scholarly journals Real-Time Dynamic Modeling: Data Information Requirements and Flight Test Results

2008 ◽  
Author(s):  
Eugene Morelli ◽  
Mark Smith
Keyword(s):  
Real Time ◽  
Dynamic Modeling ◽  
Flight Test ◽  
Test Results ◽  
Information Requirements ◽  
Time Dynamic ◽  
Modeling Data

scholarly journals Seismic energy response analysis of equipment-structure system via real-time dynamic substructuring shaking table testing

2019 ◽  
Vol 23 (1) ◽  
pp. 37-50 ◽  
Author(s):  
Jihong Bi ◽  
Lanfang Luo ◽  
Nan Jiang
Keyword(s):  
Real Time ◽  
Shaking Table Test ◽  
Seismic Energy ◽  
Shaking Table ◽  
Energy Calculation ◽  
Test Results ◽  
Structure System ◽  
Time Dynamic ◽  
Dynamic Substructuring ◽  
Shaking Table Testing

Dynamic equations are presented that have been deduced for a real-time dynamic substructuring shaking table test of an equipment-structure system, based on the branch mode substructure method. The equipment is adopted as the experimental substructure, which is loaded by the shaking table, while the structure is adopted as the numerical substructure. Real-time data communication occurs between the two substructures during the test. A real-time seismic energy calculation method was proposed for the calculation of energy responses, both in the experimental substructure and the numerical substructure. Taking a representative four-story steel frame/equipment model, real-time dynamic substructuring shaking table tests and overall model tests were executed. The proposed real-time dynamic substructuring shaking table testing method was verified by comparing the test results with shaking table test results for the overall model. The energy responses of each component in the equipment-structure system, using different connection types, also were studied. Changes in the connection types can lead to changes in the energy responses of the equipment-structure system, especially with respect to the equipment. The choice of the connection for the equipment-structure coupled system should take into account the operational performance objective of the equipment.

Real-time dynamic modeling of hydrogen PEMFCs

2008 ◽  
Vol 345 (2) ◽  
pp. 182-203 ◽  
Author(s):  
Y.H. Hung ◽  
P.H. Lin ◽  
C.H. Wu ◽  
C.W. Hong
Keyword(s):  
Real Time ◽  
Dynamic Modeling ◽  
Time Dynamic

Selection of an Efficient Linear State Estimator for Unified Real Time Dynamic State Estimation in Indian Smart Grid

2019 ◽  
Vol 20 (4) ◽  
Author(s):  
Naheed Mobin ◽  
Mohd Rihan ◽  
Mohd Zuhaib
Keyword(s):  
Smart Grid ◽  
State Estimation ◽  
Real Time ◽  
Power Grid ◽  
Dynamic State ◽  
Test Results ◽  
State Estimator ◽  
Time Dynamic ◽  
Linear State ◽  
Selection Of

Abstract As electricity demand is continuously increasing, the complexity and criticality of the power system infrastructure is increasing. After suffering from world’s largest power blackout on 30th and 31st July, 2012, the Indian power grids are now adopting the smart grid technologies to prevent such massive power failures. It initiated with the installation of PMUs on each strategically selected critical location in the entire grid under the Real Time Dynamic State measurements (URTDSM) scheme to attain the complete observability of the system. However, attainment of complete observability further demands selection of accurate and fast SE algorithm which is still a challenging task. The aim of this paper is to recommend the most efficient and accurate state estimator based on Linear State Estimation (LSE) algorithms for efficient real time monitoring and control of the grid, especially for Indian Power Grid. The performance test was conducted on two different SE algorithms, the Weighted Least Square (WLS) algorithm and recursive Kalman Filter algorithm. The test results are obtained on Indian 75-bus transmission system and validation is done by comparing the test results with the results obtained on standard IEEE-39 bus test system. Based on the performance comparison of the two algorithms, the selection of most suitable algorithm to be implemented in Indian power grid is achieved.

Cognitive Multi-UAV Formation Flight: Principle, Low-Cost UAV Testbed, Controller Tuning and Experiments

2011 ◽  
Author(s):  
Long Di ◽  
Haiyang Chao ◽  
Jinlu Han ◽  
YangQuan Chen
Keyword(s):  
Real Time ◽  
Control Structure ◽  
Low Cost ◽  
Flight Test ◽  
Formation Flight ◽  
Experimental Setup ◽  
Test Results ◽  
Test Protocol ◽  
Multi Agent ◽  
Multi Uav

Cooperative UAV systems can have great advantages over isolated UAV systems, regarding application, safety, efficiency and many other perspectives. Motivated by challenges from practical multiple UAV formation flight, this paper presents our approaches towards cognitive formation flight. It introduces the principle ofmulti-UAV cognitive formation flight and the control structure utilized in our development, describes a low-cost UAV testbed developed by ourselves, and details the tuning procedures of the implemented multi-agent flight controller for stable and consistent formation flights. Different formation flight scenarios are also discussed and the experimental setup is presented including real-time issues and the formation flight test protocol. Routinized comprehensive flight test results are also shown at the end.

Dynamic Modeling of a Flexible Cantilever Beam: An Experimental Technique

2003 ◽  
Author(s):  
S. M. Megahed ◽  
A. A. Nada
Keyword(s):  
Real Time ◽  
Cantilever Beam ◽  
Dynamic Modeling ◽  
Measurement Techniques ◽  
Beam Theory ◽  
Time Estimation ◽  
Beam Deflection ◽  
Strain Gauges ◽  
Time Dynamic ◽  
Polynomial Curve

This paper describes an investigation of real time dynamic modeling method for a flexible cantilever beam, as an approximation of a robot flexible link, using experimental measurement techniques. The beam is equipped with a number of strain gauges to measure the corresponding nodal flexibilities and using them to obtain its real-time dynamic model. Two methods are proposed; the 1st method is based on approximating the beam deflection by a 4th order polynomial curve while the 2nd one is based on the solution of its Eulerbernoulli equation. The polynomial constants are obtained by real-time estimation using the readings of the strain gauges. These readings are used to estimate the beam temporal solution in the 2nd method. The error in the beam tip deflection is reduced to 0.01% by relocating the last strain gauge at 12.5% instead of 30% from its tip. Both methods show good agreement with the general beam theory and results.

Hierarchical Bayesian continuous time dynamic modeling.

2018 ◽  
Vol 23 (4) ◽  
pp. 774-799 ◽  
Author(s):  
Charles C. Driver ◽  
Manuel C. Voelkle
Keyword(s):  
Dynamic Modeling ◽  
Continuous Time ◽  
Hierarchical Bayesian ◽  
Time Dynamic

Control Model Constructing on Real-Time Dynamic Reversible Lane in Condition of Connected Vehicle

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Lina Mao ◽  
Wenquan Li ◽  
Pengsen Hu ◽  
Guiliang Zhou ◽  
Huiting Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Control Model ◽  
Connected Vehicle ◽  
Time Dynamic
Sign in / Sign up

Export Citation Format

Share Document