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Fault Detection, Supervision and Safety for Technical processes (SAFEprocess), CAA Symposium on

作者： Chen GuangWu Ba DeJun Zhou Xin Li Peng School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou China Gansu Provincial Key Laboratory of Highland Traffic Information Engineering and Control Institute of Automatic Control Lanzhou Jiaotong University Lanzhou China

ISBN: (纸本)9781665401166

During the drilling process of an oil rig, the torsional stiffness decrease as the drilling depth increase and it leads to a stick-slip vibration of the drill string system. The dynamic process of torsional stiffness and moment of inertia is complex while it is hard to control the influence of model parameters. In this paper, the mechanism of vibration that meets the actual situation is analyzed, and a new method that uses fuzzy PID control with the Smith predictor is proposed. The simulation and results show that the proposed method can suppress the hysteresis effect and has a certain inhibitory effect on the occurrence of stick-slip vibration.

关键词： Vibrations Drilling PI control Oils Fault detection process control Safety

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Peak Time-Windowed Mean Estimation using Convex Optimization

Peak Time-Windowed Mean Estimation using Convex Optimization

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IEEE Conference on Decision and control

作者： Jared Miller Niklas Schmid Matteo Tacchi Didier Henrion Roy S. Smith Department of Information Technology and Electrical Engineering (D-ITET) ETH Zürich Physikstrasse 3 Automatic Control Laboratory (IfA) and NCCR Automation Zürich Switzerland Univ. Grenoble Alpes CNRS Grenoble INP (Institute of Engineering Univ. Grenoble Alpes) GIPSA-lab Grenoble France LAAS-CNRS Université de Toulouse CNRS Toulouse France and the Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper presents an algorithmic approach towards bounding the peak time-windowed average value attained by a state function along trajectories of a dynamical system. An example includes the maximum average current flowing across a power line in any 5 -minute window. The peak time-windowed mean estimation task may be posed as a finite-dimensional but nonconvex optimization problem in terms of an initial condition and stopping time. This problem can be lifted into an infinite-dimensional linear program in occupation measures, where no conservatism is introduced under compactness and dynamical regularity assumptions. The peak time-windowed mean estimation linear program is in turn truncated into a convergent sequence of semidefinite programs using the moment-Sum-of-Squares hierarchy. Bounds of the time-windowed mean are computed for example systems.

关键词： Heuristic algorithms Current measurement Estimation Convex functions Trajectory Dynamical systems Optimization

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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on automatic control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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Domain transfer spatiotemporal graph network for predicting key indicators in multimode chemical processes

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Chemical engineering Science 2025年 314卷

作者： Zhu, Jialiang Yang, Chao Liu, Yi Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

In modern chemical industries, soft sensors related to key indicators are crucial in production processes, particularly for multimode chemical processes. However, challenges arise from scarce labeled data and constant process shifts, which complicate the development of these sensors. Additionally, complex process variable interactions hinder prediction interpretability. This work develops a domain transfer spatiotemporal graph network (DTSGN) regression model, tailored for cross-domain modeling in multimode chemical processes. The DTSGN model first employs graph convolution to capture spatial dependencies and gating mechanism to track temporal relationships. A Bernoulli-distributed perturbation graph enhances model robustness. By embedding constraints into conditional distribution discrepancy, the parameters for DTSGN are calibrated under the fine-tuning paradigm for new mode. Therefore, the model extends its applicability to address process shifts in multimode chemical processes. A graph explainer finally ensures interpretability by aligning learned variable graph with process knowledge, and two multimode cases evidence the proposed model's efficacy and superiority. © 2025 Elsevier Ltd

关键词： Chemical industry

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Teaching MPC: Which Way to the Promised Land?

