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International Conference on Mechatronics (ICM)

作者： Chuanqi Hu Jin Wang Yikai Chen Yonghang Chen Zhi Zheng Jun Meng Guodong Lu Polytechnic Institute Zhejiang University Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou China Robotics Research Center of Yuyao City Ningbo China School of Mechanical Engineering Zhejiang University Hangzhou China National Elite Institute of Engineering Chongqing University Chongqing China College of Electrical Engineering Zhejiang University Hangzhou China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This paper presents a trajectory tracking algorithm designed for autonomous quadrotors to navigate complex dy-namic environments that is resilient to collision disturbances. Since it combines Model Predictive Contouring control (MPCC) with the capabilities of neural networks, our method is named MPCC Integrated with Neural Augmentation (MINA). By equip-ping the quadrotor with PVDF piezoelectric sensors, we achieve real-time perception of collision forces and utilize S4D-Conv Adaptive Net Architecture (SANA) for state computation, a network that integrates the advantages of S4D and convolutional kernels to enhance the accuracy of state predictions. We incorporate the predicted results into the MPCC framework to optimize the thrust output of the quadrotor for the next time step, ensuring precise trajectory tracking. Experimental validation demonstrates the reliability and effectiveness of our algorithm, providing an innovative solution for autonomous flight and trajectory tracking of quadrotors in complex dynamic environments.

关键词： Trajectory tracking Navigation Heuristic algorithms Neural networks Reliability theory Predictive models Prediction algorithms Real-time systems Sensors Quadrotors

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Muscle Synergy Analysis Based on NMF for Lower Limb Motor Function Assessment

Muscle Synergy Analysis Based on NMF for Lower Limb Motor Fu...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Xiang, Kexin Wang, Weiqun Hou, Zeng-Guang Zhang, Chutian Wang, Jiaxing Shi, Weiguo Jiao, Yuze Lin, Tianyu The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Intelligence University of Chinese Academy of Sciences Beijing100049 China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology 999078 China Macau Institute of Systems Engineering Macau University of Science and Technology China The CASIAMUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology China

ISBN: (纸本)9781665481090

Accurate rehabilitation assessments are essential for designing effective rehabilitation methods and helping patients recover better. It's well known that commonly used scale assessment methods for neurorehabilitation suffer from the issue of subjectivity, thus investigation of objective assessment methods is very necessary. Muscle synergy analysis can be uesd to assess limb motor functions from the perspective of neuromuscular control. In this paper, a method for evaluation of human lower limb motor functions based on muscle synergy analysis is presented. Muscle synergy modules are designed using surface electromyography (sEMG) signals of the subjects' lower limbs by non-negative matrix factorizations (NMF). By comparing the cosine similarities of these synergy modules, it can be seen that muscle synergies of healthy subjects and patients are significantly different, while they are similar among healthy subjects. Therefore, a reference synergy module (RSM) is designed by averaging the muscle synergy modules for healthy subjects, and the similarities can be calculated by comparing the synergy modules for healthy subjects or patients with the RSM. In the experiment carried out in this study, average similarities of the three synergy modules for healthy subjects are respectively 0.97166, 0.87368 and 0.84932, and on the other hand, the average similarities for the three synergy modules for patients are respectively 0.59979, 0.56426 and 0.69042. Therefore, the similarities for healthy subjects are much higher than those for SCI patients, which denotes that the similarity between an individual synergy module and the RSM can be used as an objective assessing index for evaluating patients' motor function. © 2022 IEEE.

关键词： Non-negative matrix factorization

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Multi-Objective Optimal control of Wastewater Treatment Processes Constrainted by Energy-Saving and Effluent Qualities

Multi-Objective Optimal Control of Wastewater Treatment Proc...

