Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2503.15362 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909543281197056 |
|---|---|
| author | Lu, Fangmin Chen, Zheng Wang, Kun |
| author_facet | Lu, Fangmin Chen, Zheng Wang, Kun |
| contents | An optimal guidance law for impact time control with field-of-view constraint is presented. The guidance law is derived by first converting the inequality-constrained nonlinear optimal control problem into an equality-constrained one through a saturation function. Based on Pontryagin's maximum principle, a parameterized system satisfying the necessary optimality conditions is established. By propagating this system, a large number of extremal trajectories can be efficiently generated. These trajectories are then used to train a neural network that maps the current state and time-to-go to the optimal guidance command. The trained neural network can generate optimal commands within 0.1 milliseconds while satisfying the field-of-view constraint. Numerical simulations demonstrate that the proposed guidance law outperforms existing methods and achieves nearly optimal performance in terms of control effort. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15362 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nonlinear Optimal Guidance for Impact Time Control with Field-of-View Constraint Lu, Fangmin Chen, Zheng Wang, Kun Optimization and Control An optimal guidance law for impact time control with field-of-view constraint is presented. The guidance law is derived by first converting the inequality-constrained nonlinear optimal control problem into an equality-constrained one through a saturation function. Based on Pontryagin's maximum principle, a parameterized system satisfying the necessary optimality conditions is established. By propagating this system, a large number of extremal trajectories can be efficiently generated. These trajectories are then used to train a neural network that maps the current state and time-to-go to the optimal guidance command. The trained neural network can generate optimal commands within 0.1 milliseconds while satisfying the field-of-view constraint. Numerical simulations demonstrate that the proposed guidance law outperforms existing methods and achieves nearly optimal performance in terms of control effort. |
| title | Nonlinear Optimal Guidance for Impact Time Control with Field-of-View Constraint |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2503.15362 |