QP-Based Inner-Loop Control for Constraint-Safe and Robust Trajectory Tracking for Aerial Robots

Accurate trajectory tracking is crucial in aerial robotics. Optimal control methods such as Nonlinear Model Predictive Control (NMPC) are able to track trajectories exploiting the full nonlinear dynamics while respecting constraints. However, the NMPC model-based nature makes it sensitive to mismatches among nominal and real models. A common workaround to mitigate the effects of model uncertainties is to implement an inner-loop controller which robustifies the NMPC outer-loop. However, this inner-loop is usually based on purely feedback-based controllers such as PID or Incremental Nonlinear Dynamic Inversion (INDI), which do not allow to consider any constraint (such as limited actuation) or optimization criteria. In contrast, in this work we propose an optimization-based innerloop controller inspired by Time Delay Control (TDC), that, thanks to a Quadratic Program (QP) formulation, is able to respect constrains and can thus preserve stability in presence of input saturation and model mismatches. Furthermore, thanks to the use of acceleration feedback, the knowledge of inertial parameters is not required by the proposed inner-loop which therefore makes it even more robust against model uncertainties. The overall architecture is validated on a fully-actuated hexarotor under model mismatches and aggressive trajectories. The experiments clearly show that our QP-based inner-loop improves the NMPC tracking performance while preserving the stability in conditions where a non-optimal (and more classical) inner-loop controllers would fail.