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IEEE International Conference on Robotics and Biomimetics

作者： Chenming Zhang Jiahao Jiang Xiang Li Zhelong Wang Sen Qiu Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of the Ministry of Education and the School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Non-intrusive Load Monitoring (NILM) represents a cost-efficient technological advancement in meticulously monitoring electricity consumption patterns. However, external noise interference in practical load identification poses challenges for accurate monitoring. To address this, a multi-task learning framework is proposed, concurrently tackling load identification and signal de-noising. To extract effective information from load signals, a novel feature representation called variational mode decomposition-instantaneous amplitude (VMD-IA) was introduced. To ensure optimal performance across the different tasks, a multi-gate mixture of expert architecture (MMOE) module is integrated into the proposed model. The proposed approach was evaluated on a self-collected load dataset, as well as public NILM datasets. The experimental results indicate that the proposed model outperforms state-of-the-art methods in all tasks.

关键词： Load monitoring Noise Logic gates Feature extraction Multitasking Robustness Signal denoising Data mining Robots Load modeling

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Development of an Automatic Sweet Pepper Harvesting Robot and Experimental Evaluation

Development of an Automatic Sweet Pepper Harvesting Robot an...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Qinghui Pan Dong Wang Jie Lian Yongxiang Dong Chaochao Qiu Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education and the School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The aging population and diminishing working population in agriculture motivate the development of autonomous harvesting robots. Although autonomous harvesting is expanding rapidly, the commercial application of sweet pepper harvesting robots still faces challenges. This paper presents the development of a sweet pepper harvesting robot and reports its experimental verification, which mainly includes end-effector design, visual perception, and grasping pose control. The end-effector adopts electrical control, mainly composed of a servo-electric two-finger parallel clamping module, a swing-cutting module, and a fruit recovery device. Equipped with a tactile sensor array, it can accurately sense the sweet pepper peduncle position and the end-effector state (harvesting failure) to complete the precise cutting. An end-effector grasping pose control algorithm of the manipulator is proposed, which can control the end-effector to grasp along the direction of the fruit peduncle and perpendicular to the tangent direction of the picking point by estimating the pose of the sweet pepper peduncle. Finally, the robot and proposed method were verified in a plant factory. The experimental findings demonstrate that the developed harvesting robot can complete robust detection of fruit peduncles and non-destructive picking of sweet pepper, with an average picking time of about 15 seconds.

关键词： Sociology Tactile sensors Grasping End effectors Production facilities Sensors Statistics

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A Study on Task Offloading Based on Multi-Agent Advantage Actor Critic Algorithms in IIoT

A Study on Task Offloading Based on Multi-Agent Advantage Ac...

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Computers and Artificial Intelligence Technologies (CAIT), International Conference on

作者： Yanchao Xu Zheng Lv Jun Zhao Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798331530891

ISBN: (纸本)9798331530907

With the rapid development of Industry 4.0, resource-constrained industrial devices are often unable to meet different demands of compute-intensive and latency-sensitive tasks in modern factories. To cope with this challenge, offloading tasks from industrial devices to edge servers or a cloud computing center to obtain lower delay is a good solution. Most of the traditional task offloading approaches rely on centralized decision-making systems, which may not be able to efficiently cope with device heterogeneity and the specific needs of different tasks in industrial environments. To address this problem, this study proposes a more comprehensive edge computing network model for Industrial Internet of Things (IIoT). By integrating an action masking technique, we establish a Markov decision model with a constrained action space. Additionally, we propose a task offloading framework based on the Multi-Agent Advantage Actor Critic (MAA2C) algorithm, adopting the Centralized Training Decentralized Execution (CTDE) architecture, aiming to achieve task offloading decision-making in modern factories and reduce the overall system delay. Validated through numerical simulation experiments, our proposed framework not only performs well in reducing system delay but also shows good adaptability in different industrial environments.

关键词： Training Computational modeling Decision making Computer architecture Numerical simulation Production facilities Delays Servers Industrial Internet of Things Edge computing

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Gas Holder Levels Prediction with Multi-Working Conditions Identified by Change Point Detection

Gas Holder Levels Prediction with Multi-Working Conditions I...

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Asian control Conference

作者： Zihe Yang Long Chen Jun Zhao Wei Wang Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The operation of gas holders in steel industry presents complex characteristics due to various factors such as ambient temperature and piston tilt, leading to multiple working conditions. To address this, the paper proposes a gas holder levels prediction method based on time series change point detection, where the real-time data are identified to find the shifts in working conditions and thereby achieves the enhancement of prediction accuracy. To investigate the relationship between the difference of the production and consumption flows and gas holder levels, a deviation series is constructed to capture the deviation between by-product gas production-consumption difference and changes in gas holder levels. Subsequently, the Bayesian method is employed for online detection of the change of the mean in these deviation series. Building upon the outcomes of change point detection, a multi-model switching-based approach employing least squares support vector machine (LSSVM) is designed for online prediction of gas holder levels. Using the actual production data from a steel enterprise for experiments, the proposed method is verified that it can effectively detect the change point across multiple operating conditions, and substantially improve gas holder levels prediction accuracy.

关键词： Employee welfare Training Adaptation models Accuracy Time series analysis Production Switches

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Active Fault Diagnosis for Gas-Steam Combined Cycle Power Plant Considering Variable Working Conditions

Active Fault Diagnosis for Gas-Steam Combined Cycle Power Pl...

