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Neural Network and ANFIS based auto-adaptive MPC for path tracking in autonomous vehicles

Abstract : Self-driving cars operate in constantly changing environments and are exposed to a variety of uncertainties and disturbances. These factors render classical controllers ineffective, especially for the lateral control in such vehicles. Therefore, an adaptive MPC controller is designed in this paper for the path tracking task, the developed controller is tuned by an improved particle swarm optimization algorithm. Furthermore, online parameter adaption is performed using Neural Networks and ANFIS. The designed controller showed promising results and adaptation capability against the standard MPC in a triple lane change scenario and a general trajectory test.
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https://hal.archives-ouvertes.fr/hal-03467943
Contributor : Frédéric Davesne Connect in order to contact the contributor
Submitted on : Monday, December 6, 2021 - 6:05:26 PM
Last modification on : Tuesday, December 21, 2021 - 9:49:13 AM

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  • HAL Id : hal-03467943, version 1

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Yassine Kebbati, Naima Ait Oufroukh, Vincent Vigneron, Dalil Ichalal. Neural Network and ANFIS based auto-adaptive MPC for path tracking in autonomous vehicles. 18ème IEEE International Conference on Networking, Sensing and Control (ICNSC 2021), Dec 2021, Xiamen, China. ⟨hal-03467943⟩

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