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robotics, control and Automation Engineering (RCAE), International Conference on

作者： Jing Zhang Haoming Yan Hongtian Chen Shuiqing Xu Yiqi Liu Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering South China University of Technology Guangzhou China Department of Automation Shanghai Jiao Tong University Shanghai China College of Electrical Engineering and Automation Hefei University of Technology Hefei China

Considering the high energy consumption problem in the wastewater treatment process, this paper proposes a multi-objective optimization control scheme based on NSGA-II algorithm. The proposed scheme can not only ensures meet effluent quality requirements but also can achieves energy cost savings. This method takes the aeration energy consumption and pumping energy consumption of the system as the optimization goal with the effluent water quality being the constraint condition. At the same time, the adaptive fuzzy PID controller is designed to ensure the control accuracy and stability of the controlled system. The results of simulation experiments validated in benchmark simulation platform, BSM1, show that, compared with PI control and single-objective optimization control, the proposed optimization scheme in this paper can achieve the desired quality of the effluent water withmore energy-saving.

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Optimization of input shaping parameters based on genetic algorithm 4

Optimization of input shaping parameters based on genetic al...

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4th International Symposium on Power Electronics and control Engineering, ISPECE 2021

作者： Lu, Xu Jia, Danping Zhang, Hualiang Zhao, Bingjie Zhang, Tao Liu, Yang Yan, Hongyu School of Artificial Intelligence Shenyang University of Technology Shenyang China School of Information Science and Engineering Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781510650374

ISBN: (纸本)9781510650367

In view of the position control vibration problem of permanent magnet synchronous motor, the control model of permanent magnet synchronous motor is simulated by using Simulink software. At the same time, according to the structure of the system model, an input shaper is added to the input end of the system to suppress the vibration of the system. However, the input parameters of the shaper are highly dependent on the parameters of the system model, so it is usually necessary to accurately identify the system model. However, the identification of permanent magnet synchronous motor is often not accurate enough, which makes it difficult to determine the input parameters of the shaper. Aiming at the problem that the input shaper is too dependent on the system, a method of input shaper parameter optimization based on genetic algorithm is designed and verified by experiments. The results show that the parameters of the input shaper with better effect can be selected by genetic optimization, and the dependence of the input shaper on the system model is reduced. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computer control systems

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Curved path planning based on 3D vision water immersion ultrasonic nondestructive testing 2

Curved path planning based on 3D vision water immersion ultr...

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2nd International Conference on Computer Vision and Data Mining, ICVDM 2021

作者： Chen, Zheng Wang, Jian Zhang, Hualiang School of Information Engineering Shenyang University of Chemical Technology Liaoning Shenyang110142 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Liaoning Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110169 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 nondestructive 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. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Robotic arms

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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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Adaptive Current Protection Technology for Distribution Network with Distributed Power Sources Based on Local Information

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Mobile Information systems 2021年 2021卷

作者： Cui, Shijie Zeng, Peng Wang, Zhongfeng Zuo, Yue 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 University of Chinese Academy of Sciences Beijing100049 China State Grid Huludao Power Supply Company Huludao125000 China

Because the distributed power supply is connected to the distribution network, its structure has also changed the original traditional method. The original single power supply method was originally used. The network provided is a radial network and it is connected to the distributed power supply. Later, it became a structure with small- and medium-sized power supplies inside, and its network power supply became dual or multiple power supplies. Such a structural change affected the coordination of power distribution protection devices, which would impose current on the distribution network. Protection has a negative impact. This paper aims to study the adaptive current protection technology of distribution network with distributed power based on local information. On the basis of analyzing the characteristics of distributed power, the classification of distributed power, and the operation mode of distributed power, according to the access of IIDG, as well as different influences of relay protection of distribution network, an improved protection scheme with adaptive current quick-break protection as the main protection is proposed, so that different fault characteristics of the distribution network have different setting values, and then simulation experiments are carried out on this scheme. The experimental results show that the setting value of three-phase short-circuit fault proposed in this paper can well protect the protection range, which is larger than the traditional protection setting and self-adaptive setting. © 2021 Shijie Cui et al.

