Learning-based control of autonomous vehicles using an adaptive neuro-fuzzy inference system and the linear matrix inequality approach
This paper proposes a learning-based control approach for autonomous vehicles. An explicit Takagi–Sugeno (TS) controller is learned using input and output data from a preexisting controller, employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. At the same time, the vehicle model is...
| Autores: | , |
|---|---|
| Formato: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/408850 |
| Acesso em linha: | https://hdl.handle.net/2117/408850 https://dx.doi.org/10.3390/s24082551 |
| Access Level: | acceso abierto |
| Palavra-chave: | Automobiles -- Automatic control ANFIS controller Linear matrix inequality Takagi–Sugeno Autonomous driving Automòbils -- Control automàtic Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Resumo: | This paper proposes a learning-based control approach for autonomous vehicles. An explicit Takagi–Sugeno (TS) controller is learned using input and output data from a preexisting controller, employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. At the same time, the vehicle model is identified in the TS model form for closed-loop stability assessment using Lyapunov theory and LMIs. The proposed approach is applied to learn the control law from an MPC controller, thus avoiding the use of online optimization. This reduces the computational burden of the control loop and facilitates real-time implementation. Finally, the proposed approach is assessed through simulation using a small-scale autonomous racing car. |
|---|