MPC with Learned Residual Dynamics with Application on Omnidirectional MAVs

TL;DR

This paper introduces two model-based control methods for omnidirectional micro aerial vehicles, optimizing trajectory tracking and disturbance rejection by leveraging learned models and actuator constraints, validated through real-world experiments.

Contribution

It proposes novel model-based control strategies that incorporate learned residual dynamics and actuator constraints for improved OMAV performance.

Findings

Both control approaches are effective in real-world experiments.

The methods outperform traditional model-free controllers.

Real-time implementation is feasible with the proposed techniques.

Abstract

The growing field of aerial manipulation often relies on fully actuated or omnidirectional micro aerial vehicles (OMAVs) which can apply arbitrary forces and torques while in contact with the environment. Control methods are usually based on model-free approaches, separating a high-level wrench controller from an actuator allocation. If necessary, disturbances are rejected by online disturbance observers. However, while being general, this approach often produces sub-optimal control commands and cannot incorporate constraints given by the platform design. We present two model-based approaches to control OMAVs for the task of trajectory tracking while rejecting disturbances. The first one optimizes wrench commands and compensates model errors by a model learned from experimental data. The second one optimizes low-level actuator commands, allowing to exploit an allocation nullspace and to consider constraints given by the actuator hardware. The efficacy and real-time feasibility of both approaches is shown and evaluated in real-world experiments.