An Iterative Convex Programming Method for Rocket Landing Trajectory Optimization

Wang, Jinbo; Li, Huixu; Chen, Hongbo

doi:10.1007/s40295-020-00235-y

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…The validity of the approach is ensured by an ad-hoc choice of the static trust regions. The weak point in this case might be a slow convergence, and further strategies might be needed to improve the quality of the process [16,27]. In the latter strategy some variable transformations are introduced to try to keep in the limits of the constraints of the problem some features of the nonlinear formulation.…”

Section: Introductionmentioning

confidence: 99%

Onboard Guidance for Reusable Rockets: Aerodynamic Descent and Powered Landing

Sagliano

Heidecker

Hernandez

et al. 2021

AIAA Scitech 2021 Forum

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This paper describes a novel general on-board guidance strategy which can be applied to both the aerodynamically-controlled descent and the powered landing phase of reusable rockets. The proposed guidance method is based on sequential convex optimization applied to a Cartesian representation of the equations of motion. The contributions are an exploitation of convex and non-convex contributions, which are processed separately to maximize the computational efficiency of the approach, the inclusion of highly nonlinear terms represented by aerodynamic accelerations, a complete reformulation of the problem based on the use of Euler angle rates as control means, an improved transcription based on the use of a generalized hp pseudospectral method, and a dedicated formulation of the aerodynamic guidance problem for reusable rockets. The problem is solved for a 40 kN-class reusable rocket. Results show that the proposed technique is a very effective methodology able to satisfy all the constraints acting on the system, and can be potentially employed online to solve the entire descent phase of reusable rockets in real-time.

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Section: Introductionmentioning

confidence: 99%

Onboard Guidance for Reusable Rockets: Aerodynamic Descent and Powered Landing

Sagliano

Heidecker

Hernandez

et al. 2021

AIAA Scitech 2021 Forum

View full text Add to dashboard Cite

show abstract

“…Direct methods involve transforming OCP problems into finite-dimensional parameter optimization problems through discretization methods, subsequently solved using nonlinear solvers. By combining convex optimization theory and the pseudospectral method, direct methods offer advantages in real-time performance and solution accuracy and have been successfully applied to solving many OCP problems [12][13][14]. Ref.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time and Optimal Hypersonic Entry Guidance Method Using Inverse Reinforcement Learning

Su,

Wang,

Chen

2023

Aerospace

Self Cite

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The mission of hypersonic vehicles faces the problem of highly nonlinear dynamics and complex environments, which presents challenges to the intelligent level and real-time performance of onboard guidance algorithms. In this paper, inverse reinforcement learning is used to address the hypersonic entry guidance problem. The state-control sample pairs and state-rewards sample pairs obtained by interacting with hypersonic entry dynamics are used to train the neural network by applying the distributed proximal policy optimization method. To overcome the sparse reward problem in the hypersonic entry problem, a novel reward function combined with a sophisticated discriminator network is designed to generate dense optimal rewards continuously, which is the main contribution of this paper. The optimized guidance methodology can achieve good terminal accuracy and high success rates with a small number of trajectories as datasets while satisfying heating rate, overload, and dynamic pressure constraints. The proposed guidance method is employed for two typical hypersonic entry vehicles (Common Aero Vehicle-Hypersonic and Reusable Launch Vehicle) to demonstrate the feasibility and potential. Numerical simulation results validate the real-time performance and optimality of the proposed method and indicate its suitability for onboard applications in the hypersonic entry flight.

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Real-time guidance for powered landing of reusable rockets via deep learning

Wang

et al. 2022

Neural Comput & Applic

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An Iterative Convex Programming Method for Rocket Landing Trajectory Optimization

Cited by 9 publications

References 30 publications

Onboard Guidance for Reusable Rockets: Aerodynamic Descent and Powered Landing

Onboard Guidance for Reusable Rockets: Aerodynamic Descent and Powered Landing

A Real-Time and Optimal Hypersonic Entry Guidance Method Using Inverse Reinforcement Learning

Real-time guidance for powered landing of reusable rockets via deep learning

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