A major issue in active fault-tolerant control (FTC) is to rely on uncertain information provided by the fault detection and identification (FDI) algorithm. To achieve a reliable FTC, the controller needs to be robust against FDI uncertainties, namely missed "small" faults and fault detection delays. In this work, the FDI system only needs to indicate which actuator is faulty after a predefined amount of time, but does not need to estimate the actuator fault value or its faulty position. The goal is to design a FTC, which is robust against potentially undetected "small" actuator faults and which guarantees boundedness of the unstable poles for all possible actuator fault types. An adapted μ analysis is developed to analyse the unstable system poles, due to the presence of a fault. It is integrated within a DK-iteration approach to synthesize a controller in a robust control framework with H1-design objectives. The synthesis is shown on a fixedwing UAV, where fault-tolerance against single-actuator faults is achieved. Real flight experiments of a UAV with aileron and flap faults show the validity of the approach.

Keywords: Multi-rotor UAVs, Hexacopter, Control Allocation, Weighted Pseudo-inverse Matrix This paper presents practical methods to improve the flight performance of an unmanned multi-rotor helicopter by using an efficient control allocation strategy. The flying vehicle considered is an hexacopter. It is indeed particularly suited for long missions and for carrying a significant payload such as all the sensors needed in the context of cartography, photogrammetry, inspection, surveillance and transportation. Moreover, a stable flight is often required for precise data recording during the mission. Therefore, a high performance flight control system is required to operate the UAV. However, the flight performance of a multi-rotor vehicle is tightly dependent on the control allocation strategy that is used to map the virtual control vector v = [T, L, M, N ]^T composed of the thrust and the torques in roll, pitch and yaw, respectively, to the propellers' speed. This paper shows that a control allocation strategy based on the classical approach of pseudo-inverse matrix only exploits a limited range of the vehicle capabilities to generate thrust and moments. Thus, in this paper, a novel approach is presented, which is based on a weighted pseudo-inverse matrix method capable of exploiting a much larger domain in v. The proposed control allocation algorithm is designed with explicit laws for fast operation and low computational load, suitable for a small microcontroller with limited floating-point operation capability.

This paper considers a hybrid vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). By tilting its propellers, the aircraft can transition from rotary-wing (RW) multirotor mode to fixed-wing (FW) mode and vice versa. A novel architecture of a neural network-based controller (NNC) is presented. An “imitative learning” approach is employed to train the NNC to mimic the response of an expert but computationally expensive model predictive controller (MPC). The resulting NNC approximates the MPC’s solution while significantly decreasing the computational cost. The NNC is trained on the longitudinal axis. Successful simulations and real flight tests prove that the NNC is suitable for the longitudinal axis control of a complex nonlinear system such as the tilt-rotor VTOL UAV through a sequence of transitions between the RW mode to the FW mode, and vice versa, in a forward flight.