Hybrid Optimal Control Framework for Mission Planning

With the progressive sophistication of future missions, it has become increasingly apparent that a new framework is necessary for efficient planning, analysis, and optimization of various concepts of operations (CONOPS). In recognizing that CONOPS involve categorical variables, we propose a hybrid optimal control framework that mathematically formalizes such problems. Hybrid optimal control theory extends ordinary optimal control theory by including categorical variables in the problem formulation. The proposed formalism frees mission planners to focus on high-level decision making by automating and optimizing the details of the inner loops. The eventual goal of this formalism is to develop efficient tools and techniques to support the objective of increasing autonomy for future systems. In using the pseudospectral knotting method to solve hybrid optimal control problems, we generate a mixed-variable programming (MVP) problem. A simple, feasible integer programming subproblem is identified that reduces the combinatorial complexity of solving the MVP. In addition to developing the framework using various examples from aerospace engineering, we provide details for a two-agent benchmark problem associated with a multiagent launch system. The entire process is illustrated with a numerical example.