@article {2020l-BicMazFarCarFra,
	title = {Nonlinear Model Predictive Control with Enhanced Actuator Model for Multi-Rotor Aerial Vehicles with Generic Designs},
	journal = {Journal of Intelligent and Robotic Systems},
	volume = {100},
	year = {2020},
	pages = {1213-1247},
	abstract = {In this paper, we propose, discuss, and validate an online Nonlinear Model Predictive Control (NMPC) method for multi-rotor aerial systems with arbitrarily positioned and oriented rotors which simultaneously addresses the local ref- erence trajectory planning and tracking problems. This work brings into question some common modeling and control design choices that are typically adopted to guarantee ro- bustness and reliability but which may severely limit the at- tainable performance. Unlike most of state of the art works, the proposed method takes advantages of a unified nonlinear model which aims to describe the whole robot dynamics by explicitly including a realistic physical description of the ac- tuator dynamics and limitations. As a matter of fact, our so- lution does not resort to common simplifications such as: 1) linear model approximation, 2) cascaded control paradigm used to decouple the translational and the rotational dynam- ics of the rigid body, 3) use of low-level reactive trackers for the stabilization of the internal loop, and 4) unconstrained optimization resolution or use of fictitious constraints. More in detail, we consider as control inputs the derivatives of the propeller forces and propose a novel method to suit- ably identify the actuator limitations by leveraging experi- mental data. Differently from previous approaches, the con- straints of the optimization problem are defined only by the real physics of the actuators, avoiding conservative {\textendash} and often not physical {\textendash} input/state saturations which are present, e.g., in cascaded approaches. The control algorithm is im- plemented using a state-of-the-art Real Time Iteration (RTI) scheme with partial sensitivity update method. The perfor- mances of the control system are finally validated by means of real-time simulations and in real experiments, with a large spectrum of heterogeneous multi-rotor systems: an under- actuated quadrotor, a fully-actuated hexarotor, a multi-rotor with orientable propellers, and a multi-rotor with an unex- pected rotor failure. To the best of our knowledge, this is the first time that a predictive controller framework with all the valuable aforementioned features is presented and exten- sively validated in real-time experiments and simulations},
	doi = {10.1007/s10846-020-01250-9},
	attachments = {https://homepages.laas.fr/afranchi/robotics/sites/default/files/2020l-BicMazFarCarFra.pdf},
	author = {Davide Bicego and Jacopo Mazzetto and Marcello Farina and Ruggero Carli and Antonio Franchi}
}
@article {2019i-RosTogCarSchCorFra,
	title = {Cooperative Aerial Load Transportation via Sampled Communication},
	journal = {IEEE Control Systems Letters},
	volume = {4},
	year = {2019},
	month = {06/2019},
	pages = {277-282},
	abstract = {In this work, we propose a feedback-based motion planner for a class of multi-agent manipulation systems with a sparse kinematics structure. In other words, the agents are coupled together only by the transported object. The goal is to steer the load into a desired configuration. We suppose that a global motion planner generates a sequence of desired configurations that satisfy constraints as obstacles and singularities avoidance. Then, a local planner receives these references and generates the desired agents velocities, which are converted into force inputs for the vehicles. We focus on the local planner design both in the case of continuously available measurements and when they are transmitted to the agents via sampled communication. For the latter problem, we propose two strategies. The first is the discretization of the continuous-time strategy that preserves  stability and guarantees exponential convergence regardless of the sampling period. In this case, the planner gain is static and computed off-line. The second strategy requires to collect the measurements from all sensors and to solve online a set of differential equations at each sampling period. However, it has the advantage to provide doubly exponential convergence. Numerical simulations of these strategies are provided for the cooperative aerial manipulation of a cable-suspended load.},
	doi = {10.1109/LCSYS.2019.2924413},
	attachments = {https://homepages.laas.fr/afranchi/robotics/sites/default/files/2019i-RosTogCarSchCorFra.pdf},
	author = {Enrica Rossi and Marco Tognon and Ruggero Carli and Luca Schenato and Juan Cort{\'e}s and Antonio Franchi}
}
@article {2018d-FraCarBicRyl,
	title = {Full-Pose Tracking Control for Aerial Robotic Systems with Laterally-Bounded Input Force},
	journal = {IEEE Trans. on Robotics},
	volume = {34},
	year = {2018},
	month = {04/2018},
	pages = {534-541},
	abstract = {A class of abstract aerial robotic systems is introduced, the Laterally Bounded Force (LBF) vehicles, in which most of the control authority is expressed along a principal thrust direction, while in the lateral directions a (smaller and possibly null) force may be exploited to achieve full-pose tracking. This class approximates platforms endowed with non-collinear rotors that can modify the orientation of the total thrust in body frame. The proposed SE(3)-based control strategy achieves, if made possible by the force constraints, the independent tracking of position-plus-orientation trajectories. The method, which is proven using a Lyapunov technique, deals seamlessly with both under- and fully-actuated platforms, and guarantees at least the position tracking in the case of an unfeasible full-pose reference trajectory. Several experimental tests are presented, that clearly shown the approach practicability and the sharp improvement with respect to state of-the-art.},
	doi = {10.1109/TRO.2017.2786734},
	attachments = {https://homepages.laas.fr/afranchi/robotics/sites/default/files/2018d-FraCarBicRyl.pdf , https://homepages.laas.fr/afranchi/robotics/sites/default/files/2018d-FraCarBicRyl-1.mp4 , https://homepages.laas.fr/afranchi/robotics/sites/default/files/2018d-FraCarBicRyl-2.mp4 , https://homepages.laas.fr/afranchi/robotics/sites/default/files/2018d-FraCarBicRyl-3.mp4},
	author = {Antonio Franchi and Ruggero Carli and Davide Bicego and Markus Ryll}
}
@conference {2016d-CarTodCarFraSch,
	title = {Multi-Robot Localization via GPS and Relative Measurements in the Presence of Asynchronous and Lossy Communication},
	booktitle = {15th European Control Conference},
	year = {2016},
	month = {07/2016},
	address = {Aalborg, Denmark},
	author = {Andrea Carron and Marco Todescato and Ruggero Carli and Antonio Franchi and Luca Schenato}
}
