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Optimization of Robot End-Effector Load Gravity Compensation for Unmanned Live Work in Electric Power Distribution Networks Based on Improved PSO Algorithm

Optimization of Robot End-Effector Load Gravity Compensation...

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2023 China automation Congress, CAC 2023

作者： Chen, Jinge Chen, Xin Hu, Chenyu Huang, Guangxian Sun, Shu Jian, Xu School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering-Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China State Grid Hubei Electric Power Co. Ltd. Xianning Power Supply Company Distribution Department Xianning437000 China State Grid Hubei Electric Power Co. Ltd. Electric Power Science Research Institute Equipment Technology Center Wuhan430077 China

ISBN: (纸本)9798350303759

In the field of electric power distribution network operation, most tasks involve contact operations. A key technology to enable the flexible operation of robots in live distribution network tasks is the installation of six-dimensional force sensors on the robot's end flange. These sensors allow the robot to perceive external environmental contact forces. However, the presence of the end load interferes with the accurate perception of external forces. Aiming at the gravity compensation of robot end load, an optimization algorithm of gravity compensation based on particle swarm optimization is proposed. In this method, the optimal solution model is established with the objective of minimum sum of squared errors, and the particle swarm optimization algorithm is used to estimate the mounting declination angle of the force sensor and improve the precision of gravity compensation. The experiments show that the proposed method can effectively reduce the maximum gravity compensation error and the average gravity compensation error in each direction after optimization, in which the maximum gravity compensation error of the resultant force is reduced by 9.11% and the average gravity compensation error of the resultant force is reduced by 12.34%. © 2023 IEEE.

关键词： Error compensation

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Spatially Dense Multi-Target Track Segment Association Algorithm

Spatially Dense Multi-Target Track Segment Association Algor...

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Chinese control Conference (CCC)

作者： Yan Yao Liping Yan Yuanqing Xia Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

In the field of radar data processing, track interruption seriously affects target tracking, track fusion, and other tasks. The existing track segment association algorithms have low correlation accuracy in dense distributed or long-time interruption situations. To this purpose, a dense multi-target track segment association (DMTTSA) algorithm is proposed. Firstly, two identical networks based on the multi-head probability sparse (ProbSparse) self-attention are used to capture the long-term dependencies of the tracks. Then, the bidirectional quadruplet hard sample loss (BiQuaHard loss) is constructed to make the tracks belonging to the same targets closer and the tracks belonging to the different targets farther. Finally, DMTTSA takes the closest track pairs in the feature space as the associated tracks and divides the unassociated tracks into the birth and dead tracks in chronological order. Some comparative experiments are carried out to show the anti-noise performance of the DMTTSA, as well as the effectiveness of solving the problem of dense multi-target track interruption.

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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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Improved BP Neural Network Utilizing Bayesian optimization Algorithm for Iron-making Process control

Improved BP Neural Network Utilizing Bayesian optimization A...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Shaoran Wang Jinhua She Seiichi Kawata Feng Wang Juan Zhao Qifu Chen Zhenhong Liao 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)9798350394085

ISBN: (纸本)9798350394092

The manufacturing sector covers a wide range of areas, focusing mainly on major equipment manufacturing and process industries, including new energy and new materials manufacturing. The ultimate goal is to realize intelligent manufacturing. Iron and steel metallurgy is an important pillar of the national economy, especially in the field of process industry. In the ironmaking process, a large number of process parameters are systematically collected, forming high-dimensional time series big data. These parameters contain hundreds of influencing factors. key production indicators such as yield, energy consumption and iron quality are closely related to a controlled intermediate variable in the smelting process, the furnace temperature. Silicon content [Si] and the mass percentage of Si in the molten iron is a good example. In order to predict and control the blast furnace ironmaking process more effectively, this paper introduces a BP neural network prediction model, and optimizes and compares the neural network model with genetic algorithm and Bayesian optimization technique.

