Tube-Based Model Predictive Control with Uncertainty Identification for Autonomous Spacecraft Maneuvers

Abstract

Autonomous spacecraft must be robust toward uncertainty and disturbances in the system while achieving required levels of performance. As an example, robotic servicing spacecraft must intercept target objects with potentially unknown dynamic properties during active debris removal efforts. This work presents tube-based model predictive control (MPC) with uncertainty identification as a strategy to enhance performance while maintaining robustness in autonomous maneuvers. The proposed algorithm, which is an extension of the standard tube-based MPC framework, measures and predicts the exogenous input to the system (i.e., the uncertainty) online. This in turn enables the robust tube to be shrunk and grown appropriately as the trajectory progresses in both predicted time and actual time. The algorithm is demonstrated in a simulated intercept maneuver with a tumbling target whose inertia tensor is uncertain. Results indicate two improvements over the standard tube-based algorithm: first, better performance is obtained when the initial exogenous input bounds are overconservative, and, second, there is greater flexibility in encouraging robustness when the exogenous input bounds are overly optimistic since the robust tube is updated online. As such, tube-based MPC with uncertainty identification represents an incremental step in enhancing the flexibility of autonomous spacecraft in addressing uncertain scenarios and environments.