An adaptive predictor–corrector reentry guidance based on self-definition way-points

An adaptive predictor–corrector reentry guidance algorithm with self-defined way-points is proposed. In the guidance process, the reentry trajectory is divided into the predictor–corrector phase and the trajectory onboard generation and tracking phase which is near to the endpoint position of reentry and utilized to improve the accuracy and adaptivity of the guidance. In the first phase, the predictor–corrector algorithm is applied to solve the guidance problem between the self-defined way-points. Moreover the position parameters of reentry trajectory are translated into the parameters related to the reentry plane by orthogonal transformation in the spherical coordinate to improve robustness of guidance algorithm. In addition, the predictor–corrector algorithm is implemented using a brain emotional learning based intelligence controller (BELBIC). In the second phase, the trajectory from the current point to the endpoint is generated onboard and the linear–quadratic regulator (LQR) theory is employed for trajectory tracking. The effectivity of the proposed guidance is validated by simulations in conditions of the nominal case, the environment dispersed case and the endpoint maneuvering case. The advantages of this guidance in coping with disturbances, reducing time of numerical trajectory prediction and being suitable for maneuver endpoint are analyzed with the simulation results.