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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Zheng, Xiao-Yi Yan, Hong-Sen Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University School of Automation Nanjing210096 China

ISBN: (纸本)9798350320831

For stochastic nonlinear systems with input saturation, few of the existing control methods use a suitable auxiliary system to solve input saturation problem, and most of them are for deterministic systems. In this study, according to the backstepping approach and the Lyapunov stability theorem, utilizing multi-dimensional Taylor network (MTN), an adaptive controller is presented to secure that all closed-loop signals keep bounded in probability, in which MTN is implemented to approach the uncertain function during the controller design. Moreover, the impact raised by input saturation is mitigated by designing an appropriate auxiliary system, in which the order of the auxiliary system is the same as that of the controlled system. Furthermore, unlike the majority of prior findings, the final output signal could be constrained in a mean quartic sense by an explicit expression of the input saturation error (the difference between the control signal and the saturation input) and the parameters used for design. Finally, the efficiency of the suggested control methodology is confirmed by providing an example. © 2023 IEEE.

关键词： Stochastic systems

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Composite-mask GAN based on refined optical flow and disparity map for SLAM visual odometry

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Optoelectronics Letters 2025年

作者： JI Yuehui JIANG Jingwei LIU Junjie SONG Yu GAO Qiang School of Electrical Engineering and Automation， Tianjin University of Technology Tianjin Key Laboratory of Control Theory and Application for Complex Systems

Although deep learning methods have been widely applied in slam visual odometry over the past decade with impressive improvements， the accuracy remains limited in complex dynamic environments. In this paper， a composite mask-based generative adversarial network is introduced to predict camera motion and binocular depth maps. Specifically， a perceptual generator is constructed to obtain the corresponding parallax map and optical flow from between two neighboring frames. Then， an iterative pose improvement strategy is proposed to improve the accuracy of pose estimation. Finally， a composite mask is embedded in the discriminator to sense structural deformation in the synthesized virtual image， thereby increasing the overall structural constraints of the network model， improving the accuracy of camera pose estimation， and reducing drift issues in the Visual Odometer. Detailed quantitative and qualitative evaluations on the KITTI dataset show that the proposed framework outperforms existing conventional， supervised learning and unsupervised depth VO methods， providing better results in both pose estimation and depth estimation.

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LSTM-TD3-based control for delayed drone combat strategies 19th

LSTM-TD3-based control for delayed drone combat strategies

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19th Chinese Intelligent systems Conference, CISC 2023

作者： Ji, Bingyu Wang, Jun Zhang, Hailin Zhang, Ya School of Automation Southeast University Nanjing210096 China Key Laboratory Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9789819968817

Reinforcement learning has made great achievements in the field of game confrontation, and military rendition intelligence is also imminent. In this paper, we propose a game confrontation game model based on LSTM and deep reinforcement learning for the red and blue sides of the drones in the delayed *** action buffer pair is added to the original battlefield environment to create a large latency environment, a TD3 algorithm is designed to join LSTM to predict the state trend, and a historical data pool is built using PER. The training results in the simulation platform show that the model has a significant improvement in effectiveness in the large latency environment, with a 0.22 improvement in battle damage ratio and a 34.76% improvement in win rate over the original model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Reinforcement learning

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Two Degree of Freedom Active Disturbance Rejection control Strategy for Time Delay systems 42

Two Degree of Freedom Active Disturbance Rejection Control S...

