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8th International Conference on Electrical, Mechanical and Computer engineering, ICEMCE 2024

作者： Li, Wenyu Cao, Shun Liu, Xianda Wang, Minghao Sheng, Shuan Shenyang University of Chemical Technology School of Information Engineering Liaoning Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control System Liaoning Shenyang China Shenyang National Research Institute of Intelligent Robots Co. Ltd. Liaoning China

ISBN: (纸本)9798331506230

Indoor distance measurement plays a critical role in positioning services. The complexity of indoor environments, coupled with a limited and small sample size, results in suboptimal distance accuracy. This paper proposes a method for non-line-of-sight distance measurement based on a gradient boosting tree model, effectively addressing the issue of sample size dependency. The random hyperparameter settings of the gradient boosting tree model lead to prolonged fitting times. Additionally, the model utilizes a fixed learning rate, which limits the analysis of features in small samples. To tackle these concerns, we develop a grid optimization algorithm that identifies the best hyperparameter combinations through a hyperparameter grid search. We also implement a weighted value approach and a cosine annealing algorithm to precisely update the learning rate according to training conditions, thus maximizing the learning of small sample features. Our proposed model significantly enhances distance accuracy in non-line-of-sight environments under constraints of training sample size and variety. Comparative experiments demonstrate that our model improves distance accuracy by 63% relative to other machine learning models, with a mean square error of only 0.168m. © 2024 IEEE.

关键词： Distance measurement

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Research on geomembrane leakage detection method based on quadruped robot

Research on geomembrane leakage detection method based on qu...

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2024 China automation Congress, CAC 2024

作者： Zhang, Pengcheng Hu, Ningning Wang, Xiao Xi, Qingbiao Zhao, Haohao Wu, Xingchen Qin, Linfeng Yuan, Dongli Investigation & Testing Research Institute Shanghai Investigation Design & Research Institute Co. Ltd. Shanghai China System Department 2 Xi'an ASN Technology Group Co. Ltd Xi'an China School of Electronic and Control Engineering Chang'an University Xi'an China School of Automation Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368604

Aiming at the problem of leakage detection of seepage control geomembranes in landfills, a fully autonomous dipole method detection system based on a quadruped robot equipped with a dipole sensor is designed. The feasibility and superiority of using a quadruped robot are analyzed on the basis of the detection principle of the dipole method. Due to the problems existing in the process of upgrading the artificial static detection to autonomous dynamic detection, a dynamic detection method based on a quadruped robot is designed while the structure construction of the dipole method on the quadruped robot is completed. The experimental results show that the efficiency of geomembrane leakage detection can be improved with detection system method employed in this paper. © 2024 IEEE.

关键词： Geomembranes

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive control For Tractor-trailer Trajectory Tracking With Unknown Parameters

K-BMPC: Derivative-based Koopman Bilinear Model Predictive C...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Zehao Wang Han Zhang Jingchuan Wang Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC.

关键词： Trajectory tracking Computational modeling Estimation Predictive models control systems Data models Nonlinear dynamical systems

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An OpenPLC-based Active Real-time Anomaly Detection Framework for Industrial control systems

An OpenPLC-based Active Real-time Anomaly Detection Framewor...

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2022 Chinese automation Congress, CAC 2022

作者： Zheng, Chengxu Wang, Xiaopeng Luo, Xiaoyu Fang, Chongrong He, Jianping Shanghai Jiao Tong University The Key Laboratory of System Control and Information Processing Ministry of Education of China And Shanghai Engineering Research Center of Intelligent Control and Management The Dept. of Automation Shanghai China NSFOCUS Technologies Group Co. Ltd Beijing China

ISBN: (纸本)9781665465335

In recent years, the design of anomaly detectors has attracted a tremendous surge of interest due to security issues in industrial control systems (ICS). Restricted by hardware resources, most anomaly detectors can only be deployed at the remote monitoring ends, far away from the control sites, which brings potential threats to anomaly detection. In this paper, we propose an active real-time anomaly detection framework deployed in the controller of OpenPLC, which is a standardized open-source PLC and has high scalability. Specifically, we add adaptive active noises to control signals, and then identify a linear dynamic system model of the plant offline and implement it in the controller. Finally, we design two filters to process the estimated residuals based on the obtained model and use χ2 detector for anomaly detection. Extensive experiments are conducted on an industrial control virtual platform to show the effectiveness of the proposed detection framework. © 2022 IEEE.

