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11th IEEE Data Driven control and Learning systems Conference, DDCLS 2022

作者： Han, Yu Chen, Yaran Li, Haoran Ma, Mingjun Zhao, Dongbin Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China University of Chinese Academy of Sciences Beijing100000 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In autonomous driving and mobile robotic systems, obtaining the depths of objects in real-time is crucial. The current network-based methods usually design complex network to achieve 3D object detection or monocular depth estimation for the whole image, resulting in too slow to be applied to mobile robots. The perspective projection-based method can achieve real-time, which calculates the object depth based on the camera parameters, the object sizes in the world coordinates and in image coordinates. While it relies heavily on the accuracy of object size in images coordinates, and the size is usually obtained with errors through detector network. Combining the perspective projection-based methods and network-based methods, we propose a fast object depth estimation method by designing a neural network to learn perspective projection, called Fast-Depth-NPP: 1) Instead of considering the whole image, we only consider the local depth of the image;2) Using local image patches as network inputs avoids measurement errors of object size with detector;3) the use of global information is enhanced by incorporating position encoding. Our method is validated on the mobile robot public dataset Neurons Perception dataset, achieving excellent results and meeting the real-time requirements. © 2022 IEEE.

关键词： Object detection

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Convection-UNet: A Deep Convolutional Neural Network for Convection Detection based on the Geo High-speed Imager of Fengyun-4B

Convection-UNet: A Deep Convolutional Neural Network for Con...

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Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA), International Conference on

作者： Yufei Wang Baihua Xiao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences & University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Deep convection can cause a variety of severe weather conditions such as thunderstorms, strong winds, and heavy rainfall. Satellite observations provide all-weather and multi-directional observations, facilitating the timely detection of such weather systems, which is crucial to saving lives and property. However, previous methods based on channel feature extraction and threshold filtering did not make full use of information in satellite images, which led to limitations on such complex problems as strong convection detection. In this study, we propose a novel framework of a deep learning-based model Convection-UNet to detect convection. We use channel 4 to 7 of FY-4B GHI that we select according to the microphysical properties of convection as input and radar reflectivity as label. We combine the detailed training time and test time data augmentation strategies and build a deep neural network to automatically extract spatial context features and achieve end-to-end learning. Results show that the performance of our method far exceeds the previous channel extraction combined with threshold filtering methods such as BT and BTD at least 0.24 on Fi-measure. We also show that our channel selection and data augmentation strategies are of great significance to detect convection.

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A LESO Based Backstepping controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of systems Science & complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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Distributed Event-Triggered Formation control for a Multi-robotic Fish System

Distributed Event-Triggered Formation Control for a Multi-ro...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Dai, Shijie Wu, Zhengxing Tan, Min Yu, Junzhi School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9789811966125

This paper investigates the formation control problem for a multi-robotic fish system and a distributed event-triggered-based formation control framework is proposed. The framework consists of a communication topology, a distributed formation control law, and a dynamic event-triggered communication mechanism. In particular, a position-based formation control law is presented to drive the multi-robotic fish system to an anticipated configuration based on local measurements while keeping pace with a moving target. Meanwhile, a dynamic event-triggered mechanism is utilized to measure importance of the interactive information and decide the communication timing. The proposed formation control method shows its merits to greatly reduce communication consumption with a limited loss of formation control performance. Finally, simulations with quantitative comparisons are presented to verify the effectiveness of the proposed framework. This formation control framework provides a solid foundation for future marine cooperative control of the multi-robotic fish system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Interval Type-2 Fuzzy Hierarchical Adaptive Cruise Following-control for Intelligent Vehicles

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1658-1672页

作者： Hong Mo Yinghui Meng Fei-Yue Wang Dongrui Wu the College of Electric and Information Engineering Changsha University of Science and TechnologyChangsha 410114China the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Qingdao Academy of Intelligent Industries Qingda 266109 the Center of China Economic and Social Security University of Chinese Academy of SciencesBeijing 100190China the Ministry of Education Key Laboratory on Image Information Processing and Intelligent Control the School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

