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Chinese Automation Congress (CAC)

作者： Hongrui Wang Tao Zou Zhijia Yang Hongyu Zheng State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Shenyang China College of Mechanical and Electrical Engineering Guangzhou University Guangzhou China Institute of Intelligent Manufacturing Huanghuai University Zhumadian China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In the dual time-scale system, the time for the fast and slow model to reach steady-state values is inconsistent. Based on the "point" model strategy of integral variables, this paper compares and analyzes the slow model variables with the integral variables and designs a two-layer structure predictive controller for the dual time-scale system under the model truncation. The steady-state optimization layer uses the step response characteristics of slow model variables to derive the multi-step controlled variable prediction values after the truncation point. Based on the improved displacement matrix, the error correction coefficient is modified by introducing an error compensation factor in the dynamic control layer to reduce the accumulated error of model truncation. Under the truncated dual time-scale system model, the improved strategy proposed in the paper can not only maintain the amount of calculation of the predictive control but also solve the problem that the steady-state target calculation is not accurate enough due to model mismatch. Finally, the elfectiveness of the improvement strategy is verified by simulation comparison.

关键词： Robots Optimized production technology Manufacturing control systems Automation Target tracking Steady-state

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sEMG-based Endpoint Stiffness Estimation of Human Arm using Gene Expression Programming

sEMG-based Endpoint Stiffness Estimation of Human Arm using ...

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2019 3rd International Conference on Artificial Intelligence, Automation and control Technologies (AIACT 2019)

作者： Jiang Zainan Yang Fan Li Chongyang Liu Daxiang Wang Chenliang Li Tianhui Liu Hong State Key Laboratory of Robotics and Systems Harbin Institute of Technology

The endpoint stiffness of the human arm has been long recognized as a key factor in the smooth contact between humans and environment. And the endpoint stiffness of the human arm is highly correlated with the surface electromyography(sEMG) produced by the contraction of the muscles. In this paper, the Gene Expression Programming(GEP) Algorithm is proposed to estimate the endpoint stiffness of human arm based on s EMG. This paper improves the traditional decoding method of GEP. Instead of generating an expression tree, it is directly decoded by looking for the effective length of the gene. And experimental results show that nonlinear models such as GEP models in this paper have higher correlation and lower RMSE(root mean square error) than regression stiffness using linear regression models. Selecting different feature of EMG signals, the correlation coefficient and the root mean square error of the model is very different. For the GEP model in this paper, WPTSVD(Wavelet Package Transform Singular Value Decomposion) and WTSVD(Wavelet Transform Singular Value Decomposion) are selected as the feature of s EMG signals have high performances and the correlation can reach 57% ±12.1%.

关键词： sEMG-based Endpoint Stiffness Estimation of Human Arm using Gene Expression Programming Arm

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Domain-invariant similarity activation map contrastive learning for retrieval-based long-term visual localization

arXiv

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arXiv 2020年

作者： Hu, Hanjiang Wang, Hesheng Liu, Zhe Chen, Weidong Autonomous Robot Lab Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The State Key Laboratory of Robotics and System HIT The Department of Computer Science and Technology University of Cambridge United Kingdom

Visual localization is a crucial component in the application of mobile robot and autonomous driving. Image retrieval is an efficient and effective technique in image-based localization methods. Due to the drastic variability of environmental conditions, e.g. illumination, seasonal and weather changes, retrieval-based visual localization is severely affected and becomes a challenging problem. In this work, a general architecture is first formulated probabilistically to extract domain-invariant feature through multi-domain image translation. And then a novel gradient-weighted similarity activation mapping loss (Grad-SAM) is incorporated for finer localization with high accuracy. We also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised manner. The final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models without and with Grad-SAM loss. Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons dataset. The strong generalization ability of our approach is verified on RobotCar dataset using models pre-trained on urban part of CMU-Seasons dataset. Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision, especially under the challenging environments with illumination variance, vegetation and night-time images. The code and pretrained models are available on https://***/HanjiangHu/DISAM. Copyright © 2020, The Authors. All rights reserved.

关键词： Image retrieval

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Target tracking control based on dual model fusion

Target tracking control based on dual model fusion

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

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

ISBN: (纸本)9781728163215

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. © 2019 IEEE.

关键词： Target tracking

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Cover Image, Volume 40, Number 6, September 2023

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Journal of Field robotics 2023年第6期40卷

作者： Guoteng Zhang Shugen Ma Jinguo Liu Xianwu Zeng Lingyu Kong Yibin Li School of Control Science and Engineering Shandong University Jinan China Department of Robotics Ritsumeikan University Shiga Japan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Yibin Li School of Control Science and Engineering Shandong University Jinan China. Intelligent Robot Research Centre Zhejiang Lab Hangzhou China

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Research on PMSM Vector control System Based on Fuzzy PI Parameter Self-Tuning

Research on PMSM Vector Control System Based on Fuzzy PI Par...

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IEEE International Conference on Power and Energy systems (ICPES)

作者： Jianhua Ding Tao Zhang Ran Tao Anna Wang Northeastern University 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 Dalian University of Technology Dalian China

ISBN: (数字)9781665404945

ISBN: (纸本)9781665404952

Based on the analysis of the permanent magnet synchronous motor (PMSM) mathematical model, a PMSM rotor field-oriented double closed-loop vector control system is constructed. Aiming at the defects of slow speed response, large overshoot, severe torque ripple, and poor anti-disturbance ability in the traditional PI controller used in the system speed loop, a fuzzy PI parameter self-tuning controller is proposed. The controller can set the two parameters of proportional and integral online according to fuzzy control rules. The system simulation model is built based on Matlab/Simulink. The simulation results show that the fuzzy PI parameter self-tuning controller has the advantages of fast dynamic response, high steady-state precision and strong anti-disturbance capability.

