Relative control of an ion beam shepherd satellite in eccentric orbits

Khoroshylov, S.V.

doi:10.1016/j.actaastro.2020.06.027

Cited by 21 publications

(5 citation statements)

References 24 publications

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“…To analyze the performance, we compare the time required to de- termine the force using the NNMs and previously proposed approaches. As already noted in the introduction, the method of force determination based on the central projection of the target is considered to be the most effective so far [11,12]. Therefore, this method was used for comparison.…”

Section: Model Performance Analysismentioning

confidence: 99%

“…One of the key tasks within the concept of "Ion beam shepherd" is the determination of the force transmitted to the SDO by the shepherd. Knowledge of this force is necessary for the successful implementation of the selected removal program and can also be useful for solving problems of navigation and relative control of the "shepherd -SDO" system [2,11,12]. Determining this force is not an easy task since its value depends in a complex way not only on the properties of the IT plume but also on the relative position and orientation of the SDO.…”

Section: Introductionmentioning

confidence: 99%

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Determination of the Force Impact of an Ion Thruster Plume on an Orbital Object via Deep Learning

Redka¹,

Khoroshylov²

2022

Kosm. nauka tehnol.

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The subject of research is the process of creating a neural network model (NNM) for determining the force impact of an ion thruster (IT) plume on an orbital object during non-contact space debris removal. The work aims to develop NNMs and study the influence of various factors on the accuracy of determining the force transmitted by the ion plume of the thruster to a space debris object (SDO). The tasks to resolve are to choose the structures of the NNMs, form a data set and use this data to train and validate the NNMs, and to explore the influence of the model structure and optimizer parameters on the accuracy of force determination. The methods used are plasma physics, computer simulation, deep learning, and optimization using an improved version of stochastic gradient descent. As a result of research, three NNMs have been developed, which differ in the number of hidden layers and neurons in hidden layers. For training and validation of the NNMs, a data set was generated for an SDO approximated by a cylinder using an autosimilar description of the ion plasma propagation. The data set was obtained for various relative positions and orientations of the object in the process of its removal from an orbit. Using this data set, the NNM parameters were optimized with the supervised learning method. The optimizer and its parameters are selected, providing a small error at the stage of validating learning outcomes. It was found that the accuracy of determining the force depends on the relative position and orientation of the SDO, as well as the architecture of the NNM, and the features of this influence were identified. The approach applied allows us to obtain the possibility of using methods of deep learning to determine the force impact of the IT plume on the SDO. The proposed models provide the accuracy of the force impact determination, which is sufficient for solving the considered class of problems. At the same time, NNM makes it possible to obtain results much faster in comparison with the methods used previously. This fact makes the NNMs promising to use both on-board and in mathematical modeling of missions to remove space debris.

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Section: Model Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of the Force Impact of an Ion Thruster Plume on an Orbital Object via Deep Learning

Redka¹,

Khoroshylov²

2022

Kosm. nauka tehnol.

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“…In Ref. [23], an algorithm to control the time-periodic plant is synthesized by solving the state dependant Riccati equation. However, this approach requires solving the Riccati equation onboard during SC operation.…”

Section: Research and Engineering Innovation Projects Of The National...mentioning

confidence: 99%

“…where Q, R are the cost matrices. Among various positive properties, such a controller demonstrates impressive robustness, which allows designers to use it for systems whose actual parameters differ significantly from the nominal ones [23]. According to this methodology the control is given in the following form:…”

Section: Control Designmentioning

confidence: 99%

Time-Periodic Spacecraft Attitude Control with the Use of Slewing Permanent Magnets

Khoroshylov

Lapkhanov

2022

Sci. innov.

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Introduction. Electromagnetic actuators are widely used in spacecraft (SC) attitude control systems. These actuators can be modified by using slewing permanent magnets (ASPM) as sources of torque instead of electromagnets. These modified devices consume less onboard electricity for SC attitude control than the conventional ones.Problem Statement. An algorithm for attitude stabilization of a SC using ASPM was proposed in previous studies, where the pole placement technique and pulse-width modulation (PWM) were used to design the controller. However, this approach does not allow the designers to find optimal values of the required magnetic torques, which may result in frequent engagement of the stepper motors of the ASPMs and their significant energy consumption. This controller has such a drawback because its gains are selected without taking into account time-periodic properties of the Earth magnetic field.Purpose. The purpose of the study is to design the algorithm for the SC angular stabilization by the ASPMs taking into account time-periodic properties of the Earth magnetic field.Materials and Methods. The solution of the time-periodic Riccati equation was used for the controller design. Mathematical modeling and computer simulation of SC motion was applied to build the model of the plan and validate the results.Results. A time-periodic based SC attitude control algorithm has been designed. Taking into consideration the time-periodic properties of the magnetic field of Earth allow us to optimize the required magnetic control torques. This algorithm minimizes the frequency of the actuation of the ASPM sashes, and thus reduces onboard energy consumption.Conclusions. The designed algorithm increases the control efficiency of SC attitude control by using jointly the ASPMs, time-periodic linear-quadratic regulator and pulse-width modulator.

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“…The main idea of this concept is to use the momentum from an ion thruster plume to transmit the deorbiting momentum to the SDO. To implement the IBS concept, it is necessary to have an efficient technique to determine the force that transmits the ion thruster (IT) to the SDO because these values are required for mission planning and relative control tasks [2]. The conventional approach for this task results in computationally complex algorithms since they are based on integration of the elementary forces over the SDO surface.…”

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confidence: 99%

Deep Learning for Space Debris Removal

Serhii,

Mikhailo

2024

ITMM

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The advances in deep learning have revolutionized the field of artificial intelligence. These advances, as well as new tasks and requirements in space exploration, have led to an increased interest in these deep learning methods among space scientists and practitioners. The problems of controlling the attitude and relative motion of spacecraft are considered for both traditional and new missions, such as contactless space debris removal. Both supervised and reinforcement learning is used to solve such problems based on various architectures of artificial neural networks, including convolutional ones. The possibility of using deep learning together with methods of control theory is analyzed to solve the considered problems more efficiently. The difficulties that limit the application of these methods for space applications are highlighted. The necessary research directions for solving these problems are indicated.

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Relative control of an ion beam shepherd satellite in eccentric orbits

Cited by 21 publications

References 24 publications

Determination of the Force Impact of an Ion Thruster Plume on an Orbital Object via Deep Learning

Determination of the Force Impact of an Ion Thruster Plume on an Orbital Object via Deep Learning

Time-Periodic Spacecraft Attitude Control with the Use of Slewing Permanent Magnets

Deep Learning for Space Debris Removal

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