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A Multiagent Quantum Deep Reinforcement learning Method for Distributed Frequency control of Islanded Microgrids

ieee TRANSACTIONS ON control OF NETWORK systems 2022年第4期9卷 1622-1632页

作者： Yan, Rudai Wang, Yu Xu, Yan Dai, Jiahong Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

This article proposes a data-driven method for distributed frequency control of islanded microgrids based on multiagent quantum deep reinforcement learning (DRL). The proposed method combines the conventional DRL framework with quantum machine learning, and can adaptively obtain the optimal cooperative control strategy. The microgrid secondary frequency control is organized in a distributed manner in which each agent performs the control action only based on the local and neighboring information. To solve the DRL problem, the deep deterministic policy gradient algorithm is derived to tune the agents' parameters. Simulation tests are performed on an islanded microgrid with four distributed generators and a 13-bus microgrid. The results demonstrate that the proposed method can effectively regulate the frequency with better time-delay tolerance, and displays the quantum advantage in parameter reduction.

关键词： Frequency control Qubit Reinforcement learning Neural networks Mathematical models Machine learning Quantum circuit data driven frequency control microgrid multiagent deep reinforcement learning (MA-DRL) quantum machine learning

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6th International conference on Recent Advances in Intelligent Computational systems, RAICS 2024

6th International Conference on Recent Advances in Intellige...

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6th ieee International conference on Recent Advances in Intelligent Computational systems, RAICS 2024

ISBN: (纸本)9798350381689

The proceedings contain 119 papers. The topics discussed include: efficient adder designs for realizing addition subset of RISC-V P-SIMD instructions;artificial intelligence driven portfolio management;assessing knee osteoarthritis severity: a deep learning approach with enhanced ResNet152;pulmonary disease multiclassification of chest X-ray images using deep learning techniques;CT image denoising using autoencoder and generative adversarial networks;power quality analyzer for grid connected inverter;ensuring privacy of data in machine learning;high gain buck boost converter with single switch and low voltage stress;load frequency control using adaptive sliding mode control for a two area interconnected power system;and revolutionizing document summarization: examining key influences on the adoption of ChatPDF for enhanced student learning.

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data-driven identification of quadrotor dynamics: a tutorial 20

Data-driven identification of quadrotor dynamics: a tutorial

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20th IFAC Symposium on System Identification (SYSID)

作者： Wi, Yejin Cescon, Marzia Univ Houston Dept Mech Engn Houston TX 77204 USA

In this work, we provide a tutorial-style exposition of quadrotor dynamics model identification from experimental data collected in closed-loop. Our objective is to provide guidelines for the scientist who is approaching model-based flight control design for multirotor UAVs for which a model is not readily available, but also to provide instructors with pedagogical material that can be utilized to develop experiential learning opportunities complementing face-to-face lectures in the system identification and control engineering curricula. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： System identification and modelling learning for control Unmanned aerial vehicles Teaching identification

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Knowledge-Infused learning: A Sweet Spot in Neuro-Symbolic AI

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ieee INTERNET COMPUTING 2022年第4期26卷 5-11页

作者： Gaur, Manas Gunaratna, Kalpa Bhatt, Shreyansh Sheth, Amit Univ South Carolina AI Inst Columbia SC 29208 USA Samsung Res Amer Mountain View CA 94043 USA Amazon Inc Seattle WA 98121 USA

Deep learning has revolutionized the artificial intelligence (AI) landscape by enhancing machine capabilities to understand data-dependant relationships. On the other hand, knowledge may not directly correlate or depend on the data but represents facts that are true. Combining knowledge with the data-driven deep learning techniques improves upon what can be learned from data alone, resulting in improved performance with reduced training, user-level explainability, modeling uncertainty in deep learning, achieving context-sensitivity, and better control over the behavior of AI systems such as to assure the safety or avoid toxic behavior. We refer to the approach of combining various types of explicit knowledge as knowledge-infused learning (KiL). Knowledge infusion brings symbolic AI into data-driven AI, giving us a class of neuro-symbolic AI methods. The work on KiL has already developed a suite of context-adaptive algorithms that infuses various knowledge into deep learning methods in various ways, broadly categorized as a shallow infusion, semi-deep infusion, and deep infusion. This special issue allows interdisciplinary researchers and practitioners from diverse fields such as natural language processing, recommender systems, and computer vision to contribute their research on the infusion of external and expert-curated knowledge in data-driven learning methodologies for consistency and robustness in outcomes.