关键词： Anomaly detection

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Minimizing Epistemic Uncertainty for Predictive control of General Anesthesia

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IFAC-PapersOnLine 2024年第24期58卷 490-495页

作者： Clara M. Ionescu Robin De Keyser Dana Copot Erhan Yumuk Isabela R. Birs Erwin Hegedus Cristina I. Muresan Martine Neckebroek Ghent University Department of Electromechanics System and Metal Engineering Ghent Belgium Technical University Cluj-Napoca Department of Automation Memorandumului Street 20 Cluj Romania Istanbul Technical University Department of Control and Automation Engineering Maslak 34469 Istanbul Turkey Ghent University Hospital Department of Anesthesia Corneel Heymanslaan 10 Ghent 9000 Belgium

Epistemic uncertainty refers to the lack of information from knowledge and in medical field it is often paired with decision making situations and systems largely dependent on context and expertise. Our approach is uncertainty-centric and digitalizes knowledge with high certainty level, in order to improve the efficacy of knowledge-driven decision support system for anesthesia regulation. We propose to digitize some of surgical actions as part of minimizing uncertainty in closed loop control. Notable disturbances are those induced by the surgery actions, but they abide clinical protocols and semantic transformation enables their use in computers. We introduce a predictive control methodology to accommodate the augmented information of the system and evaluate its impact on the closed loop performance. Simulations of closed loop control of anesthesia for various surgery protocols indicate a high relevance and applicability of proposed approach. The proposed control structure allows the use of medical semantics in decision making process of anesthesia regulation.

关键词： closed loop control of anesthesia digitalized disturbance predictive control human in the loop medical semantics decision making systems epistemic uncertainty

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Data selection strategy in data-driven model predictive control of uncertain systems

Data selection strategy in data-driven model predictive cont...

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第34届中国过程控制会议

作者： Lingyi Yang dewei Li Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai Engineering Research Center of Intelligent Control and Management

In this paper,a novel data-driven methodology for MPC with data selection strategies is *** novel selection strategy involves two ***,we check the similarity between the current data and the past data items already existing in the data *** enables the controller to pick a set of informative data items which contain sufficient information on each direction of the signal ***,for the data items within the data oracle,we integrate multiple similar data vectors into a single vector to cut down amount of *** simulation examples displays the upgraded performance and smaller amount of calculation of selection strategies.

关键词：

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Smart Plant Application of Autonomous Decentralized systems with the Introduction of Edge Nodes 15

Smart Plant Application of Autonomous Decentralized Systems ...

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15th International Conference on Advanced Computer Theory and engineering, ICACTE 2022

作者： Wang, Jingyang Zou, Tao Yang, Zhijia Wang, Hongrui Key Laboratory Of Networked Control System Institutes For Robotics And Intelligent Manufacturing Shenyang Institute Of Automation Chinese Academy Of Sciences Shenyang China Guangzhou University College Of Mechanical And Electrical Engineering Guangzhou China State Key Laboratory Of Robotics Key Laboratory Of Networked Control System Institutes For Robotics And Intelligent Manufacturing Shenyang Institute Of Automation Chinese Academy Of Sciences Shenyang China University Of Chinese Academy Of Sciences Beijing China

ISBN: (数字)9781665481809

ISBN: (纸本)9781665481809

This paper illustrates the application of an autonomous decentralized system introducing edge nodes in a smart factory. The nodes include atomic nodes, the most fundamental autonomic units constituting an autonomous decentralized system that are capable of independently extracting information from the data field and processing that information internally and simultaneously transmitting the processing results and other internal information to the data field proactively in a broadcast mode, where the transmitted information circulates in the data field. The group-based data field enables the sharing of information by atomic nodes. Grouped management nodes are the managers of the whole group, and edge nodes are the terminal intelligent control units of an intelligent autonomous decentralized system. Improvements in system decentralization, fault tolerance characteristics and flexibility of the system can lead to homogenization, autonomous control and autonomous coordination in complex manufacturing environments, as well as excellent online fault tolerance, online expansion and online maintenance of the system. In addition, it enhances the ever-changing and evolving control requirements by deconstructing the complexity in the system. © 2022 IEEE.

关键词： Fault tolerance

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NBA-guided heuristic sampling algorithm for multi-robot task planning under temporal logic

NBA-guided heuristic sampling algorithm for multi-robot task...

