GPU-Accelerated Pseudospectral Methods for Optimal Control Problems
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| Publicat a: | Mathematics vol. 13, no. 20 (2025), p. 3252-3267 |
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| Autor principal: | |
| Altres autors: | |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3265920403 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2227-7390 | ||
| 024 | 7 | |a 10.3390/math13203252 |2 doi | |
| 035 | |a 3265920403 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231533 |2 nlm | ||
| 100 | 1 | |a Zou Yilin | |
| 245 | 1 | |a GPU-Accelerated Pseudospectral Methods for Optimal Control Problems | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Sparsity | ||
| 653 | |a Accuracy | ||
| 653 | |a Interplanetary trajectories | ||
| 653 | |a Applied mathematics | ||
| 653 | |a Trajectory optimization | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Graphics processing units | ||
| 653 | |a Optimization | ||
| 653 | |a Effectiveness | ||
| 653 | |a System dynamics | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Optimal control | ||
| 653 | |a Aerospace engineering | ||
| 653 | |a Energy consumption | ||
| 653 | |a Nonlinear programming | ||
| 653 | |a Derivatives | ||
| 653 | |a Computing time | ||
| 653 | |a Linear algebra | ||
| 653 | |a Robotics | ||
| 653 | |a Spectral methods | ||
| 700 | 1 | |a Jiang Fanghua | |
| 773 | 0 | |t Mathematics |g vol. 13, no. 20 (2025), p. 3252-3267 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3265920403/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3265920403/fulltextwithgraphics/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3265920403/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |