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检索条件"机构=Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex System"

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Manual Alignment of Dual End-Effectors for Piezo-Driven Microhand

Manual Alignment of Dual End-Effectors for Piezo-Driven Micr...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Pang, Jichao Liu, Dan Tang, Xiaoqing Chen, Yan Huang, Qiang Arai, Tatsuo Liu, Xiaoming Key Laboratory of Biomimetc Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Advanced Innovation Center for Intelligent Robots and Systems School of Mechatronical Engineerin Beijing Institute of Technology Beijing100081 China The University of Electro-Communications Center for Neuroscience and Biomedical Engineering Tokyo182-8585 Japan

ISBN: (纸本)9798350372601

In recent years, micromanipulator with dual end-effectors called microhand has been widely used in industrial and biological fields. Whether the tip positions of the two end-effectors of the microhand can be accurately aligned seriously affects the precise grasping of the microhand. However, few researchers have presented practical ways for precisely aligning the tip positions of a 6-DOF microhand's two end- effectors. This paper proposes a novel, inexpensive, lightweight, robust, and easy-to-use microhand dual end-effectors clamping device that can precisely and quickly align the two end-effectors of a 6-DOF microhand. First, we describe the microhand dual end-effectors clamping device's lightweight structural design. After that, we simplify the microhand dual end-effectors clamping device model and create a mathematical model to determine the relationship between the end-effectors' tip position and the rotation angles of screw No.1, screw No.2, screw No.3 of the microhand dual end-effectors clamping device. Through experimentation, we are able to attain precise alignment of the two end-effectors tip positions of the 6-DOF microhand, so we confirm that our mathematical model is true. Finally, we use the impulse response of PZT in the microhand's lower module to confirm the stability of the microhand dual end- effectors clamping device. We believe that the proposed the microhand dual end-effectors clamping device has great potential in industrial and biological applications as a universal tool for precise alignment of the tip positions of the two end-effectors of a 6-DOF microhand. © 2024 IEEE.

关键词： Clamping devices

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation system (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Blast Furnace Gas Utilization Rate Prediction Model based on Northern Goshawk Optimization and Long Short-Term Memory in Massive Data Set

Blast Furnace Gas Utilization Rate Prediction Model based on...

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Industrial Cyber-Physical systems (ICPS)

作者： Yue Zhou Weihua Cao Zhuofu Zhang Yan Yuan School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

Affected by the global New Crown Pneumonia epidemic, energy prices in the global market continue to rise. The high quality production and energy saving of steel companies have become an important task. The blast furnace is the front-end core of the steel manufacturing process. The blast furnace gas utilization rate can effectively characterize the internal airflow distribution, blast furnace operation and energy consumption level of the blast furnace. The blast furnace Gas Utilization Rate (GUR) can effectively characterize the internal airflow distribution, blast furnace operation and energy consumption level. Most of the existing studies are based on data-driven models. The shallow neural network model is selected. But blast furnace iron making has complex uncertainty. Massive data samples under industrial information technology need to be processed. The robustness and generalization ability of the prediction model are not satisfactory. To address the above problems, this paper proposes a prediction model based on NGO-LSTM regression. The model parameter searchs can be intelligently. It achieves high-precision prediction results for massive data samples. Firstly, the selection of feature parameters is completed by maximal information coefficient. Secondly, the strong coupling of blast furnace ground needs to fully predict the relationship between the characteristic parameters. The Long Short-Term Memory (LSTM) neural network with certain memory capability is selected. And multiple parameters of this neural network model are optimized by the Northern Goshawk Optimization (NGO) algorithm. An NGO-LSTM regression prediction model is established. In this paper, experiments are carried out using actual production data. The experimental results show that the proposed method can accurately predict the blast furnace gas utilization rate. This can provide a reference for improving blast furnace product quality, reducing costs and increasing efficiency.

关键词： Energy consumption Neural networks Production Predictive models Blast furnaces Prediction algorithms Robustness

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Estimation of Bottom-Hole Pressure Based on Bayesian Optimization and Bidirectional Long Short-Term Memory Neural Network 43

Estimation of Bottom-Hole Pressure Based on Bayesian Optimiz...

