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15th German Microwave conference (GeMiC)

作者： Zeng, Xianshi Buehlmeyer, Jonas Weigel, Robert Irnstorfer, Florian Friedrich Alexander Univ Erlangen Nurnberg Inst Elect Engn Erlangen Germany

ISBN: (纸本)9783982039732

With the development of Intelligent Reflecting Surface (IRS) technology, the network performance of the control channel has an increasing impact on IRS systems. This paper presents a hardware-in-the-loop (HiL) simulation methodology for the IRS controller using real network devices. We demonstrate the behaviour of a control channel connected to the simulated IRS-based communication platform. This provides test results that correlate with wired and wireless network connection. In addition, previous work has investigated the mobile user tracking (UT) scheme and proposed methods to achieve a higher signal-to-noise ratio (SNR) in the downlink case, which will provide a theoretical upper bound. Our experimental results are analysed and compared with previous simulation methods. The results show that the proposed HiL simulation methodology can validate the functionality of the UT scheme and emphasise the importance of considering the impact of the non-ideal control link on the IRS system.

关键词： IRS Hardware-in-the-loop control Link Communication System

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Artificial Neural network-based Simplified Model Predictive control for Enhancing Voltage Source Inverters: CMV Reduction and Weighting Factor Optimization 50

Artificial Neural Network-based Simplified Model Predictive ...

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50th Annual conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Shokrani, Mohammad Imanlou, Arash Behkam, Reza Nazari-Heris, Morteza Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Polytechnic Tehran Iran Department of Engineering East Carolina University GreenvilleNC United States

ISBN: (纸本)9781665464543

controlling voltage source inverters (VSIs) is crucial for ensuring the efficient operation of inverter-based systems. Model predictive control (MPC), notably finite control set MPC (FCS-MPC), is increasingly recognized as an effective control strategy within power electronics, particularly for addressing multi-objective control problems. This article proposes a novel approach for controlling voltage source inverters using a simplified FCS-MPC scheme with common-mode voltage (CMV) reduction, limited switching frequency, and optimized weighting factors (WFs). The proposed method simplifies the MPC controller by replacing the computational burden of current predictions with a single voltage reference estimation, thereby reducing computational complexity. Moreover, offline optimization based on the non-dominated sorting genetic algorithm ii (NSGA-ii) is employed to determine optimized weighting factors for the cost function, as well as enhance the controller's performance. For further alleviation of computational burden, the controller is imitated using an artificial neural network (ANN) trained offline with data labeled by the original FCS-MPC algorithm. A simulation study is presented to control both conventional two-level VSI (2L-VSI) and three-level neutral point clamped (3L-NPC) inverters to demonstrate the effectiveness and validity of the proposed method. The results underline that the ANN imitator of simplified MPC reduces computational complexity while maintaining performance comparable to optimal control. © 2024 IEEE.

关键词： Cost functions

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Implementation of Plant Model of an Air Vented Dryer system using Data Driven Approach in MATLAB/Simulink

Implementation of Plant Model of an Air Vented Dryer system ...

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control Instrumentation System conference (CISCON) - Guiding Tomorrow - Emerging Trends in control, Instrumentation, and systems Engineering

作者： Gonde, Dhanashree Y. Thosar, Archana G. Sethi, Itishree COEP Technol Univ Dept Elect Engn Pune Maharashtra India Whirlpool Corp Software Engn Pune Maharashtra India

ISBN: (纸本)9798350375480;9798350375497

Now a days, physics-based models are extensively employed across diverse domains to accurately replicate the real-world physics of systems. These systems use mathematical equations and rules derived from physics laws. However, the deployment of physics-based models in simulation environment poses significant challenges because they are high fidelity and computationally complex models. There is a need to leverage these models in control and validation environments to ensure accuracy, stability, and optimal performance. One approach to leverage these models is through the use of Functional Mockup Interface (FMI) standards, which bridges the gap between physical modeling and control system design. But there are also challenges while deploying them in a simulation environment. To address the challenges associated with it, a new approach of data driven modeling using neural network has been studied in this paper. The developed neural network model aims to capture the dynamic behavior of the Air-vented dryer system, enabling smooth integration with MATLAB for Model-In-Loop and Hardware-In-Loop simulation. This approach helps better for simulating high fidelity systems, benefiting various industries which need dynamic system simulations.

关键词： Artificial neural network Functional mock-up unit Feedforward neural network MATLAB

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Evaluation of the performance of a multiphase queuing system with retrial orbit using neural network model 8

Evaluation of the performance of a multiphase queuing system...

