atefeh hoseinzadeh; Amirhossain Adami; Asghar Ebrahimi
Volume 10, Issue 4 , March 2018, , Pages 29-40
Abstract
The atmospheric re-entry phase is one of the most significantmission steps in the space missions;hence, theguidance and control of reentry vehicles in this phase of mission is important. In this article, a reentry vehicle guidance algorithm has been proposed which has suitable robustness in the presence ...
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The atmospheric re-entry phase is one of the most significantmission steps in the space missions;hence, theguidance and control of reentry vehicles in this phase of mission is important. In this article, a reentry vehicle guidance algorithm has been proposed which has suitable robustness in the presence of initial reentry parameters uncertainties. Here,it has been tried to minimize the landing errors at terminal point using Nonlinear Quadratic Tracking (NQT) and chasing a reference trajectory. In order to define several trajectories with different initial states using evolutionary genetic algorithm with changes in weighting matrices Q and R, it hasbeen tried to reduce the errors of landing at terminal point. The reentry position of the reentry vehicles may be different from the desired ones with respect to several events. In this situation, reentry vehicles start to move in a new trajectory which is not suitable. Therefore, the reentry vehicles should be guided to come back into the desired trajectory or a new optimum trajectory needs to be redesignedto have the same target position on the ground. To do this, we need optimum weighting matrices R and Q for every new trajectory. In this article, this problem has been resolved using partial least squares regression; meanwhile, obtaining the optimal matrices by genetic algorithms needed many times. Also,it is shown that using this method, in the presence of reentry uncertainties, weighting matrices for each new initial condition hasbeen quickly derived. Additionaly,through the matrices obtained and the nonlinear quadratic tracking controller, reentry vehicle was directedto the target with a good accuracy. The Monte Carlo analysis has been used to evaluate the performance of the proposed algoritm. According to the results, the proposed algoritm has a suitable accuracy level and it can generate the online optimum trajectory.