关键词： PI control systems simulation Steady-state Mathematical model Torque measurement Machine vector control Matlab

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On PID control for synchronization of complex dynamical network with delayed nodes

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Science China(Technological Sciences) 2019年第8期62卷 1412-1422页

作者： GU HaiBo Lv JinHu LIN ZongLi Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University Beijing 100083 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 USA

Over the past two decades, synchronization, as an interesting collective behavior of complex dynamical networks, has been attracting much attention. To reveal and analyze the inherent mechanism of synchronization in complex dynamical networks with time delays in nodes, this paper attempts to use PD and PI control protocols to achieve synchronization. Based on a classical network model, we investigate the PD and PI control for synchronization of complex dynamical networks with delayed nodes and obtain some sufficient conditions. By using Lyapunov functions and appropriate state transformations, we prove that global synchronization can be achieved via the above control protocols. Finally, some simulation examples are illustrated to validate the effectiveness of the proposed theoretical results.

关键词： synchronization PID control complex dynamical network networked control system delayed node

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Distributed Computation Offloading using Deep Reinforcement Learning in Internet of Vehicles

Distributed Computation Offloading using Deep Reinforcement ...

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IEEE International Conference on Communications in China (ICCC)

作者： Chen Chen Zheng Wang Qingqi Pei Ci He Zhibin Dou State Key Laboratory of Integrated Services Networks Xidian University Xi'an P.R.China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing China The Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China

ISBN: (数字)9781728173276

ISBN: (纸本)9781728173283

In this paper, we first take the moving vehicles as a RP (resource pool), by which we proposed a distributed computation offloading scheme to fully utilize the available resources and reduce task execution time in I0V (Internet of Vehicles). After that, we divide a complex task into many sub-tasks and indicate that how to assign these small tasks to satisfy the task execution time in RP is a NP problem. The executing time of a task is modeled as the longest calculation time among all small tasks, which is actually a min-max problem. For a dynamically vehicular environment, a distributed computing offloading strategy based on deep reinforcement learning is proposed to find the best offloading scheme to minimize the execution time of a task. Numerical results demonstrate that our scheme is better than Partial Flooding Algorithm and can make full use of the available computing resources of surrounding vehicles by considering the mobility of vehicles, the delay of communication transmission, and the separability of the tasks, thus greatly reducing the execution time of the computing tasks.

关键词： Computational modeling Simulation Reinforcement learning User experience Delays Internet Task analysis

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Observer-based Dynamic Surface control for Robotic Manipulator with Fixed-time Convergence

Observer-based Dynamic Surface Control for Robotic Manipulat...

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International Conference on Informative and Cybernetics for Computational Social systems (ICCSS)

作者： Chengzhi Zhu Lei Zuo Ning Wang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China School of Electronic and Control Engineering Chang'an University Xi'an China School of Information Engineering Chang'an University Xi'an China

ISBN: (数字)9781728162461

ISBN: (纸本)9781665419352

A state feedback dynamic surface tracking control algorithm is proposed for parametric linearized manipulator model in this paper. Different from ordinary finite-time control schemes, fixed-time convergence can be achieved in the proposed algorithm via both of high and low-degree terms in the controller such that the settling time can be preestablished by the designed parameters independent of the system initial conditions. Moreover, the problem of the “explosion of complexity” and the singularity can be avoided by constructing an auxiliary controller with a first-order filter. The semi-global fixed-time uniform convergence of all the signals in the closed-loop system can be proved by Lyapunov theorem. In order to avoid the employment of acceleration signals and obtain better tracking performance during the procedure, a kind of observer based on uniform exact differentiators was used to compensate the uncertainties online. Finally the superiority of the proposed method has been verified by simulations based on a 2-DOF robot manipulator.

关键词： Maximum likelihood detection Heuristic algorithms Nonlinear filters Observers Explosions Manipulator dynamics Convergence

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Double Closed Loop controller with Current Sharing of Interleaved DC/DC Converter for Dynamic Wireless Power Transfer System

Double Closed Loop Controller with Current Sharing of Interl...

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International Conference on control, Automation, robotics and Vision (ICARCV)

作者： Wenjie Chen Zhitao Liu Hongye Su Liyan Zhang Faculty of State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Faculty of School of Automation Wuhan University of Technology Wuhan China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Dynamic wireless power transfer (DWPT) of electric vehicles (EVs) is considered as an economic and effective solution on traffic electrification. However, the coupling coefficient between the transmitting and receiving coils changes dynamically with the movement of EV, resulting in a sharp drop in energy transfer power and efficiency. In this case, the double closed loop controller with current sharing of interleaved DC/DC converter is proposed to against coupling coefficient variation. And two layers double D coil structure is presented to make the distribution of magnetic density and temperature more uniform. Moreover, Simulation studies are carried out in PLECS to verify the proposed strategy in DWPT system. Results show that the proposed algorithm can not only significantly improve the energy transfer power and efficiency, but also ensure the current sharing of interleaved DC/DC converter, so as to enhance the robustness and stability of DWPT system.

关键词： Coils Couplings Transmitters Magnetic cores Receivers Inductors Vehicle dynamics

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