Delay compensation of multi-input distinct delay nonlinear systems via neural operators
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909799382253568 |
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| author | Bajraktari, Filip Bhan, Luke Krstic, Miroslav Shi, Yuanyuan |
| author_facet | Bajraktari, Filip Bhan, Luke Krstic, Miroslav Shi, Yuanyuan |
| contents | In this work, we present the first stability results for approximate predictors in multi-input non-linear systems with distinct actuation delays. We show that if the predictor approximation satisfies a uniform (in time) error bound, semi-global practical stability is correspondingly achieved. For such approximators, the required uniform error bound depends on the desired region of attraction and the number of control inputs in the system. The result is achieved through transforming the delay into a transport PDE and conducting analysis on the coupled ODE-PDE cascade. To highlight the viability of such error bounds, we demonstrate our results on a class of approximators - neural operators - showcasing sufficiency for satisfying such a universal bound both theoretically and in simulation on a mobile robot experiment. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_17131 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Delay compensation of multi-input distinct delay nonlinear systems via neural operators Bajraktari, Filip Bhan, Luke Krstic, Miroslav Shi, Yuanyuan Systems and Control Machine Learning Robotics Dynamical Systems In this work, we present the first stability results for approximate predictors in multi-input non-linear systems with distinct actuation delays. We show that if the predictor approximation satisfies a uniform (in time) error bound, semi-global practical stability is correspondingly achieved. For such approximators, the required uniform error bound depends on the desired region of attraction and the number of control inputs in the system. The result is achieved through transforming the delay into a transport PDE and conducting analysis on the coupled ODE-PDE cascade. To highlight the viability of such error bounds, we demonstrate our results on a class of approximators - neural operators - showcasing sufficiency for satisfying such a universal bound both theoretically and in simulation on a mobile robot experiment. |
| title | Delay compensation of multi-input distinct delay nonlinear systems via neural operators |
| topic | Systems and Control Machine Learning Robotics Dynamical Systems |
| url | https://arxiv.org/abs/2509.17131 |