Fast Desensitized Optimal Control for Rocket-Powered Descent and Landing

This research revisits desensitized optimal control (DOC) theory for its application to a computationally challenging benchmark: a rocket descent and landing scenario. 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 accuracy. 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 their relative computational efficiency. Subsequently, the application of this methodology to the target problem is thoroughly examined with an a priori performance index and characterized to reach the most efficient formulation, through the introduction of the idea of dominant sensitivities. Case-dependent modifications are explained and implemented to improve the methodology performances, resulting in 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.