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An Extended Stochastic Region of Attraction Estimation Method for Power Systems Under Stochastic Sustained Disturbances

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IEEE Transactions on Power Systems 2025年

作者： Yang, Yuerong Lin, Shunjiang Liu, Mingbo Deng, Leyi Xie, Huifan Li, Qifeng South China University of Technology School of Electric Power Engineering Guangzhou510640 China Power Dispatching and Control Center CSG Guangzhou510663 China University of Central Florida Department of Electrical and Computer Engineering OrlandoFL32816 United States

Stochastic sustained disturbances (SSDs) in renewable energy output impact power system transient stability. This study addresses the dynamic performance of power systems under SSDs using Lévy-driven stochastic differential algebraic equations (LDSDAEs). The stochastic region of attraction for LDSDAEs is divided into a stable region and a probabilistically stable region, collectively termed the extended stochastic region of attraction (ESRA). Traditional methods for estimating the deterministic region of attraction (RA) are inadequate. To address this issue, this study proposes an estimation method for the ESRA of LDSDAEs based on escape probability. We first derive an integro-differential equation for escape probabilities of initial system states near the attractor of LDSDAEs. We then explore the connection between escape probability and ESRA. Finally, we propose a wavelet basis functions-based extended projection method to solve the integro-differential equation, providing an analytical expression for escape probabilities and thus determining the ESRA. Case studies on the modified IEEE 39- and 118-bus systems demonstrate the proposed method's high computational accuracy and efficiency for power systems under Lévy disturbances. © 1969-2012 IEEE.

关键词： Integrodifferential equations

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A predictive model of death from cerebrovascular diseases in intensive care units

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Intelligent Medicine 2023年第4期3卷 267-279页

作者： Mohammad Karimi Moridani Seyed Kamaledin Setarehdan Ali Motie Nasrabadi Esmaeil Hajinasrollah Department of Biomedical Engineering Faculty of HealthTehran Medical SciencesIslamic Azad UniversityTehranIran Control and Intelligent Processing Centre of Excellence School of Electrical and Computer EngineeringCollege of EngineeringUniversity of TehranTehranIran Departments of Biomedical Engineering Shahed UniversityTehranIran Loghman Medical Center Shahid Beheshti University of Medical SciencesTehranIran

Objective This study aimed to explore the mortality prediction of patients with cerebrovascular diseases inthe intensive care unit(ICU)by examining the important signals during different periods of admission in theICU,which is considered one of the new topics in the medical *** approaches have been proposed forprediction in this *** of these methods has been able to predict mortality somewhat,but many of thesetechniques require recording a large amount of data from the patients,where recording all data is not possiblein most cases;at the same time,this study focused only on heart rate variability(HRV)and systolic and diastolicblood *** The ICU data used for the challenge were extracted from the Multiparameter Intelligent Monitoring inIntensive Care II(MIMIC-II)Clinical *** proposed algorithm was evaluated using data from 88 cerebrovascular ICU patients,48 men and 40 women,during their first 48 hours of ICU *** electrocardiogram(ECG)signals are related to lead II,and the sampling frequency is 125 *** time of admission and time ofdeath are labeled in all *** this study,the mortality prediction in patients with cerebral ischemia is evaluated using the features extracted from the return map generated by the signal of HRV and blood *** the patient’s future condition,the combination of features extracted from the return mapping generatedby the HRV signal,such as angle(𝛼),area(A),and various parameters generated by systolic and diastolic bloodpressure,including DBPMax−Min SBPSD have been ***,to select the best feature combination,the geneticalgorithm(GA)and mutual information(MI)methods were *** sample t-test statistical analysis was usedto compare the results of two episodes(death and non-death episodes).The P-value for detecting the significancelevel was considered less than *** The results indicate that the new approach presented in this paper can be compared with other methodsor l

关键词： Death prediction Cerebrovascular diseases Intensive care unit Heart rate variability Systolic and diastolic blood pressure Return map

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Energy-Based control Approaches for Weakly Coupled Electromechanical Systems

arXiv

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arXiv 2024年

作者： Javanmardi, Najmeh Borja, P. Yazdanpanah, M.J. Scherpen, J.M.A. Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen Netherlands School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

This paper addresses the regulation and trajectory-tracking problems for two classes of weakly coupled electromechanical systems. To this end, we formulate an energy-based model for these systems within the port-Hamiltonian framework. Then, we employ Lyapunov theory and the notion of contractive systems to develop control approaches in the port-Hamiltonian framework. Remarkably, these control methods eliminate the need for solving partial differential equations or implementing any change of coordinates, and are endowed with a physical interpretation. We also investigate the effect of coupled damping on the transient performance and convergence rate of the closed-loop system. Finally, the applicability of the proposed approaches is illustrated in two applications of electromechanical systems via simulations. © 2024, CC BY.

