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A New Recursive Composite Adaptive controller for Robot Manipulators

Space(Science & Technology) 2021年第1期2021卷 77-83页

作者： Jianfei Li Yaobing Wang Zhiyong Liu Xin Jing Chengwei Hu The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing Institute of Spacecraft System EngineeringBeijing 100094China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China

In this paper,a new recursive implementation of composite adaptive control for robot manipulators is *** investigate the recursive composite adaptive algorithm and prove the stability directly based on the Newton-Euler equations in matrix form,which,to our knowledge,is the first result on this point in the *** proposed algorithm has an amount of computation O(n),which is less than any existing similar algorithms and can satisfy the computation need of the complicated multidegree *** manipulator of the Chinese Space Station is employed as a simulation example,and the results verify the effectiveness of this proposed recursive algorithm.

关键词： Robot manipulators robot

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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Multi-Modal Based Fuzzy Atmosfield in Human-Robot Interaction

Multi-Modal Based Fuzzy Atmosfield in Human-Robot Interactio...

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Chinese control Conference (CCC)

作者： Xin-Heng Li Zhen-Tao Liu School of Automation China University of Geosciences (Wuhan) Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

Recently, the emotional robot has basic functions of perceiving and expressing emotions, but it still hard to communicate naturally between humans and robots. One major reason is that communication atmosphere is seldom considered in Human-robot Interaction (HRI). We propose Multi-modal (i.e., music background, speech, and semantics) Based Fuzzy Atmosfield (FA), which can not only realize the dynamic adjustment of FA but also dynamically regulate human emotions. In the experiment, a Pepper robot is used and one hundred volunteers are invited for HRI, and soothing piano pieces are used as background music. Questionnaires were filled by the volunteers after the experiments, from which the results show that 90% of the volunteers felt the dynamic changes in the communication atmosphere and 77% of the volunteers felt significant emotional regulation, which demonstrates the effectiveness of our method.

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Prediction Method of Truck Travel Time in Open Pit Mines Based on LSTM Model

Prediction Method of Truck Travel Time in Open Pit Mines Bas...

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第42届中国控制会议

作者： Mengting Ao Changhe Li Shengxiang Yang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Computer Science and Informatics De Montfort University

Aiming at the prediction of truck travel time in open pit mines,we established a prediction model based on long short-term memory(LSTM).This model fully accounts for 11 factors,including the nature of trucks,weather,road conditions,and driver's behaviors,as well as the influence of neighbor road segments in the route on the current predicted road *** experiment shows that the error of the LSTM prediction model is significantly reduced compared with S VR and BP *** addition,the maximum absolute mean error under different conditions is less than 12 seconds.

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Multi view 3D reconstruction method for weak texture objects based on "eye-in-hand" model

Multi view 3D reconstruction method for weak texture objects...

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2021 China automation Congress, CAC 2021

作者： Gong, Xuan Chen, Xin Jian, Xu He, Wenpeng School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9781665426473

Size measurement and defect detection of ceramics are required in the production process of ceramic products. Therefore, it is necessary to perform high-precision and fast three-dimensional reconstruction of such targets with smooth surfaces and weak textures. However, the reconstruction of a large number of point clouds from multiple perspectives is time-consuming due to the matching of adjacent feature points. Based on this, a fast point cloud registration strategy based on the "eye-in-hand" model is proposed, which can obtain RGB-D images at multi-view around weak-textured objects with RGB-D camera for rapid point cloud registration and reconstruction. Meanwhile, a 3D point cloud segmentation method based on voxel-grid and euclidean distance clustering is applied to divide the scene point and obtain an accurate 3D model. The experimental results indicate that the multi view 3D reconstruction speed of this method is faster and the accuracy of objects point cloud is within 2 mm. Hence, this method can be well applied in reconstructing the 3D point clouds model for ceramic products. © 2021 IEEE

关键词： Textures

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Thermal analysis and package optimization for the multi-wavelength laser bar module

Thermal analysis and package optimization for the multi-wave...

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2022 International Symposium on Semiconductor and Electronic Technology, ISSET 2022

作者： Sun, Wanjun Yang, Lijiao Xie, Jianan Zhang, Xiaxia Sha, Yang Mi, Shuai Lin, Tao College of Automation and Information Engineering Xi’an University of Technology Shaanxi Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Shaanxi Xi’an710048 China

To meet the demand of the laser application market for semiconductor laser function diversification, this paper proposes a handheld multi-wavelength laser stack packaging module. The 808 nm, 915 nm, and 980 nm semiconductor laser bars are selected for the integrated assembly of the multi-wavelength module. However, with the continuous increase of the optical power density of the integrated packaging module, thermal management has become a key factor affecting the performance and life of the device. In this paper, the finite element analysis method is used to analyze the influence of the arrangement of three laser stripe chips in the stacked packaging structure on the heat dissipation of the multi-wavelength module. The simulation results show that the heat dissipation efficiency of the multi-wavelength module can be improved by 13.74 % by changing the arrangement of chips. A back heat dissipation packaging structure with higher heat dissipation efficiency is proposed, and the influence of chip arrangement and working mode on the thermal characteristics of the device in the new packaging structure is simulated and analyzed. According to the simulation results, a chip arrangement that can make the heat dissipation effect of the multi-wavelength module achieve the best is determined. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Semiconductor lasers