关键词： Special Issues And Sections Deep learning Artificial Intelligence Behavioral Sciences Recommender systems Natural Language Processing data Models Training data Machine learning Context Awareness Knowledge Discovery

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Reduced- and Full-order Concurrent learning Extended State Observers for Fully Adaptive Anti-disturbance Surge Speed Tracking control of Autonomous Surface Vehicles 25

Reduced- and Full-order Concurrent Learning Extended State O...

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ieee 25th International conference on Intelligent Transportation systems (ITSC)

作者： Cui, Kai Li, Huijuan Jing, Yun Liu, Lu Wang, Dan Dalian Maritime Univ Sch Marine Elect Engn Dalian 116026 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

This paper considers surge speed tracking control of an autonomous surface vehicle (ASV) model subject to fully unknown internal dynamic, external disturbance, and unknown control input gain. A fully adaptive anti-disturbance control method is proposed for ASV without using any model parameters. Specifically, reduced- and full-order data-driven concurrent learning extended state observers (CLESOs) by utilizing real-time and historical data are designed to estimate the unknown model parameters of the ASV and ensure the convergence of the estimation without requiring persistent excitation. Then, an anti-disturbance surge speed tracking control law is designed. The stability of the data-driven CLESO and surge speed tracking control law are analyzed by using input-state stability (ISS) theory. Simulation results validate the effectiveness of the proposed data-driven CLESO for surge speed tracking of the autonomous surface vehicle with fully unknown dynamic model.

关键词： Adaptation models Uncertainty Simulation Observers Stability analysis Real-time systems Surges

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Leveraging Machine learning Insights at the Hot Strip Rolling Mill, Tata Steel, Port Talbot

Leveraging Machine Learning Insights at the Hot Strip Rollin...

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ieee International Instrumentation and Measurement Technology conference (I2MTC)

作者： Gibbs, Robert Giannetti, Cinzia Baynes, Thomas Pleydell-Pierce, Cameron Swansea Univ Fac Sci & Engn Swansea W Glam Wales Tata Steel Uk Ltd Hot Rolled Prod Port Talbot England

ISBN: (纸本)9798350380903;9798350380910

The Hot Strip Mill at Tata Steel, Port Talbot is a long-established, heavily instrumented, steel strip rolling mill. The mechanical properties of the finished strip depend on the cooling of the strip as it passes along the run-out table, decided before the coil-run from a physics-based simulation. Working with the MATLAB machine learning toolbox, this investigation details the process of developing an accurate (best final Pearson Coefficient of 0.8780) data-driven prediction of the two-phase cooling to final Coiling Temperature, independent of the physical model. Though ultimately trained on practical events resulting from the physics-driven control system, data-driven insights are isolated from the direct assumptions made within that existing simulation - thus affirming or providing insights into those established assumptions. The insights gained inform new visualizations of the complex datasets, for clearer illustration to industrial partners and process experts and highlight where further instrumentation would be most usefully invested. The finding that the cooling profile can be approximated as a two-phase linear process has implications for the assumptions made about the completeness and location of the metallurgical transition point in the current physical model. In addition to the parameters decided by the process model, it is the gauge of the strip that is the most important intrinsic factor affecting coiling temperature. Labelling by gauge highlights coils for which CT is over and under predicted, to aid further root cause analysis and the most efficient deployment of new instrumentation.

关键词： machine learning hot strip mill run-out table cooling profile coiling temperature visualization

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Distributed Q-learning Secondary Voltage control of Isolated AC Microgrids with Saturated Input 59

Distributed Q-Learning Secondary Voltage Control of Isolated...

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59th ieee/IAS Industrial and Commercial Power systems Technical conference, I and CPS 2023

作者： Lin, Shih-Wen Chu, Chia-Chi National Tsing Hua University Department of Electrical Engineering Hsinchu Taiwan

ISBN: (纸本)9798350396515

A model-free data-driven Q-learning algorithm for distributed control in isolated AC MGs is proposed for achieving the autonomous voltage restoration. First, by using the feedback linearization technique to each DG, a second-order linear system is defined. Then the distributed voltage restoration problem is re-formulated a consensus problem. We study this consensus problem subject to saturated inputs. An iterative model-free datadriven Q-learning algorithm is adopted to achieve the voltage restoration. Simulation of an isolated micro-grids have performed to demonstrate the effectiveness of the proposed method. © 2023 ieee.

关键词： Reinforcement learning

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Swarm learning-Based Dynamic Optimal Management for Traffic Congestion in 6G-driven Intelligent Transportation System

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2023年第7期24卷 7831-7846页

作者： Liu, Yibing Huo, Lijun Wu, Jun Bashir, Ali Kashif Univ Informat Engn State Key Lab Math Engn & Adv Comp Zhengzhou 450000 Peoples R China Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha 410072 Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Fukuoka 8080135 Japan Manchester Metropolitan Univ Dept Comp & Math Manchester M15 6BH England Woxsen Univ Sch Business Hyderabad 520345 India Lebanese Amer Univ Dept Comp Sci & Math Beirut Lebanon

As city boundaries expand and the vehicles continues to proliferate, the transportation system is increasingly overloaded, greatly increasing people's commuting burden and extending the resulting negative effects to all areas of work and life. It is a big issue that needs to be solved urgently. However, due to the development of infrastructure and technologies in 6G-driven Intelligent Transportation systems (ITS), it becomes possible to alleviate urban congestion. Existing solutions either optimize the path planning of each vehicle, or only focus on solving the problem of resource allocation of a single road, neither can take advantage of self-organizing networks and easily fall into local optimum. Combining the above reasons, we propose the Direction Decide as a Service (DDaaS) scheme. First, it contains a novel three-layer service architecture based on Swarm learning (SL), which enables orderly transmission of traffic data and control instructions and protects user privacy. Second, an improved local model and aggregation method is incorporated into DDaaS, which enables to make accurate predictions when the road resources at a single intersection are insufficient. Third, we propose a dynamic traffic control algorithm to provide signal light switching decisions for rapidly changing ITS. Finally, constructing an urban road simulation experiment combined with SUMO, we prove that DDaaS can reduce traffic congestion effectively and has significant advantages compared to other schemes.

关键词： Roads 6G mobile communication Switches data models Vehicle dynamics Hidden Markov models Computational modeling 6G-driven ITS swarm learning neural networks traffic congestion optimization intelligent decision

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Optimal Containment control of Multiple Quadrotors via Reinforcement learning

Optimal Containment Control of Multiple Quadrotors via Reinf...

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ieee International conference on Robotics and Automation (ICRA)

作者： Cheng, Ming Liu, Hao Liu, Deyuan Gu, Haibo Wang, Xiangke Beihang Univ Sch Astronaut Beijing 100191 Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

ISBN: (纸本)9798350384581;9798350384574

This paper explores the optimal containment control problem for nonlinear and underactuated quadrotors with multiple team leaders governed by nonlinear dynamics, employing the reinforcement learning. A cascade controller is formulated, comprising a position control component to ensure containment achievement and an attitude control component to govern rotational channel. The proposed optimal control protocols derived from historical data collected from quadrotor systems without requirement for exact knowledge of vehicle dynamics. The simulation illustrates the effectiveness of the proposed controller in managing a quadrotor team with multiple leaders.

关键词： Reinforcement learning

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EcoSprout: Machine learning-Powered Smart Sprinkler System 6

EcoSprout: Machine Learning-Powered Smart Sprinkler System

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6th ieee International conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Reddy Gottam, Sai Nithin Pathri, Bhargav Prajwal Ganduri, Krishna Vamshi Venkata Prasanna Sathwik, Tulasi Lalitendra Reddy, Samanthula Thatwik Vaishnavi, Varanasi School of Technology Woxsen University Hyderabad India

ISBN: (纸本)9798350306613

Smart sprinkler systems employ machine learning to forecast soil moisture levels based on environmental parameters and optimize water usage. This novel system incorporates temperature, humidity, and other environmental data to assist users in determining when and how much to water. Sensors like humidity, temperature, and soil moisture sensors are used to collect data for the system. Machine learning approaches and supervised learning models such as regression, decision trees are used to forecast and plan correlations between environmental conditions and soil moisture levels for historical data sets. Smart sprinkler systems allow you to control the amount of water available on specific crops through specific irrigation schedules based on the needs and preferences of each crop. The system's machine learning algorithms can make data-driven irrigation decisions that deliver the optimal amount of soil moisture for each crop's growing cycle taking into account your crop's exact water requirements Provides yields and its quality is enhanced by watering the plants properly and maintaining adequate water levels and drainage or root rot problems of the poor Avoid Smart sprinklers adaptable to different fields of water the variability of irrigation that can result from over-irrigation is determined by its flexibility. © 2024 ieee.

关键词： Arduino Machine learning Prediction Regression Smart Sprinkler

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