This paper explores the application of Nonlinear Model Predictive Control (NMPC) techniques, based on the Pontryagin Minimum Principle, for a minimum-propellant autonomous rendezvous maneuver in non-Keplerian Lunar orbits. The relative motion between the chaser and the target is described by the nonlinear dynamics of the circular restricted three body-problem, posing unique challenges due to the complex and unstable dynamics of near-rectilinear halo orbits. Key aspects of the proposed NMPC include trajectory optimization, maneuver planning, and real-time control, leveraging on its ability to satisfy complex mission requirements while ensuring safe and efficient spacecraft operations and in the presence of input and nonlinear/non-convex state constraints. The proposed formulation allows the design of a minimum-propellant controller, whose optimal control signal results to be bang–bang in time. A case study based on the Artemis III mission – where the docking of the Orion spacecraft to the Gateway station is planned – is illustrated in order to demonstrate the efficiency of the proposed approach, showcasing its potential for enhancing target tracking accuracy, while reducing propellant consumption.
This paper deals with simultaneous credible bands (SCBs) for transfer function estimates based on Gaussian posteriors of the impulse response vector derived from identification of high-order FIR models, where SCBs quantify estimation errors of functions over their entire domain. Though conservative, SCBs for step responses and gain/phase functions are obtained by maximizing and minimizing them over the uncertainty sets specified by critical values of statistics associated with the Gaussian posterior. This procedure also applies to deriving (exact) pointwise credible bands (PCBs) using relevant critical values. In numerical studies, we compute the failure rates that SCBs fail to include the true step response or gain function over their respective domains; thereby an empirical method for computing less conservative SCBs is developed.
One of the most important decisions in any manufacturing company is how to schedule the operations on the available machines. In several industries, the nature of the job imposes certain constraints to operations scheduling. In a no-wait flowshop, once a job starts on the first machine, it has to continue being processed on the next ones, without any interruptions. As an extension of the flowshop scheduling problem, the no-wait version is also very difficult to be solved to optimality within a reasonable time, and many heuristics have been proposed for it. This paper aims to classify existing solution algorithms proposed to solve the no-wait flowshop scheduling problem with setup times and some of its variants. Our classification is based on the type of setup considered; we also review all available performance measures in the literature. We show how combining a heuristic to generate a good initial solution, local search procedures, insertion and swapping of job positions and techniques developed originally to solve transportation problems are among the popular and efficient techniques for this problem. We identify the main available benchmark instance sets and propose several promising avenues to guide future research.