In an effort to pursue more advanced missions in space, improved on-board trajectory optimization and path (re)planning capabilities are necessary. Over the past decades, numerous missions have pushed the state of the art in autonomous rendezvous and proximity operations (RPOs). Regardless of the mission, any RPO guidance algorithm must be able to react to a dynamic environment while generating a fuel-efficient trajectory. An adaptive artificial potential function (AAPF) guidance exhibiting these properties has been experimentally evaluated on a spacecraft air-bearing test bed and its performance compared to traditional APF and other real-time guidance methods.

In this paper, a sampling-based stochastic model predictive control (SMPC) 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 for 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 limitation for the adoption of SMPC 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 noisy situations, while improving the spacecraft performance in terms of fuel consumption.

A battery/supercapacitor hybrid energy storage system (HESS) is over-actuated in the sense that there are two power sources providing a single power output. This feature of HESS is exploited in this paper to simultaneously achieve accurate identification of the battery states/parameters and high system efficiency. By actively injecting current signals, the State of Charge (SoC) and State of Health (SoH), together with other battery parameters, can be identified sequentially. Sufficient richness in the input (i.e., battery current) is necessary to ensure identification accuracy. Since signal richness for identification can be in conflict with efficient operation, a novel model predictive control (MPC) strategy is used to simultaneously consider both objectives to determine the optimal power distribution between supercapacitor and battery. The tradeoff between identification accuracy and system efficiency is investigated. Simulation results show that the proposed MPC can significantly improve identification accuracy at the expense of a slight decrease in system efficiency when compared to the baseline MPC, which does not consider the signal richness. Therefore, it is validated that the proposed MPC can effectively achieve simultaneous identification and efficient operation.

This paper reviews the integrated perturbation analysis - sequential quadratic programming (IPA-SQP) approach. The IPA-SQP approach has been proposed to address computational challenges in nonlinear model predictive control (MPC) problems. This approach combines the complementary features of perturbation analysis and sequential quadratic programming in a unified framework. An overview of the IPA-SQP approach is provided, its methodological extension to adaptive MPC is discussed, and computational issues related to nonlinear MPC are discussed. Several successful applications of the IPA-SQP will also be highlighted. References to relevant literature are included.