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18th Annual ieee International systems conference (SysCon)

作者： Al Maruf, Md Azim, Akramul Ontario Tech Univ Dept Elect Comp & Software Engn Oshawa ON Canada

ISBN: (纸本)9798350358810;9798350358803

Integrated vehicle dynamics control systems require real-time communication among their components to improve performance and process efficiency. This communication relies on the use of sensor data, hardware interfaces, transmission protocols, and control strategies, which all have an impact on the system's reliability. However, as the number of functionalized electronic control units (ECUs) and wiring systems increases, advanced control systems encounter complex functional and cybersecurity issues. To mitigate this complexity, the automotive industry widely employs the controller Area Network (CAN) communication bus. Nevertheless, the inherent vulnerabilities of CAN and the rich interfaces with external environments increase the systems' susceptibility to soft errors caused by uncertainty factors such as process changes. Therefore, detecting abnormalities in automotive CAN communication is crucial. This paper introduces a machine learning (ML)-based anomaly detection framework to identify anomalies through CAN messages, extracting key features and employing ML models for predictive analysis. It also uses Triple Modular Redundancy (TMR) for trusted ML computation in anomaly detection. The study provides a comparative analysis of various ML algorithms, highlighting the effectiveness of Deep Neural Networks in identifying anomalies within both synthetic and real Hyundai CAN data for a wheel speed control system, showcasing the framework's capability to enhance system reliability and security.

关键词： CAN Communication Anomaly Detection Embedded systems

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Centroidal State Estimation Based on the Koopman Embedding for Dynamic Legged Locomotion

Centroidal State Estimation Based on the Koopman Embedding f...

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2024 International conference on Intelligent Robots and systems

作者： Khorshidi, Shahram Dawood, Murad Bennewitz, Maren Univ Bonn Humanoid Robots Lab Bonn Germany Lamarr Inst Machine Learning & Artificial Intelli Bonn Germany

ISBN: (纸本)9798350377712;9798350377705

In this paper, we introduce a novel approach to centroidal state estimation, which plays a crucial role in predictive model-based control strategies for dynamic legged locomotion. Our approach uses the Koopman operator theory to transform the robot's complex nonlinear dynamics into a linear system, by employing dynamic mode decomposition and deep learning for model construction. We evaluate both models on their linearization accuracy and capability to capture both fast and slow dynamic system responses. We then select the most suitable model for estimation purposes, and integrate it within a moving horizon estimator. This estimator is formulated as a convex quadratic program to facilitate robust, real-time centroidal state estimation. Through extensive simulation experiments on a quadruped robot executing various dynamic gaits, our data-driven framework outperforms conventional Extended Kalman Filtering technique based on nonlinear dynamics. Our estimator addresses challenges posed by force/torque measurement noise in highly dynamic motions and accurately recovers the centroidal states, demonstrating the adaptability and effectiveness of the Koopman-based linear representation for complex locomotive behaviors. Importantly, our model based on dynamic mode decomposition, trained with two locomotion patterns (trot and jump), successfully estimates the centroidal states for a different motion (bound) without retraining.

关键词： Extended Kalman filters

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Voltage control by Smart Sustainable Buildings: data-driven vs OPF-based Techniques

Voltage Control by Smart Sustainable Buildings: Data-Driven ...

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ieee Belgrade PowerTech conference

作者： Ge, Chunlei Yu, Runze Avramidis, Iason I. Deconinck, Geert Katholieke Univ Leuven Dept Elect Engn ESAT Leuven Belgium UK Power Networks Ltd UKPN London England

ISBN: (纸本)9781665487788

The concept of smart sustainable buildings (SSBs) is an important part of the smart city. It includes a variety of controllable devices that provide flexibility to the grid and support its sustainable development. The best way to control these buildings is an undertreated question, with some going for centralized model-based approaches and others opting for local control schemes derived from data. In this paper, through AC-Optimal Power Flow (OPF) modeling of the distribution network, the optimal solution of each controllable equipment is obtained based on historical data. Next, we use machine learning algorithms to design data-driven local control schemes for each controllable device, which can approximate the optimal solution of AC-OPF. Then, the control scheme is applied to specific cases to verify its feasibility and viability as compared to the traditional OPF control scheme, which is popular within the research community. This scheme can promote the safe operation of distribution networks with higher penetration rate (30% to 50%) of controllable devices.

