![]() The reader will come away with a well-rounded understanding of the state-of-the-art in each space vehicle control application, and will be well positioned to tackle important current open problems using convex optimization as a core technology. The primary focus is on the last ten years of progress, which have seen a veritable rise in the number of applications using three core technologies: lossless convexification, sequential convex programming, and model predictive control. The considered applications include planetary landing, rendezvous and proximity operations, small body landing, constrained reorientation, endo-atmospheric flight including ascent and re-entry, and orbit transfer and injection. This survey paper provides a detailed overview of recent advances, successes, and promising directions for optimization-based space vehicle control. #Circular orbit softwareAs we enter the new decade, optimization theory, algorithms, and software tooling have reached a critical mass to start seeing serious application in space vehicle guidance and control systems. At the same time, the last four decades of optimization research have put a suite of powerful optimization tools at the fingertips of the controls engineer. The search for new science and life beyond Earth calls for spacecraft that can deliver scientific payloads to geologically rich yet hazardous landing sites. Space mission design places a premium on cost and operational efficiency. Some recent advances for indirect methods include homotopy methods Pan and Pan, 2020 Cerf et al., 2011), optimal switching surfaces (Taheri and Junkins, 2019), the RASHS and CSC approaches from Section 2.5.2 (Saranathan and Grant, 2018 Taheri et al., 2020a,b), and simultaneous optimization (also known as co-optimization) of the trajectory and the spacecraft design parameters (Arya et al., 2021). Numerous solution methods have been studied, including methods based on primer vector theory (Russell, 2007 Petropoulos and Russell, 2008 Restrepo and Russell, 2017), direct methods based on solving an NLP (Betts, 2000 Arrieta-Camacho and Biegler, 2005 Ross et al., 2007 Starek and Kolmanovsky, 2012 Rao, 2015, 2016), and indirect methods (Alfano and Thorne, 1994 Fernandes, 1995 Kechichian, 1995 Haberkorn et al., 2004 Gong et al., 2008 Gil-Fernandez and Gomez-Tierno, 2010 Zimmer et al., 2010 Pan et al., 2012 Pontani and Conway, 2013 Cerf, 2016 Taheri et al., 2016Taheri et al.,, 2017. ![]() Traditionally, the problem has been solved using optimal control theory from Section 2.1, and for this we can cite the books (Longuski et al., 2014 Lawden, 1963 Bryson Jr. Furthermore, this study has verified that this algorithm can be directly integrated with the existing formation control algorithm. ![]() In addition, it requires a 75% shorter time than the natural precession method in achieving the desired plane change and is 36% more fuel efficient than the direct firing method. Results from the simulation have shown that the required V of this proposed method is similar to the theoretical impulsive burn method. Monte Carlo simulations have been conducted to benchmark the proposed method against the theoretical impulsive thrusts in terms of V, and the commonly adopted natural precession-based plane change method in terms of the total time taken. To overcome these constraints on the orbital plane-change maneuver process, this paper proposes an optimized algorithm by minimizing Edelbaum’s equation using Selective Constrained Ensemble Kalman Filter that includes propulsion’s performance variation and firing duration constraints. In addition, the self-pressurized chemical propulsion system used in this satellite mission experiences a performance degradation effect over prolonged use in terms of specific impulse and thrust force. While the optimization of orbital plane-change maneuver has been well-studied, the constraints of propulsion system such as the power limitation, battery charging cycle, and finite thrust durations are often neglected. The cross-track baseline requirement in a satellite formation flying mission requires an orbital plane-change maneuver during the acquisition phase. ![]()
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