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2024 China automation Congress, CAC 2024

作者： Liu, Xiao Chen, Luefeng Lu, Chengda Fan, Haipeng Wu, Min Pedrycz, Witold School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey

ISBN: (纸本)9798350368604

During the coal seam drilling process, the drill string is subject to compressive deformation, compounded by unpredictable variations in formation hardness and borehole wall friction, leading to challenges in maintaining a stable feeding speed. This paper presents a novel approach by introducing uncertain parameters to describe the effects of formation hardness and borehole wall friction. Drill string axial movement model is modeled as a polyhedral system based on a lumped parameter representation. To meet industrial performance requirements, we design a robust H∞ controller to achieve consistent feeding speed control. Our simulation results demonstrate the controller’s effectiveness in ensuring system stability despite fluctuations in formation hardness and drill string friction. © 2024 IEEE.

关键词： Coal deposits

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DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds

DELFlow: Dense Efficient Learning of Scene Flow for Large-Sc...

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International Conference on Computer Vision (ICCV)

作者： Chensheng Peng Guangming Wang Xian Wan Lo Xinrui Wu Chenfeng Xu Masayoshi Tomizuka Wei Zhan Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University Mechanical Systems Control Laboratory University of California Berkeley

Point clouds are naturally sparse, while image pixels are dense. The inconsistency limits feature fusion from both modalities for point-wise scene flow estimation. Previous methods rarely predict scene flow from the entire point clouds of the scene with one-time inference due to the memory inefficiency and heavy overhead from distance calculation and sorting involved in commonly used farthest point sampling, KNN, and ball query algorithms for local feature aggregation. To mitigate these issues in scene flow learning, we regularize raw points to a dense format by storing 3D coordinates in 2D grids. Unlike the sampling operation commonly used in existing works, the dense 2D representation 1) preserves most points in the given scene, 2) brings in a significant boost of efficiency, and 3) eliminates the density gap between points and pixels, allowing us to perform effective feature fusion. We also present a novel warping projection technique to alleviate the information loss problem resulting from the fact that multiple points could be mapped into one grid during projection when computing cost volume. Sufficient experiments demonstrate the efficiency and effectiveness of our method, outperforming the prior-arts on the FlyingThings3D and KITTI dataset. Our source codes will be released on https://***/IRMVLab/DELFlow.

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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Simulation Analysis and Model Modification of the LWR Traffic Flow Theory

Simulation Analysis and Model Modification of the LWR Traffi...

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IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO

作者： Yang Chen Jun Fu Anton Selivanov State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Department of Automatic Control and Systems Engineering University of Sheffield UK

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

The surge in urbanization and ensuing traffic congestion underscores the imperative for contemporary traffic flow control models. This paper explores the Lighthill-Whitham-Richards (LWR) theory in the macroscopic domain of traffic flow control. It evaluates the accuracy and mathematical logic of simulation performance of related models, namely Greenshields, Newell-Daganzo, Greenberg, Underwood, and California. This paper employs thorough simulations that utilize a holistic three-dimensional(3D) analysis based on time, location, and density. It provides a comprehensive comparison and discusses specific features of each model. Additionally, a modification to the macroscopic model is proposed. The modified model overcomes the shortcomings of performing well only in some specific short ranges and achieves a full range of application effects. The study offers valuable guidance for real-life scenario selections and applications in the traffic domain.

关键词： Analytical models Accuracy Urban areas Transportation Predictive models Mathematical models Numerical models

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On Uniform Observability of Linear Time-varying systems with respect to Subspaces and Its Applications

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IEEE Transactions on Automatic control 2025年

作者： Liu, Tao Huang, Jie Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Shenzhen518055 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin Hong Kong

Recently, the notion of uniform observability with respect to a subspace was proposed in [21] and was utilized to solve the synchronization problem for single-integrator multi-agent systems with time-varying matrix-weighted couplings. This paper further explores the concept of uniform observability of a linear time-varying system with respect to a subspace. We will first establish the invariance property of uniform observability with respect to some invariant subspace under time-varying output feedback control. Then, based on this invariance property, we will establish an exponential convergence result with respect to an invariant subspace for a class of linear switched systems. Leveraging this exponential convergence result, we will solve the exponential synchronization problem for general linear multi-agent systems over jointly connected switching networks. It is noted that the exponential synchronization problem was also studied recently by some more sophisticated methods. Compared with these methods, our method is much simpler. Moreover, our method leads to an explicit characterization of the exponential synchronization rate. © 1963-2012 IEEE.

关键词： Linear time-invariant system

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Fast Model Predictive control of Input-Affine systems: Application to the Hindmarsh-Rose Neuron Model

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IFAC-PapersOnLine 2024年第28期58卷 354-359页

作者： Alexandra Grancharova Junhong Xie Sorin Olaru Department of Industrial Automation University of Chemical Technology and Metallurgy Bul. Kl. Ohridski 8 Sofia 1756 Bulgaria Laboratory of Signals and Systems CentraleSupélec-CNRS-Université Paris-Sud Université Paris-Saclay Gif-sur-Yvette Cedex France

The paper presents a low complexity nonlinear MPC design for the class of constrained input-affine systems. Essentially, it builds on the idea of adding a contractive constraint in the NMPC problem formulation, which would ensure the closed-loop system stability when using a small prediction horizon. In particular, the one-step ahead NMPC problem with contractive constraint is considered and an approach to obtain an efficient online solution of the associated convex quadratically constrained quadratic programming problem is developed. The proposed technique is shown to be effective for embedded convex NMPC of input-affine systems, since it will reduce the computational complexity of the online NMPC and simplify the software and hardware implementation. The methodological developments are illustrated with simulations on the Hindmarsh-Rose neuron model.