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8th International conference on Information, control, and Communication Technologies, ICCT 2024

作者： Dang, Minh Cong Moscow Institute of Physics and Technology Russia

ISBN: (纸本)9798331517564

Multiphase queuing systems are suitable model for evaluating the performance of wireless networks with linear topology. Analytical studies of such queuing systems had been provided in previous researches. However, for a variant of multiphase queuing system with single common retrial orbit queue, exact analytical results are not available and simulation method is slow in the case of complex model. We investigate the possibility of using a neural network model to predict the performance of a multiphase queuing system with single common retrial orbit queue. Experiment results have shown that by choosing suitable neural network architecture for the corresponding queuing system model, the accuracy of neural network model was significantly improved. © 2024 IEEE.

关键词： Queueing networks

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Reinforcement Learning Based Position control of 1-DOF Rotational Hydraulic Actuator 24

Reinforcement Learning Based Position Control of 1-DOF Rotat...

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24th International conference on control, Automation and systems

作者： Kang, Hokwon Kim, Jin Tak Park, Sangsin Cho, Jungsan Univ Sci & Technol UST Dept Robot Engn Daejeon 34113 South Korea Korea Inst Ind Technol KITECH Hydraul Robot Lab Robot R&D Grp Ansan 15588 South Korea

ISBN: (纸本)9798331517939;9788993215380

In this study, we propose a reinforcement learning (RL) based position control method for a one-degree-of-freedom (1-DOF) rotational hydraulic actuator. controlling hydraulic actuators is challenging due to their nonlinear characteristics and complex structure. Reinforcement learning offers the advantage of enabling control through learning without requiring a detailed understanding of the model, using virtual environments. However, simulators do not perfectly replicate real-world conditions. To address this, we aim to enhance the fidelity of the simulation and improve the performance of reinforcement learning-based controllers by integrating real-world data into the simulation using deep neural networks (DNN). The reinforcement learning is trained using the Proximal Policy Optimization (PPO) algorithm, and it is validated through experiments with step and sine inputs.

关键词： Artificial Intelligent systems Neural network systems Reinforcement Learning Hydraulic System control Position control

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Wavelet neural network algorithm for hybrid GA in infrared CO2 gas sensor

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systems AND SOFT COMPUTING 2024年 6卷

作者： Wang, Jun Wang, Yuanxi Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China

As the economy develops and the environmental impact of the greenhouse effect becomes more apparent, the need for precise measurement of specific gas concentrations in the air has become increasingly pressing. Nevertheless, as a representative of greenhouse gases, CO2 gas detectors are susceptible to environmental temperature fluctuations, which impairs the accuracy of detection. To address this issue, the research team innovatively combined the genetic algorithm (GA) and the wavelet neural network (WNN) to develop a solution for the temperature compensation problem of the infrared CO2 gas sensor. The non-dominant sorted genetic algorithm ii (NSGA-ii) was integrated into the GA to achieve a balance between the accuracy, complexity, and temperature performance of the model through multi-objective optimization. The results showed that compared with other existing models, the GA-WNN model proposed in this study can significantly reduce the difference between the detected values and the actual environmental values under various temperature conditions when processing data. Especially at an ambient temperature of 49 degrees C, for a true CO2 concentration of 2000 ppm, the detection value processed by the GA-WNN algorithm was 2046 ppm, with a relative error of only 2.3 %, far lower than the 9.8 % of Faster RCNN algorithm and 11.5 % of WNN algorithm. The contribution of the research is the proposal of a novel temperature compensation method that significantly enhances the precision of infrared CO2 gas sensors. This is of paramount importance for enhancing the accuracy of gas detection in environmental monitoring and industrial control.

关键词： WNN NSGA-ii Carbon dioxide gas checker Greenhouse gases

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Precision landing of autonomous parafoil system via deep reinforcement learning

Precision landing of autonomous parafoil system via deep rei...

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IEEE Aerospace conference

作者： Wei, Zhenyu Shao, Zhijiang Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9798350304626

This paper focuses on utilizing deep reinforcement learning (DRL) to achieve the precision landing of an autonomous parafoil system. Specifically, we present an integrated guidance and control algorithm realized by a policy network, which maps the state observation collected by the onboard sensor to the flap deflection control command. The reward function is designed to guide the policy network in minimizing both the touchdown error and the terminal anti-wind angle of the parafoil system. To ensure a smooth control profile during the time interval, an action space parameterization method based on Lagrange interpolation is proposed. On this basis, the policy network is trained by the proximal policy optimization algorithm, a type of on-policy DRL method. The parafoil dynamics are described using the six-degree-of-freedom model. Randomly distributed initial conditions are considered to account for the accumulated guidance error prior to terminal landing. Additionally, the Dryden wind disturbance is incorporated to account for the uncertainty of the wind field. The numerical results demonstrate that: i) The trained policy network effectively provides a smooth control profile for the autonomous parafoil system;ii) the policy network exhibits the potential to provide real-time command;iii) The integrated guidance and control algorithm based on DRL successfully enables precise terminal landing of the parafoil system, with a mean touchdown error smaller than 100m. The findings from this study will benefit the design of guidance and control algorithms for future autonomous parafoil systems employed in payload recovery missions or space exploration missions.

