Nonlinear MPC for Quadrotor Fault-Tolerant Control (RAL 2022) скачать в хорошем качестве

Nonlinear MPC for Quadrotor Fault-Tolerant Control (RAL 2022)

The mechanical simplicity, hover capabilities, and high agility of quadrotors lead to a fast adaption in the industry for inspection, exploration, and urban aerial mobility. On the other hand, the unstable and underactuated dynamics of quadrotors render them highly susceptible to system faults, especially rotor failures. In this work, we propose a fault-tolerant controller using the nonlinear model predictive control (NMPC) to stabilize and control a quadrotor subjected to the complete failure of a single rotor. Differently from existing works that either rely on linear assumptions or resort to cascaded structures neglecting input constraints in the outer-loop, our method leverages full nonlinear dynamics of the damaged quadrotor and considers the thrust constraint of each rotor. Hence, this method can seamlessly transition from nominal to rotor failure flights, and effectively perform upset recovery from extreme initial conditions. Extensive simulations and real-world experiments are conducted for validation, which demonstrates that the proposed NMPC method can effectively recover the damaged quadrotor even if the failure occurs during aggressive maneuvers, such as flipping and tracking agile trajectories. If you are interested to know how to restabilize a quadrotor subject to rotor failure with onboard cameras, check our previous work:    • Autonomous Quadrotor Flight despite Rotor ...   Reference F. Nan, S. Sun, P. Foehn, D. Scaramuzza Nonlinear MPC for Quadrotor Fault-Tolerant Control Robotics and Automation Letters (RAL), 2022 Paper: https://rpg.ifi.uzh.ch/docs/ICRA22_Na... More on our research in Agile Drone Flight: http://rpg.ifi.uzh.ch/aggressive_flig... More on our research in Autonomous Drone Racing: http://rpg.ifi.uzh.ch/research_drone_... Affiliations: F. Nan, S. Sun, and D. Scaramuzza are with the Robotics and Perception Group, Dep. of Informatics, University of Zurich, and Dep. of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland http://rpg.ifi.uzh.ch/