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This work brings into question some common modeling and control design choices that are typically adopted to guarantee robustness and reliability but which may severely limit the attainable 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 actuator dynamics and limitations. As a matter of fact, our solution does not resort to common simplifications such as: (1) linear model approximation, (2) cascaded control paradigm used to decouple the translational and the rotational dynamics 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 suitably identify the actuator limitations by leveraging experimental data. Differently from previous approaches, the constraints of the optimization problem are defined only by the real physics of the actuators, avoiding conservative \u2013 and often not physical \u2013 input\/state saturations which are present, e.g., in cascaded approaches. The control algorithm is implemented using a state-of-the-art Real Time Iteration (RTI) scheme with partial sensitivity update method. The performances 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<\/jats:italic> quadrotor, a fully-actuated<\/jats:italic> hexarotor, a multi-rotor with orientable<\/jats:italic> propellers, and a multi-rotor with an unexpected rotor failure<\/jats:italic>. 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 extensively validated in real-time experiments and simulations.<\/jats:p>","DOI":"10.1007\/s10846-020-01250-9","type":"journal-article","created":{"date-parts":[[2020,9,26]],"date-time":"2020-09-26T09:02:47Z","timestamp":1601110967000},"page":"1213-1247","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Nonlinear Model Predictive Control with Enhanced Actuator Model for Multi-Rotor Aerial Vehicles with Generic Designs"],"prefix":"10.1007","volume":"100","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-3423-0969","authenticated-orcid":false,"given":"Davide","family":"Bicego","sequence":"first","affiliation":[]},{"given":"Jacopo","family":"Mazzetto","sequence":"additional","affiliation":[]},{"given":"Ruggero","family":"Carli","sequence":"additional","affiliation":[]},{"given":"Marcello","family":"Farina","sequence":"additional","affiliation":[]},{"given":"Antonio","family":"Franchi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,26]]},"reference":[{"issue":"1-4","key":"1250_CR1","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/s10846-011-9560-x","volume":"65","author":"L Merino","year":"2012","unstructured":"Merino, L., Caballero, F., Mart\u00ednez-De-Dios, J.R., Maza, I., Ollero, A.: An unmanned aircraft system for automatic forest fire monitoring and measurement. 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