GPU-Accelerated Pseudospectral Methods for Optimal Control Problems

Yilin Zou and Fanghua Jiang ()
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Yilin Zou: School of Aerospace Engineering, Tsinghua University, Beijing 100084, China
Fanghua Jiang: School of Aerospace Engineering, Tsinghua University, Beijing 100084, China

Mathematics, 2025, vol. 13, issue 20, 1-15

Abstract: 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.

Keywords: optimal control; direct collocation; hardware acceleration (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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