关键词： Heuristic algorithms Neural networks Process control Predictive models Prediction algorithms Silicon Iron

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An Informer Based Alarm Early Prediction Method Over Consecutive Alarm Monitoring Periods

An Informer Based Alarm Early Prediction Method Over Consecu...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Wenbin Jiang Wenkai Hu Zhe Liu Fei Wang 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 SINOPEC-SK (WUHAN) Petrochemical Company Limited Wuhan China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Alarm systems serve as the first layer of protection for modern process industries to monitor industrial processes and ensure operational safety. However, the presence of alarm floods is common in alarm systems and may distract operators from critical alarms. To provide decision supports to operators during alarm flood situations, alarm prediction becomes a potential effective solution. The difficulty of alarm prediction lies in how to achieve high prediction accuracy over long-term consecutive alarm monitoring periods, including situations with either high or low alarm rates. Consequently, this paper presents an industrial alarm prediction method for both alarm flood periods and non-alarm flood periods. Specifically, a strategy of combining fixed-length and fixed-time sliding windows is designed. Further, an alarm prediction model based on Informer is proposed for alarm prediction under different alarm rates. A case study is presented to prove the validity of the proposed alarm prediction method.

关键词： Industries Adaptation models Analytical models Accuracy Predictive models Alarm systems Floods Protection Informatics Monitoring

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A Research on Attitude control of Quadrotor UAV Based on Equivalent-Input-Disturbance Approach and Model Predictive control

A Research on Attitude Control of Quadrotor UAV Based on Equ...

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intelligent systems, International Workshop on

作者： Zihao Ning Jinhua She 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 discusses the problem of attitude control of a quadrotor unmanned aerial vehicles (UAV). As external disturbances and system nonlinearities will affect the attitude control of quadcopter UAV, making it difficult for general control methods to achieve satisfactory results. To address this problem, this paper applies the equivalent-input-disturbance (EID) approach and the model predictive control (MPC) to the attitude control of a quadcopter UAV to ensure the reference tracking and suppress the influence of the total disturbances on the attitude of the quadcopter UAV, and the simulation results prove the effectiveness of the method.

关键词： Attitude control Simulation Conferences Autonomous aerial vehicles Prediction algorithms control systems intelligent systems Quadrotors Predictive control intelligent control

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Enhanced Gesture Recognition via Interpretable Temporal Convolutional Networks Utilizing Spatio-Temporal sEMG Features

Enhanced Gesture Recognition via Interpretable Temporal Conv...

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Pervasive and intelligent Computing (PICom), IEEE Conference on

作者： Xiaohu Ao Jinhua She Feng Wang Seiichi Kawata Edwardo Fumihiko Fukushima Jinseok Woo 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)9798331522742

ISBN: (纸本)9798331522759

Rehabilitation robots and human-robot interaction systems are receiving increasing attention in modern healthcare and engineering. Motion intent recognition techniques based on surface electromyography (sEMG) signals provide important support for these systems. However, motion intent recognition models rarely focus on the spatiotemporal properties of sEMG signals simultaneously. There is a lack of interpretable analysis of these spatiotemporal properties. The purpose of this study is to perform gesture recognition of sEMG signals from the Ninapro database using a temporal convolutional network (TCN). The sEMG signals are converted into sEMG images, which are used as input to the TCN model. The TCN model is utilized to perform gesture recognition on these images. The results show that after fusing the spatial and temporal features of the sEMG signal, the recognition accuracy of the TCN model is significantly higher than that of models using only spatial features or only temporal features. To understand the decision-making process of the model, we used an interpretable method based on Shapley values to perform a detailed interpretive analysis of the recognition results. The experimental results show that the TCN model has significant accuracy and stability in the sEMG signal gesture recognition task, while the Shapley value-based interpretable method can effectively reveal the contribution of different features to the prediction results, thus improving the transparency and user trust in the model. This study not only provides a new technical tool for sEMG signal gesture recognition but also demonstrates the value of interpretable methods in deep learning models.

