Publication: Weighted Maximum Likelihood for Controller Tuning
Weighted Maximum Likelihood for Controller Tuning
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Romero, A., Govil, S., Yilmaz, G., Song, Y., & Scaramuzza, D. (2023). Weighted Maximum Likelihood for Controller Tuning. Proceedings of the IEEE International Conference on Robotics and Automation, 2023-May, 1334–1341. https://doi.org/10.1109/ICRA48891.2023.10161417
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Recently, Model Predictive Contouring Control (MPCC) has arisen as the state-of-the-art approach for model-based agile flight. MPCC benefits from great flexibility in trading-off between progress maximization and path following at runtime without relying on globally optimized trajectories. However, finding the optimal set of tuning parameters for MPCC is challenging because (i) the full quadrotor dynamics are non-linear, (ii) the cost function is highly non-convex, and (iii) of the high dimensionality of the hyperparameter space. This
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- Romero, Angel
- Govil, Shreedhar
- Yilmaz, Gonca
- Song, Yunlong
- Scaramuzza, Davide
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Romero, A., Govil, S., Yilmaz, G., Song, Y., & Scaramuzza, D. (2023). Weighted Maximum Likelihood for Controller Tuning. Proceedings of the IEEE International Conference on Robotics and Automation, 2023-May, 1334–1341. https://doi.org/10.1109/ICRA48891.2023.10161417
