A differential flatness based model predictive control approach

Kändler, Christoph; Ding, Steven X.; Koenings, Tim; Weinhold, Nick; Schultalbers, Matthias

doi:10.1109/cca.2012.6402435

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…In [15], [5], the differential flatness of the standard quadrotor model is utilized to generate minimum snap and jerk trajectories. In [23], see Fig. 3, the authors use the inverse of the nonlinear model (from input u to output y), a combination of the feedforward term (10) and a term F y→z,v that maps the output y to the flat state and flat input.…”

Section: Related Workmentioning

confidence: 99%

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Model Predictive Path-Following for Constrained Differentially Flat Systems

Greeff,

Schoellig

2017

Preprint

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For many tasks, predictive path-following control can significantly improve the performance and robustness of autonomous robots over traditional trajectory tracking control. It does this by prioritizing closeness to the path over timed progress along the path and by looking ahead to account for changes in the path. We propose a novel predictive pathfollowing approach that couples feedforward linearization with path-based model predictive control. Our approach has a few key advantages. By utilizing the differential flatness property, we reduce the path-based model predictive control problem from a nonlinear to a convex optimization problem. Robustness to disturbances is achieved by a dynamic path reference, which adjusts its speed based on the robot's progress. We also account for key system constraints. We demonstrate these advantages in experiment on a quadrotor. We show improved performance over a baseline trajectory tracking controller by keeping the quadrotor closer to the desired path under nominal conditions, with an initial offset and under a wind disturbance.

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