Robust stabilization of hyperbolic PDE-ODE systems via Neural Operator-approximated gain kernels
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913975849975808 |
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| author | Lyu, Kaijing Biccari, Umberto Wang, Junmin |
| author_facet | Lyu, Kaijing Biccari, Umberto Wang, Junmin |
| contents | This paper investigates the mean square exponential stabilization problem for a class of coupled PDE-ODE systems with Markov jump parameters. The considered system consists of multiple coupled hyperbolic PDEs and a finite-dimensional ODE, where all system parameters evolve according to a homogeneous continuous-time Markov process. The control design is based on a backstepping approach. To address the computational complexity of solving kernel equations, a DeepONet framework is proposed to learn the mapping from system parameters to the backstepping kernels. By employing Lyapunov-based analysis, we further prove that the controller obtained from the neural operator ensures stability of the closed-loop stochastic system. Numerical simulations demonstrate that the proposed approach achieves more than two orders of magnitude speedup compared to traditional numerical solvers, while maintaining high accuracy and ensuring robust closed-loop stability under stochastic switching. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03242 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust stabilization of hyperbolic PDE-ODE systems via Neural Operator-approximated gain kernels Lyu, Kaijing Biccari, Umberto Wang, Junmin Optimization and Control This paper investigates the mean square exponential stabilization problem for a class of coupled PDE-ODE systems with Markov jump parameters. The considered system consists of multiple coupled hyperbolic PDEs and a finite-dimensional ODE, where all system parameters evolve according to a homogeneous continuous-time Markov process. The control design is based on a backstepping approach. To address the computational complexity of solving kernel equations, a DeepONet framework is proposed to learn the mapping from system parameters to the backstepping kernels. By employing Lyapunov-based analysis, we further prove that the controller obtained from the neural operator ensures stability of the closed-loop stochastic system. Numerical simulations demonstrate that the proposed approach achieves more than two orders of magnitude speedup compared to traditional numerical solvers, while maintaining high accuracy and ensuring robust closed-loop stability under stochastic switching. |
| title | Robust stabilization of hyperbolic PDE-ODE systems via Neural Operator-approximated gain kernels |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2508.03242 |