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Asian control Conference

作者： Yang Han Demin Xu Fan Zhou Ying Liu Jun Zhao Wei Wang Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The gas-steam combined cycle power plant (CCPP) is one of the important energy conversion equipment in iron and steel energy systems., which can utilize the by-product gas to supply power in industrial parks. Due to the complexity of the environment in which the equipment is located., the components of the equipment., including actuators., compressors and combustion chambers., are prone to fault. Especially., the fault information contained in the data is not sufficient when the early fault occurs., it is difficult to fault diagnosis. For this problem., this paper proposes an active fault diagnosis (AFD) method for CCPP considering variable working conditions. Firstly., the system state is estimated by the zonotopic observer., and then the state output set affected by the unknown disturbance is described by the zonotopes. Finally., in order to realize the application of AFD under variable working conditions of the equipment., the auxiliary signal is designed to sperate the state set of the normal model and the fault model under variable working conditions. The experimental results show that the proposed method has good fault diagnosis ability under variable working conditions.

关键词： Employee welfare Fault diagnosis Measurement Energy conversion Observers Iron Compressors

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A Novel optimization Method for Raw Coal Blending in Coal Preparation Plants Based on Interval Programming

A Novel Optimization Method for Raw Coal Blending in Coal Pr...

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2023 China Automation Congress, CAC 2023

作者： Lei, Jiahui Liu, Hanwen Zhao, Jun Wang, Wei Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China Chn Energy Wuda Coal Processing Co. Ltd. Wuhai China

ISBN: (纸本)9798350303759

In the process of coal blending and washing in coal preparation plants, there are various raw coals with different quality that changes greatly, which results in poor quality and low profit. To solve these problems, a novel optimization method for raw coal blending based on interval programming is proposed. Interval numbers are introduced to represent the fluctuation range of ash and sulfur content at each density level of raw coals. A predictive model of quantity and quality of dense medium cyclone products is constructed, which uses Hermite interpolation to obtain washability curves of each raw coal and adopts fitting methods to obtain the distribution curve. In order to obtain optimal coal blending schemes, considering operation and maintenance costs of raw coal feeding line, a mixed integer nonlinear programming (MINLP) model is built, whose objective is to maximize profit with the main constraints of clean coal quality and inventory of raw coal. Interval possibility is introduced to transform constraints with interval number and a customized branch and bound algorithm is used to solve it. In order to verify the effectiveness of the method in this paper, experiments are conducted using manufacturing data from a domestic coal preparation plant. The experimental results indicate that the proposed method is practical and has better economic performance. © 2023 IEEE.

关键词： Profitability

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Event-Triggered Prediction of Blast Furnace Gas Generation Based on a Trend Self-Adaption Scheme

Event-Triggered Prediction of Blast Furnace Gas Generation B...

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Asian control Conference

作者： Guanglin Cong Long Chen Jun Zhao Feng Jin Wei Wang Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The prediction of blast furnace gas (BFG) generation in ironmaking processes is crucial for the BFG scheduling work. Due to frequent switching of working conditions of the ironmaking process, the generation of BFG under each working condition fluctuates greatly. It is difficult to predict accurately during the transition of working conditions. In order to address these problems, this paper proposes a data-driven BFG generation prediction method based on a combination of an event-triggered scheme and a trend self-adaption scheme. In this method, different working conditions are divided by events such as the change of supply of hot air and oxygen. Considering the process characteristics of air reduction and restoration, the decrease and increase trends are predicted by a parameter adaption-based linear logarithmic regression. The model parameters are corrected online to realize trend self-discrimination. Besides, as for the residual sequence after trend fitting, it is modeled by using the long short-term memory (LSTM) network. To validate the effectiveness of the proposed method, actual industrial field data of a steel plant in China are utilized. Simulation experimental results show that the proposed method significantly improves prediction accuracy of the BFG generation flow.

关键词： Employee welfare Adaptation models Atmospheric modeling Time series analysis Switches Predictive models Market research

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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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Switching Model Predictive control With Actuator Saturation And Average Dwell Time

Switching Model Predictive Control With Actuator Saturation ...

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Chinese control and Decision Conference, CCDC

作者： Lei Xie Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) College of Internet of Things Engineering Jiangnan University Wuxi

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In this paper, for discrete-time switched systems with uncertainty, we study its saturation model predictive control (SMPC) under the average dwell time switching rule. Firstly, a factor greater than 1 is introduced in the differential operation of the Lyapunov function, allowing it to increase at the switching instant, thereby increasing the design freedom to adjust the average dwell time, that is, the switching frequency; Introducing auxiliary matrix and convex combination technology to transform actuator saturation constraints into constraints on the system state; Obtain the SMPC design method under the double constraints of average dwell time and state space. Secondly, an appropriate switching invariant ellipsoid is constructed to obtain a feasible solution for the SMPC control gain. SMPC combined with the average dwell time constraint can ensure the feasibility of the sufficiency condition for solving the control gain, the robustness and stability of the closed-loop switched system.

关键词： Switched systems Actuators Uncertainty Switches control systems Stability analysis Robustness

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Multimodal process monitoring based on transition-constrained Gaussian mixture model

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Chinese Journal of Chemical Engineering 2020年第12期28卷 3070-3078页

作者： Shutian Chen Qingchao Jiang Xuefeng Yan Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China

Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is *** multimodal monitoring methods rely on the assumption that the modes are independent of each other,which may not be appropriate for practical *** study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process *** technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online *** process enables the identified modes to reflect the stability of actual working conditions,improve mode identification accuracy,and enhance monitoring reliability in cases of mode *** studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach inmultimodal process monitoring with mode overlap.

关键词： Multimodal process monitoring Gaussian mixture model State transition matrix process control process systems Systems engineering

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