Electronic mail Output feedback Actuators Nonlinear systems Vehicle dynamics

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利用双RGB-D传感器融合增强对未知环境的自主探索和地图构建

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Engineering 2019年第1期 355-373页

作者：于宁波王石荣 Institute of Robotics and Automatic Information Systems Nankai University Tianjin Key Laboratory of Intelligent Robotics Nankai University State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences

对未知环境的自主探索和地图构建具有广泛的应用价值和重要的现实意义。现有方法多采用距离传感器生成二维栅格地图。红/绿/蓝深度(red/green/blue-depth,RGB-D)传感器提供环境的颜色和深度信息,从而生成三维(three-dimensional,3D)点... 详细信息

对未知环境的自主探索和地图构建具有广泛的应用价值和重要的现实意义。现有方法多采用距离传感器生成二维栅格地图。红/绿/蓝深度(red/green/blue-depth,RGB-D)传感器提供环境的颜色和深度信息,从而生成三维(three-dimensional,3D)点云地图,便于人类直观感知。本文提出了一种利用双RGB-D传感器实现未知室内环境自动探测和测绘的系统方法。通过同步处理RGB-D数据,生成定位点,逐步构建三维点云图和二维栅格地图。紧接着,探索方法被建模为一个部分可观测的马尔科夫决策过程,将局部地图推演和全局边界搜索方法相结合进行自主探索,将动态行为约束用于运动控制。这有效避免了局部最优,保证了探测效果。在单连通和多分支区域的实验表明,该方法具有较好的鲁棒性和较高的效率。

关键词：自主探索 RGB-D 传感器融合点云局部地图推演全局边界搜索

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Online 4D Ultrasound-Guided Robotic Tracking Enables 3D Ultrasound Localisation Microscopy with Large Tissue Displacements

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IEEE Transactions on Medical Imaging 2025年

作者： Yan, Jipeng Tan, Qingyuan Kawara, Shusei Zhu, Jingwen Wang, Bingxue Toulemonde, Matthieu Liu, Honghai Tan, Ying Tang, Meng-Xing Imperial College London Ultrasound Lab for Imaging and Sensing Department of Bioengineering LondonSW7 2AZ United Kingdom Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin150001 China Imperial College London Department of Bioengineering LondonSW7 2AZ United Kingdom University of Portsmouth School of Computing PortsmouthPO1 2UP United Kingdom University of Melbourne Department of Mechanical Engineering ParkvilleVIC3010 Australia

Super-Resolution Ultrasound (SRUS) imaging through localising and tracking microbubbles, also known as Ultrasound Localisation Microscopy (ULM), has demonstrated significant potential for reconstructing microvasculature and flows with sub-diffraction resolution in clinical diagnostics. However, imaging organs with large tissue movements, such as those caused by respiration, presents substantial challenges. Existing methods often require breath holding to maintain accumulation accuracy, which limits data acquisition time and ULM image saturation. To improve image quality in the presence of large tissue movements, this study introduces an approach integrating high-frame-rate ultrasound with online precise robotic probe control. Tested on a microvasculature phantom with slow but large translation motions, up to 5 mm/s and 20 mm- twice the aperture size of the matrix array used, our method achieved real-time tracking of the moving phantom and imaging volume rate at 85 Hz, keeping majority of the target volume in the imaging field of view. ULM images of the moving cross channels in the phantom were successfully reconstructed in post-processing, demonstrating the feasibility of super-resolution imaging under large tissue motions. This represents a significant step towards ULM imaging of organs with large motion. © 1982-2012 IEEE.

关键词： Motion tracking

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An Incremental Learning Framework for Skeletal-based Hand Gesture Recognition with Leap Motion

An Incremental Learning Framework for Skeletal-based Hand Ge...

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

作者： Jie Li Junpei Zhong Fei Chen Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control School of Automation Science and Engineering South China University of Technology Guangzhou China School of Science and Technology Nottingham Trent University Nottingham UK Istituto Italiano di Tecnologia via Morego 30 Genova Italy Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

Hand gesture recognition has become the focus of researchers lately because of its manifold applications in various fields. Leap Motion (LM) is a device to obtain useful and accurate information of the hand action, which is suitable for collecting the three-dimensional (3D) human hand gesture. In this paper, a novel framework which consists of an incremental learning (IL) algorithm without deep structure is proposed and applied to hand gestures classification that explicitly aimed to the LM data. The same datasets are used to train the proposed framework and the conventional Long Short Term Memory Recurrent Neural Network (LSTM-RNN). Due to the structural advantage of the proposed model, the recognition performance is improved distinctly in robustness and training time than the LSTM network. Moreover, convincing experiment results are given to illustrate that the solution is more efficient in static gesture classification.

