scholarly journals A real time Pegasus propulsion system model for VSTOL piloted simulation evaluation

1981 ◽  
Author(s):  
J. MIHALOEW ◽  
S. ROTH ◽  
R. CREEKMORE
Keyword(s):  
Real Time ◽  
Propulsion System ◽  
System Model ◽  
Simulation Evaluation

scholarly journals Real-time Pegasus propulsion system model V/STOL-piloted simulation evaluation

1984 ◽  
Vol 7 (1) ◽  
pp. 77-84 ◽  
Author(s):  
James R. Mihaloew ◽  
Stephen P. Roth ◽  
Robert Creekmore
Keyword(s):  
Real Time ◽  
Propulsion System ◽  
System Model ◽  
Simulation Evaluation

An improved compact propulsion system model based on batch normalize deep neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Juan Fang ◽  
Qiangang Zheng ◽  
Haibo Zhang ◽  
Chongwen Jin
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Real Time ◽  
Data Storage ◽  
High Accuracy ◽  
Propulsion System ◽  
System Model ◽  
Support Vector ◽  
Baseline Model ◽  
Model Based

Abstract Aero-engine on-board steady state model is an important part of many advanced engine control algorithms. In order to build a high accuracy and real-time steady-state onboard model in a large envelope, an ICPSM (improved compact propulsion system model) based on batch normalize neural network is proposed in this paper. Compared with piecewise linearization model and support vector machine model, conventional CPSM which is mainly composed of baseline model and nonlinear sub model has the advantages of high real-time performance and small data storage. However, as the similarity conversion error increases with the distance from the design point, the cumulative error of the conventional baseline model also increases, which makes the model unable to maintain high accuracy in the full envelope. Thus, a high accuracy baseline model in full envelope based on batch normalize neural network is proposed in this paper. The simulation result shows that compared with the conventional compact propulsion system model, the percentage error of parameters of the improved compact propulsion system model based on the batch neural network is reduced by two times, the single step operation time is reduced by 18%, and the data storage of the onboard model is reduced as well.

scholarly journals Real-time Simulation of Ship Propulsion System

2004 ◽  
Vol 39 (12) ◽  
pp. 913-921
Author(s):  
Xianfu Hu ◽  
Hideaki Sugita ◽  
Takeshi Nakazawa ◽  
Yihuai Hu ◽  
Kengo Tanaka
Keyword(s):  
Real Time ◽  
Propulsion System ◽  
Real Time Simulation ◽  
Ship Propulsion ◽  
Time Simulation

Robust State Estimation for Linear Systems

1993 ◽  
Vol 115 (1) ◽  
pp. 193-196
Author(s):  
S. S. Garimella ◽  
K. Srinivasan
Keyword(s):  
Dynamic System ◽  
State Estimation ◽  
Real Time ◽  
Linear Systems ◽  
Upper Bound ◽  
Estimation Error ◽  
System Model ◽  
Linear Dynamic ◽  
Modeling Errors ◽  
System States

Real-time state estimation of a linear dynamic system using an observer, in the presence of modeling errors in the system model used by the observer and uncertainty in the initial system states, is considered here. A guideline for designing observers for multioutput systems is established, based on an expression for an upper bound on the norm of the state estimation error derived in this paper. An example is presented to illustrate the usefulness of this guideline.

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