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arXiv 2023年

作者： Piao, Shiyuan Gu, Hong Wang, Aina Qin, Pan Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education The School of Control Science and Engineering Dalian University of Technology Dalian116024 China

Maxwell’s equations are a collection of coupled partial differential equations (PDEs) that, together with the Lorentz force law, constitute the basis of classical electromagnetism and electric circuits. Effectively solving Maxwell’s equations is crucial in various fields, like electromagnetic scattering and antenna design optimization. Physics-informed neural networks (PINNs) have shown powerful ability in solving PDEs. However, PINNs still struggle to solve Maxwell’s equations in heterogeneous media. To this end, we propose a domain-adaptive PINN (da-PINN) to solve inverse problems of Maxwell’s equations in heterogeneous media. First, we propose a location parameter of media interface to decompose the whole domain into several sub-domains. Furthermore, the electromagnetic interface conditions are incorporated into a loss function to improve the prediction performance near the interface. Then, we propose a domain-adaptive training strategy for da-PINN. Finally, the effectiveness of da-PINN is verified with two case studies. Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse problems

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control Method for Desorption Tower Liquid Level in Fluidized Catalytic Cracking Based on LESO-DMC

Control Method for Desorption Tower Liquid Level in Fluidize...

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Chinese Automation Congress (CAC)

作者： Jinkun Xiao Haibo Zhu Fan Zhou Kaibiao Sun Jun Zhao Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology School of Control Science and Engineering Dalian University of Technology Dalian China

Fluidized catalytic cracking (FCC) units are crucial equipment in the petroleum refining process, and the level of control directly impacts product quality and yield. To improve the control level of the desorption tower in FCC, this paper proposes a dynamic matrix control (DMC) approach based on the active disturbance rejection strategy. Based on DMC, a linear extended state observer (LESO) is constructed to compensate for model plant mismatch (MPM) and unknown disturbances. The observer bandwidth parameters are determined to optimize robustness, enabling the implementation of active disturbance rejection model predictive control in FCC units. A comparative experimental study is conducted, comparing the proposed method with the traditional DMC approach that does not account for uncertainties and disturbances. The impact of different disturbances on the control strategy is discussed. Experimental results demonstrate that the proposed method exhibits superior control performance and stronger disturbance rejection capability for the liquid level in the desorption tower of the FCC unit.

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Assessment of crystal image sharpness during crystallization based on multi-scale feature extraction

Assessment of crystal image sharpness during crystallization...

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第33届中国过程控制会议

作者： Jiayu He Tao Liu Ji Fan Yongcan Shuang Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Institute of Advanced Control Technology Dalian University of Technology

For the sharpness assessment of crystal images in-situ snapshotted from crystallization process,a novel no-reference sharpness assessment method based on multi-scale feature extraction in spatial and spectral domains is proposed in this ***,the multi-scale re-blurring is performed on crystal images to obtain the multi-scale ***,the sharpness features of crystal images are extracted in multi-scale *** extracting the gradient features,a local maximum gradient pooling algorithm is designed to acquire good ***,the crystal images are down-sampled multiple times to extract multi-scale resolution discrete cosine transform(DCT) domain ***,support vector regression is used to map the feature vectors to quality *** results on the cooling crystallization experiments of β-form L-glutamic acid well demonstrate the effectiveness and advantages of the proposed assessment method.

关键词： Image sharpness assessment in-situ image analysis multi-scale space maximum gradient singular value decomposition multi-scale resolution entropy

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Research on Adaptive Fault-Tolerant control of Quadrotor UAV Based on Improved Nonlinear Extended State Observer

Research on Adaptive Fault-Tolerant Control of Quadrotor UAV...

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International Conference on Information Science and technology (ICIST)

作者： Zongshan Cai Kaibiao Sun Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology School of Control Science and Engineering Dalian University of Technology Dalian China

In this paper, a parameter-uncertain quadrotor UAV system with unknown external disturbances and actuator failures is studied, and a parameter adaptive fault-tolerant control scheme is based on an improved nonlinear extended state Man-machine trajectory tracking control. First, considering the influence of the unknown multi-source external environmental disturbance on the quadrotor UAV, the external disturbance suffered by the system is estimated and compensated in real-time by introducing an improved nonlinear extended state observer. Secondly, a new type of sliding mode reaching law is designed by using the continuous function with relay characteristics combined with the exponential function, which improves the sliding mode response speed of the system and eliminates the chattering of the sliding mode. Finally, a parameter adaptive law is designed for the actuator fault, and an adaptive fault-tolerant controller is designed by combining the backstepping sliding mode method, and the stability of the control system is proved by using the Lyapunov stability theory.

