QP-Based Inner-Loop Control for Constraint-Safe and Robust Trajectory Tracking for Aerial Robots
Lorenzo Balandi, Paolo Robuffo Giordano, Marco Tognon
AI summary
Problem
Standard inner-loop controllers used to robustify NMPC for aerial robots lack constraint handling and optimization, causing instability and constraint violations under model mismatches or actuator saturation.
Approach
The authors embed Time Delay Control into a Quadratic Program to create a high-frequency inner-loop that uses acceleration feedback to compensate for model uncertainties while strictly enforcing actuator constraints.
Key results
- Derivation of a TDC-based inner-loop eliminating the need for precise inertial parameters
- QP formulation guaranteeing strict actuator constraint satisfaction
- Experimental validation on a hexarotor showing reduced tracking errors under aggressive maneuvers
- Maintained closed-loop stability under severe model mismatches and input saturation
Why it matters
Enables safer and more reliable deployment of aerial robots in real-world scenarios where model inaccuracies and actuator limits are unavoidable.
Abstract
Accurate trajectory tracking is crucial in aerial robotics. Optimal control methods such as Nonlinear Model Predictive Control (NMPC) are able to track trajectories ex- ploiting 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 inner- loop 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 exper- iments 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.