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Two-Degree-of-Freedom Feedback Loop Factorization controller for Oscillating Plant with Uncertain Time Delay and Astatism Using Structured Singular Value

Two-Degree-of-Freedom Feedback Loop Factorization Controller...

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International Conference on control, automation and Diagnosis (ICCAD)

作者： Marek Dlapa Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University in Zlin Zlin Czech Republic

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Application of Robust control Toolbox for Time Delay Systems implemented in the Matlab system to the oscillating plant with uncertain time delay and astatism using the D-K iteration and algebraic approach. The algebraic approach combines the structured singular value, algebraic theory and algorithm of global optimization solving remaining issues in structured singular value framework. The algorithm for global optimization can be alternated with direct search methods such as Nelder-Mead simplex method giving solutions for problems with one local extreme. As a global optimization method, Differential Migration is used proving to be reliable in solving this type of problems. The D-K iteration represents a standard method in the structured singular value theory. The results obtained from the D-K iteration are compared with the algebraic approach.

关键词： Robust control Feedback loop Delay effects Search methods Optimization methods Reliability theory Mathematical models

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Data-driven Fault Diagnosis Method Based on Fusion of Multi-direction and Multi-source Signals 5

Data-driven Fault Diagnosis Method Based on Fusion of Multi-...

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5th International Conference on Intelligent Autonomous Systems, ICoIAS 2022

作者： Yang, Yang Wang, Junda Li, Ning Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

As a pivotal mechanical component of turbine drive-chain, gearbox suffers from various accidents and fault diagnosis could remarkably enhance its reliability and maintain running safety and lifetime. Vibration-based analysis on single-channel is mainly adopted in previous diagnostic, which cannot catch full vibration inner information in gearbox sensors and bring model performance down. In this paper, a novel method based on the fusion of multi-direction and multi-source signals and the convolutional neural network (MDMS-CNN) is presented. This method can obtain multi-dimensional feature maps from MDMS and preserve the time correlation among different signal sequences. First, features from MDMS are extracted via a corrosion conversion method that converts signals into two-dimensional (2-D) gray images. Then, homologous images are spliced into same channels, while heterogeneous images are superimposed to different channels. Finally, fusion features are input into CNN for fault classification. In experiments, the method is validated on a gearbox diagnosis case. Results have shown that the proposed method achieves 99.86% recognition accuracy and faster convergence speed compared to others. © 2022 IEEE.

关键词： Failure analysis

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Sizing a Battery Energy Storage System for Hybrid Renewable Power Plants Based on Optimal Market Participation Under Different Market Scenarios

Sizing a Battery Energy Storage System for Hybrid Renewable ...

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作者： Martinez-Rico, Jon Zulueta, Ekaitz De Argandona, Ismael Ruiz Armendia, Mikel Fernandez-Gamiz, Unai Automation and Control Unit Tekniker Eibar20600 Spain University of the Basque Country Department of Energy Engineering College of Engineering at Vitoria-Gasteiz Vitoria-Gasteiz01006 Spain University of the Basque Country Department of Systems Engineering and Control College of Engineering at Vitoria-Gasteiz Vitoria-Gasteiz01006 Spain

This article presents a method for selecting the best battery sizing based on an optimal market participation strategy in a hybrid renewable power plant. The proposed formulation considers different scenarios to ensure the reserve provision compliance and includes two terms. The first one maximizes the day-ahead and automatic frequency restoration reserve (aFRR) market revenues. The second minimizes the battery degradation, accounting for both cycling and calendar ageing effects. The Iberian Electricity Market is selected as the study case, and two different market scenarios are compared: the current market scenario and the upcoming one, in which the upward and downward aFRR band offers are independent. The operation of a set of batteries is simulated until their end of life, and results reveal that energy/power ratios higher than 3 h for the current market scenario, and than 4 h for the upcoming one, are less cost effective. Regarding the best battery energy storage systems sizing, the selection varies considering incomes or profitability criteria. Moreover, keeping the current prices, in the upcoming market scenario hybrid renewable power plants are encouraged to participate more in the downward than in the upward aFRR market. © 1972-2012 IEEE.

