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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Liu, Jianhui Yang, Chunxi Zhang, Xiufeng Li, Yiming Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Yunnan650500 China

ISBN: (纸本)9798350361674

Modularized aerial vehicles are now receiving more and more attention for its flexibility and scalability. How to achieve fast and safe traversal among modular unmanned aerial vehicle (UAV) swarms in the limited space is a critical issue for them to accomplish complex missions. Motivated by cooperative control method of UAV swarms in structured airspace, this paper takes the modular flight array platform developed by our research team as an object. And then an advection model is established to discuss the dynamic process of multiple modular flight arrays. By designing two regions, a type of collision avoidance schedule is proposed. Moreover, a distributed controller based on the group of heterogeneous modular flight arrays is developed to solve air traffic problem among them under limited space constraints. Formal analyses are made to show that all modular flight arrays can reach their respective destination lines and avoid collisions. In order to make the modular flight arrays complete their respective missions quickly, a collision avoidance strategy is proposed, which shorten the convergence time and effectively avoids the oscillation phenomenon. Finally, simulation example is given to verify the effectiveness of the proposed method. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Reinforcement learning Environment for Cyber-Resilient Power Distribution System

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ieee ACCESS 2023年 11卷 127216-127228页

作者： Sahu, Abhijeet Venkatraman, Venkatesh Macwan, Richard Natl Renewable Energy Lab Boulder CO 80305 USA

Recently, numerous data-driven approaches to control an electric grid using machine learning techniques have been investigated. Reinforcement learning (RL)-based techniques provide a credible alternative to conventional, optimization-based solvers especially when there is uncertainty in the environment, such as renewable generation or cyber system performance. Efficiently training an agent, however, requires numerous interactions with an environment to learn the best policies. There are numerous RL environments for power systems, and, similarly, there are environments for communication systems. Most cyber system simulators are based in a UNIX environment, while the power system simulators are based in the Windows operating system. Hence the generation of a cyber-physical, mixed-domain RL environment has been challenging. Existing co-simulation methods are efficient, but are resource and time intensive to generate large-scale data sets for training RL agents. Hence, this work focuses on the development and validation of a mixed-domain RL environment using OpenDSS for the power system and leveraging a discrete event simulator Python package, SimPy for the cyber system, which is operating system agnostic. Further, we present the results of co-simulation and training RL agents for a cyber-physical network reconfiguration and Volt-Var control problem in a power distribution feeder.

关键词： OpenDSS SimPy OpenAI gym network reconfiguration re-routing Reinforcement learning

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Receding-Constraint Model Predictive control using a Learned Approximate control-Invariant Set

Receding-Constraint Model Predictive Control using a Learned...

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ieee International conference on Robotics and Automation (ICRA)

作者： Lunardi, Gianni La Rocca, Asia Saveriano, Matteo Del Prete, Andrea Univ Trento Ind Engn Dept Via Sommarive 11 I-38123 Trento Italy

ISBN: (纸本)9798350384581;9798350384574

In recent years, advanced model-based and data-driven control methods are unlocking the potential of complex robotics systems, and we can expect this trend to continue at an exponential rate in the near future. However, ensuring safety with these advanced control methods remains a challenge. A well-known tool to make controllers (either Model Predictive controllers or Reinforcement learning policies) safe, is the so-called control-invariant set (a.k.a. safe set). Unfortunately, for nonlinear systems, such a set cannot be exactly computed in general. Numerical algorithms exist for computing approximate control-invariant sets, but classic theoretic control methods break down if the set is not exact. This paper presents our recent efforts to address this issue. We present a novel Model Predictive control scheme that can guarantee recursive feasibility and/or safety under weaker assumptions than classic methods. In particular, recursive feasibility is guaranteed by making the safe-set constraint move backward over the horizon, and assuming that such set satisfies a condition that is weaker than control invariance. Safety is instead guaranteed under an even weaker assumption on the safe set, triggering a safe task-abortion strategy whenever a risk of constraint violation is detected. We evaluated our approach on a simulated robot manipulator, empirically demonstrating that it leads to less constraint violations than state-of-the-art approaches, while retaining reasonable performance in terms of tracking cost, number of completed tasks, and computation time.

关键词： Predictive control systems

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Geometry-agnostic Melt-pool Homogenization of Laser Powder Bed Fusion through Reinforcement learning

Geometry-agnostic Melt-pool Homogenization of Laser Powder B...

