Trade-Offs in Robust Trajectory Optimization Based on Sensitivity Minimization

Rapid developments in aerospace technologies demand reliable procedures to plan ro- bust missions with high safety. To increase safety under uncertainties in model parameters or environmental conditions, multi-objective robust optimization methods via sensitivity minimization can be used. An acceptable trade-off between a nominal operational cost (e.g., time, energy) and robustness is searched for to plan missions that are less prone to disturbances. The presented analysis considers open-loop and closed-loop sensitivity min- imization approaches and utilizes multi-objective optimization to assess the performance and the limitations of both approaches. To solve the multi-objective optimization prob- lems, scalarization techniques are employed using weighted sums and cost bounds. By varying weights and cost bounds, multiple optima can be calculated, resulting in an ap- proximate Pareto front and giving rise to an overview of the trade-off between optimality and robustness of the solutions. The analysis is performed for robust unmanned aerial vehicle (UAV) trajectory optimization minimizing positional sensitivities.