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arXiv 2022年

作者： Yu, Wanke Wu, Min Huang, Biao Lu, Chengda The School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China The Department of Chemical and Materials Engineering University of Alberta EdmontonABT6G 2G6 Canada

Many multivariate statistical analysis methods and their corresponding probabilistic counterparts have been adopted to develop process monitoring models in recent decades. However, the insightful connections between them have rarely been studied. In this study, a generalized probabilistic monitoring model (GPMM) is developed with both random and sequential data. Since GPMM can be reduced to various probabilistic linear models under specific restrictions, it is adopted to analyze the connections between different monitoring methods. Using expectation maximization (EM) algorithm, the parameters of GPMM are estimated for both random and sequential cases. Based on the obtained model parameters, statistics are designed for monitoring different aspects of the process system. Besides, the distributions of these statistics are rigorously derived and proved, so that the control limits can be calculated accordingly. After that, contribution analysis methods are presented for identifying faulty variables once the process anomalies are detected. Finally, the equivalence between monitoring models based on classical multivariate methods and their corresponding probabilistic graphic models is further investigated. The conclusions of this study are verified using a numerical example and the Tennessee Eastman (TE) process. Experimental results illustrate that the proposed monitoring statistics are subject to their corresponding distributions, and they are equivalent to statistics in classical deterministic models under specific restrictions. Copyright © 2022, The Authors. All rights reserved.

关键词： Process monitoring

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Effective Throughput Optimization of SAG Milling Process Based on BPNN and Genetic Algorithm

Effective Throughput Optimization of SAG Milling Process Bas...

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Industrial Cyber-Physical systems (ICPS)

作者： Zhenhong Liao Ce Xu Wen Chen Qifu Chen Feng Wang Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Mineral Resources Development and Utilization Changsha Research Institute of Mining and Metallurgy CO. LTD. Chagnsha China Hunan Valin Lianyuan Iron And Steel Co. Ltd. Loudi China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

Grinding is an energy-consuming process in mineral processing industry. Improving grinding processing capacity per unit power consumption is an effective means to reduce grinding production cost. In this paper, a new index for evaluating the effective processing throughput of SAG milling is proposed. The production process model is established by BP neural network (BPNN). Through combining the process mechanism and production constraints, the genetic algorithm is adopted to optimize the operating parameters of the SAG milling process to maximize the effective throughput, thus improving the grinding efficiency. The experimental results showed that through optimization of effective throughput function proposed in this paper, the SAG mill processing capacity has been increased by 4% and the operating power drawn reduced by 1.12%. It has important guiding significance for the actual production process.

关键词： Industries Power demand Neural networks Milling Throughput Minerals Materials processing

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Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

arXiv

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arXiv 2024年

作者： Hu, Xuemin Chen, Pan Wen, Yijun Tang, Bo Chen, Long School of Artificial Intelligence Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Wuhan430062 China Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China WAYTOUS Inc. Beijing100083 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an710049 China

Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but the occurring probability of an unsafe state is still high, which is unacceptable in autonomous driving tasks. Moreover, these methods are difficult to achieve a balance between the cost and return expectations, which leads to learning performance degradation for the algorithms. In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL. The short-term constraint aims to enhance the shortterm state safety that the vehicle explores, while the long-term constraint enhances the overall safety of the vehicle throughout the decision-making process, both of which are jointly used to enhance the vehicle safety in the training process. In addition, we develop a safe RL method with dual-constraint optimization based on the Lagrange multiplier to optimize the training process for end-to-end autonomous driving. Comprehensive experiments were conducted on the MetaDrive simulator. Experimental results demonstrate that the proposed method achieves higher safety in continuous state and action tasks, and exhibits higher exploration performance in long-distance decision-making tasks compared with state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Adaptive Sliding Mode control for Random Teleoperation System with Time-Varying Delay

Adaptive Sliding Mode Control for Random Teleoperation Syste...

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第40届中国控制会议

作者： Haitao Jian Shiqi Zheng Chenghao Zhao Haiming Li Shihao Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

This paper focuses on the control problem of a class of random teleoperation systems. To overcome the difficulties caused by the random environment, a new adaptive sliding mode control method for random teleoperation system is *** with the previous work, the model in this paper is built by random differential equations. In addition, different time-varying delays are introduced between the two communication channels. Furthermore, a new design scheme for random teleoperation system with varying-time delay is proposed. Radial Basis Function neural network（RBFNN） is introduced to deal with the unknown nonlinearities of the system. Using this method, good position tracking performance and stability can be obtained.

关键词： Adaptive control Sliding mode Teleoperation Random Lagrangian system Time-varying delay

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Design of a prediction and optimization system for plate shape in roller quenching process

Design of a prediction and optimization system for plate sha...

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第40届中国控制会议

作者： Wen Zhang Min Wu Cong Chen Sheng Du Luefeng Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Plate shape is one of the key quality indices of steel plates after *** is of great significance to realize the prediction and optimization of plate shape for obtaining high quality steel *** paper designs a prediction and optimization system for plate shape in roller quenching ***,the roller quenching process is described in detail,the design objectives are analyzed,and the architecture of the system is ***,the system is designed from four parts:the prediction model of plate shape,the comprehensive evaluation model of plate shape,the intelligent optimization model of operating parameters and the case ***,the prediction and optimization system is applied to the industrial *** results of preliminary tests show that the system improves the quality of plate shape.

