Incremental Nonlinear Dynamic Inversion for Aerial Manipulation
D. Liu (TU Delft - Mechanical Engineering)
S. Sun – Mentor (TU Delft - Learning & Autonomous Control)
T. Keviczky – Mentor (TU Delft - Team Tamas Keviczky)
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Abstract
The field of aerial manipulation has many applications, such as autonomous maintenance in difficult-to-reach or hazardous locations. However, achieving precise control of a floating-base multi-body system presents considerable challenges, particularly due to the need for robust performance under strong external disturbances in uncontrolled environments. To address these challenges, this work investigates the Incremental Nonlinear Dynamic Inversion (INDI) method as a promising control framework.
While INDI has commonly been applied to multi-rotor platforms and other single-body aerial vehicles, its use in aerial manipulation remains largely unexplored. Moreover, existing applications have focused on reference tracking in free flight, leaving the application of INDI in Aerial Physical Interaction (APhI) tasks unaddressed. To fill this research gap, we propose a novel Nonlinear Model Predictive Control (NMPC) formulation, building upon state-of-the-art works in multi-rotor control and aerial manipulation, and combine it with INDI as an inner-loop controller. Two control schemes are developed and evaluated: one in which INDI is applied solely to attitude control, and one in which INDI is used in both position and attitude control.
The aerial manipulator platform studied in this work is a Differential Shoulder Aerial Manipulator (DSAM), consisting of an underactuated quadrotor base equipped with a two-degrees-of-freedom (DoF) robotic arm.
The proposed controllers are validated through both simulation and real-world experiments, including free-flight maneuvers and APhI sliding tasks. Real-world results demonstrate that the baseline controller without an INDI inner loop is unable to successfully perform the sliding tasks, whereas both INDI-augmented schemes are able to simultaneously track end-effector position, attitude, and desired reference contact force. This is demonstrated across several trajectories on both a whiteboard using a marker and a blackboard using a crayon, as well as for simultaneous end-effector pose and force tracking in different directions in the world frame.
Finally, it has been experimentally validated that extending the controller with an INDI position control layer reduces end-effector position tracking error compared to a controller without this layer. In a real-world experiment tracking a figure-eight trajectory while simultaneously maintaining a desired contact force under external wind disturbance, the INDI-augmented controller reduced the maximum end-effector position tracking error by 45%.