Trajectory Design and Guidance for Space Missions with Rendezvous and Proximity Operations

Rendezvous and Proximity Operations (RPO) missions are becoming increasingly commonplace due to the heightened interest in Active Debris Removal (ADR), On-orbit Servicing (OOS), and asteroid inspection, mining, and deflection missions. Although frequently discussed in literature, routine, commercially viable RPO missions require further technical advancements. Reviews of current missions have pinpointed technology gaps in RPO missions that need addressing to enhance their efficiency, autonomy, reliability, and safety. This thesis aims to develop novel techniques to close these gaps, enhancing the transfer, far-range, and close-range hovering phases of RPO missions. Optimal trajectory design ensures spacecraft can rendezvous with their targets using minimal fuel and time. Traditional optimization methods often struggle with real-world complexities, particularly under low-thrust propulsion. This thesis introduces an indirect optimization method using scaling constants to generate fuel- and time-optimal trajectories while accounting for orbital perturbations and eclipses. Scaling factors are used to link the energy-optimal problem to the fuel- and time-optimal problems, leading to faster convergence. Additionally, a high-accuracy Preliminary Mission Design Tool (PMDT) is developed for rapid trajectory design of multi-target, low-thrust missions, considering J2 perturbations, eclipses, duty cycles, and drag. Transfer phase guidance of an RPO mission is also challenging, requiring precise adjustments to unforeseen conditions. This thesis develops a robust, convex-based Model Predictive Control (MPC) guidance algorithm that follows PMDT-generated references. Tested with thrust uncertainties, perturbations, and missed thrust events, the algorithm performs reliably, allowing the spacecraft to reach its target despite uncertainties. Far-range operations also present notable gaps, particularly in accurate relative navigation, robustness to uncertainties, and safety. This research introduces a dual-step methodology for the trajectory design and guidance of far-range operations under Angles-Only Navigation (AON), utilizing Particle Swarm Optimization (PSO) and Reinforcement Learning (RL). This approach successfully generates robust, safe, and observable trajectories despite initial state variations and thrust uncertainties while minimizing fuel consumption. Lastly, this thesis addresses gaps in close-range operations by developing control laws for autonomous spacecraft hovering near asteroids. These laws are applied in a planetary defense scenario, where the spacecraft performs Ion Beam Deflection (IBD) operations. The study also evaluates IBD effectiveness for deflecting asteroids of varying sizes, densities, and rotational characteristics. Results indicate that the efficacy of IBD is marginally influenced by spin and shape and that deflection becomes more challenging as the asteroid’s density and diameter increase.