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Stabilization of Continuous-Time systems Against Stochastic Network Uncertainties:Fundamental Variance Bounds

Journal of systems Science & Complexity 2021年第5期34卷 1858-1878页

作者： QI Tian CHEN Jie School of Automation Science and Engineering Key Laboratory of Autonomous Systems and Networked ControlSouth China University of TechnologyGuangzhou 510640China Department of Electrical Engineering City University of Hong KongHong KongChina

This paper studies the stabilizability and stabilization of continuous-time systems in the presence of stochastic multiplicative *** authors consider multi-input,multi-output(MIMO)linear time-invariant systems subject to multiple static,structured stochastic uncertainties,and seek to derive fundamental conditions to ensure that a system can be stabilized under a mean-square *** the stochastic control framework,this problem can be considered as one of optimal control under state-or input-dependent random noises,while in the networked control setting,a problem of networked feedback stabilization over lossy communication *** authors adopt a mean-square small gain analysis approach,and obtain necessary and sufficient conditions for a system to be meansquare stabilizable via output *** single-input,single-output(SISO)systems,the condition provides an analytical bound,demonstrating explicitly how plant unstable poles,nonminimum phase zeros,and time delay may impose a limit on the uncertainty variance required for mean-square *** MIMO minimum phase systems with possible delays,the condition amounts to solving a generalized eigenvalue problem,readily solvable using linear matrix inequality optimization techniques.

关键词： Mean-square small gain theorem multiplicative stochastic uncertainty networked control

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ES-MSCNN: An Echo State Multi-Scale Convolutional Neural Network for Multi-Step Prediction of Wastewater Quality Variables

ES-MSCNN: An Echo State Multi-Scale Convolutional Neural Net...

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Robotics, control and Automation Engineering (RCAE), International Conference on

作者： Yue Sun Yiqi Liu Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science & Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

Given the limitations of hardware sensors together with strong nonlinearities and nexus between variables in wastewater treatment processes (WWTPs), the measurement of key quality variables is usually inaccurate or even impossible. Moreover, due to the short data sampling period, the single-output single-step prediction model cannot meet the actual demand. Therefore, this paper proposes a new soft sensor, termed as the Echo State Multi-Scale Convolutional Neural Network (ES-MSCNN), designed for accurate multivariate multi-step prediction. ES-MSCNN first generates an echo state representation (ESR) containing temporal information through an echo state network (ESN). Subsequently, a convolutional neural network (CNN) is employed to avoid the shallowness of ESN and further extract high-dimensional feature information from the data. Besides, a multi-scale learning strategy is used in the convolutional module to capture multi-timescale features and improve the multivariate prediction accuracy. Validation on the benchmark simulation model 2 platform and comparison with several models indicate that the proposed ES-MSCNN has higher accuracy and stronger robustness in multi-step prediction tasks with multi-input and multi-output.

关键词： Accuracy Soft sensors Predictive models Feature extraction Prediction algorithms Robustness Convolutional neural networks Wastewater Wastewater treatment Robots

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Robust Output Voltage Tracking with Prescribed Performance for Single-Phase UPS Inverters

Robust Output Voltage Tracking with Prescribed Performance f...

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Chinese Automation Congress (CAC)

作者： Jialei Luo Wei Liu Key Laboratory of Autonomous Systems and Networked Control of Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper deals with the robust output voltage tracking problem with prescribed performance for single-phase voltage source uninterruptible power supply inverters. We construct two linear internal models to reproduce the steady-state information, based on which we transform our problem into the robust stabilization problem with prescribed performance of an augmented system. Then, we incorporate a novel error transformation into the existing framework for the robust output regulation problem to design a stabilization controller, based on which we synthesize our control law to make the tracking error converge to zero asymptotically while always satisfying the prescribed performance.

关键词： Automation Power supplies Transforms Inverters Regulation Steady-state Voltage control Nonlinear systems Uninterruptible power systems Lyapunov methods

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Analysis and diagnosis system for various industrial network protocols 6

Analysis and diagnosis system for various industrial network...

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6th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2022

作者： Wang, Jian Jin, Ni Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences LiaoNing ShenYang China

ISBN: (纸本)9781450397148

For a comprehensive and systematic analysis of the communication quality in the industrial field, it is important for manufacturing enterprises to study effective diagnosis and evaluation methods and develop software tool to meet the actual needs of industrial communication networks for high quality, rapid diagnosis and positioning of anomalies. By analyzing the characteristics of various industrial network protocols, this paper studies the measurement methods of the key indicators of the real-time performance of industrial communication networks and the methods of communication anomaly diagnosis, and on this basis, designs and implements the analysis and diagnosis system for various industrial network protocols. © 2022 Association for Computing Machinery.

关键词： Quality control

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Research on automatic switching of hydraulic feed dual process in honing processing 4

Research on automatic switching of hydraulic feed dual proce...