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43rd Chinese control Conference, CCC 2024

作者： Zhang, Zihan Lu, Chengda Wang, Yawu Zhou, Yang Wu, Min She, Jinhua China University of Geosciences School of Future Technology 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 China University of Geosciences School of Automation Wuhan430074 China Tokyo University of Technology School of Engineering Hachioji192-0982 Japan

ISBN: (纸本)9789887581581

Bottom-hole pressure (BHP) plays a crucial role in a drilling process. The accurate estimation of BHP ensures safe and efficient drilling operations. Artificial neural networks can predict BHP indirectly by analyzing the non-linear relationship with wellhead variables. This paper introduces a BHP estimation model that leverages Bayesian optimization and a bidirectional long short-term memory neural network (BO-BiLSTM). The model employs Bayesian optimization to determine optimal hyperparameters, including the number of hidden layers, initial learning rate, and L2 regularization parameter. The BO-BiLSTM model achieves a mean absolute percentage error of 2.056% and an R-squared score of 0.94596. The results demonstrate that the presented model enhances estimation precision and outperforms other methods examined in this study. This method facilitates indirect measurement of BHP using wellhead data, and contributes to the exploration of deep-sea resources in complex environments. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Bottom hole pressure

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Optimized Hybrid control for Attitude Adjustment of Downhole Directional Drilling Tools

Optimized Hybrid Control for Attitude Adjustment of Downhole...

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Annual Conference of Industrial Electronics Society

作者： Jing Xiao She Wang Zhen Cai School of Information Engineering Wuhan City Polytechnic University Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This study delves into the critical role of attitude control in downhole directional drilling tools, focusing on the complex coupling between inclination and azimuth in the motion model, particularly the time-varying nature of azimuth adjustments. By implementing separate control strategies for inclination and azimuth, the research integrates a comprehensive hybrid control system. It rigorously examines the stability of the closed-loop systems and validates the effectiveness and applicability of the hybrid control method through extensive simulations. The system demonstrates significant robustness and adaptability, enhancing drilling precision and efficiency.

关键词： Drilling Couplings Industrial electronics Azimuth Attitude control Simulation Focusing Stability analysis Robustness Linear matrix inequalities

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Design of a Novel Autonomous Docking system With a Supporting Robotic Fish

Design of a Novel Autonomous Docking System With a Supportin...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Dong, Huijie Zhao, Chunyang Qiao, Tiezhu Wang, Jian Chen, Di Meng, Yan Yu, Junzhi Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex System Institute of Automation Beijing100190 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Guangdong Ocean University College of Electronics and Information Engineering Zhanjiang524088 China

ISBN: (纸本)9798350327182

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named 'CourierFish'. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks. © 2023 IEEE.

关键词： Fish

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Trajectory-prediction-based Dynamic Tracking of a UGV to a Moving Target under Multi-disturbed Conditions

Trajectory-prediction-based Dynamic Tracking of a UGV to a M...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Jinge Si Bin Li Yongkang Xu Liang Wang Chencheng Deng Shoukun Wang Junzheng Wang State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology School of Automation Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Tracking dynamic targets poses a significant challenge for Unmanned Ground Vehicles (UGVs). Existing methods often lack research on multi-disturbed conditions. To address this issue, we propose a trajectory-prediction-based dynamic tracking scheme, which includes target localization, trajectory prediction, and UGV control. Firstly, an estimation algorithm based on the Extended Kalman Filter (EKF) is employed to mitigate noise and estimate the absolute states of the target accurately. To enhance robustness, we present an Adaptive Trajectory Prediction (ATP) algorithm based on prediction anchors. In this method, a quantization standard for trajectory disturbance is designed for adaptive control. Subsequently, we iteratively solve prediction anchor points based on two motion models to robustly predict the target trajectory even in the presence of unknown disturbances. Finally, the Linear Time-Varying Model Predictive control (LTV-MPC) is utilized in the UGV controller for dynamic tracking. Experimental results demonstrate that the ATP exhibits superior prediction robustness and accuracy in perturbed environments compared to other prediction algorithms. In addition, the proposed scheme effectively achieves dynamic tracking of the Unmanned Aerial Vehicle (UAV) by the UGV under multi-disturbed conditions. Specifically, when the target moves at a speed of 1.0 m/s, the UGV can maintain a tracking error within 0.346 m.

