Nonlinear MPC for Quadrotor Fault-Tolerant Control

Abstract

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 nonlinear model predictive control (NMPC) to stabilize and control a quadrotor subjected to the complete failure of a single rotor. Differently from existing works, which 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 could 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.

Uniform Fault-Tolerant Control of a Quadcopter With Rotor Failure

“…In [9], [22], [25], the dynamic control allocation is used to avoid the controller switching between the normal control and the rotor failure control. Some studies validate their FTC methods under laboratory conditions, such as [10], [12]- [14].…”

“…1 0.25 1 diag(0 0.7 0.6 0.8) 0.1718 0.644 0.7592 diag(0 0.1 0.1 0.1) 0.1 0.025 0.1 According to (10) and (11), the dynamics of un,1 is obtained as…”

“…i) In terms of FDD, it plays an essential role. However, it is imposed a severe burden on computation, and the FDD delay can adversely affect system safety [9], [10]. ii) In terms of reconfigurable control, the faulty system is often assumed states are controllable as the original system implicitly.…”

Learning Agile, Vision-Based Drone Flight: From Simulation to Reality

“…Furthermore, prior work on autonomous quadrotor navigation heavily relies on control techniques such as PID control [5][6][7] or model predictive control (MPC) [8,9]. While these methods have demonstrated impressive feats in controlled environments [5,7,9,10], they require substantial amount of tuning, and are difficult, if not impossible, to scale to complex dynamics models without large penalties in computation time.…”

“…While such modularization of the system is beneficial in terms of interpretability and allows to easily exchange modules, it results in a substantial increase in latency from perception to action and results in errors being propagated from one module to the next. Furthermore, prior work on autonomous quadrotor navigation heavily relies on control techniques such as PID control [5][6][7] or model predictive control (MPC) [8,9]. While these methods have demonstrated impressive feats in controlled environments [5,7,9,10], they require substantial amount of tuning, and are difficult, if not impossible, to scale to complex dynamics models without large penalties in computation time.…”