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Non-linear model predictive control (nMPC) is a powerful approach to controlcomplex robots (such as humanoids, quadrupeds, or unmanned aerial manipulators(UAMs)) as it brings important advantages over other existing techniques. Thefull-body dynamics, along with the prediction capability of the optimal controlproblem (OCP) solved at the core of the controller, allows to actuate the robotin line with its dynamics. This fact enhances the robot capabilities andallows, e.g., to perform intricate maneuvers at high dynamics while optimizingthe amount of energy used. Despite the many similarities between humanoids orquadrupeds and UAMs, full-body torque-level nMPC has rarely been applied toUAMs.  This paper provides a thorough description of how to use such techniques inthe field of aerial manipulation. We give a detailed explanation of thedifferent parts involved in the OCP, from the UAM dynamical model to theresiduals in the cost function. We develop and compare three different nMPCcontrollers: Weighted MPC, Rail MPC, and Carrot MPC, which differ on thestructure of their OCPs and on how these are updated at every time step. Tovalidate the proposed framework, we present a wide variety of simulated casestudies. First, we evaluate the trajectory generation problem, i.e., optimalcontrol problems solved offline, involving different kinds of motions (e.g.,aggressive maneuvers or contact locomotion) for different types of UAMs. Then,we assess the performance of the three nMPC controllers, i.e., closed-loopcontrollers solved online, through a variety of realistic simulations. For thebenefit of the community, we have made available the source code related tothis work.

Full-Body Torque-Level Non-linear Model Predictive Control for Aerial Manipulation