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IEEE International Conference on robotics and Automation (ICRA)

作者： Lijun Han Hesheng Wang Weidong Chen Jingchuan Wang Jianjun Yuan Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Shanghai Robotics Institute School of Mechatronic Engineering and Automation Shanghai University Shanghai China

This study proposes a hybrid vision/force control scheme for interaction with the inner surface of the bottle-like object. Based on the geometry of the object, a new generalized constraint called the bottleneck (BN) constraint is proposed, which ensures the tool passes through a fixed 3-D region and avoid collisions with the boundary of the region. To realize the hybrid vision/force control under the BN constraint, a novel dynamic controller is designed inspired by the hierarchical operational space, which can complete the different tasks defined in the decoupled subspace. To enhance the robustness of the algorithm, we develop a data-driven method and an adaptive method to estimate the Jacobian matrix online in force space and image space, respectively. The asymptotic stability of the closed-loop system is rigorously proved by the Lyapunov theory. Experiments are conducted to validate the performance of the proposed method.

关键词： Jacobian matrices Geometry Heuristic algorithms Robot vision systems Force Aerospace electronics Tools

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A heterogeneous master-slave teleoperation method for 7-DOF manipulator

A heterogeneous master-slave teleoperation method for 7-DOF ...

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Chinese Automation Congress (CAC)

作者： Rong Su Ke Xu Liang Zhao Peng Yu School of Information and Control Engineering Shenyang Jianzhu University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426480

Teleoperation is widely used because of its ability to extend human behavior and perception to slave robots. In this paper, we use a wearable exoskeleton as the master device for human-robot interaction, which can obtain the movement posture of the operator naturally and intuitively. The slave device uses a 7-DOF redundant manipulator similar to the human arm structure, which improves flexibility without being over redundant. In addition, an inverse kinematics method is proposed to meet the requirements of anthropomorphic remote operation of master-slave heterogeneity. Simulation experiments are carried out in combination with MATLAB and ROS RVIZ environment to prove the performance of the heterogeneous teleoperation control method.

关键词： Performance evaluation Exoskeletons Position control Human-robot interaction Kinematics Manipulators Main-secondary

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Research on fault feature extraction of rotating machinery based on adaptive VMD and optimized CYCBD

Research on fault feature extraction of rotating machinery b...

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Chinese control and Decision Conference, CCDC

作者： Ran Xie Ling Xiong Binbin Dan Zeyu Ren Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China Key Laboratory of Metallurgical Equipment and Control of Ministry of Education Wuhan China School of Physics and Electronic Science Hunan Institute of Science and Technology Yueyang China

ISBN: (纸本)9781665478977

To solve the problem that early fault features of rotating machinery are difficult to extract, an adaptive k-value hierarchical variational mode decomposition (H-VMD) combined with optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) fault feature extraction method is proposed in this paper. Mode decomposition of the vibration signal is performed with H-VMD. and then the noise dominant component is denoised by wavelet threshold denoising (WTD). Furthermore, the improved autocorrelation-weighted correlated kurtosis (ACK) when CYCBD enhances the periodic shock component of the denoised signal is the fitness function of ChOA, and the envelope demodulation analysis of the feature-enhanced signal is performed using the teager energy operator (TEO). Simulation analysis and experimental results show that the interference of background noise can be effectively removed and the periodic shock component of the vibration signal be enhanced by the proposed method, which is a new feature extraction method for the fault diagnosis of rotating machinery.

关键词： Vibrations Fault diagnosis Electric shock Noise reduction Interference Feature extraction Linear programming

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Ddos attack detection based on one-class svm in sdn 6th

Ddos attack detection based on one-class svm in sdn

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Zhao, Jianming Zeng, Peng Shang, Wenli Tong, Guoyu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789811581007

Software Defined Networking (SDN) is a new type of network architecture, which provides an important way to implement automated network deployment and flexible management. However, security problems in SDN are also inevitable. DDoS attack belongs to one of the most serious attack types, which is fairly common for today’s Internet. In SDN security fields, DDoS attack detection research has been received more and more attention. In this paper, a DDoS attack detection method based on one-class SVM in SDN is proposed, which provides a better detection accuracy. Furthermore, two new feature vectors, including middle value of flow table item’s duration and protocol data traffic percentage, are extracted to integrate into the item of 11 feature vectors. Additionally, basing on selection and construction method of the 11 feature vectors, a DDoS attack behavior model is established by using one-class SVM algorithm, and the self-adaptation genetic algorithm is designed to optimize the corresponding parameters of the Gaussian kernel of one-class SVM. The experimental results in SDN show that, the proposed new feature vectors are shown to more better detection accuracy, and the proposed method is more feasible by comparing with the BP neural network and RBF neural network algorithms under the same 11 features vectors. © Springer Nature Singapore Pte Ltd 2020.

