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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2508.15616 |
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| _version_ | 1866912547399008256 |
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| author | Detailleur, Alvaro Wahby, Dalim Ducard, Guillaume Onder, Christopher |
| author_facet | Detailleur, Alvaro Wahby, Dalim Ducard, Guillaume Onder, Christopher |
| contents | This work newly establishes the feasibility and practical value of a sum of squares (SOS)-based stability verification procedure for applied control problems utilizing neural-network-based controllers (NNCs). It successfully verifies closed-loop stability properties of a NNC synthesized using a generalizable procedure to imitate a robust, tube-based model predictive controller (MPC) for a two-wheeled inverted pendulum demonstrator system. This is achieved by first developing a state estimator and control-oriented model for the two-wheeled inverted pendulum. Next, this control-oriented model is used to synthesize a baseline linear-quadratic regulator (LQR) and a robust, tube-based MPC, which is computationally too demanding for real-time execution on the demonstrator system's embedded hardware. The generalizable synthesis procedure generates an NNC imitating the robust, tube-based MPC. Via an SOS-based stability verification procedure, a certificate of local asymptotic stability and a relevant inner estimate of the region of attraction (RoA) are obtained for the closed-loop system incorporating this NNC. Finally, experimental results on the physical two-wheeled inverted pendulum demonstrate that the NNC both stabilizes the system, and improves the control performance compared to the baseline LQR in both regulation and reference-tracking tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15616 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Synthesis and SOS-based Stability Verification of a Neural-Network-Based Controller for a Two-wheeled Inverted Pendulum Detailleur, Alvaro Wahby, Dalim Ducard, Guillaume Onder, Christopher Systems and Control This work newly establishes the feasibility and practical value of a sum of squares (SOS)-based stability verification procedure for applied control problems utilizing neural-network-based controllers (NNCs). It successfully verifies closed-loop stability properties of a NNC synthesized using a generalizable procedure to imitate a robust, tube-based model predictive controller (MPC) for a two-wheeled inverted pendulum demonstrator system. This is achieved by first developing a state estimator and control-oriented model for the two-wheeled inverted pendulum. Next, this control-oriented model is used to synthesize a baseline linear-quadratic regulator (LQR) and a robust, tube-based MPC, which is computationally too demanding for real-time execution on the demonstrator system's embedded hardware. The generalizable synthesis procedure generates an NNC imitating the robust, tube-based MPC. Via an SOS-based stability verification procedure, a certificate of local asymptotic stability and a relevant inner estimate of the region of attraction (RoA) are obtained for the closed-loop system incorporating this NNC. Finally, experimental results on the physical two-wheeled inverted pendulum demonstrate that the NNC both stabilizes the system, and improves the control performance compared to the baseline LQR in both regulation and reference-tracking tasks. |
| title | Synthesis and SOS-based Stability Verification of a Neural-Network-Based Controller for a Two-wheeled Inverted Pendulum |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2508.15616 |