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arXiv 2025年

作者： Zhou, Bei Hu, Cheng Zeng, Jun Li, Zhouheng Betz, Johannes Xie, Lei Su, Hongye The State Key Laboratory of Industrial Control Technology Zhejiang University 38 Zheda Road Xihu District Hang zhou Zhejiang310027 China Hybrid Robotics Group The Department of Mechanical Engineering UC Berkeley United States Technical University of Munich Garching85748 Germany

Drift vehicle control offers valuable insights to support safe autonomous driving in extreme conditions, which hinges on tracking a particular path while maintaining the vehicle states near the drift equilibrium points (DEP). However, conventional tracking methods are not adaptable for drift vehicles due to their opposite steering angle and yaw rate. In this paper, we propose an adaptive path tracking (APT) control method to dynamically adjust drift states to follow the reference path, improving the commonly utilized predictive path tracking methods with released computation burden. Furthermore, existing control strategies necessitate a precise system model to calculate the DEP, which can be more intractable due to the highly nonlinear drift dynamics and sensitive vehicle parameters. To tackle this problem, an adaptive learning-based model predictive control (ALMPC) strategy is proposed based on the APT method, where an upper-level Bayesian optimization is employed to learn the DEP and APT control law to instruct a lower-level MPC drift controller. This hierarchical system architecture can also resolve the inherent control conflict between path tracking and drifting by separating these objectives into different layers. The ALMPC strategy is verified on the Matlab-Carsim platform, and simulation results demonstrate its effectiveness in controlling the drift vehicle to follow a clothoid-based reference path even with the misidentified road friction parameter. Copyright © 2025, The Authors. All rights reserved.

关键词： Predictive control systems

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Reinforcement Learning-Driven Plant-Wide Refinery Planning Using Model Decomposition

arXiv

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arXiv 2025年

作者： Li, Zhouchang Lin, Runze Su, Hongye Xie, Lei Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

In the era of smart manufacturing and Industry 4.0, the refining industry is evolving towards large-scale integration and flexible production systems. In response to these new demands, this paper presents a novel optimization framework for plant-wide refinery planning, integrating model decomposition with deep reinforcement learning. The approach decomposes the complex large-scale refinery optimization problem into manageable submodels, improving computational efficiency while preserving accuracy. A reinforcement learning-based pricing mechanism is introduced to generate pricing strategies for intermediate products, facilitating better coordination between submodels and enabling rapid responses to market changes. Three industrial case studies, covering both single-period and multi-period planning, demonstrate significant improvements in computational efficiency while ensuring refinery profitability. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Serrated chip formation in machining of difficult to cut metallic materials: a comprehensive review

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Critical Reviews in Solid state and Materials Sciences 2025年

作者： Zhao, Zejia Liu, Chujun Xu, Bo Wang, Jingwei Yao, Yingxue Yin, Tengfei To, Suet Yang, Yang Institute of Semiconductor Manufacturing Research College of Mechatronics and Control Engineering Shenzhen University Shenzhen China State Key Laboratory of Ultra-precision Machining Technology Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Kowloon Hong Kong Department of Materials Science Shenzhen MSU-BIT University Shenzhen China Shenzhen China

Serrated cutting chips are usually observed in the machining of difficult to cut metallic materials such as titanium alloys, steels, and nickel-based alloys. Understanding the formation mechanism and influencing factors to generate serrated chips is meaningful for improving the machinability of the difficult to cut materials since the formation of serrated chips poses high risks on the vibration of cutting tools and further affects the surface quality of the workpiece. To date, extensive studies have been conducted on the investigation of serrated chip formation in the machining of difficult to cut materials, but there is still a lack of a comprehensive review to clarify the formation mechanism and summarize the factors affecting the serrated chip characteristics. In this review, two commonly used mechanisms, i.e., adiabatic shear band (ASB) theory and periodical crack formation (PCF) theory, are summarized and discussed with respect to theoretical analyses and experimental verifications. Serrated chip formation in different difficult to cut alloys was also systematically reviewed, considering materials inherent properties such as high hardness, poor thermal conductivity, or low fracture toughness. Besides, some critical factors, including cutting speeds, feed rates, cutting tools, external energy sources, humidity, and temperature are highlighted to illustrate their effects on the serrated chip formation. In addition, challenges and perspectives about the serrated chip formation in machining of difficult to cut metallic materials are proposed to give instruction to future research. © 2025 Taylor & Francis Group, LLC.