关键词： AC-OPF Active power curtailment Distribution systems Machine learning Voltage control Volt-Var control

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Broadband Digital Over-the-Air Computation for Wireless Federated Edge learning

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ieee TRANSACTIONS ON MOBILE COMPUTING 2024年第5期23卷 5212-5228页

作者： You, Lizhao Zhao, Xinbo Cao, Rui Shao, Yulin Fu, Liqun Xiamen Univ Sch Informat Dept Informat & Commun Engn Xiamen 361005 Fujian Peoples R China Xiamen Univ Shenzhen Res Inst Shenzhen 518000 Guangdong Peoples R China Univ Macau State Key Lab Internet Things Smart City Taipa 999078 Macau Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

This article presents the first orthogonal frequency-division multiplexing(OFDM)-based digital over-the-air computation (AirComp) system for wireless federated edge learning, where multiple edge devices transmit model data simultaneously using non-orthogonal OFDM subcarriers, and the edge server aggregates data directly from the superimposed signal. Existing analog AirComp systems often assume perfect phase alignment via channel precoding and utilize uncoded analog transmission for model aggregation. In contrast, our digital AirComp system leverages digital modulation and channel codes to overcome phase asynchrony, thereby achieving accurate model aggregation for phase-asynchronous multi-user OFDM systems. To realize a digital AirComp system, we develop a medium access control (MAC) protocol that allows simultaneous transmissions from different users using non-orthogonal OFDM subcarriers, and put forth joint channel decoding and aggregation decoders tailored for convolutional and LDPC codes. To verify the proposed system design, we build a digital AirComp prototype on the USRP software-defined radio platform, and demonstrate a real-time LDPC-coded AirComp system with up to four users. Trace-driven simulation results on test accuracy versus SNR show that: 1) analog AirComp is sensitive to phase asynchrony in practical multi-user OFDM systems, and the test accuracy performance fails to improve even at high SNRs;2) our digital AirComp system outperforms two analog AirComp systems at all SNRs, and approaches the optimal performance when SNR >= 6 dB for two-user LDPC-coded AirComp, demonstrating the advantage of digital AirComp in phase-asynchronous multi-user OFDM systems.

关键词： Federated edge learning over-the-air computation multiple access channel codes real-time implementation

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2024 ieee 7th International conference on Industrial Cyber-Physical systems, ICPS 2024

2024 IEEE 7th International Conference on Industrial Cyber-P...

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7th ieee International conference on Industrial Cyber-Physical systems, ICPS 2024

ISBN: (纸本)9798350363012

The proceedings contain 99 papers. The topics discussed include: automated derivation of application workload models for design space exploration of industrial distributed cyber-physical systems;multi-robot formation control with human-on-the-loop;vision-based navigation for the affine formation control of a multi-robot team;cooperative plug-and-play-KalmanNet for 4D situational awareness in autonomous driving;analyzing MLOps and its applicability in the maritime domain through a systematic mapping study;multi-modal learning with joint image-text embeddings and decoder networks;a study of a data-driven cyber-physical control system using an assessment mechanism;uncertainty quantification of a cyber-physical system for monitoring aircraft performance;and STRIDE-based methodologies for threat modeling of industrial control systems: a review.

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data-driven short term load forecasting with deep neural networks: Unlocking insights for sustainable energy management

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ELECTRIC POWER systems RESEARCH 2024年 232卷

作者： Waheed, Waqar Xu, Qingshan Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China

In today's smart grid and building infrastructure, it is strongly suggested to implement short-term demand forecasting for future power generation. There is a growing demand for improved accuracy in forecasting, from the level of individual users to that of the power system in the context of the emerging energy market and the creation of a smart grid infrastructure. There is also a need for better rules to control the supply and demand equilibrium. This study demonstrates and incorporates a deep learning neural network model with time series analysis and feature selection in order to forecast the complex and variable hourly load demand and involved a comprehensive comparison between DNN approach and other alternatives for short-term load forecasting. The deep feed -forward and wide and deep neural network models incorporate with stacking hidden layers of CNN such as LSTM/GRU units for handling uncertainty. While, random forest regression with GridSearchCV has been used for the identification of optimal set of feature selection. This proposed model has increased temporal dependencies, a large influence, and a strong capacity for learning in order to accurately predict loads based on the hourly driven power consumption of ieee dataset. Examining the resulting limitations and influencing factors allows for the validation of the experimental findings.

关键词： Short term load forecasting Demand response Deep neural networks (DNN) Environmental influences

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Scalable Multi-Agent Reinforcement learning for Residential Load Scheduling Under data Governance

IEEE TRANSACTIONS ON INDUSTRIAL CYBER-PHYSICAL SYSTEMS

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ieee TRANSACTIONS ON INDUSTRIAL CYBER-PHYSICAL systems 2025年 3卷 351-361页

作者： Qin, Zhaoming Dong, Nanqing Liu, Di Wang, Zhefan Cao, Junwei Ecole Polytech Fed Lausanne Automat Control Lab CH-1015 Lausanne Switzerland Shanghai Artificial Intelligence Lab Shanghai 200232 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China

As a data-driven approach, multi-agent reinforcement learning (MARL) has made remarkable advances in solving cooperative residential load scheduling problems. However, centralized training, the most common paradigm for MARL, limits large-scale deployment in communication-constrained cloud-edge environments. As a remedy, distributed training shows unparalleled advantages in real-world applications but still faces challenge with system scalability, e.g., the high cost of communication overhead during coordinating individual agents, and needs to comply with data governance in terms of privacy. In this work, we propose a novel MARL solution to address these two practical issues. Our proposed approach is based on actor-critic methods, where the global critic is a learned function of individual critics computed solely based on local observations of households. This scheme preserves household privacy completely and significantly reduces communication cost. Simulation experiments demonstrate that the proposed framework achieves comparable performance to the state-of-the-art actor-critic framework without data governance and communication constraints.

