GPU-Accelerated Pseudospectral Methods for Optimal Control Problems

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Detalles Bibliográficos
Publicado en:	Mathematics vol. 13, no. 20 (2025), p. 3252-3267
Autor Principal:	Zou Yilin
Outros autores:	Jiang Fanghua
Publicado:	MDPI AG
Materias:	Sparsity Accuracy Interplanetary trajectories Applied mathematics Trajectory optimization Fourier transforms Graphics processing units Optimization Effectiveness System dynamics Methods Algorithms Optimal control Aerospace engineering Energy consumption Nonlinear programming Derivatives Computing time Linear algebra Robotics Spectral methods
Acceso en liña:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumo:	Pseudospectral methods are effective tools for solving optimal control problems, but they result in large-scale nonlinear programming (NLP) problems that are computationally demanding. A major bottleneck is the repeated evaluation of the objective function, system dynamics, path constraints, and their derivatives. This paper presents an approach to accelerating these computations using Graphics Processing Units (GPUs). We offload the evaluation of the NLP functions and their first and second derivatives to the GPU by developing custom CUDA kernels that exploit the parallelism in the discretized problem structure. The effectiveness of this method is demonstrated on a low-thrust interplanetary trajectory optimization problem. A comparison with a CPU implementation shows that the GPU-accelerated approach reduces the overall computational time. This work demonstrates the potential of GPU acceleration and provides a foundation for future research into fully GPU-native optimal control solvers.
ISSN:	2227-7390
DOI:	10.3390/math13203252
Fonte:	Engineering Database