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ieee/ASME International conference on Advanced Intelligent Mechatronics (AIM)

作者： Park, Bumsoo Mishra, Sandipan Rensselaer Polytech Inst Dept Mech Aerosp & Nucl Engn 110 8th St Troy NY 12180 USA

ISBN: (纸本)9781665476331

This work explores the feasibility of a geometry-agnostic laser power control strategy for laser powder bed fusion (L-PBF) using reinforcement learning. The controller is designed to anticipate and compensate geometry-induced process inhomogeneities, as well respond to in-process uncertainty through feedback control. To train the reinforcement learning controller, first a reduced-order simulation model is fit to experimental data. Then, the optimal control strategy is found through reinforcement learning on this reduced-order model. After the training, we demonstrate that the learned control strategy can reduce up to 55% of the error 2-norm and 59% of the standard deviation with respect to a given reference value. Moreover, the learned control strategy is applicable to novel build geometries without any additional tuning, or modification of the controller, in which we find that the controller attenuated 2-norm error by 62% and variation levels by 60% when deployed on a new (test) geometry, presenting the efficacy of the proposed controller. Finally, the experimental validation of the algorithm in a 'playback' setting resulted in a 24% reduction of both 2-norm error and variation levels, highlighting its potential in an industrial L-PPBF system.

关键词： Geometry

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Cybersecurity Analysis of data-driven Power System Stability Assessment

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ieee INTERNET OF THINGS JOURNAL 2023年第17期10卷 15723-15735页

作者： Zhang, Zhenyong Zuo, Ke Deng, Ruilong Teng, Fei Sun, Mingyang Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China Guizhou Univ Coll Comp Sci & Technol Guiyang 550025 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

Machine learning-based intelligent systems enhanced with Internet of Things (IoT) technologies have been widely developed and exploited to enable the real-time stability assessment of a large-scale electricity grid. However, it has been extensively recognized that the IoT-enabled communication network of power systems is vulnerable to cyberattacks. In particular, system operating states, critical attributes that act as input to the data-driven stability assessment, can be manipulated by malicious actors to mislead the system operator into making disastrous decisions and thus cause major blackouts and cascading events. In this article, we explore the vulnerability of the data-driven power system stability assessment, with a special emphasis on decision tree-based stability assessment (DTSA) approaches, and investigate the feasibility of constructing a physics-constrained adversarial attack (PCAA) to undermine the DTSA. The PCAA is formulated as a nonlinear programming problem considering the misclassification constraint, power limits, and bad data detection, computing potential adversarial perturbations that reverse the "stable/unstable" prediction of the real-time input while remaining invisible/stealthy. Extensive experiments based on the ieee 68-bus system are conducted to evaluate the impact of PCAAs on predictions of DTSA and their transferability.

关键词： Adversarial examples cyber security decision trees (DTs) physics-constrained machine learning (ML) transient stability assessment (TSA)

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Observer-Informed Deep learning for Traffic State Estimation With Boundary Sensing

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2024年第2期25卷 1602-1611页

作者： Zhao, Chenguang Yu, Huan Hong Kong Univ Sci & Technol Guangzhou Thrust Intelligent Transportat Guangzhou 511400 Guangdong Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China

Traffic state estimation (TSE) refers to the inference of macroscopic traffic states, including density, speed, and flow, based on partially observed traffic data and some prior knowledge of traffic dynamics. TSE plays a key role in traffic management since traffic control relies on accurate estimation of traffic states. This paper proposes a novel hybrid TSE approach called Observer-Informed Deep learning (OIDL), which integrates a Partial Differential Equation (PDE) observer and deep learning paradigm to estimate spatial-temporal traffic states from boundary sensing data. The proposed OIDL consists of two modules, an Observer-Uninformed Neural Network (OUNN) to generate preliminary traffic state estimation, and an Observer-Informed Neural Network (OINN) constructed from a boundary observer with theoretical convergence guarantee to regularize the estimation. Furthermore, we propose Adaptive OIDL (aOIDL) to simultaneously estimate traffic states and model parameters. Experiments on the NGSIM dataset demonstrate that the proposed OIDL reduces the estimation error by up to 30 percent compared to the model-based observer, data-driven neural networks, and some hybrid TSE approaches. The OIDL also has smaller variance of the estimation error and presents more accurate pattern for congested traffic.

关键词： Traffic state estimation boundary observer adaptive observer observer-informed deep learning

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Model-Guided learning for Wind Farm Power Optimization

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ieee TRANSACTIONS ON control systems TECHNOLOGY 2024年第2期32卷 428-439页

作者： Xu, Zhiwei Chu, Bing Geng, Hua Nian, Xiaohong Zhang, Chenghui Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Cent South Univ Sch Automat Changsha 410075 Peoples R China

In a wind farm, the interactions between turbines caused by wakes can significantly reduce the power output of the wind farm. Accurately modeling the interactions is challenging due to the highly complex nature of the wakes and this limits the performance of model-based wind farm power optimization methods. There are also data-driven approaches, which do not require a system model. However, they generally require a large number of measurement data and the convergence speed can be slow. To address these limitations, this article proposes a model-guided learning (MGL) method for wind farm to improve its power output by leveraging the knowledge of the available simplified power generation model and learning from the real-time power generation data. The proposed method can quickly increase the power output of the wind farm, guarantee implemented control actions to satisfy the control constraints of all turbines, and have the ability to find the optimal solution of the power optimization problem. The presented method is then extended to deal with time-varying wind conditions using a hierarchical framework. Simulation results indicate that the proposed scheme can efficiently improve the power output of the wind farm in different wind conditions compared with some benchmarks. It shows a power efficiency gain of 2.5% over greedy policy and 1.2% than the model-based gradient method in given complex wind conditions, which are substantial improvements in the performance for the considered wind farm power optimization problem.