关键词： Titanium alloys

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Applications of Acoustic Emission Monitoring in Grinding: A Review

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International Journal of Precision Engineering and Manufacturing - Green technology 2025年 1-26页

作者： Jia, Jianfei Guo, Bing Krajnik, Peter Gu, Yan Wu, Guicheng Yao, Honghui Zhao, Huan Li, Kenan Yang, Wei Zhao, Qingliang Center for Precision Engineering School of Mechatronics Engineering Harbin Institute of Technology P.O. Box 413 Harbin150001 China Department of Industrial and Materials Science Chalmers University of Technology Gothenburg Sweden Chongqing Research Institute Harbin Institute of Technology Chongqing401120 China Sichuan Aerospace Fenghuo Servo Control Technology Co. Ltd Chengdu611130 China Zhongshan Sprecision Technology Co. Ltd Zhongshan528437 China State Key Laboratory of Intelligent Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory for High Performance Tools Zhengzhou Abrasive Grinding Research Institute Co. Ltd Henan450001 China

Grinding is typically the final operation in many manufacturing chains owing to its ability to achieve high surface quality. However, any failure in this process can lead to irreparable damage to the workpiece and increased costs. Reliable, online, and onsite monitoring techniques must be implemented to avoid these problems. Over the past decades, various technologies have been developed to monitor grinding processes, utilizing a range of signals. Among these, acoustic emission (AE) has demonstrated significant advantages, including high sensitivity to fine dynamic interactions between the grinding wheel and workpiece, as well as excellent performance in filtering mechanical noise. Consequently, AE is widely recognized as one of the most effective monitoring and detection methods in grinding. However, comprehensive literature reviews on AE monitoring in grinding remain scarce. This paper provides an overview of the history and sources of AE monitoring in grinding. Subsequently, it outlines the process of AE monitoring in grinding and introduces different methods applied in AE monitoring. Lastly, research gaps and future trends for the applications of AE monitoring in grinding are highlighted. Overall, this paper systematically summarizes the monitoring system, analysis methods, and practical applications of AE in grinding, while also identifying knowledge gaps and scope for future work. © The Author(s), under exclusive licence to Korean Society for Precision Engineering 2025.

关键词： Precision engineering

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Dynamic Route Optimization for Multi-Vehicle Systems with Diverse Needs in Road Networks Based on Preference Games

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Computers, Materials and Continua 2025年第3期83卷 4167-4192页

作者： Wang, Jixiang Wei, Jing Chen, Siqi Yu, Haiyang Ren, Yilong School of Transportation Science and Engineering Beihang University Beijing 100191 China School of Electrical and Control Engineering North China University of Technology Beijing 100144 China Hangzhou Innovation Institute Beihang University Hangzhou 310023 China The State Key Lab of Intelligent Transportation System Beijing 100191 China

