This chapter addresses the fault-tolerant control (FTC) of a quadrotor, framed as a benchmark problem. When one of the quadrotor’s four rotors fails, causing deviations in position and attitude, a fault-tolerant controller is activated to restore the quadrotor’s original position and maintain stable flight. FTC for quadrotors is an important practical issue for ensuring dependability as these platforms find broader societal applications and roles. This is particularly challenging because a linear model is no longer valid for control design owing to the large attitude motions and limitations on available thrusts. We first present a nonlinear model and control objectives of a quadrotor and then define the benchmark problem and performance metrics. For future comparative studies, we also provide software tools for implementing nonlinear model predictive control (NMPC) as a baseline solution for the benchmark problem. The users can easily modify the software tools to implement and evaluate other control strategies. Finally, we present a design example of the NMPC and assess its efficacy through numerical simulations covering failure scenarios.

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Fault-Tolerant Control of a Quadrotor Based on a Nonlinear Model

  • Kenta Hoshino,
  • Toshiyuki Ohtsuka

摘要

This chapter addresses the fault-tolerant control (FTC) of a quadrotor, framed as a benchmark problem. When one of the quadrotor’s four rotors fails, causing deviations in position and attitude, a fault-tolerant controller is activated to restore the quadrotor’s original position and maintain stable flight. FTC for quadrotors is an important practical issue for ensuring dependability as these platforms find broader societal applications and roles. This is particularly challenging because a linear model is no longer valid for control design owing to the large attitude motions and limitations on available thrusts. We first present a nonlinear model and control objectives of a quadrotor and then define the benchmark problem and performance metrics. For future comparative studies, we also provide software tools for implementing nonlinear model predictive control (NMPC) as a baseline solution for the benchmark problem. The users can easily modify the software tools to implement and evaluate other control strategies. Finally, we present a design example of the NMPC and assess its efficacy through numerical simulations covering failure scenarios.