Scalable iterative Gramian synthesis for control-affine systems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866909055067357184 |
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| author | Yu, Zongxi Tamekue, Cyprien Chen, Ruiqi Ching, ShiNung |
| author_facet | Yu, Zongxi Tamekue, Cyprien Chen, Ruiqi Ching, ShiNung |
| contents | This article presents a scalable implementation of nonlinear Gramian-based control synthesis for control-affine systems, including a minimum energy control construction. These synthesis advances are achieved by addressing key computational bottlenecks inherent to iterative synthesis map formulations, yielding a computational scheme that exhibits rapid convergence and high-precision. The efficacy of this synthesis framework is demonstrated across five canonical nonlinear control systems and 100-dimensional recurrent neural network models, including underactuated systems. Empirical scaling results further indicate that convergence is primarily governed by intrinsic system properties, such as nonlinearity and controllability, rather than by state-space dimensionality. This work provides a practical, scalable computational pathway for translating rigorous nonlinear synthesis theory into high-dimensional control applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19003 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scalable iterative Gramian synthesis for control-affine systems Yu, Zongxi Tamekue, Cyprien Chen, Ruiqi Ching, ShiNung Optimization and Control Systems and Control This article presents a scalable implementation of nonlinear Gramian-based control synthesis for control-affine systems, including a minimum energy control construction. These synthesis advances are achieved by addressing key computational bottlenecks inherent to iterative synthesis map formulations, yielding a computational scheme that exhibits rapid convergence and high-precision. The efficacy of this synthesis framework is demonstrated across five canonical nonlinear control systems and 100-dimensional recurrent neural network models, including underactuated systems. Empirical scaling results further indicate that convergence is primarily governed by intrinsic system properties, such as nonlinearity and controllability, rather than by state-space dimensionality. This work provides a practical, scalable computational pathway for translating rigorous nonlinear synthesis theory into high-dimensional control applications. |
| title | Scalable iterative Gramian synthesis for control-affine systems |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2605.19003 |