The real-time path optimization for heterogeneous vehicle fleets in large-scale road networks presents significant challenges due to conflicting traffic demands and imbalanced resource allocation. While existing vehicle-to-infrastructure coordination frameworks partially address congestion mitigation, they often neglect priority-aware optimization and exhibit algorithmic bias toward dominant vehicle classes—critical limitations in mixed-priority scenarios involving emergency vehicles. To bridge this gap, this study proposes a preference game-theoretic coordination framework with adaptive strategy transfer protocol, explicitly balancing system-wide efficiency (measured by network throughput) with priority vehicle rights protection (quantified via time-sensitive utility functions). The approach innovatively combines (1) a multi-vehicle dynamic routing model with quantifiable preference weights, and (2) a distributed Nash equilibrium solver updated using replicator sub-dynamic models. The framework was evaluated on an urban road network containing 25 intersections with mixed priority ratios (10%–30% of vehicles with priority access demand), and the framework showed consistent benefits on four benchmarks (Social routing algorithm, Shortest path algorithm, The comprehensive path optimisation model, The emergency vehicle timing collaborative evolution path optimization method) showed consistent benefits. Results show that across different traffic demand configurations, the proposed method reduces the average vehicle traveling time by at least 365 s, increases the road network throughput by 48.61%, and effectively balances the road loads. This approach successfully meets the diverse traffic demands of various vehicle types while optimizing road resource allocations. The proposed coordination paradigm advances theoretical foundations for fairness-aware traffic optimization while offering implementable strategies for next-generation cooperative vehicle-road systems, particul

关键词： different needs dynamic route selection large-scale road network Preference game vehicle road coordination

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Online process monitoring for complex systems with dynamic weighted principal component analysis

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Chinese Journal of Chemical Engineering 2016年第6期24卷 775-786页

作者： Zhengshun Fei Kangling Liu School of Automation and Electrical Engineering Zhejiang University of Science and Technology Hangzhou 310023 China State Key Lab of Industrial Control Technology Institute of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou 310027 China

Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivated to propose a new monitoring method by compensating the principal component analysis with a weight *** proposed monitor consists of two tiers. The first tier uses the principal component analysis method to extract cross-correlation structure among process data, expressed by independent components. The second tier estimates auto-correlation structure among the extracted components as auto-regressive models. It is therefore named a dynamic weighted principal component analysis with hybrid correlation structure. The essential of the proposed method is to incorporate a weight approach into principal component analysis to construct two new subspaces, namely the important component subspace and the residual subspace, and two new statistics are defined to monitor them respectively. Through computing the weight values upon a new observation, the proposed method increases the weights along directions of components that have large estimation errors while reduces the influences of other directions. The rationale behind comes from the observations that the fault information is associated with online estimation errors of auto-regressive models. The proposed monitoring method is exemplified by the Tennessee Eastman process. The monitoring results show that the proposed method outperforms conventional principal component analysis, dynamic principal component analysis and dynamic latent variable.

关键词： Principal component analysisWeightOnline process monitoringDynamic

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Boosting multi-features with prior knowledge for mini unmanned helicopter landmark detection

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2008年第2期9卷 256-261页

作者： Qin-yuan REN Ping LI Bo HAN The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China

Without sufficient real training data,the data driven classification algorithms based on boosting method cannot solely be utilized to applications such as the mini unmanned helicopter landmark image *** this paper,we propose an approach which uses a boosting algorithm with the prior knowledge for the mini unmanned helicopter landmark image *** stage forward stagewise additive model of boosting is analyzed,and the approach how to combine it with the prior knowledge model is *** approach is then applied to landmark image detection,where the multi-features are boosted to solve a series of problems,such as rotation,noises affected,*** of real flight experiments demonstrate that for small training examples the boosted learning system using prior knowledge is dramatically better than the one driven by data only.

关键词： Boosting Prior knowledge model Landmark detection

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Model of atomized droplets average particle size and verification of eco-friendly hybrid lubrication (CAMQL)

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Friction 2025年第5期13卷

作者： Liu, Mingzheng Li, Changhe Jia, Dongzhou Liu, Xin Zhang, Yanbin Yang, Min Cui, Xin Gao, Teng Dambatta, Yusuf Suleiman Li, Runze Key Lab of Industrial Fluid Energy Conservation and Pollution Control Ministry of Education Qingdao University of Technology Qingdao266520 China Qingdao Jimo Qingli Intelligent Manufacturing Industry Research Institute Qingdao266201 China College of Mechanical Engineering and Automation Liaoning University of Technology Jinzhou121001 China School of Mechanical Engineering Dalian University of Technology Dalian116081 China School of Engineering Department of Biomedical Engineering Massachusetts Institute of Technology Boston02139 United States

