Model-based deep reinforcement learning with heuristic search for satellite attitude control

Xu, Ke; Wu, Fengge; Zhao, Junsuo

doi:10.1108/ir-05-2018-0086

Cited by 3 publications

(2 citation statements)

References 14 publications

(14 reference statements)

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“…Reference [55] proposes an approach based on deep RL to increase the adaptability and autonomy of the satellite control system. It is a model-based algorithm that can find solutions in fewer training episodes than model-free algorithms.…”

Section: Attitude Controlmentioning

confidence: 99%

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%

Deep learning for spacecraft guidance, navigation, and control

Khoroshylov¹,

Redka²

2021

Kosm. nauka tehnol.

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“…With the advent of DRL, it has also been used for more advanced tasks such as enabling intelligent cooperation between multiple UAVs [34], and for specific control problems such as landing [35]. RL algorithms have also been proposed for attitude control of other autonomous vehicles, including satellites [36] and underwater vehicles. Carlucho et al [37] applies an actorcritic DRL algorithm to low-level attitude control of an autonomous underwater vehicle (AUV) -similar to the proposed method in this paper -and find that the derived control law transfers well from simulation to real world experiments.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy optimization

Bøhn

Coates

Moe

et al. 2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

141

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Contemporary autopilot systems for unmanned aerial vehicles (UAVs) are far more limited in their flight envelope as compared to experienced human pilots, thereby restricting the conditions UAVs can operate in and the types of missions they can accomplish autonomously. This paper proposes a deep reinforcement learning (DRL) controller to handle the nonlinear attitude control problem, enabling extended flight envelopes for fixed-wing UAVs. A proof-of-concept controller using the proximal policy optimization (PPO) algorithm is developed, and is shown to be capable of stabilizing a fixed-wing UAV from a large set of initial conditions to reference roll, pitch and airspeed values. The training process is outlined and key factors for its progression rate are considered, with the most important factor found to be limiting the number of variables in the observation vector, and including values for several previous time steps for these variables. The trained reinforcement learning (RL) controller is compared to a proportional-integralderivative (PID) controller, and is found to converge in more cases than the PID controller, with comparable performance. Furthermore, the RL controller is shown to generalize well to unseen disturbances in the form of wind and turbulence, even in severe disturbance conditions.

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Satellite attitude tracking control of moving targets combining deep reinforcement learning and predefined-time stability considering energy optimization

Zhao

Wang

et al. 2022

Advances in Space Research

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Model-based deep reinforcement learning with heuristic search for satellite attitude control

Cited by 3 publications

References 14 publications

Deep learning for spacecraft guidance, navigation, and control

Deep learning for spacecraft guidance, navigation, and control

Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy optimization

Satellite attitude tracking control of moving targets combining deep reinforcement learning and predefined-time stability considering energy optimization

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