Trajectory tracking control with data fusion and geometric algorithm

https://doi.org/10.58571/CNCA.AMCA.2025.074

Resumen: In this research, the problem of path tracking and data fusion is addressed using the Extended Kalman Filter to improve the accuracy in states estimating of the bicycle kinematic model. The pure pursuit algorithm based on vehicle geometry is employed to achieve path tracking. A novel nonlinear control strategy is implemented for controlling electric motors in a differential traction system. The Ackermann geometry model is used to establish the reference speeds of the motors and enhance maneuver stability in curved paths. Finally, experimental results are presented for tracking a Lemniscate path under different position update data times.

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Martínez-Ramírez, L., Guerrero-Castellanos, J., Ambrosio-Lázaro, R., Mino-Aguilar, G., Mino-Aguilar, J. & Munoz-Hernandez, G. (2025). Trajectory tracking control with data fusion and geometric algorithm. Memorias del Congreso Nacional de Control Automático 2025, pp. 433-438. https://doi.org/10.58571/CNCA.AMCA.2025.074

Palabras clave

Kinematic bicycle model, Model-free control, Kalman filter, Trajectory tracking, Pure pursuit algorithm, electronic differential.

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