Driving Intention Identification Model Based on Long and Short-Term Memory Network

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Tian Yuan ◽  
Hua Chai ◽  
Ke-Xin Ma
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Model Based ◽  
Memory Network ◽  
Identification Model

Aerodynamic surrogate model based on deep long short-term memory network: An application on high-lift device control

2021 ◽  
pp. 095441002110270
Author(s):  
Yi Zhang ◽  
Shuyue Wang ◽  
Gang Sun ◽  
Jun Mao
Keyword(s):  
Surrogate Model ◽  
Short Term Memory ◽  
Short Term ◽  
Longitudinal Stability ◽  
High Lift ◽  
Term Memory ◽  
Unsteady Aerodynamic ◽  
Model Based ◽  
Memory Network ◽  
Long Short Term Memory

An unsteady aerodynamic surrogate model based on the deep LSTM (long short-term memory) network is proposed for predicting unsteady aerodynamic coefficients. Deflection angles and deflection velocities of control surfaces are introduced to input values of the surrogate model to enhance the capability of identifying different motion states so that accumulative error can be controlled. Longitudinal stability is extremely important for flight safety while few studies have worked on unsteady aerodynamics of airfoils/wings with moving high-lift device (HLD) motion. Longitudinal static margin sequence of the HLD extending process is studied, and nonlinearity and hysteresis of coefficients in HLD motion are validated. The surrogate model is then applied in HLD motion control with the particle swarm optimization (PSO) method. Additionally, the results are then performed in three-dimensional aircraft HLD control. Validation computations show that longitudinal stability of optimized configuration is promoted with lift coefficient unchanged.

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