关键词： Damping

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Securing Mobile Robotic Networks Against Replacement Attack

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IEEE Transactions on Automatic control 2025年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping Cai, Lin Shanghai Jiao Tong University Dept. of Automation China Ministry of Education of China The Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China University of Victoria Dept. of Electrical and Computer Engineering BC Canada

The security of mobile robotic networks (MRNs) has been an active research topic in recent years. This paper aims to secure the ubiquitous formation control of MRNs against the replacement attack, where an external robot can replace a formation robot by compromising the communication and physically interfering with the victim simultaneously. To counter this advanced attack, the novel idea of this work is to leverage the physical proximity of the formation shape and the interaction topology among robots for defense design. First, from the physical proximity perspective, we propose the convex neighbor polygon (CNP) to capture the geometric characteristic of the formation robots, and design a CNP-based security mechanism for the robots during the replacement attack. Then, from the interaction perspective, we introduce the indirect controllability to characterize the possibility that the attacker leverages the interaction between robots to deactivate the CNP mechanism, and establish the conditions regarding the topology structure and the attack input to counter the replacement. Finally, based on the obtained conditions, we demonstrate how to design the initiatory topology among the formation robots to enhance the defense performance. Comparative simulations verify the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Topology

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Multi-Agent Path Finding Using Imitation-Reinforcement Learning with Transformer

Multi-Agent Path Finding Using Imitation-Reinforcement Learn...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Chen, Lin Wang, Yaonan Miao, Zhiqiang Mo, Yang Feng, Mingtao Zhou, Zhen School of Electrical and Information Engineering Hunan University Changsha410082 China National Engineering Research Center for Robot Visual Perception and Control Technology Changsha410082 China School of Computer Science and Technology Xidian University Xian710126 China

ISBN: (纸本)9781665481090

Multi-Agent Path Finding is a problem of finding the optimal set of paths for multiple agents from the starting position to the goal without conflict, which is essential to large-scale robotic systems. Imitation and reinforcement learning are applied to solve the MAPF problem and have achieved certain results, which provides a feasible solution for the path planning problem of large-scale robot systems. The current method improves the performance of distributed strategy-guided agent planning paths in complex environments by introducing the communication between graph neural networks and agents but dramatically reduces the system's robustness. This paper develops a novel imitation reinforcement learning framework by introducing Transformer, which enables algorithms to perform well in complex environments without relying on communication between agents. Compared with its counterparts, experiments show that the policy trained by our method guides the agent to drive from the initial position to the goal without collision and achieve better performance. © 2022 IEEE.

关键词： Reinforcement learning

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Magnetoelastic coupling driven magnon gap in a honeycomb antiferromagnet

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Physical Review B 2024年第10期110卷 104419页

作者： Jeongheon Choe Andrey Baydin Gaihua Ye Bowen Ma Cynthia Nnokwe Martin Rodriguez-Vega David Lujan Swati Chaudhary Fuyang Tay Jiaming He Hiroyuki Nojiri Jianshi Zhou Gregory A. Fiete Junichiro Kono Rui He Xiaoqin Li Department of Physics Center of Complex Quantum Systems Center for Dynamics and Control of Materials Department of Electrical and Computer Engineering Smalley–Curl Institute Department of Physics Applied Physics Graduate Program Smalley–Curl Institute Department of Mechanical Engineering Institute for Materials Research Department of Physics and Astronomy Department of Materials Science and NanoEngineering

Cobalt titanate, CoTiO3, is a honeycomb antiferromagnet recently confirmed experimentally to host Dirac magnons, topological spin-orbit excitons, and chiral phonons. Here, we investigate a magnon gap at the zone center which calls for a refined spin Hamiltonian. We propose a microscopic model for the magnon gap and attribute it to a lattice-distortion (phonon)-induced higher-order spin interaction. Strong magnetoelastic coupling in CoTiO3 is also evident in Raman spectra, in which the magnetic order exerts a stronger influence on phonons corresponding to in-plane ionic motions than those with out-of-plane motions. We further examine the evolution of the zone-center magnons in a high magnetic field up to 18.5 T via THz absorption spectroscopy measurements. Based on this field dependence, we propose a spin Hamiltonian that not only agrees with magnon dispersion measured by inelastic neutron scattering but also includes fewer exchange constants and a realistic anisotropy term. Our work highlights the broad implications of magnetoelastic coupling in the study of topologically protected bosonic excitations.