关键词： Battery storage

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Complex Permeability Determination for EE Core Based on Flexible PCB Fixture and Magnetic Flux Path Length Analysis 2

Complex Permeability Determination for EE Core Based on Flex...

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2nd China Power Supply Society Electromagnetic Compatibility Conference, CPEMC 2024

作者： Jie, Huamin Zhao, Zhenyu Chang, Yongqi Wang, Changdong Jiang, Shiqi Gao, Richard Xian-Ke See, Kye Yak School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering National Univeristy of Singapore Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Institute of High Performance Computing A*STAR Singapore Singapore

ISBN: (纸本)9798331509552

The complex permeability of magnetic cores acts as a vital factor in electromagnetic interference (EMI) filtering choke design and optimization. Many efforts have been reported for toroid cores, but fewer target those with other shapes, such as EE, EI, UU, and UI cores. Owing to their commonly used in planar EMI filters, determining the complex permeability of them is of significance. Targeting EE cores, this paper presents a flexible printed circuit board (PCB)-based fixture adapter tailored designed for performing the measurement, while the corresponding core permeability can be calculated through magnetic flux path length analysis. The experiments validate the effectiveness and accuracy of the proposed methods, spanning a wide frequency range up to 120 MHz. ©2024 IEEE.

关键词： Magnetic flux

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Certificated Actor-Critic: Hierarchical Reinforcement Learning with control Barrier Functions for Safe Navigation

arXiv

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arXiv 2025年

作者： Xie, Junjun Zhao, Shuhao Hu, Liang Gao, Huijun Department of Automation School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

control Barrier Functions (CBFs) have emerged as a prominent approach to designing safe navigation systems of robots. Despite their popularity, current CBF-based methods exhibit some limitations: optimization-based safe control techniques tend to be either myopic or computationally intensive, and they rely on simplified system models;conversely, the learning-based methods suffer from the lack of quantitative indication in terms of navigation performance and safety. In this paper, we present a new model-free reinforcement learning algorithm called Certificated Actor-Critic (CAC), which introduces a hierarchical reinforcement learning framework and well-defined reward functions derived from CBFs. We carry out theoretical analysis and proof of our algorithm, and propose several improvements in algorithm implementation. Our analysis is validated by two simulation experiments, showing the effectiveness of our proposed CAC algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Deep Reinforcement Learning or Lyapunov Analysis?A Preliminary Comparative Study on Event-Triggered Optimal control

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1702-1704页

作者： Jingwei Lu Lefei Li Qinglai Wei Fei-Yue Wang IEEE the Department of Industrial Engineering Tsinghua UniversityBeijing 100084China The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter develops a novel method to implement event-triggered optimal control(ETOC) for discrete-time nonlinear systems using parallel control and deep reinforcement learning(DRL), referred to as Deep-ETOC. The developed Deep-ETOC method introduces the communication cost into the performance index through parallel control, so that the developed method enables control systems to learn ETOC policies directly without triggering conditions.

关键词： Deep letter enable

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Reinforcement Fuzzy Q-Learning Incorporated with Genetic Kinematics Analysis for Self-organizing Holonomic Motion control of Six-Link Stewart Platforms

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International Journal of Fuzzy Systems 2023年第3期25卷 1239-1255页

作者： Huang, Hsu-Chih Xu, Sendren Sheng-Dong Chen, Yu-Xiang Chen, Chien-Ming Department of Electrical Engineering National Ilan University Yilan260007 Taiwan Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei106335 Taiwan Advanced Manufacturing Research Center National Taiwan University of Science and Technology Taipei106335 Taiwan

