Intelligent control of spacecraft attitude using reinforcement leaning

Khoroshylov, S.V.; Redka, M.O.

doi:10.15407/itm2019.04.029

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The goal of Refs. [28][29][30]40] is to develop an effective algorithm for SC intelligent control based on RL methods. To increase the RL efficiency, a statistical model of SC dynamics based on the concept of Gaussian processes is used.…”

Section: Attitude Controlmentioning

confidence: 99%

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Deep learning for spacecraft guidance, navigation, and control

Khoroshylov¹,

Redka²

2021

Kosm. nauka tehnol.

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The advances in deep learning have revolutionized the field of artificial intelligence, demonstrating the ability to create autonomous systems with a high level of understanding of the environments where they operate. 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 goal of this review article is to analyze the latest advances in deep learning for navigation, guidance, and control problems in space. The problems of controlling the attitude and relative motion of spacecraft are considered for both traditional and new missions, such as orbital service. The results obtained using these methods for landing and hovering operations considering missions to the Moon, Mars, and asteroids are also analyzed. Both supervised and reinforcement learning are used to solve such problems based on various architectures of artificial neural networks, including convolutional and recurrent 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 the reviewed methods for space applications are highlighted. The necessary research directions for solving these problems are indicated.

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Section: Attitude Controlmentioning

confidence: 99%

“…Guarantees of stability of the SC motion taking into account the uncertainty of the dynamic model, are obtained using the method of Lyapunov functions [30]. The cost function is chosen as a candidate for the Lyapunov function.…”

Section: Attitude Controlmentioning

confidence: 99%

Deep learning for spacecraft guidance, navigation, and control

Khoroshylov¹,

Redka²

2021

Kosm. nauka tehnol.

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Relative control of an underactuated spacecraft using reinforcement learning

Khoroshylov¹,

Redka²

2020

Teh. Meh.

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The aim of the article is to approximate optimal relative control of an underactuated spacecraft using reinforcement learning and to study the influence of various factors on the quality of such a solution. In the course of this study, methods of theoretical mechanics, control theory, stability theory, machine learning, and computer modeling were used. The problem of in-plane spacecraft relative control using only control actions applied tangentially to the orbit is considered. This approach makes it possible to reduce the propellant consumption of reactive actuators and to simplify the architecture of the control system. However, in some cases, methods of the classical control theory do not allow one to obtain acceptable results. In this regard, the possibility of solving this problem by reinforcement learning methods has been investigated, which allows designers to find control algorithms close to optimal ones as a result of interactions of the control system with the plant using a reinforcement signal characterizing the quality of control actions. The well-known quadratic criterion is used as a reinforcement signal, which makes it possible to take into account both the accuracy requirements and the control costs. A search for control actions based on reinforcement learning is made using the policy iteration algorithm. This algorithm is implemented using the actor–critic architecture. Various representations of the actor for control law implementation and the critic for obtaining value function estimates using neural network approximators are considered. It is shown that the optimal control approximation accuracy depends on a number of features, namely, an appropriate structure of the approximators, the neural network parameter updating method, and the learning algorithm parameters. The investigated approach makes it possible to solve the considered class of control problems for controllers of different structures. Moreover, the approach allows the control system to refine its control algorithms during the spacecraft operation.

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Intelligent control of spacecraft attitude using reinforcement leaning

Cited by 2 publications

References 7 publications

Deep learning for spacecraft guidance, navigation, and control

Deep learning for spacecraft guidance, navigation, and control

Relative control of an underactuated spacecraft using reinforcement learning

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