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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/2511.22123 |
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| _version_ | 1866917108351238144 |
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| author | Waterman, Adam Guay, Martin |
| author_facet | Waterman, Adam Guay, Martin |
| contents | We present a framework for optimal trajectory generation in flow-driven systems governed by the Navier-Stokes equations, combining a Proper Orthogonal Decomposition (POD) reduced0order model (ROM) with Model Predictive Control (MPC). The approach (i) approximates the velocity field from data via snapshot POD and orthogonal projection, (ii) derives a Galerkin-projected dynamical model in reduced coordinates, and (iii) employs MPC to plan control inputs that steer an agent through the predicted flow while satisfying state and actuation constraints. By leveraging reduced-order modeling, the method enables real-time control in high-dimensional flow environments. Simulations demonstrate accurate flow-field reconstruction and efficient trajectory generation within realistic wind environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22123 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model Predictive Path Planning in Navier-Stokes Flow with POD-Based Reduced-Order Models Waterman, Adam Guay, Martin Optimization and Control Systems and Control We present a framework for optimal trajectory generation in flow-driven systems governed by the Navier-Stokes equations, combining a Proper Orthogonal Decomposition (POD) reduced0order model (ROM) with Model Predictive Control (MPC). The approach (i) approximates the velocity field from data via snapshot POD and orthogonal projection, (ii) derives a Galerkin-projected dynamical model in reduced coordinates, and (iii) employs MPC to plan control inputs that steer an agent through the predicted flow while satisfying state and actuation constraints. By leveraging reduced-order modeling, the method enables real-time control in high-dimensional flow environments. Simulations demonstrate accurate flow-field reconstruction and efficient trajectory generation within realistic wind environments. |
| title | Model Predictive Path Planning in Navier-Stokes Flow with POD-Based Reduced-Order Models |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2511.22123 |