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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhang, Hehui Wang, Zhu Wang, Shao Xian Department of Automation Beijing102249 China

ISBN: (纸本)9798350361674

Advanced Process control is frequently applied in industrial chemical processes, where Dynamic Matrix control is widely employed for its capability to handle complex optimization control problems with multiple variables and constraints. However, during prolonged operation of industrial processes, factors such as changes in operating conditions and external environmental variations may lead to a mismatch between the predictive model and the actual plant in DMC. To address the Model-plant Mismatch, this paper proposes a dynamic trajectory rapid intelligent optimization method based on online reconstruction of the predictive model. Constructing the system model based on the known linear dynamic model of the control loop and PID parameters, the new dynamic matrix is obtained using the closed-loop step response of the system model. Then, updating the prediction model online and simultaneously updating the optimization-constrained conditions in real time ensures the long-term effectiveness of the advanced control scheme. To rapidly solve the optimization objective, this paper utilizes an improved GA-PSO algorithm to compute the Set Point trajectory of the control loop. Finally, this study conducted simulations by integrating Honeywell's UniSim Design simulation platform with the EPKS DCS control system, verifying the effectiveness of dynamic matrix online updating and demonstrating the long-term effectiveness of the advanced control scheme. © 2024 ieee.

关键词： Constrained optimization

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Implementation of Artificial Intelligence-Based Fault Classification and Anomaly Detection: A Case Study on Hydraulic Centrifugal Pumps 19

Implementation of Artificial Intelligence-Based Fault Classi...

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ieee 19th conference on Industrial Electronics and Applications (ICIEA)

作者： Turk, Mehmet Can Kazemi, Zahra Andersen, Peter Rindom Lemming, Jakob Larsen, Peter Gorm Aarhus Univ Dept Elect & Comp Engn DK-8200 Aarhus N Denmark Landia AS R&D Dept DK-6940 Lem St Denmark

ISBN: (纸本)9798350360875;9798350360868

Monitoring systems have been used in hydraulic centrifugal pumps for fault detection and predictive maintenance, which are critical to prevent costly unplanned shutdowns. Machine learning-driven monitoring systems that are based on pump vibration data have widely become popular in this context. The success of these techniques crucially depends on the quality of the vibration data received from the pump data acquisition system. In this regard, the paper first implements a proof-of-concept custom data acquisition unit mountable on the centrifugal pumps to log data related to the vibration, pressure, voltage, current, and temperature. Second, advanced machine learning techniques including Random Forest (RF), Multi-Layer Perceptron Classifier (MLPClassifier), Autoencoders (AEs), and Voting Classifier (VC) are applied to detect anomalies and classify 11 known faults. Performance metrics of the models are compared based on several tests on different experimental datasets. The accuracy of the implemented system has ranged from 98.5% to 100% regarding the classification of faults and detection of anomalies.

关键词： Anomaly Detection Deep learning Fault Classification Hydraulic Pump Machine learning Monitoring System

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Establishment and Analysis of Passive Localization Model for a Class of Unmanned Aerial Vehicle systems 13

Establishment and Analysis of Passive Localization Model for...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Yu, Liwen Yang, Qianru Li, Yuqian Zhang, Ruikun School of Mathematics and Physics Qingdao University of Science and Technology Qingdao266061 China

ISBN: (纸本)9798350361674

In order to avoid the interference of external magnetic field, UAV swarms should keep electromagnetic silence while flying, and using pure azimuth passive localization to adjust the position of UAVs is an effective method. The pure azimuth passive localization problem for UAVs includes two scenarios: known and unknown positions and quantities of UAVs emitting signals. For the case that the number and position of the three UAVs transmitting signals are known, a localization model based on the sine-cosine theorem, iteration and other methods for passively receiving signal UAVs is established for adjusting the position of the UAVs in order to realize the effective adjustment of the position of the UAVs. For the case that the number and quantity of some UAVs transmitting signals are unknown, this paper proposes an effective localization model of UAVs with a slight deviation of a certain position based on the polar coordinate localization method and the characteristics of the hidden circle model. Meanwhile, the relevant properties of the sine-cosine function are utilized to determine the uniqueness of the solution. Finally, the accuracy of the model is verified by numerical simulation. The results show that the theoretical credibility of the method proposed in this paper is strong and provides a more reliable theoretical support for the research in the field of UAV technology. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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learning Input driven Dynamic Bayesian Networks with Measurement Noise 23

Learning Input Driven Dynamic Bayesian Networks with Measure...