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Conual Information Fusion for Small Object Detection 40

Conual Information Fusion for Small Object Detection

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40th Chinese control Conference, CCC 2021

作者： Chen, Jun Chen, Xiaoqiang Luo, Linbo Wang, Ganbei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9789881563804

In view of the difficulty and low accuracy of small object detection for remote sensing images, this paper proposes a small object detection algorithm based on conual information fusion to solve the problem of real-time detection accuracy of small object. In this paper, we use bottom-up VGG16 network to realize multi-scale feature extraction to deal with the problem of insufficient image feature extraction. To direct at the problem that the feature information of each feature layer is single, the shallow feature layer and the deep feature layer are fused through the feature fusion module, which achieves the purpose that some feature layers have more abundant fusion features in the structure level. Aiming at the problem that the detection objects in remote sensing images are mainly small and medium-sized objects, this paper proposes to use the multivariate information of four different scale feature layers for classification prediction and regression prediction, so as to reduce the complexity of network model. The experimental results show that the proposed small object detection algorithm based on the fusion of four scale deep and shallow conual information can obtain good accuracy and real-time performance on the NWPU VHR-10 dataset, improve the detection accuracy on the basis of ensuring the real-time detection, and perform well in the small object detection task of remote sensing images. © 2021 Technical Committee on control Theory, Chinese Association of automation.

关键词： Remote sensing

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A Robust Multi-AGV Cooperative Path Planning Method Considering Spatiotemporal Distance 5

A Robust Multi-AGV Cooperative Path Planning Method Consider...

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5th International Conference on Computer Engineering and intelligent control, ICCEIC 2024

作者： Chen, Maolin Chen, Songhang Lian, Mingchang School of Advanced Manufacturing Fuzhou University Quanzhou China Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou China Fujian College University of Chinese Academy of Sciences Fuzhou China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China

ISBN: (纸本)9798331507992

With the widespread application of Automated Guided Vehicles (AGVs) in warehousing and logistics systems, the optimization of multi-AGV path planning has become a critical issue. Current methods primarily focus on minimizing operating time and energy consumption but often overlook spatiotemporal distances between paths, leading to frequent path conflicts and interferences in the system. This paper proposes a neural network based time window estimation model, designed to generate time windows that better align with dynamic environments. Based on this model, we introduce a collaborative multi-AGV path planning method that optimizes spatiotemporal distances between paths. Compared to the Conflict-Based Search (CBS) method, this approach significantly improves the spatiotemporal distance between AGVs during task execution, enhancing the robustness of AGV clusters while only marginally increasing time costs. © 2024 IEEE.

关键词： Automatic guided vehicles

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PI Tuning and Optimization for Speed control of PMSM Based on Multivariable Sliding-Mode Extremum Seeking

PI Tuning and Optimization for Speed Control of PMSM Based o...

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第34届中国控制与决策会议

作者： Fan Li Wei Shen Chuanyu Ge Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology

ISBN: (纸本)9781665478977

The PI controller is the most basic and widely used for the permanent magnet synchronous motor speed *** cumbersome parameter adjustment process makes it difficult for the PI controller to balance convenience and *** this end,this paper presents a new PI tuning and optimization scheme based on multivariable sliding-mode extremum ***,by analyzing the frequency domain model of permanent magnet synchronous motor,the analytical calculation formulas of controller parameters and motor parameters are derived as the initial value of PI parameter ***,a multivariable sliding-mode extremum seeking optimizer is designed to optimize the control parameters by minimizing the cost function based on the feedback error *** feasibility of the above scheme is proved by theoretical derivation,and the simulation and experimental results also verify the feasibility and effectiveness of the proposed method.

关键词： permanent-magnet synchronous motor(PMSM) multivariable sliding mode extremum seeking(MSME) self-tuning parameter optimization

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Disturbance Rejection in Lower-Limb Rehabilitation Robot with Parameter Uncertainties

Disturbance Rejection in Lower-Limb Rehabilitation Robot wit...

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intelligent systems, International Workshop on

作者： He Wang Jinhua She Feng Wang Juan Zhao Seiichi Kawata School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

This paper presents a method integrated equivalent-input-disturbance (EID) and repetitive control (RC) to mitigate the effects of non-linearity and uncertainty in a pedaling rehabilitation robot. The repetitive controller is designed to ensure accurate steady-state tracking of periodic signal. Meanwhile, the EID estimator, combined with a state observer, is responsible for estimating and compensating for the impacts of non-linearity, parameter uncertainties, and disturbances caused by patients with lower-limb impairments. We design a Lyapunov stability function to assess the stability of system. The parameters of the control gain are obtained by linear matrix inequality (LMI). The results confirm the effectiveness of the proposed method.

关键词： Uncertain systems Accuracy Uncertainty Simulation Observers Assistive robots Stability analysis Steady-state Robots Lyapunov methods

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