关键词： data governance Clouds data privacy Privacy Microgrids Training Job shop scheduling Costs Batteries Reinforcement learning data-privacy load management multi-agent systems reinforcement learning

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Research on Voltage Stability Margin control Strategy for Large Power Grids: A Deep Reinforcement learning Approach 3

Research on Voltage Stability Margin Control Strategy for La...

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3rd Asia Power and Electrical Technology conference, APET 2024

作者： Li, Tao Gong, Xue Hong, Yunfei Bai, Gang Shi, Yi Jiang, Yiqiang Chen, Tong Huang, Jingnan State Grid Yibin Electric Power Supply Company Yibin China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350367690

The dynamic nature of wide-area power systems poses significant challenges to the control of voltage stability margin, particularly under time-varying conditions. In response to this issue, this paper proposes a data-driven optimization control method for grid voltage stability margin, which derives and defines voltage stability margin using power flow data and constructs a control model for grid voltage stability margin. In this model, the control problem of voltage stability margin is transformed into a Markov decision process. Then, real-time control values are obtained through the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The simulation results show that this model can respond quickly to time-varying systems and has significant advantages in networks with load fluctuations, improving voltage stability margin and ensuring high-quality power supply. © 2024 ieee.

关键词： Deep reinforcement learning

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Experimental Validation for Artificial data-driven Tracking control for Enhanced Three-Phase Grid-Connected Boost Rectifier in DC Microgrids

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ieee TRANSACTIONS ON INDUSTRY APPLICATIONS 2023年第2期59卷 2563-2580页

作者： Soliman, Ahmed S. Amin, Mahmoud M. El-Sousy, Fayez F. M. Mohammad, Osama A. Florida Int Univ Energy Syst Res Lab Miami FL 33174 USA Manhattan Coll New York NY 10471 USA Prince Sattam Bin AbdulAziz Univ Al Kharj 16278 Saudi Arabia

This article introduces the control and operation of a grid-connected converter with an energy storage system. A complete mathematical model was presented for the developed converter and its control system. The system under study was a small microgrid comprising an AC grid that is feeding a DC load through a converter. The converter was connected to the AC grid through an R-L filter. The classical linear controllers have limitations due to their slow transient performance and low robustness against parameter variations and load disturbances. In this paper, machine-learned controllers were used to dealing with those drawbacks of the traditional controller. First, a study for conventional nested loop Proportional Integral (PI) was introduced for both outer and inner loops PI-PI controller. A data-driven Online learning (DDOL) controller was then proposed. A comparison between the normal traditional PI-PI controller and the proposed DDOL ones was made under different operating scenarios. The converter control was tested under various operational conditions, and its dynamic and steady-state behavior was analyzed. The model was done through a MATLAB Simulink to check the normal operation of the network in a grid-connected mode under different load disturbances and AC input voltage. Then, the system was designed, fabricated, and implemented in a hardware environment in our Energy systems Research Laboratory (ESRL) testbed, and the hardware test results were verified. The results showed that the proposed DDOL controller was more robust and had better transient and steady state performances.

关键词： DC microgrids energy storage grid-connected rectifier intelligent controllers neural networks online learning power converters

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data-driven Distance Metrics for Kriging-Short-Term Urban Traffic State Prediction

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第6期24卷 6268-6279页

作者： Varga, Balazs Pereira, Mike Kulcsar, Balazs Pariota, Luigi Peni, Tamas Budapest Univ Technol & Econ Fac Transportat Engn & Vehicle Engn Dept Control Transportat & Vehicle Syst H-1111 Budapest Hungary Chalmers Univ Technol Dept Math Sci S-41296 Gothenburg Sweden Mines Paris PSL Univ Dept Geosci & Geoengn F-75272 Paris France Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden Univ Naples Federico II Dept Civil Architectural & Environm Engn I-80138 Naples Italy Inst Comp Sci & Control SZTAK Syst & Control Lab H-1111 Budapest Hungary

Estimating traffic flow states at unmeasured urban locations provides a cost-efficient solution for many ITS applications. In this work, a geostatistical framework, kriging is extended in such a way that it can both estimate and predict traffic volume and speed at various unobserved locations, in real-time. In the paper, different distance metrics for kriging are evaluated. Then, a new, data-driven one is formulated, capturing the similarity of measurement sites. Then, with multidimensional scaling the distances are transformed into a hyperspace, where the kriging algorithm can be used. As a next step, temporal dependency is injected into the estimator via extending the hyperspace with an extra dimension, enabling for short horizon traffic flow prediction. Additionally, a temporal correction is proposed to compensate for minor changes in traffic flow patterns. Numerical results suggest that the spatio-temporal prediction can make more accurate predictions compared to other distance metric-based kriging algorithms. Additionally, compared to deep learning, the results are on par while the algorithm is more resilient against traffic pattern changes.

关键词： Prediction algorithms Measurement Detectors Kernel Deep learning Correlation Neural networks Kriging spatio-temporal prediction distance metric traffic flow prediction

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