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Intelligent decision-making of shield tunneling machine using deep reinforcement learning in complex geological environment

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Zhang, Wenshuai Liu, Xuanyu Zhang, Jiliang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110004 China College of Information Science and Engineering Northeastern University Shenyang110004 China School of Information and Control Engineering Liaoning Petrochemical University Liaoning Fushun113001 China

To reduce constructional hazards of manual operation, the implementation an intelligent decision-making of the shield machine will be an inevitable development trend in the future. Hence, this paper suggests an intelligent decision-making approach using the Twin Delayed Deep Deterministic Policy Gradient (TD3) agent, which is constructed under the Actor-Critic framework. A robust nonlinear correlation exists between sealed cabin pressure and tunneling parameters. Therefore, a deep reinforcement learning (DRL) training environment is established using the sealed cabin pressure mechanism model. TD3 agent is trained by exploring empirical data obtained from continuous interactions between TD3 agent and the environment. Critic networks calculate the evaluation value of the action. TD3 selects the minimum evaluation value to avoid overestimating the parameters. Actor network adopts delayed updating parameters to make control process more stable and accurate. Based on earth theory model, target pressure values under different earth conditions are calculated. An appropriate reward function is established based on the target pressure for current soil conditions. TD3 agent dynamically adjusts the propulsion speed and screw conveyor speed in real-time according to geological environment to achieve intelligent control of the sealed cabin earth pressure. The experimental results show that the decision-making error of this method is 0 Megapascal (MPa), and it better adapts to random soil variations and can complete nearly a hundred different soil transition tasks in complex environments. This study promotes technological advancements in tunnel engineering by introducing artificial intelligence (AI) technology into tunnel construction field, realizing deep integration of AI technology and civil engineering. © 2025 Elsevier Ltd

关键词： Deep reinforcement learning

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Dynamic Graph Learning Soft Sensor in process Industry

Dynamic Graph Learning Soft Sensor in Process Industry

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Data Driven control and Learning Systems (DDCLS)

作者： Mingwei Jia Danya Xu Tao Yang Yuan Yao Yi Liu Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Department of Chemical Engineering National Tsing Hua University Taiwan

The development of an accurate soft sensor modeling method in the process industry remains a great challenge because the coupling relationship between variables is always intricate and difficult to model. In this work, a dynamic graph learning (DGL) soft sensor is proposed to alleviate this problem. The proposed model realizes the ability of the soft sensor to perceive the coupling relationship in real time by automatically learning the dynamic graph. Then, a causal convolutional mechanism and a multi-hop graph attention mechanism are used to systematically construct the dependencies of variables in the spatial-temporal dimension and model their variation patterns effectively. Finally, the proposed method is tested on the penicillin fermentation process and shown to be feasible and effective. The results showed that the change of the dynamic graph in the spatial-temporal dimension was in line with the process mechanism.

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Non-Line-Of-Sight Target Localization in T-Shaped Roadways Using Simo Fmcw Millimeter-Wave Radar

SSRN

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SSRN 2024年

作者： Hu, Qingsong Luo, Yujia Wang, Liudi Cheng, Yuanxun Zhang, Yuansheng Yuan, Shuya Kou, Zhihao School of Information and Control Engineering China University of Mining & Technology Jiangsu Xuzhou221116 China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing100032 China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing100032 China

The hazardous and complex nature of underground mining imposes stringent safety requirements on autonomous driving. T-shaped roadways, common in such environments, present significant challenges for non-line-of-sight (NLOS) target localization. Traditional underground positioning technologies often fail in these settings due to severe signal attenuation and multipath effects, leading to potential accidents. This manuscript proposes a novel localization algorithm using single-input-multi-output (SIMO) Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar to address these challenges. We analyze millimeter wave multipath propagation in T-shaped tunnels and construct an FMCW multipath echo model. Using CA-CAFAR and MVDR algorithms, we accurately estimate target distance and angle information. Additionally, a theoretical false point matching elimination method is designed to improve positioning accuracy by mitigating false target interference. Field tests in typical T-shaped NLOS scenarios demonstrate the method's high-precision positioning, enhancing the safety and efficiency of autonomous vehicles in coal mines. © 2024, The Authors. All rights reserved.

