Experimental Validation of a Robust H∞ Control Method on Miniaturized Optical Image Stabilizer Prototypes

2020 ◽  
Vol 142 (12) ◽  
Author(s):  
Alireza Alizadegan ◽  
Pan Zhao ◽  
Ryozo Nagamune ◽  
Mu Chiao
Keyword(s):  
Large Scale ◽  
Closed Loop ◽  
Control Method ◽  
Controller Design ◽  
Design Method ◽  
Optical Image ◽  
Small Scale ◽  
Practical Implementation ◽  
Model Parameters ◽  
Experimental Frequency

Abstract This paper validates a robust H∞ controller design method, experimentally, on miniaturized prototypes of the magnetically actuated lens-tilting optical image stabilizers (OISs) with product variabilities. Five small-scale OIS prototypes with product variations are constructed by three-dimensional (3D) printing. For the prototypes, the model parameters are identified based on experimental frequency response data of the prototypes. Using the identified model, a robust H∞ controller is designed to guarantee the robust stability of the closed-loop system and to optimize the closed-loop performance. The experimental results reveal larger and more complex uncertainties in miniaturized OISs with mass-produced parts compared to large-scale prototypes. Despite the increased amount of uncertainties, it is demonstrated that the robust H∞ controller still outperforms the conventional controllers in terms of robust closed-loop stability, performance, and controller order for practical implementation on a mobile phone device.

A New Iterative Identification and Control Method of Closed-Loop Power System Based on Ambient Signals

2015 ◽  
Vol 798 ◽  
pp. 261-265
Author(s):  
Miao Yu ◽  
Chao Lu
Keyword(s):  
Power System ◽  
Closed Loop ◽  
Control Method ◽  
Controller Design ◽  
Design Method ◽  
Stability Margin ◽  
System Stability ◽  
Iterative Identification ◽  
Effective Performance ◽  
And Control

Identification and control are important problems of power system based on ambient signals. In order to avoid the model error influence of the controller design, a new iterative identification and control method is proposed in this paper. This method can solve model set and controller design of closed-loop power system. First, an uncertain model of power system is established. Then, according to the stability margin of power system, stability theorem is put forward. And then controller design method and the whole algorithm procedure are given. Simulation results show the effective performance of the proposed method based on the four-machine-two-region system.

Passivity-Based Stabilization of Underactuated Mechanical Systems

2013 ◽  
Vol 421 ◽  
pp. 16-22
Author(s):  
Shan Shan Wu ◽  
Wei Huo
Keyword(s):  
Closed Loop ◽  
Control Method ◽  
Controller Design ◽  
Design Method ◽  
Mechanical Systems ◽  
Matching Condition ◽  
Loop System ◽  
Closed Loop System ◽  
Underactuated Mechanical Systems ◽  
Stabilization Control

A new stabilization control method for underactuated linear mechanical systems is presented in this paper. By proper setting the desired closed-loop system, the matching condition for controller design is reduced to one equation and an adjustable parameter (damping coefficient) is introduced to the controller. Stability of the closed-loop system is proved based on passivity. As an application example, stabilization control of 2-DOF Pendubot is studied. The system is linearized at its equilibrium point and the proposed controller design method is applied to the linearized system. The procedure of solving matching condition and design controller for the Pendubot is provided. The simulation results verify feasibility of the proposed method.

Coupled Thermo-Hydro-Geochemical Models of Engineered Barrier Systems: The Febex Project

2000 ◽  
Vol 663 ◽  
Author(s):  
J. Samper ◽  
R. Juncosa ◽  
V. Navarro ◽  
J. Delgado ◽  
L. Montenegro ◽  
...  
Keyword(s):  
Large Scale ◽  
Reference Model ◽  
Numerical Models ◽  
Full Scale ◽  
Small Scale ◽  
Model Parameters ◽  
In Situ Test ◽  
Wide Range ◽  
Engineered Barrier

