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A repetitive-control method based on sliding-mode control for nonlinear systems with external disturbances 41

A repetitive-control method based on sliding-mode control fo...

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第41届中国控制会议

作者： Yibing Wang Min Wu Manli Zhang Luefeng Chen Jinhua She 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 Engineering Tokyo University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper presents a two-dimensional(2 D) repetitive-control method based on sliding-mode control for a nonlinear system with external *** a sliding-mode controller has a powerful ability to handle nonlinearity and external disturbances during the sliding motion,the reference-tracking and disturbance-suppression problems of the original nonlinear system are decomposed into two subproblems:reference tracking for a linear system and disturbance suppression for a nonlinear modified-repetitive control system(MRCS) with external *** the first one,a repetitive controller constructed with two positive time-delay loops is employed to increase the flexibility of the system design and guarantee high accuracy reference tracking.A continuous-discrete 2 D model of the linear MRCS is established to describe continuous control and discrete learning by using the 2 D property of repetitive control.A sufficient stability condition,in which three parameters are used to adjust control and learning,is given to guarantee the stability of the linear *** design of the system is transformed into parameter *** optimization algorithm is used to yield the optimal *** the other,a sliding-mode controller is developed to eliminate the effect of both nonlinearity and external disturbances on the system.A linear-matrix-inequality(LMI)-based sufficient stability condition is derived to guarantee the stability of the sliding *** condition is used to design the sliding-mode controller,which has a simple structure and does not require any specific information of nonlinearity and external ***,a numerical example and a comparison demonstrate the validity and advantage of the presented method.

关键词： Repetitive control sliding-mode control two-dimensional model

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Confidence-Driven Semi-Supervised Partial Label Learning

Confidence-Driven Semi-Supervised Partial Label Learning

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International Joint Conference on Neural Networks (IJCNN)

作者： Chengkun Liu Jun Zhang Jing Chai School of Information Science and Engineering Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming City China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Semi-supervised Partial Label Learning (SPLL) aims to learn from a dataset comprised of both partial label examples each of which is associated with a candidate label set and unlabeled examples. The mainstream of SPLL methods usually construct a confidence matrix for training examples and by which to operate label disambiguation and classifier training. However, they treat examples with different confidence levels in the same strategy during training, which might result in degenerated learning performance. In this work, a novel method named COnfidence-DRiven (CODR) is proposed to deal with the above drawback. In specific, we iteratively update the confidence matrix and predictive network, and employ different strategies to deal with high-confidence and remaining low-confidence examples. Extensive experiments on real-world datasets demonstrate the superiority of CODR in classification accuracy compared with several other state-of-the-art methods.

关键词： Training Measurement Accuracy Sensitivity analysis Noise Neural networks

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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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Dilated Autoformer: An Improved Model for Predicting the State of Transformer Bushing 43

Dilated Autoformer: An Improved Model for Predicting the Sta...

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43rd Chinese Control Conference, CCC 2024

作者： Huang, Xiangjie Lang, Xun Liu, Songhua Li, Peng Guo, Tao Yu, Li Yunnan University Department of Electronic Engineering Yunnan Kunming650091 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan Kunming650500 China Honghe Power Supply Bureau Yunnan Power Grid Corporation Yunnan Honghe China

ISBN: (纸本)9789887581581

Electric transformers play a crucial role in power systems, and extensive research has been conducted on transformer state prediction. However, despite the importance of transformer bushing as a vital component of transformers, there has been limited research focused specifically on its state prediction. This work builds upon the state-of-the-art predictive network, the Autoformer, and introduces an enhanced functionality module (the Dilated Loss module) for efficient state prediction of transformer bushing. More specifically, the aim is to predict three essential parameters of the bushing: dielectric loss, end shield current, and equivalent capacitance. By forecasting future trends in these parameters, potential bushing or transformer faults can be detected at an early stage. To evaluate the effectiveness of the proposed method, we conducted comparative experiments with attention-based frameworks (Autoformer Pyrafomer, Informer), traditional deep learning algorithms such as Temporal Convolutional Network and Long Short-Term Memory, as well as the statistical methods (Moving Average). The results show that our Dilated Autoformer outperforms the aforementioned techniques in terms of four key metrics: mean square error, mean absolute error, root mean squared error, and coefficient of determination. Our proposed model reveals the potential of attention-based algorithms in predicting the status of transformer bushings. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Dielectric losses

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Hardware-in-the-loop Simulation and Experimental System for Process Control of Marine Resources Exploration 43

Hardware-in-the-loop Simulation and Experimental System for ...

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43rd Chinese Control Conference, CCC 2024

作者： Wang, Zeyi Wang, Chenxuan Ma, Zhejiaqi Zeng, Kanghui Lu, Chengda Wang, Yawu Wu, Min China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China China University of Geosciences School of Future Technology Wuhan430074 China

ISBN: (纸本)9789887581581

Effective control techniques are essential for offshore platform systems to suppress disturbances caused by the complex marine environment and ensure the stable operation of the systems during the exploration of marine resources. This paper establishes a hardware-in-the-loop based marine resource exploration process control simulation and experiment system to simulate the effects of marine environmental factors on the offshore platform. The marine environment is simulated using JONSWAP spectra and real marine environment data. The dynamics of offshore platform is modeled using Lagrange's equation method. And the control effect is demonstrated using virtual reality technology. Ultimately, the dynamic positioning system control experiment, which uses the equivalent input disturbance method, validates the experimental system. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Hardware-in-the-loop simulation

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A Survey on IMU-and-Vision-based Human Pose Estimation for Rehabilitation 41

A Survey on IMU-and-Vision-based Human Pose Estimation for R...

