A Chance Constraint Predictive Control and Estimation Framework for Spacecraft Descent with Field Of View Constraints

Recent studies of optimization methods and GNC of spacecraft near small bodies focusing on descent, landing, rendezvous, etc., with key safety constraints such as line-of-sight conic zones and soft landings have shown promising results; this paper considers descent missions to an asteroid surface with a constraint that consists of an onboard camera and asteroid surface markers while using a stochastic convex MPC law. An undermodeled asteroid gravity and spacecraft technology inspired measurement model is established to develop the constraint. Then a computationally light stochastic Linear Quadratic MPC strategy is presented to keep the spacecraft in satisfactory field of view of the surface markers while trajectory tracking, employing chance based constraints and up-to-date estimation uncertainty from navigation. The estimation uncertainty giving rise to the tightened constraints is particularly addressed. Results suggest robust tracking performance across a variety of trajectories.Comment: Changed Section IV to reflect finalized stochastic tube MPC law. Added reference trajectory in field of view figure

Spacecraft Relative Motion Planning Using Chained Chance-Constrained Admissible Sets

With the increasing interest in proximity and docking operations, there is a growing interest in spacecraft relative motion control. This paper extends a previously proposed constrained relative motion approach based on chained positively invariant sets to the case where the spacecraft dynamics are controlled using output feedback on noisy measurements and are subject to stochastic disturbances. It is shown that non-convex polyhedral exclusion zone constraints can be handled. The methodology consists of a virtual net of static equilibria nodes in the Clohessy-Wiltshire-Hill frame. Connectivity between nodes is determined through the use of chance-constrained admissible sets, guaranteeing that constraints are met with a specified probability.Comment: Submitted to the 2020 American Control Conferenc

Chance-Constrained Controller State and Reference Governor

The controller state and reference governor (CSRG) is an add-on scheme for nominal closed-loop systems with dynamic controllers which supervises the controller internal state and the reference input to the closed-loop system to enforce pointwise-in-time constraints. By admitting both controller state and reference modifications, the CSRG can achieve an enlarged constrained domain of attraction compared to conventional reference governor schemes where only reference modification is permitted. This paper studies the CSRG for systems subject to stochastic disturbances and chance constraints. We describe the CSRG algorithm in such a stochastic setting and analyze its theoretical properties, including chance-constraint enforcement, finite-time reference convergence, and closed-loop stability. We also present examples illustrating the application of CSRG to constrained aircraft flight control.Comment: 17 pages, 8 figure