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Contraction analysis on primal-dual gradient optimization

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arXiv 2019年

作者： Su, Yanxu Shi, Yang Sun, Changyin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Southeast University Nanjing210096 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 3P6 Canada

This paper analyzes the contraction of the primaldual gradient optimization via contraction theory in the context of discrete-time updating dynamics. The contraction theory based on Riemannian manifolds is first established for convergence analysis of a convex optimization algorithm. The equality and inequality constrained optimization cases are studied, respectively. Under some reasonable assumptions, we construct the Riemannian metric to characterize a contraction region. It is shown that if the step-sizes of the updating dynamics are properly designed, the convergence rates for both cases can be obtained according to the contraction region in which the convergence can be ***, the augmented Lagrangian function which is projection free is adopted to tackle the inequality constraints. Some numerical experiments are simulated to demonstrate the effectiveness of the presented contraction analysis results on primal-dual gradient optimization algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

Hierarchical control Scheme for VSC-MTDC System with multiple Renewable Energy Sites Based on Optimal Power Flow and Droop Scheme

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Hierarchical Control Scheme for VSC-MTDC System with multipl...

IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Asif Raza Qi Huang Jian Li Olusola Bamisile Cai Dongsheng Ghalib Raza Sichuan Provincial Key Lab of Power System Wide –Area Measurement and Control School of Mechanical and Electrical Engineering University of Electronic Science & Technology of China College of Nuclear and Automation Engineering Chengdu University of Technology Sichuan Provincial Key Lab of Power System Wide–Area Measurement and Control School of Mechanical and Electrical Engineering University of Electronic Science & Technology of China University of Electronic Science & Technology of China College of Nuclear and Automation Engineering Chengdu University of Technology Sichuan P.R. China Benazir Bhutto Shaheed University of Technology & Skill Development Khairpur Pakistan

This paper designs a hierarchical control system to improve control performance in a multi-terminal high voltage direct current based on the voltage source converter (VSC-MTDC) transmission system. In this control scheme, the primary control layer employs droop control, while the secondary control layer has developed optimal power flow control (OPF) for maintaining the variation of DC voltage and regulating the active power, and simultaneously minimizing power loss. In the event of a power disturbance or a change in power generation or consumption in the DC system, based on OPF results a new stable droop operating mode is established to stabilize DC voltage variation and regulate active power in the network. A ±400 kV four-terminal VSC-MTDC test network has been developed by using PSCAD and MATlab simulation tools to assess the proposed control scheme. The primary controller is designed in PSCAD, and the secondary optimum control technique is applied in MATlab. The results of steady-state and transient operating modes demonstrate that the planned control technique can achieve DC voltage regulation and keep active power, and minimize the power losses in the MTDC system.

关键词： Renewable energy sources System integration control systems Steady-state Power conversion Voltage control PSCAD

Distributed model predictive control under inexact primal-dual gradient optimization based on contraction analysis

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arXiv 2019年

This paper develops a distributed model predictive control (DMPC) strategy for a class of discrete-time linear systems with consideration of globally coupled constraints. The DMPC under study is based on the dual problem concerning all subsystems, which is solved by means of the primal-dual gradient optimization in a distributed manner using Laplacian consensus. To reduce the computational burden, the constraint tightening method is utilized to provide a capability of premature termination with guaranteeing the convergence of the DMPC optimization. The contraction theory is first adopted in the convergence analysis of the primal-dual gradient optimization under discrete-time updating dynamics towards a nonlinear objective function. Under some reasonable assumptions, the recursive feasibility and stability of the closed-loop system can be established under the inexact solution. A numerical simulation is given to verify the performance of the proposed strategy. Copyright © 2019, The Authors. All rights reserved.

关键词： Closed loop systems

An Enhanced Fault Diagnosis Method with Uncertainty Quantification Using Bayesian Convolutional Neural Network

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An Enhanced Fault Diagnosis Method with Uncertainty Quantifi...