In the precision cutting of difficult-to-process metals, surface thermal damage to a workpiece is a significant technical challenge. Although clean minimum quantity lubrication (MQL) technology, which replaces traditional pouring cooling, is used, inadequate heat dissipation remains an issue. Cryogenic air MQL (CAMQL), an eco-friendly technology, can enhance the heat transfer performance of the lubricating film in the cutting zone, offering excellent cooling and lubrication effects. However, the influence of jet and temperature parameters on the average particle size and distribution characteristics of atomized droplets is not well understood. This study first analyzes the evolution of lubricant physical properties and establishes a quantitative mapping relationship between cryogenic air temperature and physical parameters of lubricant. Next, the unstable fluctuation in the annular liquid film at the two-phase flow nozzle exit is observed and analyzed. A thickness model of annular liquid film is developed, revealing the effect of airflow field on the annular liquid film. Finally, a model for the average particle size of atomized droplets under CAMQL is established. Numerical analysis and validation experiments under different working conditions show that the measured values align with the theoretical values. Under an air pressure of 0.4 MPa and an air flow temperature of −50 °C, the droplet particle size is 133.5 μm, with an error of 8.2%. The effect of air pressure on particle size is greater than that of air flow temperature. Additionally, the distribution spans of droplet size under different conditions are analyzed, and the results demonstrated that low temperatures help shorten the interval between particle sizes and improve the relative uniformity of particle size distribution. This research provides a theoretical basis for the application of CAMQL technology in the cutting process. © The Author(s) 2025.

关键词： Numerical models

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A fuzzy compensation-Koopman model predictive control design for pressure regulation in proten exchange membrane electrolyzer

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Chinese Journal of Chemical Engineering 2024年第12期76卷 251-263页

作者： Haokun Xiong Lei Xie Cheng Hu Hongye Su State Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou 310027China

Proton exchange membrane(PEM)electrolyzer have attracted increasing attention from the industrial and researchers in recent years due to its excellent hydrogen production *** accurate models to predict their performance is crucial for promoting and accelerating the design and optimization of electrolysis *** work developed a Koopman model predictive control(MPC)method incorporating fuzzy compensation for regulating the anode and cathode pressures in a PEM electrolyzer.A PEM electrolyzer is then built to study pressure control and provide experimental data for the identification of the Koopman linear *** identified linear predictors are used to design the Koopman *** addition,the developed fuzzy compensator can effectively solve the Koopman MPC model mismatch *** effectiveness of the proposed method is verified through the hydrogen production process in PEM simulation.

关键词： Hydrogen production PEM electrolyzer Nonlinear control Model predictive control Koopman operator Fuzzy logic system

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Simultaneous approach for simulation of a high-temperature gas-cooled reactor

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2011年第7期12卷 567-574页

作者： Yang CHEN Jiang-hong YOU Zhi-jiang SHAO Ke-xin WANG Ji-xin QIAN State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027 China

The simulation of a high-temperature gas-cooled reactor pebble-bed module(HTR-PM) plant is *** lumped parameter model has the form of a set differential algebraic equations(DAEs) that include stiff equations to model point neutron *** nested approach is the most common method to solve DAE,but this approach is very expensive and time-consuming due to inner *** paper deals with an alternative approach in which a simultaneous solution method is *** DAEs are discretized over a time horizon using collocation on finite elements,and Radau collocation points are *** resulting nonlinear algebraic equations can be solved by existing *** discrete algorithm is discussed in detail;both accuracy and stability issues are ***,the simulation results are presented to validate the efficiency and accuracy of the simultaneous approach that takes much less time than the nested one.

关键词： Differential algebraic equations(DAEs) High-temperature gas-cooled reactor(HTR) Simulation Simultaneous approach

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