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Recent advances in Small Unmanned Aerial Systems (SUAS) or drone technologies has resulted in their widespread use in a number of civilian applications, such as aerial imaging, infrastructure inspection, precision agriculture, among others. While this technology is accessible for everyone, it still requires a highly skilled operator to be able to successfully operate these drones in a safe and efficient manner. At the same time, developments in Virtual/Augmented Reality (V/AR) technologies present opportunities for combining the two into novel applications and use cases by providing an intuitive interface for interacting with the drones - opening up possibilities for safe and effective use of drones by relatively untrained operators.

In recent years, there has been an increase in the use of Unmanned Aerial Systems (UAS) in the civilian sector for various purposes. As these platforms are constrained in terms of payload and capacity, they are typically equipped with a minimal sensor suite and the use of redundant sensors is uncommon. This research effort describes the design and simulation of a Neural Network (NN) based fault tolerant flight control approach for sensor and actuator failures, implemented on a parallel and distributed computational architecture. The inter process communication is implemented using BSD sockets and Message Passing Interface (MPI). For the purpose of the sensor failure detection, identification and accommodation (SFDIA) task, it is assumed that the pitch, roll and yaw rate gyros onboard the aircraft are without physical redundancy.

A measurement device that is extremely important for Unmanned Aerial Vehicle (UAV) guidance and control purposes is the airspeed sensor. As the parameters of feedback control laws are conventionally scheduled as a function of airspeed, an incorrect reading (e.g. due to a sensor fault) of the Pitot-static tube could induce an incorrect feedback control action, potentially leading to the loss of control of the UAV. The objective of this study is to establish the accuracy and reliability of the two airspeed estimation techniques for eventual use as the basis for real-time fault detection of anomalies occurring on the Pitot-static tube sensor. The first approach is based on an Extended Kalman Filter (EKF) and the second approach is based on Least Squares (LS) modeling. The EKF technique utilizes nonlinear kinematic relations between GPS, Inertial Measurement Unit and Air Data System signals and has the advantage of independence from knowledge of the aircraft model.

Recent catastrophic air crashes have shown that physical redundancy is not a foolproof option for failures on Air Data Systems (ADS) on an aircraft providing airspeed measurements. Since all the redundant sensors are subjected to the same environmental conditions in flight, a failure on one sensor could occur on the other sensors under certain conditions such as extreme weather; this class of failure is known in the literature as “common mode” failure. In this paper, different approaches to the problem of detection, identification and accommodation of failures on the Air Data System (ADS) of an aircraft are evaluated. This task can be divided into component tasks of equal criticality as Sensor Failure Detection and Identification (SFDI) and Sensor Failure Accommodation (SFA). Data from flight test experiments conducted using the WVU YF-22 unmanned research aircraft are used.