In this paper, the Model-Free Control (MFC) is used and tested on a Multi-Input-Multi-Output (MIMO) nonlinear system, which is a quadrotor vehicle. The system is decomposed into different-dependent Single-Input-Single-Output (SISO) sub-systems, where the MFC algorithm is applied on each one of them. MFC aims at compensating the time-varying disturbances and un-modeled system dynamics that the optimal feedback controller fails to cope with. A comparison between LQR feedback controller with and without the MFC will be implemented and tested on a real quadrotor vehicle. Different flight test results validate the importance of using the MFC, and its capability to control the quadrotor system that is using a degraded feedback controller, as well.

A systematic fault tolerant control (FTC) scheme based on fault estimation for a quadrotor actuator, which integrates normal control, active and passive FTC and fault parking is proposed in this paper. Firstly, an adaptive Thau observer (ATO) is presented to estimate the quadrotor rotor fault magnitudes, and then faults with different magnitudes and time-varying natures are rated into corresponding fault severity levels based on the pre-defined fault-tolerant boundaries. Secondly, a systematic FTC strategy which can coordinate various FTC methods is designed to compensate for failures depending on the fault types and severity levels. Unlike former stand-alone passive FTC or active FTC, our proposed FTC scheme can compensate for faults in a way of condition-based maintenance (CBM), and especially consider the fatal failures that traditional FTC techniques cannot accommodate to avoid the crashing of UAVs. Finally, various simulations are carried out to show the performance and effectiveness of the proposed method

In this paper, Fault Diagnosis and Fault Tolerant Control algorithms are proposed to compensate for a bias type sensor fault applied on the quadrotor vehicle. A novel output estimator design called intelligent Output-Estimator (iOE) is introduced to detect and isolate the sensor fault. The estimation of the fault magnitude is done through a proposed structured algorithm that senses the maximum error deviation between the output estimation and the measured outputs. Then the fault compensation is achieved by regenerating the desired path. Real-flight results validated the proposed algorithms and showed the sensor fault accommodation process.

The Fault Detection and Isolation (FDI) of a quadrotor vehicle, which is described by a nonlinear model, are tackled in this paper. The nonlinear identity observer is utilized to detect the various types of sensors faults, and the Generalized Observer Scheme (GOS) is used to isolate the faulty sensor. The robustness of the observer and its scheme is investigated through several simulation results to build the Decision- Making Unit (DMU) for the residuals. Various simulation results have validated the FDI method and shown the system performance towards different fault scenarios.