Fault-Tolerant Model Predictive Control for Multirotor UAVs

This paper presents a method for advanced fault-tolerant control (FTC) of multirotor unmanned aerial vehicles (UAVs), which includes anomaly detection on sensor measurements, fault estimation on actuators, and a robust model predictive control (MPC). To detect anomalies on the sensor measurements, an Echo State Network is used. System states and faults are estimated using an adaptive extended Kalman filter. The system is further controlled using MPC. The method is tested in numerical simulations with a hexacopter dynamic model. Simulation results show the ability of the FTC to handle failure with different even and uneven actuator faults.