关键词： Genetic algorithms

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A distributed integrated energy trading solution

A distributed integrated energy trading solution

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Computer, Blockchain and Financial Development (CBFD), International Conference on

作者： Jingya Dong Chunhe Song Zeyu Zheng Tao Zhang Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing University of Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665429641

Blockchain technology can bring profound changes to the energy industry and is now becoming a new driving force for the rapid development of this sector. In order to build a secure, reliable, and stable energy service system, it is necessary to study the framework and interaction mechanism of the energy trading system. This paper introduces a distributed integrated energy trading solution, proposes a new overall architecture, and analyses the blockchain model. This paper presents an algorithm to calculate the score directly in the client. Through this algorithm, users can choose service providers, get rid of cloud control, and realize users' free choice of electricity purchase and interaction between users through the smart contract. The test results show that the system has high performance and efficiency, can meet the needs of integrated energy transactions, and provides a solution for the application of blockchain in energy trading.

关键词： Industries Cloud computing Analytical models Costs Smart contracts Force Computer architecture

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When measurements fail: using an interactive SLAM solution to fight bad odometry

When measurements fail: using an interactive SLAM solution t...

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IEEE Instrumentation and Measurement Technology Conference

作者： Robert Milijaš Juraj Oršulić Stjepan Bogdan Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9781728144603

ISBN: (纸本)9781728144610

In this paper we use the underlying pose graph structure of graph SLAM to allow the user to inspect and intervene into the Simultaneous Localization and Mapping (SLAM) process. With post-processing the map by inserting custom constraints into the optimization problem, the user can improve the quality of the map obtained as the final SLAM result. We refer to this inspection and intervention process as interactive SLAM. The mathematical background, which allows such interventions, is laid out, and a software implementation is used to improve the quality of a map created at the Faculty of Electrical Engineering and Computing, University of Zagreb.

关键词： SLAM robotics Interactive SLAM supersonic low altitude missile robotics maps software implementation ELECTRICAL ENGINEERING odometry

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Artificial Neural Network-Based Direct Power control to Enhance the Performance of a Pmsg-Wind Energy Conversion System Under Real Wind Speed and Parameter Uncertainties: An Experimental Validation

SSRN

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SSRN 2023年

作者： Majout, Btissam Bossoufi, Badre Karim, Mohammed Skruch, Pawel Mobayen, Saleh El Mourabit, Youness El Zair Laggoun, Zakaria Laboratory of Engineering Modelling and Systems Analysis SMBA University Fez Morocco Department of Automatic Control and Robotics AGH University of Science and Technology Kraków30-059 Poland Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou640301 Taiwan National School of Applied Sciences Abdelmalek Essaadi University Tetouan Morocco Laboratory of Electrotechnique Constantine Mentouri Brothers Constantine 1 University Constantine Algeria

With the increasing emphasis on embedding advanced technology into system controls, the Direct Power control (DPC) approach has garnered significant attention, owing to its straightforward and highly adaptable algorithm. It has been adopted in many applications. However, the variable frequency, harmonic distortion of the currents, and power ripples caused by Hysteresis controllers and switching tables decrease its effectiveness and robustness, affecting the system’s performance. For this reason, this paper proposed a new DPC based on Artificial Neural Network (ANN) approaches. In this approach, the hysteresis comparator and the switching table are substituted with ANN controllers and then applied on both sides: machine-side converter (MSC) and grid-side converter (GSC) of a Permanent Magnet Synchronous Generator based Wind Energy Conversion System (PMSG-WECS). Moreover, to make the system more efficient in varying wind conditions, this study expands the utilization of the artificial neural networks (ANN) to encompass the maximum power point tracking (MPPT) control strategy. To demonstrate the effectiveness of the proposed approach on the system behaviors, a simulation test was carried out in the Matlab/Simulink environment, using a real wind profile of a Moroccan city (Essaouira). In comparison to the classical DPC control, the simulation results showed the superior performance of the proposed ANN-DPC control in terms of reference tracking, response time, overshoot, precision, and its capacity to reduce the rate of power ripples and total harmonic distortion (THD) in the injected currents. Furthermore, a robustness test was also included in this work to check the robustness of the proposed control against parameters variation. In conclusion, the feasibility and effectiveness of the ANN-DPC control approach were confirmed through experimental validation using the dSPACE DS1104 board. © 2023, The Authors. All rights reserved.