关键词： Cooperative control model uncertainties power optimization wake interactions wind farm

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An Online Evaluation Based Extended-State Kalman Filter Approach to Navigation Via Signal of Opportunity 13

An Online Evaluation Based Extended-State Kalman Filter Appr...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Liu, Junyuan Chen, Xuanwen Chen, Sen Bai, Wenyan Wang, Yinghui Zhao, Zhiliang Airborne Avionics Flight Test Institute Chinese Flight Test Establishment Xi'an710089 China Beijing Aerospace Automatic Control Institute Beijing100854 China School of Automation and Electrical Engineering University of Science and Technology Beijing100083 China School of Mathematics and Statistics Shaanxi Normal University Xi'an710119 China School of Electrical and Control Engineering North Central University Taiyuan030051 China

ISBN: (纸本)9798350361674

This paper studies the problem of multi-source opportunistic signal navigation of aircraft. Firstly, the paper provides the mathematical models for five types of opportunistic signals. Furthermore, the online evaluation algorithm based on T-test is proposed for seeking the biased opportunistic signals. By disposing of the opportunistic signals causing intolerant location error, an estimated location position is obtained by trusty opportunistic signals. Finally, to handle the unknown part of flight acceleration caused by external disturbances, the extended-state Kalman filter is constructed to promote the locating accuracy. The simulation results show the effectiveness of the proposed method. © 2024 ieee.

关键词： Extended Kalman filters

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Prescribed-Time Cooperative Resilient Fuzzy control for CPVS Under DoS Attacks

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ieee TRANSACTIONS ON FUZZY systems 2024年第7期32卷 3968-3978页

作者： Liu, Yan Deng, Chao Fan, Sha Wang, Bohui Xie, Xiangpeng Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing 100091 Peoples R China

In this article, the prescribed-time cooperative resilient fuzzy tracking control problem is addressed for third-order nonlinear cyber-physical vehicle systems under denial-of-service attacks. Different from the existing results on cooperative resilient fuzzy tracking control, the developed method can achieve the cooperative resilient fuzzy tracking control objective within the prescribed time. To begin with, a data-driven-based online learning algorithm is introduced for learning the unknown and switching matrix of the reference signal. Based on the learned matrix, novel distributed resilient cooperative observers are designed to achieve prescribed-time observation by introducing an asynchronous observing method. To further improve the smoothness of the observation signal, a new improved smooth second-order prescribed-time observer is designed. Furthermore, leveraging the states of the smooth observer, a fuzzy controller is proposed to achieve precise tracking within the prescribed time, independent of initial conditions using the scaling error method and the backstepping technique. Finally, a simulation example is included to illustrate the effectiveness of the proposed methodology.

关键词： Observers Vectors Denial-of-service attack Convergence Telecommunications Fans Backstepping Distributed resilient cooperative observers denial-of-service attacks prescribed-time control

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data driven Frequency Stability Detection in Power systems Using XGBoost Model

Data Driven Frequency Stability Detection in Power Systems U...

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2024 Beyond Technology Summit on Informatics International conference, BTS-I2C 2024

作者： Ramadhan, Raja Kamal Putranto, Lesnanto Multa Irnawan, Roni Yasirroni, Muhammad Universitas Gadjah Mada Department of Electrical and Information Engineering Yogyakarta Indonesia

ISBN: (纸本)9798331508579

Detection of frequency instability has become a critical area of research in power systems because of its significant impact on the reliability and stability of interconnected grids. Traditional methods based on complete power system models are increasingly replaced by data-driven approaches to address computational burdens. This paper proposes a frequency stability detection framework using the XGBoost machine learning model. It addresses the gap in applying machine learning techniques to larger and more complex power systems, including interconnected grids beyond microgrids. A synthetic dataset was generated from the ieee 39-bus test system, simulating both normal operating conditions and N-1 contingency scenarios. The dataset incorporates variations in load demand, generator dispatch power, and frequency nadir and rebound. The proposed model achieved an average accuracy of 0.91, with strong performance in precision, recall, and F1-score, while demonstrating computational efficiency. These findings underscore the potential of XGBoost for scalable frequency stability monitoring in complex power systems. Future work will focus on validating the model with real-world utility data and developing automated frequency control mechanisms for real-time implementation. © 2024 ieee.

关键词： Frequency stability

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