Constrained Burn Optimization for the International Space Station

Abstract

Long-term trajectory planning for the International Space Station (ISS) uses simple rules of thumb embedded in a manual framework to target translation burns. This approach is limited by coarse knowledge of the problem sensitivities, it lacks automation, and it is unable to optimize these burns to minimize total . These issues can be resolved by casting the ISS trajectory planning problem as a constrained burn optimization problem. Unfortunately, the six-month to two-year time frames for long-term ISS trajectory planning preclude the use of numeric partial derivatives for accurate constraint sensitivities. A framework is presented to solve the constrained burn optimization problem for the ISS in a robust, automated fashion. The cost function for this problem is total , with constraints imposed on longitude of ascending node and semimajor axis altitude. The framework uses analytic derivatives for the cost function and variational equations using state transition matrices for the constraints. A local minimum solution is found using this framework, assuming fixed dates for the burns. Burn dates are then randomly varied to find other local solutions, which are searched for a candidate global minimum solution. This process is shown to work on real-world ISS burn-planning problems.