Energy-optimal in orbit mission planning for agile remote sensing satellites

A new hybrid algorithm combining pseudospectral method and genetic algorithm is presented in this work to solve the in orbit autonomous mission planning problem for the agile remote sensing satellite at multiple discrete observation points. The problem is broken into space resource scheduling problem and continuous optimal control problem based on the coupling of attitude motion equations. This algorithm, according to the space resource scheduling model built based on the travelling salesman problem (TSP) model, encodes the observation sequence and the relative observation time by a two-dimensional real coding structure, and calculates the observation sequence and the observation time by the genetic algorithm. The time optimal control problem in judging the observation time feasibility and the minimal energy consumption in attitude maneuvering are considered as the continuous optimal control problem, which is then solved by Gauss pseudospectral method based on Gauss pseudospectral costate mapping theorem. A comparative simulation test is carried out for the simple genetic algorithm and the proposed algorithm. The simulation results show that the energy consumption obtained by the proposed algorithm is reduced by 60% compared with that obtained by the simple genetic algorithm under typical simulation conditions.