关键词： Roller quenching process Plate shape prediction intelligent optimization Case matching

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Electroencephalogram Emotion Recognition Using Variational Modal Decomposition Based Dispersion Entropy Feature Extraction

Electroencephalogram Emotion Recognition Using Variational M...

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第40届中国控制会议

作者： Si-Jun Hu Zhen-Tao Liu Xue-Wen Ding School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Electroencephalogram（EEG） emotion recognition has gained considerable attention due to its ability to reflect people’s inner emotional states objectively and *** extraction is a critical step in EEG emotion recognition because of non-stationarity and irregularity of EEG signals.A feature extraction method using Variational Modal Decomposition（VMD）to extract Dispersion Entropy（DispEn） is proposed in this *** EEG signal is decomposed into several components,and DispEn of each component is extracted in eight emotion-related *** method was tested on DEAP dataset in which the EEG emotional states are accessed in Valence-Arousal emotional *** emotional states（i.e.,HVHA,HVLA,LVHA,LVLA） are classified by Support Vector Machine（SVM）.The experimental results show that the accuracy of emotion recognition is 77.87%,which demonstrates its effectiveness.

关键词： EEG Emotion recognition VMD Dispersion Entropy Feature Extraction SVM

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Detection Method for Overbreak and Underbreak of Tunnel Based on Three-Dimensional Laser Point Clouds

Detection Method for Overbreak and Underbreak of Tunnel Base...

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第32届中国过程控制会议(CPCC2021)

作者： Zhuoxuan Jiang Jianqi An Zhilin Tang Xin Chen Min Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

During the construction of the tunnel excavation with the method of drilling and blasting,overbreak and underbreak occur ***,overbreak and underbreak affect the cost,efficiency,and safety of tunnel *** paper presents a detection method for overbreak and underbreak of tunnels based on three-dimensional laser point ***,this paper obtains point cloud data of a tunnel by a 3 D laser scanner,preprocesses the point cloud data based on Gaussian filter,and extracts midlines of the tunnel based on random sampling consistency(RANSAC) to obtain attitude and trend information of the ***,cross-sections of the tunnel are extracted according to the midline of the ***,the position and value of the overbreak and underbreak are got according to comparing the projections of the cross-sections of the tunnel with a planned extent of the ***,this method was applied to an evaluation of overbreak and underbreak of a tunnel,and the results show that the method in this paper detects overbreak and underbreak easily,quickly,and accurately.

关键词： Overbreak and underbreak three-dimensional laser point cloud

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A New Dynamic Event-Triggered Mechanism Based Model Predictive control for systems with Polytopic Uncertainties

A New Dynamic Event-Triggered Mechanism Based Model Predicti...

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第40届中国控制会议

作者： Fan Wei Xiongbo Wan School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

This paper investigates the problem of model predictive control（MPC） for systems with polytopic uncertainties under the event-triggered communication mechanism. To save network resources, a new dynamic event-triggered mechanism（DETM） is proposed, which contains an adaptive internal dynamic variable（IDV） and a time-varying parameter. A "min-max"optimization problem is put forward to dealing with the MPC problem for systems with polytopic uncertainties. With the aid of a Lyapunov-like function dependent on the IDV of the DETM, an auxiliary optimization problem is devised with constraints in terms of linear matrix inequalities. By solving such an auxiliary optimization problem, sub-optimal feedback gains are obtained which ensure the input-to-state practical stability of the closed-loop system. A numerical example is provided to demonstrate the effectiveness of the devised MPC algorithm.

关键词： Model predictive control dynamic event-triggered mechanism networked systems input-to-state practical stability

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Event-Triggered Attack-Tolerant control for Quadrotor UAV

Event-Triggered Attack-Tolerant Control for Quadrotor UAV

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第40届中国控制会议

作者： Haiming Li Shiqi Zheng Chenghao Zhao Haitao Jian Shihao Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

In view of the loss of speed caused by the attack of the four-rotor UAV executor, an adaptive control method is designed to maintain the altitude and posture of the UAV without the attack diagnostic mechanism. Adaptive event trigger control methods also consider the mechanism of event triggering. The main impact of attacks on UAVs is the loss of thrust from UAVs. The attack-tolerant method designed in this paper can ensure that the tracking error of multi-acting device can maintain altitude and attitude when attacked is gradually convergent. At the same time, the event trigger method reduces the use of communication resources. Simulation proves the validity of the method.

关键词： Adaptive control attack-tolerant method event-triggered quadrotor UAV

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Performance Improvement of TFRC Congestion control Algorithm in Wireless Network Streaming Media Transmission

Performance Improvement of TFRC Congestion Control Algorithm...

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第40届中国控制会议

作者： Kang Li Yan Yuan Yitao Chen Zhicheng Zhao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

This paper studies a rate-based TCP-Friendly Rate control（TFRC） mechanism, which is widely used in multimedia real-time services, and analyzes its basic workflow, throughput model and calculation of key parameters. In order to meet the demand of real-time service for network transmission, the bandwidth-delay product（BDP） is applied to the congestion control of TFRC as the network congestion warning signal, and the improved TFRC algorithm is proposed. The simulation results show that this method can achieve good results in the network transmission of real-time services, and its friendliness and smoothness are improved to a certain extent.

关键词： Real-time multimedia application TFRC Bottleneck bandwidth and round-trip time（BBR） Wireless network

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