| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000607894 |
| ISSN: | 1405-5546 |
| Autores: | Rodríguez Arellano, Jesús A.1 Cruz, Víctor D.1 Aguilar, Luis T.1 Miranda Colorado, Roger2 |
| Instituciones: | 1Instituto Politécnico Nacional, Tijuana. México 2Instituto Politécnico Nacional, México 3CONAHCyT, México |
| Año: | 2024 |
| Periodo: | Abr-Jun |
| Volumen: | 28 |
| Número: | 2 |
| Paginación: | 821-836 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | This research presents a novel trajectory generation algorithm and the design of a prescribed time controller for trajectory tracking tasks for autonomous vehicles. The trajectory generation algorithm uses a hybrid combination of computer vision techniques and intelligent rail detection methods using an on-board camera. Based on the previous information, a possible trajectory is then generated that the vehicle should follow. A time-prescribed controller is then developed and implemented to track the trajectory generated by the proposed methodology. The controller uses a hybrid structure in which a time-varying feedback controller transitions into a fixed-time controller. This approach achieves stabilization in the prescribed time despite the initial conditions. To address the trajectory design, a scaled autonomous vehicle simulator was used to then evaluate the prescribed time controller compared to a finite time controller and a dynamic feedback controller. The simulation results demonstrate the effectiveness of trajectory generation and trajectory tracking control algorithms in addressing these challenges in real-world scenarios by examining two situations: unperturbed and perturbed cases. |
| Keyword: | Prescribed time stabilization, Trajectory generation, Neural networks |
| Texto completo: | Texto completo (Ver PDF) Texto completo (Ver HTML) |
