Mars atmospheric entry guidance for optimal terminal altitude

Long, Jiateng; Gao, Ai; Cui, Pingyuan; Liu, Yang

doi:10.1016/j.actaastro.2018.12.001

Cited by 13 publications

(3 citation statements)

References 21 publications

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“…Before the powered descent phase, the rover enters the Martian atmosphere during which the guidance is designed to track a pre‐optimized trajectory. There are various guidance laws of Mars entry with Low‐L/D vehicle or earth reentry 4‐6 . These guidance laws have high reference values for the Mid‐L/D vehicle, and they can be divided into three categories: trajectory planning, predictor‐corrector and tracking guidance.…”

Section: Introductionmentioning

confidence: 99%

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Mars powered descent phase guidance law based on reinforcement learning for collision avoidance

Zhang

Zeng

Guo

et al. 2023

Intl J Robust & Nonlinear

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SummaryThis paper proposes a reinforcement learning‐based guidance law for Mars powered descent phase, which is an effective online calculation method that handles the nonlinearity caused by the mass variation and avoids collisions. The reinforcement learning method is designed to solve the constrained nonlinear optimization problem by using a critic neural network. Specifically, to cope with the position constraint (i.e., glide‐slope constraint) and the thrust force limit constraint, a modified cost function is proposed, and the associated Hamilton‐Jacobi‐Bellman equation is solved online without using an actor neural network, which significantly reduces the computational burden. The convergence of the critic neural network is proven. Simulation results show the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

“…There are various guidance laws of Mars entry with Low-L/D vehicle or earth reentry. [4][5][6] These guidance laws have high reference values for the Mid-L/D vehicle, and they can be divided into three categories: trajectory planning, predictor-corrector and tracking guidance.…”

Section: Introductionmentioning

confidence: 99%

Mars powered descent phase guidance law based on reinforcement learning for collision avoidance

Zhang

Zeng

Guo

et al. 2023

Intl J Robust & Nonlinear

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show abstract

“…The indirect method uses the principle of minimal values and solves by introducing the Lagrange multiplier method. However, its initial value is difficult to obtain, and the solution is sensitive to the initial value, which limits the application of this method [7]. The direct method is to transform the landing trajectory optimization problem into a static parameter optimization problem under multiple path constraints and boundary constraints, and then solve it by numerical optimization methods.…”

Section: Introductionmentioning

confidence: 99%

Optimal Landing Zone Selection and Online Trajectory Programming Method for Lunar Lander

Deng

Ping

2023

J. Phys.: Conf. Ser.

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To achieve a safe landing on the lunar surface with complex terrain, online landing zone selection is required. A DEM-based landing zone selection technique is proposed. The method firstly obtains several candidate landing zones by analyzing the gradient and variance information from the DEM, and then selects the integrated candidate landing zone by the overlap ratio. Finally, the optimal trajectory programming is carried out with the current state of the lander as the initial value and each integrated candidate landing zone as the final position, and the optimal landing zone is selected by comparing the required fuel to each landing zone. Compared with the classical DEM-based online landing site selection technique, the proposed method does not perform terrain reconstruction, responds quickly, and achieves real-time performance, and it also reduces fuel consumption, provides residual fuel for the subsequent landing phase, and increases the reliability of the landing process. Simulation results show that the proposed method is able to select several candidate landing zones in complex terrain, meanwhile, it is capable of achieving the optimal trajectory programming with the target of these several candidate landing zones, and finally, the optimal landing zone is selected by comparing the fuel consumption of each path.

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