关键词： Landing

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Adaptive overlay selection at the SD-WAN edges: A reinforcement learning approach with networked agents

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COMPUTER networkS 2024年 243卷

作者： Botta, Alessio Canonico, Roberto Navarro, Annalisa Stanco, Giovanni Ventre, Giorgio Univ Napoli Federico II DIETI Via Claudio 21 I-80125 Naples Italy

Software-Defined Wide Area networks (SD-WANs) have emerged as a promising solution to address the connectivity demands of modern distributed enterprises. However, the effective application of the Software Defined networking (SDN) paradigm in such broad and dynamic environments remains a significant challenge. In this paper, we present two novel contributions. First, we design a decentralized control plane for SD-WANs that leverages edge-based network monitoring and overlays' configuration. Then we present a Reinforcement Learning-based orchestration plane that leverages local information for the enforcement of SD-WAN policies. Since traditional approaches suffer either a lack of scalability due to the problem's complexity or suboptimal performance due to isolated decision-making, the proposed approach leverages a cooperative Multi -Agent Reinforcement Learning framework. Our novel cooperative approach is based on per-site agents that exchange a small amount of information to enhance performance while preserving scalability. To validate the efficacy of our proposed approach, we conducted an extensive experimental evaluation considering diverse SD-WAN scenarios. Results show that our framework is able to satisfy global network policies for a multi-site SD-WAN with different QoS requirements and cost constraints.

关键词： SD-WAN SDN Multi-agent reinforcement learning Traffic engineering

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Efficiency and Reliability Enhancement in HVDC-VSC Power Transmission network with control and Planning Strategies 7

Efficiency and Reliability Enhancement in HVDC-VSC Power Tra...

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7th International conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Satish, Y. Shyam, P. Aravind, S. Chandana, V. Geetha Lakshmi, V. Sai Prasad v Potluri Siddhartha Institute of Technology Dept of Electrical and Electronics Engineering Vijayawada India

ISBN: (纸本)9798350372816

The significant roles, technical advantages, and high efficiency of High Voltage Direct Current (HVDC) systems have led to their global application. In most cases, the HVDC network improves the utilization of HVAC systems by connecting two distinct but complementary technical features. A regulated converter circuit serves as the join point system for connecting HVAC lines to HVDC. There are detrimental impacts on the electrical power quality of the systems linked on either end of the HVDC network, despite the use's significance and the link's considerable advantages. Both conventional and alternative approaches have been considered and implemented to improve the electrical grid's subpar performance. On the other hand, the system typically has to synthesise the assumption of load circumstances in order to make the improvement. This study introduces a novel approach that makes use of a real-time control mechanism. With the help of the EMTDC model, the controller is utilized, and its capacity to monitor setpoints associated with active power, reactive power, and other variables that are appropriate is evaluated. Considering the effectiveness of the suggested superior outcomes are achieved by the controller proposed in the work, which had a greater margin of stability and a shorter time having optimized response for the system. © 2024 IEEE.

关键词： HVDC power transmission

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Nonlinear Model Predictive control Approximation: Applications to Truck-Trailer control System 24

Nonlinear Model Predictive Control Approximation: Applicatio...

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24th International conference on control, Automation and systems

作者： Park, Suyong Nguyen, Duc Giap Jin, Yongsik Park, Jinrak Kim, Dohee Eo, Jeong Soo Han, Kyoungseok Hanyang Univ Dept Automot Engn Seoul 04763 South Korea Kyungpook Natl Univ Dept Mech Engn Daegu 41566 South Korea Elect & Telecommun Res Inst ETRI Robot IT Convergence Res Sect Daegu 42994 South Korea Hyundai Motor Co Dept Electrified Syst Control Res Lab Hwaseong 18280 South Korea

ISBN: (纸本)9798331517939;9788993215380

In this work, we demonstrate the effectiveness of nonlinear model predictive control (NMPC) approximation based on deep neural network (DNN). MPC has been widely adopted in autonomous driving control problems to handle multiple objectives and constraints. We first design the implicit NMPC for the forward and backward motions of a truck-trailer (TT) system, which follows the reference path while maintaining safety between the head truck (HT) and the trailer (TR). However, the computational load in implicit MPC makes it a challenge for real-time implementations. To alleviate the computational burden in implicit NMPC online, an NMPC approximation approach based on DNN is adopted in this study to achieve a parametric function approximation. We conduct a comparative study on the proposed approach and a baseline controller for control performance analysis, and the computational load is evaluated on a hardware-in-the-loop (HIL) experimental system.

关键词： Deep neural network truck-trailer system nonlinear model predictive control hardware-in-the-loop systems control policy approximation

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