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23rd ieee International conference on Machine learning and Applications, ICMLA 2024

作者： Veres, David Li, Ping Kadirkamanathan, Visakan The University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798350374889

Dynamic Bayesian Networks (DBNs) are useful tools for modelling complex systems whose network representations can be elicited a priori or learnt from data. In this paper, a maximum likelihood Doubly-Iterative Expectation Maximization (DI-EM) Algorithm is developed for the identification of grey-box ARMAX state-space model representations of DBNs involving known, noisy measurement processes. The grey-box model incorporates network dependencies among time series variables and exploits time series data of low longitudinal and high cross-sectional dimensions. A network learning procedure is developed using a score-based structure-selection method to find the underlying network of an input-driven dynamical system. By computing a finite data version of the Bayesian Information Criterion (BIC) for small sample sizes, the proposed method's performance is investigated on simulated and real-world data. The algorithm recovers the underlying ground-truth networks of simulated systems under finite data criteria with Jaccard Coefficient values of up to 0.84, and selects structures with improved weighted mean-squared error loss over a baseline black-box model fit on real-world data. © 2024 ieee.

关键词： Expectation maximization algorithm

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MDFF: A Multilevel Deep Fusion Framework of Multisource Heterogeneous data for FeO Content Prediction in Sintering Process

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ieee TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Wei, Shiyi Ye, Chenxin Zhao, Yuchen Zhang, Xinmin Song, Zhihuan Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

As the key quality indicator in the iron ore sintering process, the ferrous oxide (FeO) content in the finished sinter directly reflects its thermal state and denotes sintering quality. Currently, data-driven soft sensor modeling technology has become the mainstream method for predicting this quality indicator. However, existing approaches either fail to leverage the multimodal properties of the sintering dataset or simply neglect to explore the interrelationships between multisource heterogeneous data, thus limiting the improvement of prediction accuracy. To address this issue, we propose a multilevel deep fusion framework (MDFF) for real-time FeO content detection, which combines data fusion, feature fusion, and decision fusion methods together to form an end-to-end framework for soft sensor modeling. First, shallow features are extracted from cross section images based on expert knowledge for data-level fusion. Then, two multiscale encoding networks and a shared representation learning network are employed to realize feature-level fusion. Subsequently, the final predicted FeO content is obtained by fusing the prediction results of shared representations and modality-specific features at the decision level. By integrating multisource data and designing multilevel information fusion, the proposed MDFF method can make full exploitation of the intrinsic interactions among multisource information at various levels. The experimental results of the actual sintering process show that MDFF is superior to the existing models in terms of prediction accuracy and stability.

关键词： Sintering Feature extraction Soft sensors Predictive models data models Accuracy data mining Real-time systems Production Numerical models Deep learning end-to-end modeling ferrous oxide (FeO) content prediction iron ore sintering multisource data fusion soft sensing

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Recurrent Attentional Reinforcement learning for Fault Diagnosis of Hydraulic System 10

Recurrent Attentional Reinforcement Learning for Fault Diagn...

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10th International conference on control, Decision and Information Technologies (CoDIT)

作者： Tang, Zhenhui Liu, Ru Lou, Kang Gao, Chaojian Wang, Jingcheng SJTU Sanya Yazhou Bay Inst Deepsea Sci & Technol Sanya 572024 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Shanghai Marine Equipment Res Inst Shanghai 200040 Peoples R China Shanghai Jiao Tong Univ Dept Automat Key Lab Syst Control & Informat Proc Minist Educ China Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Shanghai Engn Res Ctr Intelligent Control & Manag Shanghai 200240 Peoples R China Xian Technol Univ Autonomous Syst & Intelligent Control Int Joint R Xian 710021 Peoples R China

ISBN: (纸本)9798350373981;9798350373974

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although data-driven fault diagnosis method attains remarkable progress by learning fault features automatically, the existing data-driven fault diagnosis pipelines learn non-discriminative feature. To deal with these problems, a new data-driven fault diagnosis method with recurrent attentional module is proposed to find a sequence of informative features by localizing distinguishing time fragments about each individual faults iteratively. The contextual sequence dependency between the localized time fragments is enhanced. Then, the fault types are further predicted on localized time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on hydraulic system show that the proposed framework achieves superior diagnosis performance and computation efficiency.

关键词： Hydraulic equipment

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Escape with Self-adaptive Decision Radius based on Deep Deterministic Policy Gradient in Pursuit Games 13

Escape with Self-adaptive Decision Radius based on Deep Dete...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhou, Xiaojie Yang, Chunxi Wang, Wenbo Sun, Pengqi Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650500 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming650500 China

ISBN: (纸本)9798350361674

The Pursuit-Evasion (PE) game of Unmanned Surface Vehicles (USVs) is a classic antagonistic problem for the intelligent agent system. To enhance the escaping success rate of evaders with better effort, this paper proposes an escape strategy based on the geometrical characteristics of Apollonius circles. An improved self-adaptive escaping strategy for the evader utilizing the deep deterministic policy gradient algorithm is proposed. Then, the criteria for successful encirclement by pursuer are given. A DDPG algorithm-based framework is proposed on the basis of markov decision process formulation. Specifically, an action space based on adaptive decision radius of evader is designed. Our simulation shows the proposed method has more advantages in terms of escape distance and escape time. © 2024 ieee.

