No abstract

This paper presents a control design strategy for the soft-landing problem on the Moon using solid propellant engines (SPEs). While SPEs have controllability issues and issues relating to the fact that they cannot be restarted, they are characterized by their reliability, simplicity, and cost-effectiveness. Consequently, our main contribution is to tackle this disadvantage by formulating a 1-dimensional landing optimization problem using an array of SPEs in a CubeSat platform, which is analyzed for different numbers of engines in the array and for three types of propellant grain cross-section (PGCS). The engines and control parameters are optimized by a genetic algorithm (GA) due to the non-linearity of the problem and the uncertainties of the state variables. Two design approaches for control are analyzed, where the robust design based on the uncertainties of the variables shows the best performance. The results of Monte Carlo simulations were used to demonstrate the effectiveness of the robust design, which decreases the impact velocity as the number of SPEs increases. Using an arrangement of ten SPEs, the landing was at −2.97 m/s with a standard deviation of 0.99 m/s; using sixteen SPEs, the landing was at −2.04 m/s with a standard deviation of 0.48 m/s. Both have regressive PGCS.

No abstract