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IFAC-PapersOnLine 2021年第6期54卷 238-243页

作者： Timm Faulwasser Sergio Lucia Moritz Schulze Darup Martin Mönnigmann Institute of Energy Systems Energy Efficiency and Energy Economics TU Dortmund University Dortmund Germany Laboratory of Process Automation Systems TU Dortmund University Dortmund Germany Control and Cyberphysical Systems TU Dortmund University Dortmund Germany Automatic Control and Systems Theory Ruhr University Bochum Bochum Germany

Since the earliest conceptualizations by Lee and Markus, and Propoi in the 1960s, Model Predictive control (MPC) has become a major success story of systems and control with respect to industrial impact and with respect to continued and wide-spread research interest. The field has evolved from conceptually simple linear-quadratic (convex) settings in discrete and continuous time to nonlinear and distributed settings including hybrid, stochastic, and infinite-dimensional systems. Put differently, essentially the entire spectrum of dynamic systems can be considered in the MPC framework with respect to both—system theoretic analysis and tailored numerics. Moreover, recent developments in machine learning also leverage MPC concepts and learning-based and data-driven MPC have become highly active research areas. However, this evident and continued success renders it increasingly complex to live up to industrial expectations while enabling graduate students for state-of-the-art research in teaching MPC. Hence, this position paper attempts to trigger a discussion on teaching MPC. To lay the basis for a fruitful debate, we subsequently investigate the prospect of covering MPC in undergraduate courses; we comment on teaching textbooks; and we discuss the increasing complexity of research-oriented graduate teaching of MPC.

关键词： Model Predictive control Teaching

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Repetitive process based design of PD-type iterative learning control laws for batch processes with time-delays

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IFAC-PapersOnLine 2023年第2期56卷 1901-1906页

作者： Robert Maniarski Wojciech Paszke Eric Rogers Hongfeng Tao Institute of Automation Electronic and Electrical Engineering University of Zielona Góra ul. Szafrana 2 65-516 Zielona Góra Poland School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi 214122 P. R. China

This paper uses linear matrix inequality techniques and the Kalman-Yakubovich-Popov lemma to design an iterative learning control law for a class of batch processes with state delays. The design procedure is based on the stability theory for repetitive processes, a class of 2D systems. A numerical example illustrates the new design and demonstrates that the design has advantages compared to the existing alternatives.

关键词： Iterative learning control repetitive processes batch processes linear matrix inequalities systems with delays

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Distributed Optimal Power Flow for VSC-MTDC Meshed AC/DC Grids Using ALADIN

Distributed Optimal Power Flow for VSC-MTDC Meshed AC/DC Gri...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Junyi Zhai Xinliang Dai Yuning Jiang Ying Xue Veit Hagenmeyer Colin N. Jones Xiao-Ping Zhang College of New Energy China University of Petroleum (UPC) China Karlsruhe Institute of Technology (KIT) Institute for Automation and Applied Informatics Germany Automatic Control Laboratory École Polytechnique Fédérale de Lausanne (EPFL) Switzerland School of Electric Power Engineering South China University of Technology (SCUT) China Department of Electronic Electrical and Systems Engineering University of Birmingham (UBir) Birmingham United Kingdom

The increasing application of voltage source converter (VSC) based high voltage direct current (VSC-HVDC) technology in power grids has raised the importance of incorporating DC grids and converters into the existing transmission network. This poses significant challenges in dealing with the resulting optimal power flow (OPF) problem. In this paper, a recently proposed nonconvex distributed optimization algorithm — Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN), is tailored to solve the nonconvex AC/DC OPF problem for emerging voltage source converter (VSC) based multiterminal high voltage direct current (VSC-MTDC) meshed AC/DC hybrid systems. The proposed scheme decomposes this AC/DC hybrid OPF problem and handles it in a fully distributed way. Compared to the existing state-of-art Alternating Direction Method of Multipliers (ADMM), which is in general, not applicable for nonconvex problems, ALADIN has a theoretical convergence guarantee. Applying these two approaches to VSC-MTDC coupled with an IEEE benchmark AC power system illustrates that the tailored ALADIN outperforms ADMM in convergence speed and numerical robustness.