关键词： Unmanned surface vehicles

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data-learning Game Output Regulation Approach for Human-Machine Cooperative Driving Toward Varied Drivers and Vehicles

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2024年第12期25卷 20190-20202页

作者： Guo, Hongyan Shi, Wanqing Guo, Jingzheng Liu, Jun Cao, Dongpu Chen, Hong Jilin Univ State Key Lab Automot Simulat & Control Changchun 130012 Peoples R China Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

For personalized human-machine cooperative (HMC) control, traditional model-driven approaches, which rely on predefined driver-vehicle-road (DVR) models, often struggle to adapt to individual driver differences. To address this, a data-learning shared control strategy based on game output regulation and adaptive dynamic programming (ADP) is presented. Firstly, considering the differences in driver's characteristics, vehicle-road dynamics and human-machine interaction, an uncertain DVR system is established. Subsequently, robust output regulation (ROR) is utilized to handle road curvature perturbations and ensure closed-loop system stability. Subsequently, a dynamic game framework between the front-wheel steering system (AFS) and the active rear-wheel steering system (ARS) is further developed to ensure both vehicle stability and path-tracking accuracy in complex environments. Finally, the AFS-ARS optimal control strategies are iteratively learned and updated by ADP, using online DVR system data, without requiring prior knowledge of specific drivers or vehicles. Through driver-in-the-loop experiments, it is demonstrated that the presented method exhibits good adaptability to different drivers.

关键词： Human-machine cooperative driving personalized drivers adaptive dynamic programming game output regulation

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Neural ODE-based Imitation learning (NODE-IL): data-Efficient Imitation learning for Long-Horizon Multi-Skill Robot Manipulation

Neural ODE-based Imitation Learning (NODE-IL): Data-Efficien...

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2024 International conference on Intelligent Robots and systems

作者： Zhao, Shiyao Xu, Yucheng Kasaei, Mohammadreza Khadem, Mohsen Li, Zhibin Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland UCL Dept Comp Sci London England

ISBN: (纸本)9798350377712;9798350377705

In robotics, acquiring new skills through Imitation learning (IL) is crucial for handling diverse complex tasks. However, model-free IL faces challenges of data inefficiency and prolonged training time, whereas model-based methods struggle to obtain accurate nonlinear models. To address these challenges, we developed Neural ODE-based Imitation learning (NODE-IL), a novel model-based imitation learning framework that employs Neural Ordinary Differential Equations (Neural ODEs) for learning task dynamics and control policies. NODE-IL comprises (1) Dynamic-NODE for learning the continuous differentiable task's transition dynamics model, and (2) control-NODE for learning a long-horizon control policy in an MPC fashion, which are trained holistically. Extensively evaluated on challenging manipulation tasks, NODE-IL demonstrates significant advantages in data efficiency, requiring less than 70 samples to achieve robust performance. It outperforms Behavioral Cloning from Observation (BCO) and Gaussian Process Imitation learning (GP-IL) methods, achieving 70% higher average success rate, and reducing translation errors for high-precision tasks, which demonstrates its robustness and accuracy, as an effective and efficient imitation learning approach for learning complex manipulation tasks.

关键词： Adversarial machine learning

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Shielding for Socially Appropriate Robot Listening Behaviors 33

Shielding for Socially Appropriate Robot Listening Behaviors

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33rd ieee International conference on Robot and Human Interactive Communication (ieee RO-MAN) - Embracing Human-Centered HRI

作者： Gillet, Sarah Marta, Daniel Akif, Mohammed Leite, Iolanda KTH Royal Inst Technol Lindstedtsvagen 24 S-10044 Stockholm Sweden

ISBN: (纸本)9798350375039;9798350375022

A crucial part of traditional reinforcement learning (RL) is the initial exploration phase, in which trying available actions randomly is a critical element. As random behavior might be detrimental to a social interaction, this work proposes a novel paradigm for learning social robot behavior-the use of shielding to ensure socially appropriate behavior during exploration and learning. We explore how a data-driven approach for shielding could be used to generate listening behavior. In a video-based user study (N=110), we compare shielded exploration to two other exploration methods. We show that the shielded exploration is perceived as more comforting and appropriate than a straightforward random approach. Based on our findings, we discuss the potential for future work using shielded and socially guided approaches for learning idiosyncratic social robot behaviors through RL.

关键词： Adversarial machine learning

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