A composite framework for co-optimization of spacecraft trajectory and propulsion system

Arya, Vishala; Taheri, Ehsan; Junkins, John L.

doi:10.1016/j.actaastro.2020.10.007

Cited by 25 publications

(7 citation statements)

References 19 publications

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“…It turns out that homotopy can also be used to encode (45) in a continuous framework, and has been successfully embedded into recent indirect trajectory optimization algorithms. In this section, we briefly introduce the relaxed autonomously switched hybrid system (RASHS) and composite smooth control (CSC) methods (Saranathan and Grant, 2018;Taheri et al, 2020a;Arya et al, 2020).…”

Section: Homotopy Methodsmentioning

confidence: 99%

“…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, , 2017Rasotto et al, 2015;Lizia et al, 2014). Some recent advances for indirect methods include homotopy methods (Pan et al, 2019;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, 2020).…”

Section: Orbit Transfer and Injectionmentioning

confidence: 99%

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Advances in Trajectory Optimization for Space Vehicle Control

Malyuta¹,

Yu²,

Elango³

et al. 2021

Preprint

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Space mission design places a premium on cost and operational efficiency. The search for new science and life beyond Earth calls for spacecraft that can deliver scientific payloads to geologically rich yet hazardous landing sites. 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. As 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. This survey paper provides a detailed overview of recent advances, successes, and promising directions for optimization-based space vehicle control. The considered applications include planetary landing, rendezvous and proximity operations, small body landing, constrained attitude reorientation, endo-atmospheric flight including ascent and reentry, and orbit transfer and injection. 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 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.

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Section: Homotopy Methodsmentioning

confidence: 99%

Section: Orbit Transfer and Injectionmentioning

confidence: 99%

Advances in Trajectory Optimization for Space Vehicle Control

Malyuta¹,

Yu²,

Elango³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…RASHS [22] CSC [23,24] STCs Slack variable [25][26][27] Multiplicative coefficient [28,29] Compound logic [30,31] Proposed method that it uses numerical optimization to solve a discretized version of the optimal control problem. This generally makes it easier to handle constraints, which are nontrivial to include in an indirect approach.…”

Section: Indirect Methodsmentioning

confidence: 99%

“…Homotopy is the core idea behind the recent relaxed autonomous switched hybrid system (RASHS) and composite smooth control (CSC) algorithms [22][23][24]. Both algorithms model the constraint (15a) by approximating the and combination with a sigmoid function.…”

Section: Existing Homotopy Methodsmentioning

confidence: 99%

“…Figure 1 illustrates how the proposed algorithm relates to existing literature. Closest to our approach are the recently published relaxed autonomous switched hybrid system (RASHS) and composite smooth control (CSC) algorithms [22][23][24]. Both RASHS and CSC belong to the indirect family of methods in the sense that they solve for the optimality conditions obtained from Pontryagin's maximum principle [32][33][34].…”

Section: A Contributionsmentioning

confidence: 99%

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Fast Homotopy for Spacecraft Rendezvous Trajectory Optimization with Discrete Logic

Malyuta¹,

Açıkmeşe²

2021

Preprint

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This paper presents a computationally efficient optimization algorithm for solving nonconvex optimal control problems that involve discrete logic constraints. Traditional solution methods for these constraints require binary variables and mixed-integer programming, which is prohibitively slow and computationally expensive. This paper targets a fast solution that is capable of real-time implementation onboard spacecraft. To do so, a novel algorithm is developed that blends sequential convex programming and numerical continuation into a single iterative solution process. Inside the algorithm, discrete logic constraints are approximated by smooth functions, and a homotopy parameter governs the accuracy of this approximation. As the algorithm converges, the homotopy parameter is updated such that the smooth approximations enforce the exact discrete logic. The effectiveness of this approach is numerically demonstrated for a realistic rendezvous scenario inspired by the Apollo Transposition and Docking maneuver. In under 15 seconds of cumulative solver time, the algorithm is able to reliably find difficult fuel-optimal trajectories that obey the following discrete logic constraints: thruster minimum impulse-bit, range-triggered approach cone, and range-triggered plume impingement. The optimized trajectory uses significantly less fuel than reported NASA design targets. Nomenclature 𝜃 appch = approach cone half-angle, rad Δ𝑡 max = maximum pulse duration, s Δ𝑡 min = minimum pulse duration, s Δ𝑡 𝑖𝑘 = pulse duration of 𝑖-th thruster at 𝑘-th control interval, s Δ𝑡 db = buffer zone around Δ𝑡 min for the wall avoidance constraint, s f𝑖 = thrust direction vector for the 𝑖-th thruster F B = body frame centered at the chaser COM F L = LVLH frame centered at the target COM 𝐹 rcs = constant thrust level generated by a thruster, N

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