Hybrid multi-objective orbit-raising optimization with operational constraints

Morante, David; Sanjurjo-Rivo, Manuel; Soler, Manuel; Sánchez-Pérez, José Manuel

doi:10.1016/j.actaastro.2020.05.022

Cited by 9 publications

(3 citation statements)

References 37 publications

(74 reference statements)

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“…A formulation of the proximity quotient based on MEE was implemented in LATOP (Lyapunov control Aided Transfer Optimizer Program) and combined with a genetic algorithm. This Q-law was also used by Morante et al [124] in the tool MOLTO-OR, to incorporate the optimization of the propulsion system along the trajectory optimization. Morante et al used it as an initial guess for a collocation method where he applied various operational constraints (e.g., slot-synchronization, avoidance of the geostationary ring).…”

Section: Predefined Control Lawsmentioning

confidence: 99%

A Survey on Low-Thrust Trajectory Optimization Approaches

2021

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In this paper, we provide a survey on available numerical approaches for solving low-thrust trajectory optimization problems. First, a general mathematical framework based on hybrid optimal control will be presented. This formulation and their elements, namely objective function, continuous and discrete state and controls, and discrete and continuous dynamics, will serve as a basis for discussion throughout the whole manuscript. Thereafter, solution approaches for classical continuous optimal control problems will be briefly introduced and their application to low-thrust trajectory optimization will be discussed. A special emphasis will be placed on the extension of the classical techniques to solve hybrid optimal control problems. Finally, an extensive review of traditional and state-of-the art methodologies and tools will be presented. They will be categorized regarding their solution approach, the objective function, the state variables, the dynamical model, and their application to planetocentric or interplanetary transfers.

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Section: Predefined Control Lawsmentioning

confidence: 99%

A Survey on Low-Thrust Trajectory Optimization Approaches

2021

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“…Cheng et al [11] developed a real-time optimal control approach based on multiscale deep neural networks for the orbit transfer problem of the solar sail spacecraft. In a recent study, Morante et al [12] proposed a multi-objective optimization approach for an orbit-raising optimization problem, in which chemical, electrical, and hybrid trajectories are considered.…”

Section: Introductionmentioning

confidence: 99%

Orbital Maneuver Optimization of Earth Observation Satellites Using an Adaptive Differential Evolution Algorithm

Luo

Peng

et al. 2022

Remote Sensing

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Earth observation satellite (EOS) systems often encounter emergency observation tasks oriented to sudden disasters (e.g., earthquake, tsunami, and mud-rock flow). However, EOS systems may not be able to provide feasible coverage time windows for emergencies, which requires that an appropriately selected satellite transfers its orbit for better observation. In this context, we investigate the orbit maneuver optimization problem. First, by analyzing the orbit coverage and dynamics, we construct three models for describing the orbit maneuver optimization problem. These models, respectively, consider the response time, ground resolution, and fuel consumption as optimization objectives to satisfy diverse user requirements. Second, we employ an adaptive differential evolution (DE) integrating ant colony optimization (ACO) to solve the optimization models, which is named ACODE. In ACODE, key components (i.e., genetic operations and control parameters) of DE are formed into a directed acyclic graph and an ACO is appropriately embedded into an algorithm framework to find reasonable combinations of the components from the graph. Third, we conduct extensive experimental studies to show the superiority of ACODE. Compared with three existing algorithms (i.e., EPSDE, CSO, and SLPSO), ACODE can achieve the best performances in terms of response time, ground resolution, and fuel consumption, respectively.

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“…These methods may be grouped in either global or local approaches, where global optimization algorithms aim at finding the global minimum of the transfer time and the corresponding steering law. Global strategies include indirect methods [15,16,17,18,19,20], and direct methods, such as pseudospectral methods [21], nonlinear programming [22,23], a combination of genetic algorithms and quadratic programming [24], and shape-based methods [25]. In this context, a comparison between the performance of direct and indirect approaches for solar sail trajectory optimization has been recently analyzed in Ref.…”

mentioning

confidence: 99%