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Coverage control in Multiagent Games on Arbitrary Connected Networks

Coverage Control in Multiagent Games on Arbitrary Connected ...

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Asian control Conference

作者： Shaoqing Zhang Pengyang Fan Weiguo Xia Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering Dalian University of Technology China Shenyang Aircraft Design and Research Institute Shenyang China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, we address the heterogeneous multiagent coverage control problem. While existing algorithms for coverage control are typically designed for specific communication network architectures, there is a notable gap between the available algorithms and the need for developing algorithms suitable for arbitrary connected communication networks. To mitigate this gap, we propose a distributed network adjustment strategy aimed at optimizing network structure to enhance the coverage performance across arbitrary connected communication networks. In terms of the coverage performance, we design a coverage utility function based on network games in dynamic networks and formulate a potential game function, thereby transforming the coverage game problem in dynamic networks into a max-min problem. Additionally, we introduce the Dynamic Network Region Coverage Algorithm to address the coverage game problem in dynamic networks. Finally, we validate the effectiveness of our algorithm through simulation experiments.

关键词： Network topology Heuristic algorithms Games Minimax techniques Numerical simulation Numerical models Communication networks

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Distributed Adaptive Robust Scheduling optimization for Energy System of Steel Industry Considering Prediction Uncertainties

SSRN

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SSRN 2023年

作者： Wang, Zhiyuan Han, Zhongyang Zhao, Jun Wang, Wei School of Control Sciences and Engineering Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian China

Trend prediction is the key foundation for the dynamic scheduling optimization of the energy system in the steel industry. However, the involved uncertainties often lead to insufficient optimization or over-adjustment. To address the above problem, a distributed optimization framework for the integrated gas-steam-electricity energy system in the steel industry is proposed in this study. A Robust optimization (RO) model is established at first to mathematically address prediction uncertainties. In this model, the gas, steam, and electricity distribution networks are considered as different participants, with their operating costs and CO2 emissions costs as the objectives for minimization. To parallelize with the three types of energy media in practice, the general Alternating Direction Multiplier Method (ADMM) is extended to a 3-block structure in this study for solving the RO model in a distributed architecture. For each block, the Column-and-Constraint Generation (C&CG) algorithm is deployed. To ensure convergence considering the interaction between the updated dual and coupling variables in each iteration of the overall ADMM and the individual C&CG, an adaptive method bridging these two algorithms is developed in this study. Finally, experiments using real data from a Chinese steel plant demonstrates superior efficiency and robustness comparing with other commonly deployed algorithms. © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Cooperative differential games guidance laws for multiple attackers against an active defense target

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Chinese Journal of Aeronautics 2022年第5期35卷 374-389页

作者： Fei LIU Xiwang DONG Qingdong LI Zhang REN School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100083China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of ChinaChongqing UniversityChongqing 400044China

This paper is concerned with a scenario of multiple attackers trying to intercept a target with active *** types of agents are considered in the guidance:The multiple attackers,the target and the defender,where the attackers aim to pursuit the target from different directions and evade from the defender *** guidance engagement is formulated in the framework of a zero-sum two-person differential game between the two opposing teams,such that the measurements on the maneuver of the target or estimations on the defending strategy of the defender can be *** of the attackers resides in two aspects:redundant interception under the threat of the defender and the relative intercept geometry with the *** miss distances,the relative intercept angle errors and the costs of the agents are combined into a single performance index of the *** formulation enables a unitary approach to the design of guidance laws for the *** minimize the control efforts and miss distances for the attackers,an optimization method is proposed to find the best anticipated miss distances to the defender under the constraint that the defender is endowed with a capture *** simulations with two cases are conducted to illustrate the effectiveness of the proposed cooperative guidance law.

关键词： Active defense Cooperative guidance Differential games Minimum effort optimization Relative intercept angle

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Multi-stage ARSVAE-based Detection Framework for DDoS Attack of Cyber-Energy System in Industrial Park

Multi-stage ARSVAE-based Detection Framework for DDoS Attack...