关键词： Logic gates Computer architecture Gesture recognition Training Feature extraction Microprocessors Three-dimensional displays

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Curved path planning based on 3D vision water immersion ultrasonic nondestructive testing

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Journal of Physics: Conference Series 2021年第1期2024卷

作者： Zheng Chen Jian Wang Hualiang Zhang School of Information Engineering Shenyang University of Chemical Technology Shenyang Liaoning 110142 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Liaoning 110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Liaoning 110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Liaoning 110169 China

Compared with traditional 2D image processing, 3D point cloud processing has become a hot technology in the industry, but there are few researches applied to nondestructive testing of curved workpieces. This article aims at the non-destructive testing of curved workpieces, using 3D point cloud and robotic arm for water immersion ultrasonic non-destructive testing. Aiming at the 3D point cloud with many outliers, using the commonly used filter in two-dimensional images-the guide filter, the algorithm is improved and used for the filtering and downsampling of the 3D point cloud, and the adjustment of the curved surface workpiece and the robot arm's pose is introduced. Finally, the workpiece was scanned by a robotic arm with a water immersion ultrasonic nondestructive testing system to prove the feasibility of the experiment and improve the detection efficiency of traditional nondestructive testing technology.

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Neural Network control Using Composite Learning for USVs with Output Error Constraints

Neural Network Control Using Composite Learning for USVs wit...

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International Conference on Unmanned systems and Artificial Intelligence

作者： Puyong Xu Chenguang Yang Shi-Lu Dai Zhaoyong Mao Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Unmanned System Research Institute Northwestern Polytechnical University Xi'an China

ISBN: (数字)9781728158594

ISBN: (纸本)9781728158600

In this paper, by focusing on trajectory tracking control of unmanned surface vessel (USV), we present a control method considering uncertain dynamics and output error constrains. Firstly, by using the properties of tan-type barrier Lyapunov function (BLF), the output tracking error can be constrained. Secondly, we use radical basis function neural network (RBF NN) to approximate the uncertain dynamics. Considering that the estimated parameters convergence cannot be achieved in the absence of persistent excitation (PE) conditions, the composite learning update law of the weight matrix in the NN is adopted to guarantee the parameters convergence under interval excitation (IE) conditions which is easier to reach. In simulation studies, it is proven that the USV have good ability to follow the pre-designed trajectory with small tracking error and the parameters convergence can be ensured.

关键词： unmanned surface vessel (USV) barrier Lyapunov function neural network composite learning Unmanned Surface Vessel Neural network outlet error network control control Methods neural network control follow error Converge Learning

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Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive controller based on Neural Network with System Error Set as Input

Parameter Self-Tuning of SISO Compact-Form Model-Free Adapti...

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Asian control Conference

作者： Chen CHEN Xueyuan LI Ye YANG Jiarong XU Zuwei LIAO Xinggao LIU Jinshui CHEN Jiangang LU State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou Zhejiang China State Key Laboratory of Chemical Engineering College of Chemical and Biological Engineering Zhejiang University Hangzhou Zhejiang China Yuyao Technology Innovation Center Robotics Institute of Zhejiang University Ningbo Zhejiang China

ISBN: (纸本)9781728102634

Model-Free Adaptive control (MFAC) is a new data-driven control method, which depends only on the input/output (I/O) measurement data rather than the mathematical model information of the actual controlled system. The SISO MFAC based on the compact-form dynamic linearization (SISO-CFMFAC) is a promising approach to control the SISO nonlinear systems. However, the parameters in the SISO-CFMFAC should be tuned carefully before being put into use. Unfortunately, so far the parameter tuning of SISO-CFMFAC is still a laborious, time-consuming and cost-consuming work. In this paper, a novel parameter self-tuning approach of SISO-CFMFAC based on back propagation Neural Network with System Error set as input (SISO-CFMFAC-NNSE) is proposed, and then verified by using a typical time-varying nonlinear SISO system. Results show that the proposed controller named SISO-CFMFAC-NNSE can achieve better control stability and accuracy than the existing controller of SISO-CFMFAC.