关键词： Target tracking Accuracy Heuristic algorithms Noise Prediction algorithms Autonomous aerial vehicles Robustness

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Model Predictive control of Decomposition Furnace Based on Equivalent-Input-Disturbances

Model Predictive Control of Decomposition Furnace Based on E...

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Chinese automation Congress (CAC)

作者： WeiKang Wu Gang Yu Wangyong He Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geoscience Wuhan China Huaxin Cement Technology Management(Wuhan) Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In the cement production process, the decomposer outlet temperature control faces challenges such as large time delay and multiple disturbances. A general control system cannot handle time delay and disturbances effectively at the same time. Therefore, in this paper, we consider the problem of disturbances in time-delay systems and suppress the disturbances present in the outlet temperature control of the decomposer by combining equivalent-input-disturbance (EID) and Model Predictive control (MPC), where EID can estimate the disturbances and apply them to the input channels of the system, which do not require any information about the disturbances. Finally simulations are carried out in the catabolizer system and the results indicate that this system is able to suppress the perturbations effectively in time delay systems.

关键词： Delay effects Perturbation methods Optimal control Production Predictive models Aerospace electronics Mathematical models Temperature control State estimation Predictive control

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Auto-Regressive with Exogenous Input Modelling Based Model Predictive control of Cement Grate Cooler

Auto-Regressive with Exogenous Input Modelling Based Model P...

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Chinese automation Congress (CAC)

作者： Tian Zhang Gang Yu Wangyong He Renjie Gui Weikang Wu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geoscience Wuhan China Huaxin Cement Technology Management(Wuhan) Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In the cement production process, the speed of grate coolers directly affects the residence time and cooling effect of cement clinker. Its effective control is of great significance to ensuring cement quality. But the adjustment of grate speed is a complex process, involving moderate cooling air, uniform clinker thickness, regulated output and other constraints. In this study, an Auto-Regressive with exogenous input (ARX) grate cooler prediction model is developed using a data-driven approach based on a company's cement production line, a model predictive control (MPC) approach is employed for the purpose of optimising the speed of the grate cooler based on the ARX model. The results demonstrate that the method of MPC exhibits greater stability than the method of PID, and that MPC technology has good efficacy and superiority in the adjustment of grate speed.

关键词： Kilns Cooling Cement industry Velocity control Production Switches Predictive models control systems Stability analysis Predictive control

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Identification and Prediction of Microvascular Invasion in Hepatocellular Carcinoma Based on Multiple Sequence Fusion Images

Identification and Prediction of Microvascular Invasion in H...

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Chinese control Conference (CCC)

作者： Mingguang Lu Yuan Zhu School of Automation China University of Geosciences Wuhan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Hepatocellular carcinoma (HCC) is one of the most difficult diseases to be treated fundamentally in the medical field in today’s world, and the recurrence rate of HCC has been increasing year by year in recent years. The study on the identification and prediction of microvascular invasion phenomenon (MVI) in hepatocellular carcinoma is beneficial to the postoperative prevention of hepatocellular carcinoma patients, which can greatly improve the survival probability of patients after surgery. In this study, a new MVI prediction method (MSF-GWO-GPR) was proposed, which combines the multi-sequence image features of imaging histology, sifts out the unimportant features through the T-test method and the dimensionality reduction of Lasso regression, and establishes the multi-sequence Gaussian process regression (GPR) prediction model for data fusion, and combined with the Gray Wolf Optimization (GWO) algorithm to design a suitable optimization objective function to optimize the hyper-parameters in the GPR, in order to get better prediction results. Compared with the existing methods, the data from different sequences of MRI images are selected for fusion in this study, which makes the feature data richer and more complete, and the model applicability is better; moreover, by optimizing the hyperparameters in the model through the GWO, the model can be made more accurate, reduce the risk of misidentified prediction, and improve the accuracy rate. The experimental results show that MSF-GWO-GPR outperforms widely used machine learning methods in terms of accuracy, F1 score, precision, recall and AUC metrics. This may provide a new idea for the identification and prediction of MVI in hepatocellular carcinoma.

关键词： Liver cancer Accuracy Magnetic resonance imaging Prevention and mitigation Surgery Predictive models Feature extraction

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