关键词： Coal mines

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Efficient Ensemble Broad Learning System Based on Dropout and Dropconnect

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The International Journal of Intelligent control and Systems 2024年第2期29卷 79-87页

作者： Yiwan Cao Fei Chu Shuai Li C.L.Philip Chen Jun Fu School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110004China

Broad learning system(BLS)is an emerging neural network characterized by its rapid processing and robust generalization ***,determining the appropriate structure for broad learning system is also a *** addition,broad learning system may perform overfitting due to the dependence between nodes in processing fully connected *** deal with these problems,an efficient ensemble broad learning system based on Dropout and Dropconnect is proposed in this *** proposed Dropout ensemble broad learning system randomly discards hidden nodes to improve diversity between individuals and reduce the synergy between nodes to improve prediction *** Dropconnect ensemble broad learning system randomly drops connection weights to generate more complementary models by adding input attribute *** experimental results on the UCI datasets confirm that the method proposed in this paper can solve the problem of model overfitting caused by the strong dependence between the nodes of ensemble broad learning *** proposed algorithm outperforms the original BLS in terms of prediction stability and classification accuracy.

关键词： Broad learning system ensemble learning Dropout Dropconnect

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Performance-Oriented Event-Triggered Merging-Following control of Multi-Lane Vehicles

Performance-Oriented Event-Triggered Merging-Following Contr...

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Chinese control and Decision Conference, CCDC

作者： Yan-Xi Liu Chen-Liang Zhang Ge Guo College of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper focuses on the merging-following control of vehicles standing on different lanes in presence of performance requirements and limited communication resources. A constraint of tracking error is developed to prescribe the performance, and then addressed via error mapping transformation, leading to a prescribed performance control (PPC) algorithm that can make the both distance and angular errors between consecutive vehicles evolve within the prescribed region. Then an event-triggered control protocol driven by the performance function is incorporated to reduce the signal transmission frequency between the controller and actuator with a performance-communication balance. Based on the Lyapunov stability proof, it is verified that all signals in the vehicular system remain uniformly ultimately bounded. A numerical simulation is presented to illustrate the effectiveness of the proposed scheme.

关键词： Actuators Protocols Numerical simulation Steady-state Planning Transient analysis Frequency control

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Adaptive Command-filtered Finite-time control of Vehicular Platoons with Inter-Vehicle Spacing Constraints

Adaptive Command-filtered Finite-time Control of Vehicular P...

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Asian control Conference

作者： Wei Liu Zhongyang Wei Zhenyu Gao Ge Guo Northeastern University Qinhuangdao Qinhuangdao China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper investigates the problem of platoon control of vehicles with inter-vehicle spacing constraints and uncertain parameters. The prescribed performance control methodology is employed to describe the spacing constraints that de-scribed by a time-varying smooth function. Based on the smooth function, asymmetric barrier lyapunov functions, neural network technique and in the context of command filtered control, a novel finite-time control scheme is proposed, by which the platoon can be achieved with all the states stabilized in a given finite-time, while guarantee that the inter-vehicle spacing constraints are not violated during the whole operation. The effectiveness and performance of the method is demonstrated by numerical simulations.

关键词： Uncertainty Backstepping control design Simulation Numerical simulation Explosions Complexity theory

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Design of indirect-type iterative learning control for continuous-time batch processes with the repetitive process setting

Design of indirect-type iterative learning control for conti...

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Asian control Conference

作者： Robert Maniarski Wojciech Paszke Hongfeng Tao Zhihe Zhuang Institute of Automation Electronic and Electrical Engineering University of Zielona Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

An indirect iterative learning control law is proposed for a class of continuous-time batch processes with time-varying uncertainties, input delay, and disturbances. First, by designing a predictor based on a state observer we are able to estimate the future state and compensate for the input delay. Then, based on the estimated state we design a robust $H$ ∞ controller in the presence of time-varying uncertainties and load disturbances. Additionally, the differential repetitive process setting for the design of the iterative learning control scheme is applied and hence the convergence of the tracking error in trial-to-trial direction occurs. Finally, the simulation results verify the new method's effectiveness.

