Trajectory optimization of a reentry vehicle based on artificial emotion memory optimization

The trajectory optimization of an unpowered reentry vehicle via artificial emotion memory optimization(AEMO) is discussed. Firstly, reentry dynamics are established based on multiple constraints and parameterized control variables with finite dimensions are designed. If the constraint is not satisfied, a distance measure and an adaptive penalty function are used to address this scenario. Secondly, AEMO is introduced to solve the trajectory optimization problem. Based on the theories of biology and cognition, the trial solutions based on emotional memory are established. Three search strategies are designed for realizing the random search of trial solutions and for avoiding becoming trapped in a local minimum. The states of the trial solutions are determined according to the rules of memory enhancement and forgetting. As the iterations proceed, the trial solutions with poor quality will gradually be forgotten. Therefore,the number of trial solutions is decreased, and the convergence of the algorithm is accelerated. Finally, a numerical simulation is conducted, and the results demonstrate that the path and terminal constraints are satisfied and the method can realize satisfactory performance.