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第41届中国控制会议

作者： Yuan Niu Jinhua She Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the rapid development of human pose estimation,the human-pose-estimation method applied in rehabilitation therapy has attracted more and more attention in today's aging *** them,inertial measurement units(IMU) and computer vision are two significant methods for human pose *** mainly pay attention to the application of these two methods in the rehabilitation therapy *** this paper,we first summarize the research status of IMU and computer vision in the field of rehabilitation ***,we find that combining the two methods can produce better ***,the main problems of the current research and possible development directions are identified.

关键词： Human Pose Estimation IMU Computer Vision Rehabilitation Therapy

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BECNN: Bias Field Estimation CNN Trained with a Dual Route Implicit Supervised Learning Strategy

BECNN: Bias Field Estimation CNN Trained with a Dual Route I...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Shuaizheng Feng, Chaolu Li, Wei Yang, Jinzhu Zhao, Dazhe Northeastern University School of Computer Science And Engineering Shenyang China Ministry of Education Northeastern University Key Laboratory of Intelligent Computing in Medical Image Shenyang China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China

ISBN: (纸本)9798350386226

Bias fields adversely affect various automatic analysis technologies. Therefore, bias field correction is essential. However, deep learning based methods encounter challenges in obtaining ground truth. Although existing methods attempt to address this problem by using training-free techniques or constructing datasets with approximations of the ground truth, the lack of task-oriented guidance, training instability, and inappropriate use of approximations still impact performance. Additionally, different approaches for bias field correction, i.e., estimating bias fields versus directly restoring clean images, exhibit different performances. However, there is no consensus on the best way, resulting in limited performance in some cases. To address these problems, we propose a bias field generation method to construct the dataset and provide task-oriented information. We then propose the concept of Equivalent of Residual Mapping (ERM) to analyze the advantages of estimating bias fields. According to ERM, we propose the Bias field Estimation Convolutional Neural Network (BECNN). Finally, we propose the Dual Route Implicit Supervised Learning (DRISL) strategy to balance the guidance derived from approximations of the ground truth with over-dependence on them. The proposed method is compared qualitatively and quantitatively with the correlated methods. Experiment results demonstrate that the proposed method performs effectively both on bias field estimation and correction. © 2024 IEEE.

关键词： Self-supervised learning

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Multi-model Coupled Marine Environment Model in Offshore Drilling Process 41

Multi-model Coupled Marine Environment Model in Offshore Dri...

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第41届中国控制会议

作者： Yujie Cao Sheng Du Chengda Lu Min Wu Jinhua She 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 Engineering Tokyo University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Safe and efficient deep-sea drilling is inseparable from accurate and timely recognition of the ocean and geological *** refers to ocean dynamics,meteorology,hydrology,ecology,chemistry,ocean sound,light physical characteristics,ocean geology and geography.A complex ocean environment is one of the challenges facing ocean engineering at *** is difficult to establish an independent ocean or geological model for controller of the deep-sea drilling *** the face of complex and changeable ocean environment,modeling and analysis of wave environmental factors alone is not suitable for the control of deep-sea drilling *** paper analyzes the influence of various marine environmental factors on the offshore drilling platform and studies the internal relationship between each ***,the models of ocean breeze,wave and ocean current were designed to realize the simulation of ocean environment and parameterized description of ocean environment for deep-sea drilling *** has laid the foundation for the design of a deep-sea drilling control system.

关键词： Marine environment model Offshore drilling process Multi-model coupled

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Research on path planning of mobile robot based on DWA-IMP-A*algorithm

Research on path planning of mobile robot based on DWA-IMP-A...

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第42届中国控制会议

作者： Zhi Liu Lin Zhang Shoukun Wang Tianwei Niu Yongkang Xu Jin Cao Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

Path planning is one of the most critical links in mobile robots. Its timeliness, security and accessibility are crucial to the development and wide application of mobile robots. However, in solving the problem of path planning, the most popular A* algorithm has some problems, such as heuristic function cannot be estimated accurately, node redundancy, path is not smooth, and obstacle avoidance cannot be achieved in real time. To solve these problems, A fusion algorithm of improved A* combined with reverse path and dynamic window method(DWA-IMP-A*) was proposed. The algorithm refines the heuristic function by incorporating the reverse path. The node optimization algorithm is used to further reduce the path length. The generated trajectories are smoothed by cubic spline interpolation. At the same time, it is integrated with the improved DWA algorithm to improve the efficiency and safety of robot path planning. The algorithm takes ROS mobile robot as the carrier and is tested under typical road conditions. Compared with A* algorithm, the planning time is reduced by 54.6% and the path length is reduced by 6.37%. Experimental results verify the effectiveness and robustness of the algorithm. The research results have certain reference significance for the path planning of various types of mobile robots and the research of driverless vehicles.

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Hand-Motion Intention Recognition Based on One-Dimensional Convolutional Neural Network 41

Hand-Motion Intention Recognition Based on One-Dimensional C...

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第41届中国控制会议

作者： Hao Wu Feng Wang Juan Zhao Jinhua She 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 Engineering Tokyo University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Surface electromyographic(sEMG) signals play an important role in human-computer interaction between patients and rehabilitation robots in rehabilitation *** paper shows the effectiveness of one-dimensional convolutional neural networks(1-D CNN) in estimating hand motion intentions from sEMG *** database 1 was employed to train the ***,the data from a single subject was used to train a model,and the accuracy was roughly maintained at 80%.Then,the whole dataset was used to train the network,and the recognition accuracy was improved to 84.16%.This shows that 1-D CNN effectively estimates hand movements and achieves high accuracy even for a small number of data.

关键词： Electromyographic hand motion intention Ninapro database one-dimensional convolutional neural network(1-D CNN)

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