In this paper, a sampling-based Stochastic Model Predictive Control algorithm is proposed for discrete-time linear systems subject to both parametric uncertainties and additive disturbances. One of the main drivers for the development of the proposed control strategy is the need of reliable and robust guidance and control strategies for automated rendezvous and proximity operations between spacecraft. To this end, the proposed control algorithm is validated on a floating spacecraft experimental testbed, proving that this solution is effectively implementable in real-time. Parametric uncertainties due to the mass variations during operations, linearization errors, and disturbances due to external space environment are simultaneously considered.The approach enables to suitably tighten the constraints to guarantee robust recursive feasibility when bounds on the uncertain variables are provided. Moreover, the offline sampling approach in the control design phase shifts all the intensive computations to the offline phase, thus greatly reducing the online computational cost, which usually constitutes the main limit for the adoption of Stochastic Model Predictive Control schemes, especially for low-cost on-board hardware. Numerical simulations and experiments show that the approach provides probabilistic guarantees on the success of the mission, even in rather uncertain and noise situations, while improving the spacecraft performance in terms of fuel consumption.

This paper outlines a method based on the theory of artificial potential fields combined with sliding mode techniques for spacecraft maneuvers in the presence of obstacles. Guidance and control algorithms are validated with a six degree-of-freed (dof) omorbital simulator. The idea of this paper is to provide computationally efficient algorithms for real time applications, in which the combination of Artificial potential field (APF) and sliding mode control shows the ability of plan trajectories, even in the presence of external disturbances and model uncertainties. A reduced frequency of the proposed controllers and a pulse width modulation (PWM) of the thrusters are considered to verify the performance of the system. The computational performance of APF as a guidance algorithm is discussed and the algorithms are verified by simulations of a complete rendezvous maneuver. The proposed algorithm appears suitable for the autonomous, real-time control of complex maneuvers with a minimum on-board computational effort.

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