IEEE International Conference on automation Science and engineering (CASE)

作者： Qihang Fang Gang Xiong Xiuqin Shang Sheng Liu Bin Hu Zhen Shen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Fault diagnosis is a vital technique to pinpoint the machine malfunctions in manufacturing systems. In recent years, the deep learning techniques greatly improve the fault detection accuracy, but there still remain some problems. If one fault is absent in the training data or the fault signal is disturbed by severe noise interference, the fault classifier may misjudge the health state. This problem limits the reliability of the fault diagnosis in real applications. In this paper, we enhance the fault diagnosis method by using Bayesian Convolutional Neural Network (BCNN). A Shannon entropy-based method is presented to quantify the prediction uncertainty. The BCNN turns the deterministic predictions to probabilistic distributions and enhances the robustness of the fault diagnosis. The uncertainty quantification method helps to indicate the wrong predictions, detect unknown faults, and discover the strong disturbances. Then, a fine-tuning strategy is applied to enhance the model performance further. The potential usability of the proposed method in monitoring the motors of 3D printers is studied. And the experiment is conducted on a motor bearing dataset provided by Case Western Reserve University. The proposed BCNN achieves 99.82% fault classification accuracy over nine health conditions. Its robustness is verified by comparing the testing accuracy with three other methods on the noisy datasets. And the uncertainty quantification method successfully detects the outlier inputs.

关键词： Fault diagnosis Uncertainty Bayes methods Circuit faults Robustness Predictive models Noise measurement

Temperature Compensation Method of Fluxgate Sensor Based on Polynomial Fitting

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arXiv 2024年

作者： Yang, Ruiping Liu, Huan Ge, Jian Chugo, Daisuke Dong, Haobin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Faculty of Engineering Kwansai Gakuin University Osaka669-1330 Japan

Fluxgate sensors are widely used in the field of low frequency and weak vector magnetic field measurement because of their good performance, such as high resolution and low power consumption. However, during the long-term continuous observation, the drift errors of the fluxgate sensor will occur due to the variable ambient temperature. This paper proposes a temperature compensation method for fluxgate sensors based on polynomial fitting. First, a physical model of the temperature & fluxgate sensor was established on the COMSOL Multiphysics simulation platform, and the influence of temperature on the measurement performance of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature compensation model of the fluxgate sensor was constructed. And compared it with other temperature compensation method, the result shows that the proposed temperature compensation method is relatively simple and can better achieve real-time compensation for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were *** temperature drift is reduced from more than 500 nT before compensation to about 1 nT. The results show that the proposed method has a good temperature compensation effect on the data measured by the fluxgate sensor within a variable temperature background. Copyright © 2024, The Authors. All rights reserved.

关键词： Simulation platform

Concept, Principle, and Modeling of Driving Risk Entropy Based on Human-Vehicle-Road Coupling Model for Autonomous Vehicle

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SSRN

SSRN 2024年

作者： Gao, Hongbo Yang, Hanqing Zhu, Juping Su, Huiping Wang, Ning Xie, Liping Li, Keqiang Department of Automation University of Science and Technology of China Hefei230026 China Institute of Advanced Technology University of Science and Technology of China Hefei230088 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Chongqing College of Mobile Communication Chongqing400044 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing100084 China

Driving risk entropy, based on entropy law, is an innovative concept proposed for intelligent driving systems. The concept deals with the driving risks caused by the human-vehicle-road system from the driving information obtained;however, driving risks are difficult to assess due to many influencing factors and great uncertainty. The risk entropy model of vehicles is visualized by introducing the concept of basic isotropic risk elliptic line (BIREL). Further incorporating the concept of information entropy, risk entropy models for vehicle interaction and vulnerable participants are established, and the risk entropy model for roads is developed using raster coordinates. Ultimately, a three-dimensional driving risk entropy model is constructed based on the human-vehicle-road coupling model, with appropriate weights assigned to each parameter through the DDPG algorithm. Experimental results demonstrate the effectiveness of the driving risk entropy model. © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

Weighted Fusion of Multiple Classifiers for Human Activity Recognition

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Weighted Fusion of Multiple Classifiers for Human Activity R...