关键词： Accuracy Intent recognition Computational modeling Gesture recognition Predictive models Stability analysis Real-time systems Spatiotemporal phenomena Convolutional neural networks Time complexity

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Dynamic Performance Prediction in Batch-Based Assembly System with Bernoulli Machines and Changeovers

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complex System Modeling and Simulation 2022年第3期2卷 224-237页

作者： Zunjun Wang Zhiyang Jia Xiuxuan Tian Jingchuan Chen Bei Pan State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Industrial and Manufacturing Systems Engineering University of Hong KongHong Kong 999077China School of Engineering Tokyo University of TechnologyTokyo 192-0982Japan

Worldwide competition and diverse demand of customers pose great challenges to manufacturing enterprises. How to organize production to achieve high productivity and low cost becomes their primary task. In the mean time, the rapid pace of technology innovation has contributed to the development of new types of flexible automation. Hence, increasing manufacturing enterprises convert to multi-product and small-batch production, a manufacturing strategy that brings increased output, reduced costs, and quick response to the market. A distinctive feature of small-batch production is that the system operates mainly in the transient states. Transient states may have a significant impact on manufacturing systems. It is therefore necessary to estimate the dynamic performance of systems. As the assembly system is a typical class of production systems, in this paper, we focus on the problem of dynamic performance prediction of the assembly systems that produce small batches of different types of products. And the system is assumed to be characterized with Bernoulli reliability machines, finite buffers, and changeovers. A mathematical model based on Markovian analysis is first derived and then, the analytical formulas for performance evaluation of three-machine assembly systems are given. Moreover, a novel approach based on decomposition and aggregation is proposed to predict dynamic performance of large-scale assembly systems that consist of multiple component lines and additional processing machines located downstream of the assemble machine. The proposed approach is validated to be highly accurate and computationally efficient when compared to Monte Carlo simulation.

关键词： flexible assembly system dynamic performance Bernoulli machines finite buffers changeovers

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Causality Analysis and Modeling of Fuel Ratio Influencing Factors in Blast Furnace Ironmaking Process

Causality Analysis and Modeling of Fuel Ratio Influencing Fa...

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Asian control Conference

作者： Binjie Qian Weihua Cao Qifu Chen Min Wu 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 Future Technology China University of Geosciences Wuhan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Blast furnace ironmaking is a key process in iron and steel production, and the fuel ratio has an important impact on the smelting efficiency, cost and environmental impact of the blast furnace. This study aims to provide new ideas and method-ological support for the optimized management and control of the blast furnace ironmaking process by exploring the effects of different factors on the fuel ratio. We firstly sorted out the factors affecting the fuel ratio based on the blast furnace process mechanism, and then deeply analyzed the causal relationship of each parameter on the fuel ratio based on the transfer entropy value, and screened out the parameters that did not have a significant effect on the fuel ratio. Taking the remaining key parameters as inputs, the LSTM and GRU models, which can capture time series information, are used to predict the mean and variance of the fuel ratio, respectively, and the two are combined to compute the final fuel ratio, which overcomes the problem of poor model generalization brought by small fluctuations in the training data of fuel ratio. The experimental results show that the convergence speed of the model is significantly improved after parameter screening, and the fuel ratio can be effectively predicted within a certain error range.

关键词： Computational modeling Time series analysis Smelting Process control Training data Predictive models Blast furnaces

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Prediction of gas utilization ratio based on singular spectrum analysis, improved PSO and KELM

Prediction of gas utilization ratio based on singular spectr...

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Chinese control and Decision Conference, CCDC

作者： Zhuang Li Jianqi An Yunpeng Guo Guoyu Zhao Min Wu 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 Future Technology China University of Geosciences Wuhan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The gas utilization ratio (GUR) in a blast furnace is directly linked to the efficiency, cost-effectiveness and environmental impact on the blast furnace ironmaking process. However, The stability of the published GUR time series prediction models need to be improved. This paper presents an improved particle swarm optimization (PSO) incorporating linearly decreasing inertia weights (LDIW) to optimize the kernel-based extreme learning machine (KELM) for single-step prediction. This paper uses singular spectrum analysis (SSA) to preprocess the data and extract the key components from the GUR time series to solve the problem of high volatility of the GUR time series. In addition, this paper introduces LDIW to improve the optimization ability of particle swarm optimization algorithm, which enhances the stability of a single-step prediction model. Then this paper uses the improved PSO algorithm to extract the optimal parameters of KELM, and establishes a single-step GUR prediction model based on the improved PSO-KELM. Finally, this paper uses the actual production process data of blast furnace to verify the prediction model. The results show that the prediction accuracy of GUR and the overall stability of the model are significantly improved, providing important guidance for the blast furnace ironmaking process.

关键词： Extreme learning machines Time series analysis Production Predictive models Blast furnaces Prediction algorithms Stability analysis

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