关键词： Tuning Data models Neural networks Nonlinear dynamical systems Adaptation models control systems

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Target Tracking control Based on Dual Model Fusion

Target Tracking Control Based on Dual Model Fusion

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IEEE International Conference on robotics and Biomimetics

作者： Kang Li Wenzhong Zha Xiangrui Meng Xiaoguang Zhao Key Laboratory of Cognition and Intelligence Technology China Electronics Technology Group Corporation Information Science Academy CETC Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Recent years have witnessed rapid progress in target tracking. To track a moving target for mobile robots, however, both performance and speed of the algorithm are indispensable. This paper proposes a dual model fusion strategy to improve target tracking drift. Among them, the spatiotemporal context model in middle-level feature space (MFSTC) is utilized to ameliorate target tracking effect when illumination or appearance changes, the mean shift based on 3D back projection (MS3D) is fused to allow the algorithm to tackle occlusion and deformation. Tracking controller based on visual servo is also designed for mobile robots. We validate the efficiency of the proposed fusion model on the university of Birmingham RGB-D Tracking Benchmark (BTB) and show that our approach compares favorably with the state-of-the-arts, mobile robots using our approach can track targets robustly under various challenging scenes.

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${H}_{\infty }$ Filtering for Fuzzy Jumping Genetic Regulatory Networks With Round-Robin Protocol: A Hidden-Markov-Model-Based Approach

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IEEE Transactions on Fuzzy systems 2019年第1期28卷 112-121页

作者： Hao Shen Yunzhe Men Jinde Cao Ju H. Park School of Electrical and Information Engineering Anhui University of Technology Ma’anshan China School of Mathematics Southeast University Nanjing China Key Laboratory of Intelligent Control and Decision of Complex Systems and School of Automation Beijing Institute of Technology Beijing China Jiangsu Provincial Key Laboratory of Networked Collective Intelligence and School of Mathematics Southeast University Nanjing China Department of Electrical Engineering Yeungnam University Kyongsan South Korea

In this article, a method to design the filter for fuzzy jumping genetic regulatory networks is explored. The case when the filters cannot directly utilize the mode information of the plant is taken into account. A hidden Markov model is introduced to address such a problem. Furthermore, a mature scheduling method, namely round-robin protocol, is employed to optimize the data transmission in genetic regulatory networks. On the basis of the fuzzy model approach and the stochastic analysis technique, some novel conditions ensuring the H ∞ performance and stochastic stability of the error system are established. The parameters of the filter can be presented via addressing the convex optimization problem. The feasibility of results is finally illustrated by considering a repressilator model subject to stochastic jumping parameters.

关键词： Hidden Markov models Proteins Genetics Protocols Markov processes Data communication

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A Vision-Based Path Planning and Following System for a Miniature Robotic Fish

A Vision-Based Path Planning and Following System for a Mini...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Yue Lu Xingyu Chen Zhengxing Wu Yan Yan Junzhi Yu 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 State Key Laboratory for Turbulence and Complex System BIC-ESAT College of Engineering Peking University Beijing China

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

Most existing path following algorithms for middle-or large-sized underwater vehicles minimize yaw difference of the control object. Breaking the canonical manner, this paper presents a vision-based path planning and following system for miniature robotic fish. As for path planning, the proposed system finds a gird path based on the AStar algorithm and selects a series of target points in the grid path. By connecting these points in order, a curve path can be planned. In terms of path following, the proposed system forgoes to give a specific yaw and only uses a global camera. It obtains coordinates of the robotic fish in real time by KCF tracking algorithm to calculate an approximate motion vector. A PID controller is employed to drive the difference angle between the motion vector and the target vector (the vector from the robotic fish to the target point) to zero. By swimming to target points one by one, the robotic fish can move to the terminus following the planned path. Finally, aquatic experiments are conducted to verify the effectiveness of the vision-based path planning and following system. The results reveal that the developed system can generate a safe and smooth path, and the robotic fish can follow the path with acceptable deviation.

关键词： Fish Robot kinematics Path planning Robot sensing systems Solenoids Magnetic resonance imaging

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