Intelligent vehicles can effectively improve traffic congestion and road traffic *** cruise followingcontrol(ACFC)is a vital part of intelligent *** this paper,a new hierarchical vehicle-following control strategy is presented by synthesizing the variable time headway model,type-2 fuzzy control,feedforward+fuzzy proportion integration(PI)feedback(F+FPIF)control,and inverse longitudinal dynamics model of ***,a traditional variable time headway model is improved considering the acceleration of the lead ***,an interval type-2 fuzzy logic controller(IT2 FLC)is designed for the upper structure of the ACFC system to simulate the driver's operating *** reduce the nonlinear influence and improve the tracking accuracy for the desired acceleration,the control strategy of F+FPIF is given for the lower control ***,the lower control method proposed in this paper is compared with the fuzzy PI control and the traditional method(no lower controller for tracking desired acceleration)***,the proportion integration differentiation(PID),linear quadratic regulator(LQR),subsection function control(SFC)and type-1 fuzzy logic control(T1 FLC)are respectively compared with the IT2 FLC in control performance under different ***,the simulation results show the effectiveness of IT2 FLC for the upper structure and F+FPIF control for the lower structure.

关键词： Adaptive cruise following-control(ACFC) feedforward+fuzzy proportion integration(PI)feedback(F+FPIF) type-2 fuzzy control variable time headway

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Distributed Tracking control of Nonlinear Multi-agent systems Against False Data Injection Attacks 41

Distributed Tracking Control of Nonlinear Multi-agent System...

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第41届中国控制会议

作者： Yanhui Zhang Jian Sun Gang Wang Yong Xu the School of Automation and the Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology the Beijing Institute of Technology Chongqing Innovation Center IEEE

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,the distributed resilient tracking control problem is investigated for nonlinear multi-agent systems with false data injection(FDI) attacks and unknown *** FDI attacks are considered in communication network between ***,the neighbor's information received by the agent may be wrong due to the attacks,which will affect tracking consensus ***,the approximate property of fuzzy logic systems are utilized to estimate unknown nonlinear *** backstepping framework,we design a distributed resilient control scheme under asymmetric *** asymmetric saturation nonlinearity,an auxiliary system is constructed to simplify designed progress of the *** on Lyapunov stability theory,it is proved that all signals in the closed-loop system are semi-globally uniformly bounded,and the followers' outputs are driven to a neighborhood near the output of ***,simulation results are presented to illustrate the effectiveness of the proposed method.

关键词： Adaptive resilient control nonlinear MASs FDI attacks asymmetric saturation

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Lane-Change Intention Prediction of Surrounding Vehicles Using BiLSTM-CRF Models with Rule Embedding

Lane-Change Intention Prediction of Surrounding Vehicles Usi...

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2022 Chinese automation Congress, CAC 2022

作者： Wang, Kai Hou, Jie Zeng, Xianlin School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing100081 China

ISBN: (纸本)9781665465335

Predicting lane-change intentions of surrounding vehicles can effectively help autonomous vehicles reduce collisions caused by lane changes and ensure driving safety. Because prediction methods based on black-box models will lead to passengers' distrust of machine prediction, the intention prediction methods used to autonomous driving need to be interpretable and trustworthy. This paper presents a method for intention prediction of surrounding vehicles by using a bidirectional long short term memory network (BiLSTM) with a conditional random field (CRF) layer above it. Compared with intention prediction methods using deep network, the proposed method can find the features that contribute most to the prediction, thereby improving the interpretability and ensuring the prediction performance. In addition, by employing the transfer characteristic of the CRF layer, traffic rules and the experience of skilled drivers can be embedded to the prediction in the form of rules. Use rules to constrain the intention prediction, thereby improving the trustworthiness of prediction results. Test results on naturalistic driving dataset show that the proposed method can predict the lane-change intention with an accuracy of 97.22%, which is higher than that of Bi-LSTM. © 2022 IEEE.

关键词： Embeddings

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Composite Model Predictive control for PMSM Servo systems Based on Backlash Cancellation and Disturbance Compensation 41

Composite Model Predictive Control for PMSM Servo Systems Ba...

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第41届中国控制会议

作者： Lei Li Zuo Wang Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Shenzhen Research Institute Southeast University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper investigates the speed regulation problem of permanent magnet synchronous motor(PMSM) servo system with backlash based on model predictive control(MPC).It is known that the existing backlash in servo systems causes steady-state errors,limit cycles and rigid *** order to counteract the adverse performance degradations of the backlash nonlinearity,a composite MPC strategy is investigated based on backlash cancellation and disturbance ***,an approximated dead-zone model of backlash is ***,a generalized proportional-integral observer(GPIO) is constructed to estimate the lumped disturbances,including backlash modelling residual error,parameter uncertainties and external *** introducing disturbance estimation and backlash transmission torque into system model,a composite MPC can be obtained based on the prediction-precision enhanced model,which brings significantly improvements to control ***,the efficacy of the presented method is demonstrated by simulation results.

关键词： Model predictive control(MPC) PMSM servo system Backlash cancellation Generalized proportional-integral observer(GPIO)

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Generalized Proportional Integral Observer Based Sliding Mode control for Biaxial Motion control System with Mismatched Disturbances 41

Generalized Proportional Integral Observer Based Sliding Mod...

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第41届中国控制会议

作者： Guancheng Ren Zuo Wang Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex systems of Engineering Ministry of Education Shenzhen Research Institute Southeast University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In the biaxial motion system,nonlinearities and mismatched disturbances always have adverse effects on the contouring accuracy,which are often *** of existing advanced control methods are developed only to suppress matched *** this paper,the necessity of taking the contour error as the control target is ***,four generalized proportional integral observers(GPIOs) are developed to estimate both matched and mismatched lumped ***,based on contour error,a sliding mode controller is developed for the biaxial motion *** introducing the disturbance estimations into the modified sliding surface design,a composite controller is *** proposed method effectively improves the contouring accuracy and suppress the mismatched disturbances,while the chattering caused by large switching gains is *** stability analysis of the system is established by means of the Lyapunov ***,to demonstrate the superiority and effectiveness of the controller,simulation results of the biaxial motion control system are presented in various working cases.

关键词： Biaxial motion control system contouring control mismatched disturbances generalized proportional-integral observer(GPIO) sliding mode control chattering reduction

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A Novel Hybrid Transformer-Based Framework for Solar Irradiance Forecasting under Incomplete Data Scenarios

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IEEE Transactions on Industrial Informatics 2024年第6期20卷 8605-8615页

作者： Zhang, Hanjin Li, Bin Su, Shun-Feng Yang, Wankou Xie, Liping Southeast University School of Economics and Management Nanjing210096 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518063 China

Accurate prediction of solar irradiance is crucial for the effective utilization of solar energy. However, in real-world scenarios, complex irradiance patterns and prevalent incomplete data pose challenges to precise forecasting, resulting in additional uncertainties and instability. To address these issues, this study proposes a novel irradiance forecasting model that integrates a Mask-Transformer data imputation module and a prediction module centered around the typical patterns representation mechanism. The Mask-Transformer leverages a mask modeling mechanism to model the context of missing data, facilitating accurate estimation of missing values and reducing noise and uncertainty in the input data. The typical patterns representation mechanism comprises a series decomposition module and a feature fusion module, providing the module with the capability to mitigate nonlinearity and nonstationarity in solar irradiance data. This enhancement leads to improved short-term forecasting performance while maintaining long-term forecasting capabilities. Experimental results on two datasets demonstrate that the proposed model exhibits sufficient robustness and accuracy, making it effective in scenarios with incomplete data. © 2005-2012 IEEE.

关键词： Forecasting

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