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第43届中国控制会议

作者： Jiong Luo Yongzhao Hua Shunfu Ding Xiwang Dong Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang University Institute of Artificial Intelligence Beihang University Institute of Unmanned System Beihang University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Aiming at the task planning problem of multi-robot under linear temporal logic(LTL), this paper proposes an improved sampling planning algorithm based on Nondeterministic Büchi automaton(NBA) guidance. Firstly, this paper defines a kind of LTL task planning problem, which mainly focuses on the meaningful key nodes in the task execution of each robot, and there are many sequential dependencies of task constraints. Then, an improved sampling algorithm framework is proposed,which uses a more efficient way to build a search tree and reduces the number of nodes in the tree. Furthermore, the sampling points are guided by NBA to ensure that sampling is conducive to the advancement of time-series tasks. And considering the task feasible region of each robot, the auction algorithm is introduced to optimize the local matching relationship, which greatly improves the quality of sampling points. Finally, numerical simulation is carried out to solve the LTL task planning problem. The optimality and stability of the algorithm are obviously improved, and the time consumption of this algorithm is obviously better than that of the existing advanced algorithms.

关键词： Multi-robot systems sampling-based planning temporal logic task planning NBA-guided

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Cooperative control Based on Distributed Model Predictive control Method for Urban Rail Transit

Cooperative Control Based on Distributed Model Predictive Co...

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IEEE International Conference on Networking, Sensing and control

作者： Jiahui Lv Jidong Lv Hongjie Liu Zhengwei Luo Bingyan Zhou Qianying Ma School of Automation and Intelligence (Beijing Jiaotong University) Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Focused on the challenge of train's high-density tracking control for urban rail transit, a cooperative control method based on Distributed Model Predictive control (DMPC) is studied. Firstly, the force of the train is analyzed and the state space model of train operation is established. Upon this foundational framework, the control objectives of the train control system and corresponding control strategies are proposed. Secondly, to ensure the safety of train operations, the DMPC is used to design a cooperative train consistency control algorithm that considers the dynamic parameters such as the position, speed, and acceleration of the front train. The stability of the train control system is analyzed and verified by the Lyapunov stability theorem. Finally, combined with the west section of Beijing Metro Line No. 11, the proposed method is simulated and verified. The experimental results show that the proposed train cooperative control method can ensure the safe operation of trains, and make the state of trains consistent and have good synchronization. Compared with the following control method, the cooperative control method can reduce the train spacing and speed difference, increasing by 85% and 76% respectively.

关键词： Rails State feedback Heuristic algorithms Simulation Stability analysis Sensors Safety Synchronization Predictive control Lyapunov methods

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Fault Diagnosis Method for On-Board Interface Equipment of CTCS-3 Based on Temporal Knowledge Graph Completion

Fault Diagnosis Method for On-Board Interface Equipment of C...

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IEEE International Conference on Networking, Sensing and control

作者： Bingyan Zhou Daqian Zhang Jidong Lv Hongjie Liu Zixu Zhao Zhengwei Luo School of Automation and Intelligence Beijing Jiaotong University Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Chinese Train control system level 3 (CTCS-3) train control onboard equipment plays a crucial role in ensuring train safety and improving operational efficiency. And the on-board interface equipment enables the interaction between the on-board Automatic Train Protection (ATP), ground equipment, drivers, and trains. While its fault accounts for a relatively high proportion of faults, therefore, this paper proposed a fault diagnosis method for on-board interface equipment based on temporal knowledge graph completion. Firstly, this method introduces temporal series to integrate travel logs and fault statistics, which extract fault phenomena and perform entity alignment, construct the temporal knowledge graph. Secondly, on this basis, we constructed a fault diagnosis network based on knowledge graph completion, which incorporates the Temporal-Translating Embedding (T - TransE) vectorization algorithm, Bidirectional Long Short-Term Memory (Bi-LSTM), and Self-Attention (SA) mechanism for temporal feature extraction. Finally, the T - TransE vectorization model was pretrained using the on-board interface equipment fault data from a railroad station in recent years for selecting the most effective temporal integration method. In order to validate the superiority of the proposed fault diagnosis method and the effectiveness of the data combination method, the diagnostic network without data combination and temporal relationship introduction, and other common fault diagnostic networks, were tested using the on -board fault data. Compared with the following fault diagnosis methods, the model proposed in this paper achieved the highest fault diagnostic accuracy of 96.8%.

关键词： Fault diagnosis Accuracy Knowledge graphs Feature extraction control systems Data models Sensors Protection Long short term memory

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