ABSTRACTFEBEX (Full-scale Engineered Barrier EXperiment) is a demonstration and research project dealing with the bentonite engineered barrier designed for sealing and containment of waste in a high level radioactive waste repository (HLWR). It includes two main experiments: an situ full-scale test performed at Grimsel (GTS) and a mock-up test operating since February 1997 at CIEMAT facilities in Madrid (Spain) [1,2,3]. One of the objectives of FEBEX is the development and testing of conceptual and numerical models for the thermal, hydrodynamic, and geochemical (THG) processes expected to take place in engineered clay barriers. A significant improvement in coupled THG modeling of the clay barrier has been achieved both in terms of a better understanding of THG processes and more sophisticated THG computer codes. The ability of these models to reproduce the observed THG patterns in a wide range of THG conditions enhances the confidence in their prediction capabilities. Numerical THG models of heating and hydration experiments performed on small-scale lab cells provide excellent results for temperatures, water inflow and final water content in the cells [3]. Calculated concentrations at the end of the experiments reproduce most of the patterns of measured data. In general, the fit of concentrations of dissolved species is better than that of exchanged cations. These models were later used to simulate the evolution of the large-scale experiments (in situ and mock-up). Some thermo-hydrodynamic hypotheses and bentonite parameters were slightly revised during TH calibration of the mock-up test. The results of the reference model reproduce simultaneously the observed water inflows and bentonite temperatures and relative humidities. Although the model is highly sensitive to one-at-a-time variations in model parameters, the possibility of parameter combinations leading to similar fits cannot be precluded. The TH model of the “in situ” test is based on the same bentonite TH parameters and assumptions as for the “mock-up” test. Granite parameters were slightly modified during the calibration process in order to reproduce the observed thermal and hydrodynamic evolution. The reference model captures properly relative humidities and temperatures in the bentonite [3]. It also reproduces the observed spatial distribution of water pressures and temperatures in the granite. Once calibrated the TH aspects of the model, predictions of the THG evolution of both tests were performed. Data from the dismantling of the in situ test, which is planned for the summer of 2001, will provide a unique opportunity to test and validate current THG models of the EBS.

scholarly journals Self-Supervised Pre-Training of Transformers for Satellite Image Time Series Classification

2020 ◽  
Author(s):  
Yuan Yuan ◽  
Lei Lin
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Large Scale ◽  
Temporal Structure ◽  
Satellite Image ◽  
Fine Tuning ◽  
Small Scale ◽  
Model Parameters ◽  
Learning Approaches ◽  
Wide Range

Satellite image time series (SITS) classification is a major research topic in remote sensing and is relevant for a wide range of applications. Deep learning approaches have been commonly employed for SITS classification and have provided state-of-the-art performance. However, deep learning methods suffer from overfitting when labeled data is scarce. To address this problem, we propose a novel self-supervised pre-training scheme to initialize a Transformer-based network by utilizing large-scale unlabeled data. In detail, the model is asked to predict randomly contaminated observations given an entire time series of a pixel. The main idea of our proposal is to leverage the inherent temporal structure of satellite time series to learn general-purpose spectral-temporal representations related to land cover semantics. Once pre-training is completed, the pre-trained network can be further adapted to various SITS classification tasks by fine-tuning all the model parameters on small-scale task-related labeled data. In this way, the general knowledge and representations about SITS can be transferred to a label-scarce task, thereby improving the generalization performance of the model as well as reducing the risk of overfitting. Comprehensive experiments have been carried out on three benchmark datasets over large study areas. Experimental results demonstrate the effectiveness of the proposed method, leading to a classification accuracy increment up to 1.91% to 6.69%. <div><b>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</b></div>

Large-Scale Evaluation of Constrained Bearing Elements Made of Thermosetting Polyester Resin and Polyester Fabric Reinforcement

2006 ◽  
Vol 128 (4) ◽  
pp. 681-696 ◽  
Author(s):  
P. Samyn ◽  
W. Van Paepegem ◽  
J. S. Leendertz ◽  
A. Gerber ◽  
L. Van Schepdael ◽  
...  
Keyword(s):  
Plastic Deformation ◽  
Large Scale ◽  
Elastic Recovery ◽  
Small Scale ◽  
Practical Implementation ◽  
Fiber Failure ◽  
Stick Slip ◽  
Composite Surface ◽  
Pull Out ◽  
Stable Sliding

Polymer composites are increasingly used as sliding materials for high-loaded bearings, however, their tribological characteristics are most commonly determined from small-scale laboratory tests. The static strength and dynamic coefficients of friction for polyester/polyester composite elements are presently studied on large-scale test equipment for determination of its bearing capacity and failure mechanisms under overload conditions. Original test samples have a diameter of 250 mm and thickness of 40 mm, corresponding to the practical implementation in the sliding surfaces of a ball-joint, and are tested at various scales for simulation of edge effects and repeatability of test results. Static tests reveal complete elastic recovery after loading to 120 MPa, plastic deformation after loading at 150 MPa and overload at 200 MPa. This makes present composite favorable for use under high loads, compared to, e.g., glass-fibre reinforced materials. Sliding tests indicate stick-slip for pure bulk composites and more stable sliding when PTFE lubricants are added. Dynamic overload occurs above 120 MPa due to an expansion of the nonconstrained top surface. A molybdenum-disulphide coating on the steel counterface is an effective lubricant for lower dynamic friction, as it favorably impregnates the composite sliding surface, while it is not effective at high loads as the coating is removed after sliding and high initial static friction is observed. Also a zinc phosphate thermoplastic coating cannot be applied to the counterface as it adheres strongly to the composite surface with consequently high initial friction and coating wear. Most stable sliding is observed against steel counterfaces, with progressive formation of a lubricating transfer film at higher loads due to exposure of PTFE lubricant. Composite wear mechanisms are mainly governed by thermal degradation of the thermosetting matrix (max. 162°C) with shear and particle detachment by the brittle nature of polyester rather than plastic deformation. The formation of a sliding film protects against fiber failure up to 150 MPa, while overload results in interlaminar shear, debonding, and ductile fiber pull-out.

