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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.08288 |
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| _version_ | 1866908585145925632 |
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| author | Ossareh, Hamid R. Shayne, William Chevalier, Samuel |
| author_facet | Ossareh, Hamid R. Shayne, William Chevalier, Samuel |
| contents | Constraint management is a central challenge in modern control systems. A solution is the Reference Governor (RG), which is an add-on strategy to pre-stabilized feedback control systems to enforce state and input constraints by shaping the reference command. While robust formulations of RG exist for linear systems, their extension to nonlinear systems is often computationally intractable. This paper develops a scenario-based robust RG formulation for nonlinear systems and investigates its parallel implementation on multi-core CPUs and CUDA-enabled GPUs. We analyze the computational structure of the algorithm, identify parallelization opportunities, and implement the resulting schemes on modern parallel hardware. Benchmarking on a nonlinear hydrogen fuel cell model demonstrates order-of-magnitude speedups (by as much as three orders of magnitude) compared to sequential implementations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08288 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CPU- and GPU-Based Parallelization of the Robust Reference Governor Ossareh, Hamid R. Shayne, William Chevalier, Samuel Systems and Control Constraint management is a central challenge in modern control systems. A solution is the Reference Governor (RG), which is an add-on strategy to pre-stabilized feedback control systems to enforce state and input constraints by shaping the reference command. While robust formulations of RG exist for linear systems, their extension to nonlinear systems is often computationally intractable. This paper develops a scenario-based robust RG formulation for nonlinear systems and investigates its parallel implementation on multi-core CPUs and CUDA-enabled GPUs. We analyze the computational structure of the algorithm, identify parallelization opportunities, and implement the resulting schemes on modern parallel hardware. Benchmarking on a nonlinear hydrogen fuel cell model demonstrates order-of-magnitude speedups (by as much as three orders of magnitude) compared to sequential implementations. |
| title | CPU- and GPU-Based Parallelization of the Robust Reference Governor |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.08288 |