Optimal Guidance for Accurate Lunar Soft Landing with Minimum Fuel Consumption using Model Predictive Static Programming

In this paper the soft lunar landing with minimum fuel expenditure is formulated as a nonlinear optimal guidance problem. The realization of pinpoint soft landing with terminal velocity and position constraints is achieved using Model Predictive Static Programming (MPSP). The high accuracy of the terminal conditions is ensured as the formulation of the MPSP inherently poses final conditions as a set of hard constraints. The computational efficiency and fast convergence make the MPSP preferable for fixed final time onboard optimal guidance algorithm. It has also been observed that the minimum fuel requirement strongly depends on the choice of the final time (a critical point that is not given due importance in many literature). Hence, to optimally select the final time, a neural network is used to learn the mapping between various initial conditions in the domain of interest and the corresponding optimal flight time. To generate the training data set, the optimal final time is computed offline using a gradient based optimization technique. The effectiveness of the proposed method is demonstrated with rigorous simulation results.

Bulk Randall-Sundrum models, electroweak precision tests, and the 125 GeV Higgs

We present up-to-date electroweak fits of various Randall-Sundrum (RS) models. We consider the bulk RS, deformed RS, and the custodial RS models. For the bulk RS case we find the lightest Kaluza-Klein (KK) mode of the gauge boson to be similar to 8 TeV, while for the custodial case it is similar to 3 TeV. The deformed model is the least fine-tuned of all which can give a good fit for KK masses < 2 TeV depending on the choice of the model parameters. We also comment on the fine-tuning in each case.