关键词： MATLAB

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An Improved Chimp Optimization Algorithm for Short-term Hydrothermal Scheduling

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第3期49卷 666页

作者： Chen, Gonggui Wang, Shumin He, Yongsheng Shang, Wanfeng Long, Hongyu Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing University of Posts and Telecommunications Chongqing400065 China State Grid Chongqing Electric Power Company Chongqing400015 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Shenzhen518055 China Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing400065 China

The short-term hydrothermal scheduling (STHS) is a complex non-linear, non-convex and high-dimensional mathematical optimization problem. Due to the fact that the original chimp optimization algorithm (ChOA) is likely to fall into local optimum and has the disadvantage of low population diversity, an improved chimp optimization algorithm (IChOA) is proposed to solve the STHS problem. Firstly, Logistic-Tent chaotic mapping is employed to initialize the population and increase the population diversity. Then a new nonlinear convergence factor is introduced to make the optimization search process more applicable to STHS problems. Next, particle swarm algorithm (PSO) and gravitational search algorithm (GSA) are combined to enhance the shortcoming and balance the global search ability and local exploration ability of ChOA. Moreover, Cauchy mutation and opposition-based learning strategy are supplemented as perturbation interference when the optimal position is not updated to improve the ability to leap out of the local optimum. Finally, a graded optimization strategy is adopted for the constraint handing of dynamic reservoir balance and thermal power balance. Three standard hydrothermal power systems are utilized to verify the practicability and validity of the proposed method. Results of simulations reveal that IChOA has significant competitive advantages in addressing the STHS problem. © 2022, IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Particle swarm optimization (PSO)

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A CNN-LSTM Hybrid Model for Ankle Joint Motion Recognition Method Based on sEMG

A CNN-LSTM Hybrid Model for Ankle Joint Motion Recognition M...

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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Hao-Ran Cheng Guang-Zhong Cao Cai-Hong Li Aibin Zhu Xiaodong Zhang Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots Shenzhen Key Laboratory of Electromagnetic Control College of Mechatronics and Control Engineering Shenzhen University Shenzhen China Institute of Robotics & Intelligent Systems Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robots Xi'an China

ISBN: (数字)9781728157153

ISBN: (纸本)9781728157160

A CNN-LSTM hybrid model for ankle joint motion recognition based on surface electromyography (sEMG) signals is proposed in this paper. The traditional recognition method is to manually extract the features from sEMG signals and then use machine learning method to train the model, which relies on prior knowledge and requires a lot of time to test and select good features to obtain high classification accuracy. In this paper, the CNN-LSTM hybrid model is used to identify four ankle joint movements (dorsiflexion, plantar flexion, foot varus and foot eversion). The hybrid model consists of two CNN layers and three LSTM layers. CNN can learn to automatically extract features and LSTM is able to capture long-term correlations of sEMG data. The experiment results show that the proposed model is effective and accurate, thus providing a basis for the subsequent research.

关键词： Feature extraction Convolution Machine learning Logic gates Neurons Kernel Robots

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3d-Swim: 3d Vision Based Seam Width Measurement for Industrial Composite Fiber Layup In-Situ Inspection

SSRN

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SSRN 2022年

作者： Zhang, Yinlong Yuan, Libiao Liang, Wei Xia, Xiaofang Pang, Zhibo State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Shenyang Pharmaceutical University China School of Computer Science and Technology Xidian University Xian710071 China Department of Automation Technology ABB Corporate Research Sweden Vasteras72178 Sweden Stockholm11428 Sweden

Composite fiber layup in-situ inspection plays an important role in the advanced manufacturing. An accurate seam width measurement is an indispensable procedure during the layup inspection. However, the traditional seam measurements may fail in presence of surface curvature variance, curved edges between sheets, to name a few. This paper intentionally designs a novel 3D vision based Seam Width Measurement for Industrial Composite Fiber Layup In-situ inspection (called 3D-SWiM). It aims to address three major challenges in measuring the seam width between the composite fiber sheets, namely, seam region over-segmentation, failures on region of interests (ROI) seam detection, seam width measurement inconsistency. Firstly, the region growing with semantic refinement is designed for 3D point cloud clustering, to overcome the over-segmentation on seam regions. Afterwards, the gridding model with edge discrimination is applied to extract the 3D potential candidates in the edge areas. Eventually, we use the cubic B-spline fitting model to describe the seam region curves and the improved particle swarm optimization model to estimate the minimum and maximum distance between the seam edges. Our method has been evaluated extensively on the developed composite fiber layup platform. The experimental results prove the competitive accuracy against the state-of-the-art methods in seam width measurement. © 2022, The Authors. All rights reserved.

关键词： Inspection

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