@Article{icces.2023.09494,
AUTHOR = {Chenhao Zuo, Hongqian Zhao, Xiaokui Yue, Honghua Dai},
TITLE = {Efficient Calculation Model and Guidance Law of Non-Contact Plasma  Plume De-Tumbling},
JOURNAL = {The International Conference on Computational \& Experimental Engineering and Sciences},
VOLUME = {27},
YEAR = {2023},
NUMBER = {1},
PAGES = {1--1},
URL = {http://www.techscience.com/icces/v27n1/54099},
ISSN = {1933-2815},
ABSTRACT = {Dramatically increase of the amount of the failed satellites is posing a serious threat to the normal orbiting 
satellites. To avoid potential collisions, it is important to remove the failed satellites, and the first step is to 
detumble these uncontrolled targets. This study proposes an efficient calculation method for the failed 
satellite de-tumbling system. The plasma plume generated by Hall effect thruster on chaser is used as noncontact de-tumbling medium, which reduces fuel consumption and collision risk [1]. The plasma plume is 
composed of a variety of particles with strong disorder, so it is difficult to calculate the plume de-tumbling 
torque. In order to solve the problem of complex plume calculation and difficult implementation of onboard 
computer, neural network is used to establish an efficient calculation model of de-tumbling to realize 
efficient and high-precision de-tumbling calculation. Traditional guidance laws are difficult to deal with 
diverse and complex tumbling states, therefore this study uses genetic algorithm to establish the optimal 
plume pointing guidance law for various motion states based on the efficient calculation model. According 
to the relative position and attitude between the target and the chaser, the trajectory and plume direction 
of the chaser can be obtained through the guidance law. The de-tumbling torque and its variation trend are 
introduced as disturbances into the model predictive control (MPC) algorithm, which can approach the 
desired trajectory and attitude of the chaser quickly. Finally, numerical simulation results indicate that the 
proposed neural network-based efficient model can reduce the model computing time by 90% and the 
optimal guidance law can stabilize the target in various motion states.},
DOI = {10.32604/icces.2023.09494}
}