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2024 4th International Conference on Mechatronics, Automation and Intelligent control, MAIC 2024

作者： Lu, Yong Pei, Hailong Li, Guang Deng, Haijun Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems School of Automation Science and Engineering South China University of Technology Guangzhou510640 China School of Information Guangdong Polytechnic of Science and Trade Guangzhou510440 China

Honing is a finishing process used for the inner cylindrical surfaces of engine blocks or liners. The process involves abrasive particles bonded to the surface of a honing head, which come into contact with the inner bore surface, performing an oscillating and rotational motion. These motions follow a specific pattern, allowing for the creation of crosshatch patterns on the inner bore surface, thereby improving dimensional accuracy, form accuracy and reducing surface roughness. Common honing processes are divided into four distinct phases: material removal, crosshatching, finish honing, and plateau honing. Each phase corresponds to specific tasks such as material removal from the bore, creating crosshatch patterns, honing the peaks in the crosshatch, and final surface finishing. In computer numerical control (CNC)-based honing machine, individual control commands are typically used to manage each of these phases separately. The commands are combined according to process requirements to achieve the desired honing results. To enhance the efficiency of the honing process, this study focuses on the hydraulic feed of the abrasive strips on the honing head, specifically examining the automatic continuous switching between finish honing and plateau honing. The research analyzes the honing principles, develops a control algorithm for dual-process automatic switching, and validates the process through machining tests. Compared to single-process control, the proposed algorithm significantly improves processing efficiency. Additionally, this algorithm can be applied to other dual-process automatic switching controls, providing valuable insights for the design of CNC honing systems. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Honing machines

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Sampled-Data Output Voltage Practical Tracking for Uninterruptible Power Supply systems with Transmission Delays

Sampled-Data Output Voltage Practical Tracking for Uninterru...

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Complex systems and Intelligent Science (CSIS-IAC), International Annual Conference on

作者： Mengqi Jia Wei Liu Key Laboratory of Autonomous Systems and Networked Control of Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798331504755

ISBN: (纸本)9798331504762

This paper aims at addressing output voltage practi-cal tracking problem for uninterruptible power supply systems by devising an aperiodic sampled-data control scheme. We consider the presence of transmission delay in the input channel. We demonstrate that the problem can be solved by a predictor-based sampled-data dynamic output feedback control law. Moreover, the tracking error can converge to a prescribed neighborhood of the origin.

关键词： Regulation Delays Voltage control Complex systems Uninterruptible power systems Output feedback

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NONLINEAR STEAM GENERATOR FEEDWATER system IDENTIFICATION BASED ON WAVELET NEURAL NETWORK 10

NONLINEAR STEAM GENERATOR FEEDWATER SYSTEM IDENTIFICATION BA...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Wang, Hongrui Yuan, Mingzhe Zhang, Pengbin Xing, Kunpeng Guangzhou Institute of Industrial Intelligence Guangzhou China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Nuclear Power Engineering Co. Ltd. Shenzhen China

ISBN: (纸本)9781837242672

Steam generator is the key equipment in the large and complex nuclear power plant, and its water supply system has the characteristics of time-varying, large delay, nonlinear. In this paper, a segmented linearized model based on wavelet neural network is developed for a steam generator feedwater system of a nuclear power plant. All the data of the automation stages of the steam generator, which are steam flow rate, feedwater flow rate and liquid level from 20% load to 100% load, are collected and divided into six stages based on the characteristics of the data. For the first four stages of data, the feedwater system was modelled using the Hammerstein-Wiener method based on wavelet neural network, which was used as the nonlinear input and nonlinear output module of Hammerstein-Wiener. And for the remaining two stages of data, the nonlinear autoregressive models based on with exogenous (ARX) inputs model approach was modelled using nonlinear ARX as the underlying framework of wavelet neural network. The proposed model can be used for model predictive control to switch between different models based on load changes. Through the verification of simulation experiments, the built model can accurately reflect the trend of actual data, and the predicted values have high accuracy and reliability. © The Institution of Engineering & Technology 2024.

关键词： Nuclear power plants

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Design of Yaw controller for a Small Unmanned Helicopter Based on ADRC

Design of Yaw Controller for a Small Unmanned Helicopter Bas...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Huang, Linjie Pei, Hailong Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou510640 China

ISBN: (纸本)9789811581540

For ensuring the stabilities of unmanned helicopters under various working conditions, this paper introduces a yaw controller based on ADRC. Because the effect of PI or PID controller is not enough in some severe environments such as working in a situation with strong wind, so ADRC is wildly used in the area of UAV. However, it takes a lot of time to adjust parameters, which is not conductive to piratical application. What this article makes is to enhance and to simplify traditional ADRC through practical experiments and theory analysis. This paper uses gradient descent to identify the actuator gain$$b:control$$, and summarizes the practical tuning experience during the tuning process. Compared with the traditional PID method, the experimental analysis claim that the method proposed in this article has significant advantages in both dynamic performance and anti-interference performance. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： controllers

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Dynamic control Allocation for a Twin Ducted Fan UAV

Dynamic Control Allocation for a Twin Ducted Fan UAV

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Meng, Chaoheng Pei, Hailong Cheng, Zihuan Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou510640 China

ISBN: (纸本)9789811581540

Twin ducted fan UAV is a novel aircraft with redundant control effectors and the interactions between actuators and control allocators can not be ignored. In this paper, dynamic control allocation is used to deal with the control allocation problem of Twin ducted fan UAV. First, the actuator rate is penalized in the optimization function of the control allocation based on the quadratic programming method. Second, a post-compensation method is applied to compensate for command increment attenuation. The simulation results show that the dynamic control allocation method with actuator dynamic compensation can prevent the high bandwidth actuator from saturating in advance, and the virtual control command is effectively allocated to actuators with different bandwidths. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Actuators

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection 30

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Cheng, Long He, Haoyang Gu, Yan Liu, Qingzhi Zhao, Zhiming Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Wageningen University and Research Information Technology Group Netherlands University of Amsterdam Research Group of Multi-Scale Networked Systems Netherlands

ISBN: (纸本)9798331515966

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of 53.18% and costs by 61.98% compared to basic techniques, and achieving 20.26% and 25.71% improvements over the latest advanced methods, respectively. © 2024 IEEE.

关键词： Deep reinforcement learning

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