In this paper, we present the design and implementation of two nonlinear observers: nonlinear extended state observer and sliding mode observer for estimating the pitch, yaw and roll angles and angular rates of a fixed-wing unmanned aerial vehicles system under a decoupled-reduced model in real flight experiments. A backstepping control law is designed for control in a decentralized way for altitude, yaw and roll of the airplane. This scheme allows us to test experimentally the feasibility of using the online estimated data from the observers in flight control, which is useful for increasing the robustness of the control and the safety of flight. Furthermore, a comparative analysis of the performance of both nonlinear observers is conducted.

Unmanned Aerial vehicles (UAVs) have captivated the interest of many researchers in recent years and are currently used in a wide range of applications such as: geographic mapping, weather forecasting, aerial photography, law enforcement, and search and rescue. Many of these applications require a multirotor UAV in order to maintain a stationary position in mid-air, and thus UAVs must be designed to be as stable as possible to reduce the need for excessive flight controller corrections and positioning errors, particularly under windy conditions. This research focuses on performing an empirical study on the effects of adding an inward propeller tilt angle, a design feature commonly used to improve stability in fixed-wing aircraft, on the flight stability of a quadrotor UAV. A ready-to-fly quadrotor UAV kit was assembled, and its propellers were tilted inward at various fixed angles of 0 o , 5 o and 10 o , and the planar motion of the UAV was computed using object visual tracking software. The results demonstrate that adding a propeller tilt angle improves quadrotor UAV flight stability.

This paper addresses the problem of robust control for a class of nonlinear dynamical systems in the continuous time domain. We deal with nonlinear models described by differential-algebraic equations (DAEs) in the presence of bounded uncertainties. The full model of the control system under consideration is completed by linear sampling-type outputs. The linear feedback control design proposed in this manuscript is created by application of an extended version of the conventional invariant ellipsoid method. Moreover, we also apply some specific Lyapunov-based descriptor techniques from the stability theory of continuous systems. The above combination of the modified invariant ellipsoid approach and descriptor method makes it possible to obtain the robustness of the designed control and to establish some well-known stability properties of dynamical systems under consideration. Finally, the applicability of the proposed method is illustrated by a computational example. A brief discussion on the main implementation issue is also included.

This work focuses on enhancing existing technology to support unmanned aerial vehicle navigation for the inspection of power plants. These inspections are a vital component of a power plant's capability to function but can be costly and dangerous to do by humans. Therefore, it has been proposed to utilize a small, unmanned quadcopter integrated with autonomous navigation by using an alternate signal in place of GPS. The quadcopter can gain the ability to navigate to desired coordinates without the need for GPS, which can often be unavailable within these structures. The UAV uses an optical flow sensor to determine the ground velocity and x-y position, and sonar for measuring the altitude. Using ROS and MAVLink, the communication can be modified to use "fake GPS" data, instead of the actual GPS signal, to be published in the appropriate ROS topics to avoid GPS failures. The optical flow and fake GPS are filtered by an Extended Kalman Filter to get a better position estimation during navigation. Through this process, autonomous flight was successfully achieved without GPS.