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42nd Chinese control Conference, CCC 2023

作者： Shan, Xiyuan Cai, Tao School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

ISBN: (纸本)9789887581543

This paper is concerned with the controller design and the theoretical analysis for time-delay systems, a two degree of freedom (feedforward and feedback) control method is proposed, which combines advantages of the Smith predictor and the active disturbance rejection control (ADRC). The feedforward part of controller is used to track the set point, the feedback part of controller (ADRC) is used to suppress interferences and the Smith predictor is used to correct time delay. The proposed control design is easy to tune parameters and has been proved to effectively control systems with large time delay. The bounded input bounded output (BIBO) stability of closed-loop system is verified. Finally, numerical simulations show the effectiveness and practicality of the proposed design. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Time delay

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Detection and Positioning of Workpiece Grinding Area in Dark Scenes with Large Exposure 1

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Guo, Zhentao Zhao, Guiyu Bian, Jinyue Ma, Hongbin School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

Workpiece grinding is a crucial process in the smart manufacturing chain. In order to meet the requirements of industrial precision and relieve heavy work, researchers have developed a vision-based grinding robot. However, the problem of workpiece grinding area detection and positioning is difficult to be solved in dark scenes with large exposure. This paper proposes a method that fuses technologies such as processing of the image, coordinate and point cloud, which can accurately detect and locate the workpiece grinding area. Firstly, A method based on YOLOv7 and improved image preprocessing is used to detect labels of the grinding area. Secondly, A model for the prediction of the coordinates based on multiple linear regression was used to predict the coordinates of missing labels for the same grinding area. Finally, Data processing of the point cloud and transformation of the system of coordinates are used to achieve the acquisition of the coordinates of positioning vertices in the grinding area and conversion from the camera coordinate system to the world coordinate system. We used several sets of data for evaluation in our experiments, and the experimental results show that our proposed method can effectively detect the workpiece grinding area. At the same time, our method can also predict the coordinates of missing labels, which provides a more stable and reliable guarantee for industrial production. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Multiple linear regression

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CFSeRec: A Contrastive Framework for Sequential Recommendation 42

CFSeRec: A Contrastive Framework for Sequential Recommendati...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Tong Dai, Yaping Shao, Shuai School of Automation Beijing Institute of Technology Beijing100081 China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

ISBN: (纸本)9789887581543

In sequential recommender systems, the main problems are the long-tailed distribution of data and noise interference. A Contrastive Framework for Sequential Recommendation (CFSeRec) is proposed to solve these two problems respectively. Token shuffling and adversarial attack data augmentation methods are used in the framework to improve the quality and quantity of training data, so that the long-tailed problem is mitigated. Through the application of projection head method, the sequence representation becomes more general and robust, rather than just adapted to the task of contrastive learning. Therefore, the impact of noise on sequence recommender systems is effectively alleviated. Experiments on four public datasets show that CFSeRec achieves state-of-the-art performance in the metrics of hit ratio and normalized discounted cumulative gain, when comparing to the seven previous frameworks. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Recommender systems

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Fault diagnosis of bearings based on deep separable convolutional neural network and spatial dropout

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Chinese Journal of Aeronautics 2022年第10期35卷 301-312页

作者： Jiqiang ZHANG Xiangwei KONG Xueyi LI Zhiyong HU Liu CHENG Mingzhu YU School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of EducationNortheastern UniversityShenyang 110819China Liaoning Province Key Laboratory of Multidisciplinary Design Optimization of Complex Equipment Northeastern UniversityShenyang 110819China Angang Steel Company Limited Anshan 114021China

Bearing pitting,one of the common faults in mechanical systems,is a research hotspot in both academia and *** fault diagnosis methods for bearings are based on manual experience with low diagnostic *** study proposes a novel bearing fault diagnosis method based on deep separable convolution and spatial dropout *** separable convolution extracts features from the raw bearing vibration signals,during which a 3×1 convolutional kernel with a one-step size selects effective features by adjusting its *** similarity pruning process of the channel convolution and point convolution can reduce the number of parameters and calculation quantities by evaluating the size of the weights and removing the feature maps of smaller *** spatial dropout regularization method focuses on bearing signal fault features,improving the independence between the bearing signal features and enhancing the robustness of the model.A batch normalization algorithm is added to the convolutional layer for gradient explosion control and network stability *** validate the effectiveness of the proposed method,we collect raw vibration signals from bearings in eight different health *** experimental results show that the proposed method can effectively distinguish different pitting faults in the bearings with a better accuracy than that of other typical deep learning methods.