International Conference on Information Fusion

作者： Kezhu Zuo Xinde Li Jean Dezert Yilin Dong School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Measurement and Control of CSE School of Automation Southeast University Nanjing China The French Aerospace Lab ONERA DTIS/MIDL Palaiseau France College of Computer Science and Engineering Shanghai Maritime University Shanghai China

Human Activity Recognition (HAR) based on wear-able device has become a hot topic of research due to its wide range of applications in health-care, fitness and smart homes. However, the classification of some activities with similar sensor readings, such as standing and sitting, is usually more challenging for the design of efficient activity recognition algorithms. Considering the inconsistent performance of different classifiers, which can provide information complementary for individual classifier, we propose a novel multi-classifier fusion method based on belief functions (BFs) theory for HAR. Specifically, at first, four classifiers are trained using time-domain and frequency-domain features to obtain basic belief assignments (BBA) of activity, respectively. Then, three assessment criteria are utilized to evaluate the reliability of the classifiers and a scoring matrix is constructed. Next, the algorithm of Belief Function based the Technique for Order Preference by Similarity to Ideal Solution (BF-TOPSIS) is employed to calculate the weighting coefficients for each classifier. Finally, the discounting and Dempster’s rules are adopted to combine the multiple classifiers and further decision making. Several experiments were conducted to illustrate the performance of the proposed method using the UCI smartphone dataset, and the results show that the proposed method is more accurate than the state-of-art methods.

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Position/force switching control based on velocity damping

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Position/force switching control based on velocity damping

International Conference on Information and automation (ICIA)

作者： Zhili Long Jiwen Fang Jiping Zhao Zhu Chao Shenzhen Key Lab for Advanced Motion Control Technology & Modern Automation Equipment Shenzhen State Key Lab for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an Shenzhen Graduate School Harbin Institute of Technology Shenzhen Shenzhen Graduate School Harbin Institute of Technology 518055 China

ISBN: (纸本)9781479913329

Stable and fast switching between displacement and pressure will directly affect precision and velocity of automated equipment. Switching control between displacement and pressure, which is based on the velocity damping, is proposed for motion switching of automated equipment. At first, the control models were established by analyzing displacement control and pressure control. To solve the shortage of direct switching between displacement and pressure, switching control based on the velocity damping is presented. The effectiveness of this method is verified by using piezoelectric sensor in experiment. Compared to direct switching control, the proposed switching control becomes more accurate and faster. The measurement data shows that the maximal impact force of control system is 0.4N and the amplitude of forces of fluctuations are within the range of ±0.03N.

关键词： Switches Damping Force Welding Pressure control Process control

Mobile Energy Transfer in Internet of Things

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arXiv 2020年

作者： Zhang, Qingqing Wang, Gang Giannakis, Georgios B. Liu, Qingwen Chen, Jie College of Electronic and Information Engineering Tongji University Shanghai201800 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN 55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200092 China

—Internet of things (IoT) is powering up smart cities by connecting all kinds of electronic devices. The power supply problem of IoT devices constitutes a major challenge in current IoT development, due to the poor battery endurance as well as the troublesome cable deployment. The wireless power transfer (WPT) technology has recently emerged as a promising solution. Yet, existing WPT advances cannot support free and mobile charging like Wi-Fi communications. To this end, the concept of mobile energy transfer (MET) is proposed, which relies critically on an resonant beam charging (RBC) technology. The adaptive (A) RBC technology builds on RBC, but aims at improving the charging efficiency by charging devices at device preferred current and voltage levels adaptively. A mobile ARBC scheme is developed relying on an adaptive source power control. Extensive numerical simulations using a 1,000mAh Li-ion battery show that the mobile ARBC outperforms simple charging schemes such as the constant power charging, the profile-adaptive charging, and the distance-adaptive charging in saving energy. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy transfer