This paper contributes to the development of reinforcement fuzzy Q-learning incorporated with genetic kinematics analysis for self-organizing holonomic motion control of six-link Stewart platforms. The fuzzy theory is integrated with the Q-learning algorithm to develop a hybrid reinforcement learning strategy. Both the actions and the Q-functions in the classical Q-learning algorithm are inferred by means of fuzzy rules. The proposed hybrid reinforcement learning is then applied to self-organizing holonomic motion control of parallel Stewart platforms for industrial applications. After the kinematics analysis using a geometrical approach and genetic algorithm (GA)-based Newton–Raphson formulation, this study develops a practical control scheme that combines fuzzy Q-learning and proportional–integral–differential (PID) control scheme, called FQPID. In the proposed FQPID holonomic motion control method, the PID control gains are online-tuned via the fuzzy Q-learning computation process. An experimental Stewart platform consisting of six servo motors, adjustable links, and two platforms is constructed to illustrate the effectiveness of the presented methods. The experimental results are compared with some existing control methods using the same evaluation criterion. Through the experimental results and comparative works, the superiority of the proposed FQPID control scheme is presented and discussed. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.

关键词： Reinforcement learning

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Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6500-6508页

作者： Chen, Luefeng Cao, Wei Zheng, Biao Wu, Min Pedrycz, Witold Hirota, Kaoru China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Macau University of Science and Technology Institute of Systems Engineering Taipa999078 China Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

In the human pose estimation task, on the one hand, 3-D pose always has difficulty in dividing different 2-D poses if the view is limited;on the other hand, it is hard to reduce the lifting ambiguity because of the lack of depth information, it is an important and challenging problem. Therefore, two-stage representation refinement based on the convex combination for 3-D human pose estimation is proposed, in which the two-stage method includes a dense-spatial-temporal convolutional network and a local-to-refine network. The former is applied to determine the features between each video frame;the latter is used to get the different scales of pose details. It aims to address the difficulty of estimating 3-D human pose from 2-D image sequences. In such a way, it can better use the relations between every frame in the sequence of the pose video to produce more accurate results. Finally, we combine the above network with a block called convex combination to help refine the 3-D pose location. We test the proposed approach on both Human3.6m and MPII datasets. The result confirms that our method can achieve better performance than improved CNN supervision, a simple yet effective baseline, and coarse-to-fine volumetric prediction. Besides, a robustness test experiment is carried out for the proposed method while the input is interrupted. The result verifies that our method shows better robustness. © 2024 IEEE.

关键词： Convolution

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Federated Intelligence for Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-5页

作者： Zhang, Weishan Zhang, Baoyu Jia, Xiaofeng Qi, Hongwei Qin, Rui Li, Juanjuan Tian, Yonglin Liang, Xiaolong Wang, Fei-Yue School of China University of Petroleum Qingdao China Department of Data Management Big Data Centre Beijing Beijing China Technology Company Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE

关键词： Intelligent vehicle highway systems

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Improved Hybrid Attention Learning System for Remaining Useful Life Prediction 42

Improved Hybrid Attention Learning System for Remaining Usef...

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42nd Chinese control Conference, CCC 2023

作者： Li, Zhi Sun, Tao Ma, Song Yang, Bin Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Tsinghua University Department of Automation Beijing100084 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9789887581543

Remaining useful life (RUL) prediction can improve the availability and efficiency of equipment or system by providing timely maintenance suggestions. In this paper, an improved hybrid attention learning system for RUL prediction is developed. By introducing adaptive weighting, a hybrid attention mechanism with self attention and external attention is designed. The mechanism enhances the attention to samples with similar performance degradation patterns, and improves the feature extraction ability of the learning system for sensor data with strong temporal correlation. Finally, we conducted a series of simulation experiments on the commercial modular aviation propulsion system simulation (C-MAPSS) dataset to verify the effectiveness and superiority of the proposed method in RUL prediction. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Learning systems

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