关键词： Electric power system protection

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Parallel Learning Based Foundation Model for networked Traffic Signal control

Parallel Learning Based Foundation Model for Networked Traff...

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International Conference on Intelligent Transportation

作者： Chen Zhao Xingyuan Dai Yuanyuan Chen Yilun Lin Yisheng Lv Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Shanghai AI Laboratory Shanghai China Macao Institute of Systems Engineering Macau University of Science and Technology Macao China

networked Traffic Signal control (NTSC) is a fundamental component of Intelligent Transportation systems (ITS) and the broader vision of smart city development. While a plethora of intelligent strategies have been developed, the Sim2Real challenge often impedes their full realization. In response, this paper introduces the Parallel Learning-based Adaptive Network for Traffic Signal control (PLANT) as a foundation model for NTSC. We employ the Wasserstein GAN with Gradient Penalty (WGAN-GP) to generate a wide range of artificial scenarios for robust PLANT training. Further, the Transformer-based Cooperation Mechanism (TCM) is integrated as the primary learner within PLANT, facilitating effective capture of traffic dynamics and knowledge accumulation. This knowledge is readily transferable to real-world applications through meticulous fine-tuning, equipping PLANT to adapt and evolve in alignment with shifting transportation paradigms. Our empirical study on the Hangzhou road network demonstrates PLANT's superiority over both traditional and emerging DRL-based approaches, emphasizing its viability as a potential foundation model for NTSC.

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Online Gyro Bias Estimation from Single Vector Measurements Using Regression Models

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IFAC-PapersOnLine 2023年第1期56卷 252-257页

作者： Bowen Yi Lei Wang Weidong Zhang Australian Centre for Field Robotics The University of Sydney NSW 2006 Australia State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China School of Information and Communication Engineering Hainan University Haikou 570228 China Department of Automation Shanghai Jiao Tong University Shanghai 200240 China

This paper addresses the problem of on-line consistent estimation of gyro bias using the measurements of a single vector and the biased angular velocity - both in the body-fixed frame. We propose two globally convergent gyro bias observers using new regression models, which are capable to deal with the cases of constant and time-varying reference vectors, respectively. Indeed, there are quite a few works discussing the latter case. To address this, we derive a nonlinear regression model, based on which a convexified gradient descent observer is designed, providing globally asymptotically convergent estimates to the gyro bias under some sufficient excitation conditions. The proposed schemes are illustrated by some numerical simulations.

关键词： Nonlinear systems observer parameter identification robotics

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Fault Diagnosis Based on Multi-scale LSTM-FCNs for Industrial Process

Fault Diagnosis Based on Multi-scale LSTM-FCNs for Industria...

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Yang, Shaojie Li, Peng Li, Shuai Zhou, Xiaofeng Jiang, Shanghong Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and IntelligentManufacturing Chinese Academy OfSciences Shenyang110169 China University of Chinese Academy OfSciences Beijing100049 China Information Center of Ansteel Mining Limited Company Anshan114031 China

ISBN: (纸本)9781665401159

Aiming at the characteristics of multi-scale, time series and nonlinear of the complex industrial process data, in order to extract process information more efficiently for improving the diagnosibility, a novel fault diagnosis method based on multi-scale long short term memory- fully convolutional networks (LSTM-FCNs) for industrial process is proposed. Firstly, variational mode decomposition (VMD) is used to obtain the multi-scale components of the process data. Then a parallel LSTM and FCNs optimized by batch normalization and random inactivation is built to extract features for each component. Finally, based on the fusion of multi-scale features, a fully connected layer is used for diagnosis. The LSTM-FCNs method is evaluated by the Tennessee Eastman (TE) benchmark industrial process, and compared with other methods, its effectiveness and superiority are verified. © 2021 IEEE.

关键词： Deep neural networks

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