关键词： Magnetism Magnons Topological materials Raman spectroscopy Terahertz spectroscopy

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MULSAM: Multidimensional Attention with Hardware Acceleration for Efficient Intrusion Detection on Vehicular CAN Bus

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Xu, He Shi, Xiaokang Liu, Hansheng Wang, Yanwen Lu, Jiwu Zeng, Haibo Li, Renfa Wu, Di Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology Hunan Changsha410082 China Hunan University College of Electrical and Information Engineering Hunan Changsha410082 China Hunan University Shenzhen Research Institute Shenzhen518000 China Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA24061 United States Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China

controller Area Network (CAN) protocol is an efficient standard enabling communication among Electronic control Units (ECUs). However, the CAN bus is vulnerable to malicious attacks because of a lack of defense features. In this article, a novel vehicle Intrusion Detection System (IDS) is developed. The challenge is that existing techniques of Intrusion Detection Systems (IDS) rarely consider attacks with small-batch, which are characterized by their small attack scale and concealed attack patterns, posing a significant threat to driving safety. To solve this problem, we developed an algorithm model that merges Multi-dimensional Long Short-Term Memory (MD-LSTM) and Self-Attention Mechanism (SAM), shortly named MULSAM. The MULSAM model was compared with other baseline models, including Stacked LSTM, MD-LSTM, etc. Experiments show that our approach has the best detection accuracy (98.98%) and training stability. Further, to speed up the inference of MULSAM on edge, the hardware accelerator is implemented on FPGA devices using technologies such as parallelization, modular, pipeline, and fixed-point quantization. Experiments show that our FPGA-based acceleration scheme has a better energy efficiency than the CPU platform. Even with a certain degree of quantification, the acceleration model for MULSAM still displays a high detection accuracy of 98.81% and a low latency of 1.88 ms. © 1982-2012 IEEE.

关键词： Intrusion detection

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Distributed Optimal Load Frequency control with Stochastic Wind Power Generation

Distributed Optimal Load Frequency Control with Stochastic W...

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2021 European control Conference, ECC 2021

作者： Silani, Amirreza Cucuzzella, Michele Scherpen, Jacquelien M. A. Javad Yazdanpanah, Mohammad University of Groningen Jan C. Willems Center For Systems and Control ENTEG Faculty of Science and Engineering Groningen Netherlands University of Tehran Control Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Tehran Iran Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (纸本)9789463842365

Motivated by the inadequacy of conventional control methods for power networks with a large share of renewable generation, in this paper we study the (stochastic) passivity property of wind turbines based on the Doubly Fed Induction Generator (DFIG). Differently from the majority of the results in the literature, where renewable generation is ignored or assumed to be constant, we model wind power generation as a stochastic process, where wind speed is described by a class of stochastic differential equations. Then, we design a distributed control scheme that achieves load frequency control and economic dispatch, ensuring the stochastic stability of the controlled network. © 2021 EUCA.

关键词： Random processes

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An Actor-Critic Reinforcement Learning Scheme for Reactive 3D Optimal Motion Planning Based on Fluid Dynamics

An Actor-Critic Reinforcement Learning Scheme for Reactive 3...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Marios Malliaropoulos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Department of Electrical and Computer Engineering University of Patras Center of AI & Robotics (CAIR) New York University Abu Dhabi

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work proposes a novel and provably correct method for three-dimensional optimal motion planning in complex environments. Our approach models the 3D motion planning problem by solving streamlines of the potential fluid flow, filling a gap in traditional motion planning techniques by guaranteeing a closed-loop, smooth and natural-looking navigation solution. Special emphasis is given to an inherent challenge of artificial potential field (APF) methods, namely establishing proofs of safety and stability over the entire optimization process. A model-based actor-critic reinforcement learning algorithm is introduced to approximate the optimal solution to the Hamilton-Jacobi-Bellman equation and update the controller parameters in a deterministic manner. Through a series of ROS-Gazebo software-in-the-loop simulations the proposed methodology demonstrates robustness and outperforms widely used methods such as the RRT ∗ , highlighting its contribution to the field of 3D optimal motion planning.

关键词： Solid modeling Three-dimensional displays Navigation Reinforcement learning Mathematical models Stability analysis Robustness Planning Safety Optimization

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Experimental Study on Motion controller based on NN-ARX and ARMAX Actuator Identification for 3-DoF Delta Parallel Robot

Experimental Study on Motion Controller based on NN-ARX and ...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Hasan Jalali Hossein Damavandi Ahmad Kalhor Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Faculty of Computer Engineering and Information Technology Sadjad University Mashhad Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

Today, system identification plays a pivotal role in control science and offering a myriad of applications. This paper places its focus on the identification of actuator models within real-world delta robots for informing controller design. Two identification methods, namely NN-ARX and ARMAX, have been employed to extract the dynamic characteristics of the robot actuators, resulting in dynamic models of these integral components being derived. These dynamic models have been utilized in simulations for controller design, and due to the disparities in the identification models, distinct controllers have been realized. Subsequently, these controllers have been practically implemented on delta robots, and their performances have been subjected to a comprehensive comparative analysis. The results demonstrate that controllers integrating the identified actuator models outperformed those designed without the incorporation of the identification models. In practice, the implementation of controllers based on NN-ARX yielded the most favorable results among all the tested controllers. This research not only underscores the importance of accurate actuator models in control system design but also highlights the superior performance of neural network-based controllers in real-world robotic applications. In particular, the practical results of NN-ARX-based controllers were able to achieve significantly lower RMSE of 2.3562, 1.9531, and 2.1185 for the three motors, respectively, as opposed to the 4.1369, 3.0125, and 3.0363 achieved by the ARMAX-based controllers.

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