scholarly journals Influence of Steering Angle Profiles on the Orbit Transfer Trajectory

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Donghun Lee ◽  
Young-Joo Song

This paper considers a planar orbit transfer trajectory design problem using finite thrust modeling. In this problem, the steering angles associated with the thrust direction are calculated from the predetermined profile format, and the unknown parameters in the profile are directly optimized. Three profile formats that were implemented in previous lunar exploration missions are considered. In addition, a steering angle profile defined in the rotating frame and the optimal steering angle profile are newly studied to compare the performances. To this end, the direct parameter optimization problem and the indirect optimization problem are formulated, and the characteristics of the steering angle profile and its influence on the transfer trajectory are analyzed.

2018 ◽  
Vol 189 ◽  
pp. 10019
Author(s):  
Hao Li ◽  
Changzhu Wei

A trajectory optimization method for RLV based on artificial memory principles is proposed. Firstly the optimization problem is modelled in Euclidean space. Then in order to solve the complicated optimization problem of RLV in entry phase, Artificial-memory-principle optimization (AMPO) is introduced. AMPO is inspired by memory principles, in which a memory cell consists the whole information of an alternative solution. The information includes solution state and memory state. The former is an evolutional alternative solution, the latter indicates the state type of memory cell: temporary, short-and long-term. In the evolution of optimization, AMPO makes a various search (stimulus) to ensure adaptability, if the stimulus is good, memory state will turn temporary to short-term, even long-term, otherwise it not. Finally, simulation of different methods is carried out respectively. Results show that the method based on AMPO has better performance and high convergence speed when solving complicated optimization problems of RLV.


2012 ◽  
Vol 2012 ◽  
pp. 1-5
Author(s):  
A. V. Wildemann ◽  
A. A. Tashkinov ◽  
V. A. Bronnikov

This paper introduces an approach for parameters identification of a statistical predicting model with the use of the available individual data. Unknown parameters are separated into two groups: the ones specifying the average trend over large set of individuals and the ones describing the details of a concrete person. In order to calculate the vector of unknown parameters, a multidimensional constrained optimization problem is solved minimizing the discrepancy between real data and the model prediction over the set of feasible solutions. Both the individual retrospective data and factors influencing the individual dynamics are taken into account. The application of the method for predicting the movement of a patient with congenital motility disorders is considered.


2012 ◽  
Vol 73 ◽  
pp. 237-249 ◽  
Author(s):  
David C. Folta ◽  
Mark Woodard ◽  
Kathleen Howell ◽  
Chris Patterson ◽  
Wayne Schlei

1982 ◽  
Vol 5 (2) ◽  
pp. 221-224 ◽  
Author(s):  
Lincoln J. Wood ◽  
Thomas P. Bauer ◽  
Keith P. Zondervan

Author(s):  
Zhong-Sheng Wang ◽  
John McVicker ◽  
Paul Anderson ◽  
Tess Doeffinger ◽  
Robert Hook ◽  
...  

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