Fast Desensitized Optimal Control for Rocket Powered Descent and Landing

This research revisits the Desensitized Optimal Control Theory for its application to a computationally challenging numerical benchmark, specifically a descent and landing scenario involving a rocket propulsion system. The primary objective is to assess the efficacy of the proposed method in mitigating the impact of perturbations on the final state, thereby establishing a framework capable of simultaneously optimizing Guidance and Control for the specified case. Additionally, our focus is on formulating a rapid and computationally efficient approach to enhance speed without compromising precision. The investigation begins with a comprehensive analysis of the fundamental components of the method, particularly the sensitivity terms and the computation of feedback gains, with a comparison of alternative formulations to evaluate competitiveness. Subsequently, the application of this methodology to the target problem is thoroughly examined and characterized to reach the most efficient formulation, through the definition of dominant sensitivities. Case-dependent modifications are implemented to improve its performances, resulting in the to the introduction of the Marginal DOC Coefficient, and the results are critically compared against those obtained using conventional methods through an extensive Monte-Carlo analysis campaign.