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On multi-body dynamic modeling and control of large multirotor aerial vehicles with tilting rotors

Grant number: 20/12314-3
Support Opportunities:Scholarships in Brazil - Post-Doctorate
Effective date (Start): May 01, 2021
Effective date (End): September 11, 2022
Field of knowledge:Engineering - Mechanical Engineering - Mechanics of Solids
Principal Investigator:Davi Antônio dos Santos
Grantee:João Francisco Silva Trentin
Host Institution: Divisão de Engenharia Mecânica (IEM). Instituto Tecnológico de Aeronáutica (ITA). Ministério da Defesa (Brasil). São José dos Campos , SP, Brazil


New ways of urban air transportation have attracted attention in the past few years. These solutions aim to reduce the traffic congestion that many people around the world have to undergo every day to get to their workplace, as well as to use clean energy. Therefore, large multirotor aerial vehicles have been studied to undertake this task in such areas. This proposal seeks to investigate a high fidelity modeling of a large Multirotor Aerial Vehicle (MAV) for urban air mobility, more precisely, an electrical Vertical Take-off and Landing aircraft, since many modeling assumptions and simplifications are carried out when modeling small MAVs. Additionally, this type of aircraft uses the distributed electric propulsion concept where multiple propellers driven by electric motors are distributed along the airframe. The goal is to provide in a single framework the model of a large MAV where it is allowed to use fixed and tilting rotors. Furthermore, new flight control strategies will be investigated relying on the new and more complete model. These new controllers must be robust against unknown external disturbances and model uncertainty while taking into account the saturation limits of the actuators. In this sense, the development of new adaptive sliding mode control strategies will be carried out. Adaptive mechanisms for the switching gain, along with the well-known properties that sliding mode control has, can provide adequate flight control strategies, thus seeking for the reduction of the magnitude of the control actions to a minimum admissible while assuring a finite-time convergence. (AU)

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