Multidisciplinary Design Optimization Approach to Integrated Space Mission Planning and Spacecraft Design

TL;DR

This paper presents an innovative iterative optimization method that decomposes a complex integrated space mission and spacecraft design problem into manageable subproblems, enabling efficient and scalable solutions for lunar exploration missions.

Contribution

The paper introduces a novel MDO-based iterative approach that decomposes the large-scale MINLP into MIQP and NLP subproblems, improving solution efficiency and scalability for integrated space mission planning and design.

Findings

Achieves better solutions with shorter computational times than existing methods.

Effectively decomposes complex problems into solvable subproblems.

Scalable and parallelizable approach for larger mission design problems.

Abstract

Space mission planning and spacecraft design are tightly coupled and need to be considered together for optimal performance; however, this integrated optimization problem results in a large-scale Mixed-Integer Nonlinear Programming (MINLP) problem, which is challenging to solve. In response to this challenge, this paper proposes a new solution approach to this MINLP problem by iterative solving a set of coupled subproblems via the augmented Lagrangian coordination approach following the philosophy of Multi-disciplinary Design Optimization (MDO). The proposed approach leverages the unique structure of the problem that enables its decomposition into a set of coupled subproblems of different types: a Mixed-Integer Quadratic Programming (MIQP) subproblem for mission planning and one or more Nonlinear Programming (NLP) subproblem(s) for spacecraft design. Since specialized MIQP or NLP solvers can be applied to each subproblem, the proposed approach can efficiently solve the otherwise intractable integrated MINLP problem. An automatic and effective method to find an initial solution for this iterative approach is also proposed so that the optimization can be performed without the need for a user-defined initial guess. In the demonstration case study, a human lunar exploration mission sequence is optimized with a subsystem-level parametric spacecraft design model. Compared to the state-of-the-art method, the proposed formulation can obtain a better solution with a shorter computational time even without parallelization. For larger problems, the proposed solution approach can also be easily parallelizable and thus is expected to be further advantageous and scalable.