Integrated Decisions on Online Product Image Configuration and Inventory Planning Using DPSO

2020 ◽  
Vol 12 (4) ◽  
pp. 1-20
Author(s):  
Kuan-Chung Shih ◽  
Yan-Kwang Chen ◽  
Yi-Ming Li ◽  
Chih-Teng Chen
Keyword(s):  
Large Scale ◽  
Operational Performance ◽  
Small Scale ◽  
Model Parameters ◽  
Inventory Planning ◽  
Comparison Results ◽  
The Face ◽  
Product Image ◽  
Online Stores ◽  
Dependent Demand

Integrated decisions on merchandise image display and inventory planning are closely related to operational performance of online stores. A visual-attention-dependent demand (VADD) model has been developed to support online stores make the decisions. In the face of evolving products, customer needs, and competitors in an e-commerce environment, the benefits of using VADD model depend on how fast the model runs on the computer. As a result, a discrete particle swarm optimization (DPSO) method is employed to solve the VADD model. To verify the usability and effectiveness of DPSO method, it was compared with the existing methods for large-scale, medium-scale, and small-scale problems. The comparison results show that both GA and DPSO method perform well in terms of the approximation rate, but the DPSO method takes less time than the GA method. A sensitivity is conducted to determine the model parameters that influence the above comparison result.

Dynamics of a Large Scale Hydraulic Capsule Pipeline System

1999 ◽  
Vol 124 (1) ◽  
pp. 191-195 ◽  
Author(s):  
Hongliu Du ◽  
Satish S. Nair
Keyword(s):  
Large Scale ◽  
Control Method ◽  
Experimental Studies ◽  
Small Scale ◽  
Pipeline System ◽  
Centrifugal Pumps ◽  
Long Distance ◽  
Column Separation ◽  
In Series ◽  
Hydraulic Capsule Pipeline

The dynamics of a booster station, which is critical for the control of a novel, long distance, hydraulic capsule pipeline, is simulated mathematically for design studies and control of the hydraulic transients caused by the valve actuators in the system. Several modifications to the pump bypass station configuration of the booster station have been studied. With the objective of eliminating column separation and reducing flow reversals, a configuration with several centrifugal pumps connected in series, and a carefully sized air chamber is found to be a viable design. A valve control method is designed to eliminate column separation and the design results in acceptable flow reversal levels in the main pipe. The simulation results match with trends in limited experimental studies performed on a small scale experimental capsule pipeline system.

scholarly journals Self-Supervised Pre-Training of Transformers for Satellite Image Time Series Classification

2020 ◽  
Author(s):  
Yuan Yuan ◽  
Lei Lin
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Large Scale ◽  
Temporal Structure ◽  
Satellite Image ◽  
Fine Tuning ◽  
Small Scale ◽  
Model Parameters ◽  
Learning Approaches ◽  
Wide Range

<div>Satellite image time series (SITS) classification is a major research topic in remote sensing and is relevant for a wide range of applications. Deep learning approaches have been commonly employed for SITS classification and have provided state-of-the-art performance. However, deep learning methods suffer from overfitting when labeled data is scarce. To address this problem, we propose a novel self-supervised pre-training scheme to initialize a Transformer-based network by utilizing large-scale unlabeled data. In detail, the model is asked to predict randomly contaminated observations given an entire time series of a pixel. The main idea of our proposal is to leverage the inherent temporal structure of satellite time series to learn general-purpose spectral-temporal representations related to land cover semantics. Once pre-training is completed, the pre-trained network can be further adapted to various SITS classification tasks by fine-tuning all the model parameters on small-scale task-related labeled data. In this way, the general knowledge and representations about SITS can be transferred to a label-scarce task, thereby improving the generalization performance of the model as well as reducing the risk of overfitting. Comprehensive experiments have been carried out on three benchmark datasets over large study areas. Experimental results demonstrate the effectiveness of the proposed method, leading to a classification accuracy increment up to 2.38% to 5.27%. The code and the pre-trained model will be available at https://github.com/linlei1214/SITS-BERT upon publication.</div><div><b>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</b></div>

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