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2022 Chinese Automation Congress, CAC 2022

作者： Lv, Xiaoquan Jin, Feng Chen, Lie Zhao, Jun Wang, Wei Dalian University of Technology Ministry of Education Key Laboratory of Intelligent Control and Optimization for Industrial Equipment China School of Control Science and Engineering Dalian University of Technology Dalian China Jiangsu HENGA Automation Equipment Co. Ltd. Yangzhou China

ISBN: (纸本)9781665465335

The informatization of industrial parks increases the risk of the cyber-energy system (CES) attacked by hackers, which affects the security and stability of the production process. Among all kinds of the network attacks, distributed denial of service (DDoS) attack is one of the main threats to the CES, which may occupy the bandwidth and the memory resources a lot so as to destroy the normal running mode of the local host. In this study, a detection framework based on multi-stage adaptive recycling skip variational auto-encoder (MARSVAE) is proposed for CES under the condition of unbalanced data. To extract the probabilistic feature in the first stage, an improved variational auto-encoder based on skip structure with adaptive parameters and recycling structure (ARSVAE) is designed to improve the convergence speed. Then, ARSVAE is presented to solve the problem of imbalanced features representation for identifying anomalies at the second stage. Experiments by employing four public datasets indicate that the proposed MARSVAE is capable of reducing false alarm rate and achieving a detection precision of more than 99%. © 2022 IEEE.

关键词： Recycling

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A Causal Fusion Inference Method for Industrial Alarm Root Cause Analysis Based on process Topology and Alarm Event Data

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Journal of Beijing Institute of technology 2022年第4期31卷 371-381页

作者： Pan Zhang Wenkai Hu Xiangxiang Zhang Jianqi An School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China

Modern industrial systems are usually in large scale,consisting of massive components and variables that form a complex system *** to the interconnections among devices,a fault may occur and propagate to exert widespread influences and lead to a variety of *** the root causes of alarms is beneficial to the decision supports in making corrective alarm *** data-driven methods for alarm root cause analysis detect causal relations among alarms mainly based on historical alarm event *** improve the accuracy,this paper proposes a causal fusion inference method for industrial alarm root cause analysis based on process topology and alarm events.A Granger causality inference method considering process topology is exploited to find out the causal relations among *** topological nodes are used as the inputs of the model,and the alarm causal adjacency matrix between alarm variables is obtained by calculating the likelihood of the topological Hawkes *** root cause is then obtained from the directed acyclic graph(DAG)among alarm *** effectiveness of the proposed method is verified by simulations based on both a numerical example and the Tennessee Eastman process(TEP)model.

关键词： roots cause analysis causality inference process topology alarm events

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Learning Adaptive Mapless Navigation Skills Through Evidential Deep Learning

Learning Adaptive Mapless Navigation Skills Through Evidenti...

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

作者： Cancan Zhao Yufeng Yan Xuhao Liu Shuai Zhang Bo Ouyang School of Management Hefei University of Technology Hefei China The Key Laboratory of Process Optimization and Intelligent Decision-Making(Ministry of Education) Hefei University of Technology Hefei China

Traditional navigation methods tend to design and train navigation skills based on the consideration of multi-scenario applicability and perform well in general scenarios. However, this results in sub-optimal navigation performance in some context-specific scenarios, such as navigating through open or crowded scenarios. While some existing navigation skills can achieve satisfactory performance in context-specific scenarios, e.g., using PID in open scenarios and reinforcement learning-based training methods in crowded scenarios. Most methods currently do without consideration of the integration of navigation skills and contextual understanding. To address this gap, we propose an adaptive mapless navigation method to utilize the optimal capabilities of each skill corresponding to the context-specific scenario. Specifically, we package several existing navigation skills and train a scenario classifier to enable the agent to automatically select the most appropriate skill based on the current scenario. Furthermore, we incorporate evidential deep learning (EDL) to assess classification uncertainty and ensure the safe switching of navigation skills. Our method was tested in both simulated and real scenarios, and the results of the experiments confirmed the effectiveness and generalization of our method. Moreover, the proposed method exhibits notably enhanced, context-sensitive efficiency within a hybrid multi-scenario setting, in comparison to a single navigation skill.

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