关键词： Predictive control Nonlinear systems Constraints Convex programming Computational complexity

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Batch-to-Batch Optimization with Model Adaptation Leveraging Gaussian Processes: The Case of Optogenetically Assisted Microbial Consortia 23

Batch-to-Batch Optimization with Model Adaptation Leveraging...

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23rd International Conference on control, automation and systems, ICCAS 2023

作者： Espinel-Rios, Sebastian Kok, Rudolph Klamt, Steffen Avalos, Jose L. Findeisen, Rolf Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Princeton University Department of Chemical and Biological Engineering Princeton United States Institute for Automation Engineering Otto von Guericke University Magdeburg Magdeburg Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

ISBN: (纸本)9788993215274

Microbial consortia are promising biotechnological production systems with the potential to divide complex metabolic pathways into smaller submodules, as well as make products and consume substrates that monocultures cannot. Maintaining optimal cell population levels and preventing mono culture formation challenge bioproduction by microbial consortia. Optogenetics allows for regulating the expression of key growth-regulatory genes through light to modulate cell population levels. Model-based dynamic optimization can determine optimal light trajectories and inoculum sizes that maximize product synthesis while satisfying system constraints, e.g., safety, economic, or technical aspects. Further-more, closed-loop dynamic optimization can address system uncertainty to a certain extent;however, its implementation is challenging due to limited online sensors. Alternatively, here we propose to perform open-loop optimization with batch-to-batch model adaptation based on Gaussian processes for maximizing bioproduction by optogenetically assisted consortia. The proposed approach enables knowledge transfer from existing to new models, improving predictability and optimization performance in each batch while avoiding costly and time-consuming modeling experiments. Compared to closed-loop optimization, this strategy is easier to implement as it does not rely on online monitoring, contributing to the state of the art in optimizing bioproduction by microbial consortia. We outline the applicability of the approach using simulation experiments of an optogenetically assisted consortium for the biosynthesis of the flavonoid naringenin, considering both parameter and model structure uncertainty. © 2023 ICROS.

关键词： Substrates

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A Modified Artificial Potential Field Method Based on Subgoal Points for Mobile Robot 16th

A Modified Artificial Potential Field Method Based on Subgoa...

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Mo, Jixue Gao, Changqing Liu, Fei Yang, Qingkai Fang, Hao Department of Mathematics and Theories Pengcheng Laboratory Shenzhen518055 China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Industrial Training Center Shenzhen Polytechnic Shenzhen518055 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819964826

In this paper, a modified artificial potential field (MAPF) method was proposed for general mobile robot navigation system. This MAPF method can effectively solve the unreachable goal problem and the local minima problem of traditional APF method. The first key idea of MAPF method is to modify the repulsive force function and thus optimize the direction of total repulsive force;the second key idea is to generate a serials of subgoal points around obstacles with specific method, so as to help the robot escape from or keep away from local minima area. By comparing with other similar algorithms in simulation environment, the MAPF algorithm can generate shorter paths efficiently. More importantly, it also can generate effective paths in multi-obstacle environment where other algorithms cannot. Finally the simulation results verified the reasonability and practicability of the proposed MAPF method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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NA-CPG: A robust and stable rhythm generator for robot motion control

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Biomimetic Intelligence & Robotics 2022年第4期2卷 30-38页

作者： Ru Tong Changlin Qiu Zhengxing Wu Jian Wang Min Tan Junzhi Yu State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China

Central pattern generators(CPGs)have been widely applied in robot motion control for the spontaneous output of coherent periodic ***,the underlying CPG network exhibits good convergence performance only within a certain range of parameter spaces,and the coupling of oscillators affects the network output accuracy in complex topological ***,CPGs may diverge when parameters change drastically,and the divergence is irreversible,which is catastrophic for the control of robot ***,normalized asymmetric CPGs(NA-CPGs)that normalize the amplitude parameters of Hopf-based CPGs and add a constraint function and a frequency regulation mechanism are ***-CPGs can realize parameter decoupling,precise amplitude output,and stable and rapid convergence,as well as asymmetric output ***,it can effectively cope with large parameter changes to avoid network oscillations and *** optimize the parameters of the NA-CPG model,a reinforcement-learning-based online optimization method is further ***,a biomimetic robotic fish is illustrated to realize the whole optimization *** demonstrated that the designed NA-CPGs exhibit stable,secure,and accurate network outputs,and the proposed optimization method effectively improves the swimming speed and reduces the lateral swing of the multijoint robotic fish by 6.7%and 41.7%,*** proposed approach provides a significant improvement in CPG research and can be widely employed in the field of robot motion control.

关键词： Normalized asymmetric CPGs Rhythm generator Motion control Robotic fish

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