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: | , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2024 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositório: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/362169 |
| Acesso em linha: | http://hdl.handle.net/10261/362169 https://api.elsevier.com/content/abstract/scopus_id/85191375965 |
| Access Level: | Acceso aberto |
| Palavra-chave: | ANFIS controller Takagi–Sugeno Autonomous driving Linear matrix inequality |
| 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. |
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