关键词： Uncertainty Simulation Batch production systems process control Observers Delays Iterative learning control

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Privacy Preservation by Intermittent Transmission in Cooperative LQG control Systems

Privacy Preservation by Intermittent Transmission in Coopera...

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Asian control Conference

作者： Wenhao Lin Yuqing Ni Wen Yang Chao Yang Department of Automation Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, we study a cooperative linear quadratic Gaussian (LQG) control system with a single user and a server. In this system, the user runs a process and employs the server to meet the needs of computation. However, the user regards its state trajectories as privacy. Therefore, we propose a privacy scheme, in which the user sends data to the server intermittently. By this scheme, the server's received information of the user is reduced, and consequently the user's privacy is preserved. In this paper, we consider a periodic transmission scheme. We analyze the performance of privacy preservation and LQG control of different transmission periods. Under the given threshold of the control performance loss, a trade-off optimization problem is proposed. Finally, we give the solution to the optimization problem.

关键词： Measurement Privacy Data privacy Networked control systems Trajectory Servers Optimization

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Sparse Causality Analysis Approach with Time-varying Parameters for Root Cause Localization of Nonstationary process 4

Sparse Causality Analysis Approach with Time-varying Paramet...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Song, Pengyu Zhao, Chunhui Huang, Biao Ding, Jinliang College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China University of Alberta Department of Chemical and Materials Engineering EdmontonABT6G 2G6 Canada Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

Root cause diagnosis (RCD) is an important technique for maintaining the safe operation of industrial processes. Traditional RCD methods usually require stationarity assumptions. However, the process inevitably shows nonstationarity due to factors such as switching of operating conditions. Although there have been some previous studies trying to overcome the challenge of nonstationarity, these methods fail to guarantee the significance of the extracted causalities and lead to redundant relationships. To address the above issues, a sparse causal analysis model with time-varying parameters is extracted in this study. First, we propose an end-to-end information fusion and prediction task to characterize predictive relationships between variables and avoid repeated modeling. Second, we design time-varying parameters for the information fusion mechanism to cope with nonstationarity and automatically identify significant causality through sparse parameter updates. We design an update strategy that constrains the gradient information to guarantee sparsity. Finally, a causal metric is constructed for the time-varying predictive relationship to comprehensively obtain the overall causal relationship, which further guarantees causal significance. The validity of the proposed method is illustrated through a real industrial example collected from a thermal power plant. © 2022 IEEE.

关键词： Information fusion

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DSTAnet: A Trajectory Distribution-aware Spatio-Temporal Attention Network for Vehicle Trajectory Prediction

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IEEE Transactions on Vehicular Technology 2025年

作者： Li, Taoying Sun, Xulei Dong, Quanyu Guo, Ge Dalian Maritime University School of Maritime Economics and Management Dalian116026 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao066004 China

Trajectory prediction is a crucial task of autonomous driving and benefits vehicles travel safely in complex traffic environments. However, most existing trajectory prediction methods suffer from low accuracy issue due to the highly complex and dynamic interactions between vehicles, as well as the uncertainty of driver intentions in real-world scenarios. To address these challenges, we propose a trajectory Distribution-aware Spatio-Temporal Attention network (DSTAnet) for vehicle trajectory prediction. The proposed DSTAnet begins with a temporal feature extractor that combines Transformer Encoder Layers (TEL) and Gated Recurrent Units (GRU), extracting both long-term teporal dependencies and short-term variations in vehicle trajectories. Next, Graph Attention Networks (GAT) are introduced to extract spatial features in dynamically changing interactions, and focus on the most noteworthy vehicles, which are beneficial for modeling interactions between vehicles. Meanwhile, Mixed Density Network (MDN) is used to learn potential trajectory distributions from spatiotemporal features, which enriches the features related to driver intentions, providing a probabilistic understanding of possible future trajectories. By predicting multiple trajectories through random sampling, DSTAnet captures the inherent uncertainty of driver intentions. Finally, the INTERACTION dataset is adopted to estimate the model performance and results indicate that the proposed method outperforms others. © 1967-2012 IEEE.

关键词： Distribution transformers

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