关键词： Batch normalization Convolutional neural network Fault diagnosis Similarity pruning Spatial dropout

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Non-Smooth Speed control of Switched Reluctance Motor Based on Generalized Proportional Integral Observer 42

Non-Smooth Speed Control of Switched Reluctance Motor Based ...

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42nd Chinese control Conference, CCC 2023

作者： Yin, Qingzhao Tian, Zheng Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9789887581543

This paper investigates the speed regulation control of switched reluctance motor (SRM) systems. To improve the antidisturbance performance of SRM, a composite non-smooth control strategy is proposed. First, the structure of SRM is analyzed, and a simplified nonlinear model is obtained based on a segmented representation of the varying phase inductance. A virtual control function is introduced to represent the nonlinear part of the torque equation, whose inverse function is cascaded to linearized the nonlinear model. Second, a generalized proportional integral observer (GPIO) is constructed to estimate the lumped disturbance of the system, which is used for feedforward compensation design. Finally, a composite speed controller is designed based on a combination of finite time proportional feedback and feed-forward compensation based on GPIO (FTP+GPIO). The speed error closed-loop system can be regarded as a first-order finite time control system with bounded disturbances. Strict analysis shows that the proposed scheme can improve the anti-disturbance performance of the closed-loop system. The effectiveness of the proposed method is verified by simulation results. Moreover, it is compared with proportional feedback combining feed-forward compensation (P+GPIO) method and proportional integral (PI) control method. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Closed loop systems

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Stability Analysis of Load Frequency control System with Time-Varying Delays

Stability Analysis of Load Frequency Control System with Tim...

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2023 China Automation Congress, CAC 2023

作者： Fan, Chunling Chen, Chaoyang Zhou, Shaowu Lian, Honghai Chen, Zuguo Li, Pei School of Information and Electrical Engineering Hunan Provincial Key Laboratory of Intelligent Control and Maintenance of Complex Systems Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798350303759

In this paper, the stability analysis of Load frequency control (LFC) systems with time-varying delay is conducted. Firstly, an augmented Lyapunov-Krasovskii (L-K) functional is designed to incorporate the relevant information of the delay state. Then, the functional derivatives are estimated using an auxiliary-function-based integral inequality in conjunction with an improved reciprocally convex inequality. Furthermore, the correlation function is processed through a quadratic function negative definite derivation. Finally, case studies established on a one-area LFC system are conducted to demonstrate the superiority of the method, and its accuracy is verified through a simulation case. © 2023 IEEE.

关键词： Electric control equipment

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Conservative-Progressive Collaborative Learning for Semi-Supervised Semantic Segmentation

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IEEE Transactions on Image Processing 2023年 32卷 6183-6194页

作者： Fan, Siqi Zhu, Fenghua Feng, Zunlei Lv, Yisheng Song, Mingli Wang, Fei-Yue Institute of Automation State Key Laboratory of Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China Tsinghua University Beijing100190 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China

Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels. Addressing that, we propose a novel learning approach, called Conservative-Progressive Collaborative Learning (CPCL), among which two predictive networks are trained in parallel, and the pseudo supervision is implemented based on both the agreement and disagreement of the two predictions. One network seeks common ground via intersection supervision and is supervised by the high-quality labels to ensure a more reliable supervision, while the other network reserves differences via union supervision and is supervised by all the pseudo labels to keep exploring with curiosity. Thus, the collaboration of conservative evolution and progressive exploration can be achieved. To reduce the influences of the suspicious pseudo labels, the loss is dynamic re-weighted according to the prediction confidence. Extensive experiments demonstrate that CPCL achieves state-of-the-art performance for semi-supervised semantic segmentation. © 1992-2012 IEEE.

关键词： Semantic Segmentation

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