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Distributed Optimal Power Flow for VSC-MTDC Meshed AC/DC Grids Using ALADIN

arXiv

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arXiv 2022年

作者： Zhai, Junyi Dai, Xinliang Jiang, Yuning Xue, Ying Hagenmeyer, Veit Jones, Colin N. Zhang, Xiao-Ping Qingdao China City & Energy Research Institute Suzhou China Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Automatic Control Laboratory EPFL Switzerland School of Electric Power Engineering South China University of Technology Guangzhou China Department of Electronic Electrical and Systems Engineering University of Birmingham Birmingham United Kingdom

The increasing application of voltage source converter (VSC) based high voltage direct current (VSC-HVDC) technology in power grids has raised the importance of incorporating DC grids and converters into the existing transmission network. This poses significant challenges in dealing with the resulting optimal power flow (OPF) problem. In this paper, a recently proposed nonconvex distributed optimization algorithm—Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN), is tailored to solve the nonconvex AC/DC OPF problem for emerging voltage source converter (VSC) based multiterminal high voltage direct current (VSC-MTDC) meshed AC/DC hybrid systems. The proposed scheme decomposes this AC/DC hybrid OPF problem and handles it in a fully distributed way. Compared to the existing state-of-art Alternating Direction Method of Multipliers (ADMM), which is in general, not applicable for nonconvex problems, ALADIN has a theoretical convergence guarantee. Applying these two approaches to VSC-MTDC coupled with an IEEE benchmark AC power system illustrates that the tailored ALADIN outperforms ADMM in convergence speed and numerical robustness. Copyright © 2022, The Authors. All rights reserved.

关键词： Constrained optimization

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Deformation Pattern Shape Sensing for Visuotactile Endoscope

Deformation Pattern Shape Sensing for Visuotactile Endoscope

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Yanzhe Yu Yuan Tian Shaowei Cui Yupeng Wang Rui Wang Shuo Wang Beijing University of Science and Technology Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

The integration of visuotactile sensor technology with endoscopy enables the provision of tactile information for minimally invasive surgeries and other operations. This paper proposes a real-time sensing framework for contact deformation pattern shape, providing a foundation for precise perception in miniaturized visuotactile sensors. Specifically, we first construct a Deformation Pattern shape Descriptor (DPSD) and then track the key points in the descriptor using optical flow to obtain contact deformation shape information. Through extensive qualitative and quantitative experiments, our proposed algorithm achieves an average perception accuracy of around 83.81% for contact pattern shapes. It demonstrates consistent perception accuracy across various contact force magnitudes, directions, and scenarios, highlighting its robustness. Additionally, compared to traditional marker-level methods, it achieves a notable 46.5% improvement in displacement field perception accuracy. This research elevates tactile perception to the dimension of contact pattern shape sensing and has the potential for application in miniaturized visual tactile sensors.

关键词： Visualization Accuracy Shape Deformation Tracking Endoscopes Shape measurement

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Quantized Distributed Nonconvex Optimization with Linear Convergence

Quantized Distributed Nonconvex Optimization with Linear Con...

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IEEE Conference on Decision and control

作者： Lei Xu Xinlei Yi Jiayue Sun Yang Shi Karl Henrik Johansson Tao Yang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper considers distributed nonconvex op-timization for minimizing the average of local cost functions, by using local information exchange over undirected communication networks. Since the communication channels often have limited bandwidth or capacity, we first introduce a quantization rule and an encoder/decoder scheme to reduce the transmission bits. By integrating them with a distributed algorithm, we then propose a distributed quantized nonconvex optimization algorithm. Assuming the global cost function satisfies the Polyak– Łojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that the proposed algorithm linearly converges to a global optimal point. Moreover, a low data rate is shown to be sufficient to ensure linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical simulation examples.

关键词： Quantization (signal) Communication channels Bandwidth Cost function Numerical simulation Communication networks Distributed algorithms

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