Graduate Student Works
Dr. Riccardo Bevilacqua
In the interest of exploiting natural forces for propellant-less spacecraft missions, this investigation proposes an adaptive control strategy to account for unknown parameters in the dynamic modeling of a reflectivity-controlled solar sail spacecraft. A Lyapunov-based control law along with integral concurrent learning is suggested to accomplish and prove global exponential tracking of the estimated parameters and states of interest, without satisfying the common persistence of excitation condition, which in most nonlinear systems cannot be guaranteed a priori. This involves estimating the solar flux or irradiance from the Sun to account for uncertainty and variation over time in this value. To illustrate potential applications, two missions are considered: (1) a geostationary debris removal case and (2) an Earth-Mars interplanetary transfer orbit following a logarithmic spiral reference trajectory. The proposed formulation demonstrates the benefit of estimating the solar flux using integral concurrent learning. Results are compared to trajectories with no estimation to illustrate the need to account for the actual solar flux.
Scholarly Commons Citation
Mendoza Zambrano, L. E., & Bevilacqua, R. (2023). Solar Sailing Adaptive Control Using Integral Concurrent Learning for Solar Flux Estimation. Acta Astronautica, 213(). https://doi.org/10.1016/j.actaastro.2023.09.025