Nonlinear Optimal Control of Reentry Vehicles Based on Deriving the State and Control Depended Systematic Matrices in State Space Form

The atmospheric reentry phase is one of the most important mission steps in space missions, therefore, the guidance and control of reentry vehicles in this phase of mission is important. In this article, a reentry vehicle guidance algorithm is proposed which has suitable robustness in the presence of initial reentry parameters uncertainty. To use any conductive method, first the motion equations must be obtained. In this paper, quadratic nonlinear control method is used to guide the vehicle. In this regard, the equations of motion of reentry vehicles are developed in form of state space and the system and control matrices depending on the state and control variables are extracted. In this article, it is tried to minimize the landing errors at terminal point using Nonlinear Quadratic Tracking (NQT) and chasing a reference trajectory. In order to define a trajectory with different initial states using evolutionary genetic algorithm with changes in weighting matrices Q and R, it is tried to reduce the errors of landing at terminal point. Monte Carlo analysis is used to evaluate the performance of the proposed algorithm. According to the results, the proposed algorithm can reduce the errors more than 90% in the presence of reentry initial parameter uncertainties.