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In this study, angle of attack estimation method is studied by using the measurable parameters of a projectile into an artificial neural network. For this study, a projectile with a set of specific aerodynamic characteristics is considered. The acceleration values of the projectile, whose aerodynamic properties are known, can be calculated depending on the angle of attack and other inputs. In this way, data sets, which are used in artificial neural network, are created. The time cost and performance that occurs with the expansion or reduction of data sets are the subject of the study. The search for the ideal artificial neural network structure for a successful prediction performance is another subject of the study. In this study, online or offline estimation of angle of attack with artificial neural network is explained. Equations \\eqref{fx1}-\\eqref{fz2} show that the accelerations are a function of $M$, $\\alpha$, $\\beta$, $\\omega$, $\\delta_a$, $\\delta_e$ and $\\delta_r$. Also aerodynamic coefficients depend on $M$, $\\alpha$, $\\beta$, $\\omega$, $\\delta_a$, $\\delta_e$ and $\\delta_r$. In order to estimate angle of attack, the relevant measurable parameters are taken as input. An artificial neural network structure is planned with accelerometer, gyro and aerodynamic surface deflection angles as inputs. There is no known instrument for measuring the angle of attack on projectiles. Known theoretical solutions make use of integrals of auxiliary instruments in practice, which accumulates errors and progressively degrades performance towards the end of the flight. Therefore, in this study, it is investigated whether it is possible to make a measurement using only the outputs of the instruments and the aerodynamic database. According to the study, the \"Equespaced Distribution (ED)\" performance is considered suitable for the estimation of the angle of attack with artificial neural networks. However, training ANN with ED is costly. In the face of this problem, training ANN with \"Normal Distribution (ND)\" is recommended as an alternative and a comparison is made in this regard. In the first study, an ANN with 1,000 data is trained with ND, but sufficient performance is not provided. Then, ANN with 10,000 data is trained with ND, the performance has increased, but it is not at the desired level. Finally, with 10,000 data sets ANN, the training is repeated for more layers (three layers with ten neurons) and the system performance reaches a sufficient level for prediction. It has been observed that the performance is improved as the data frequency increases. However, for the time cost incurred, sampled data with normal distribution are tried and the estimation performance is found at the desired level. In the full presentation of the study, how the test parameters are selected, why the input and output parameters of the artificial neural network are correlated, and the comparison of online performance in a simulation flight with conventional methods will also be presented.

This paper presents a short tutorial introduction to disturbance observer-based control approaches for the quadrotors. With this tutorial, researchers, engineers and students would be able to implement disturbance observer-based model-in-loop simulations and experiments more easily to design robust autopilot system for the quadrotors. To achieve this, first of all, the modeling and controlling of a quadrotor are explained and all linear disturbance observer-based control approaches in the literature are adapted in its overall nonlinear architecture. Disturbance observer-based control design steps are given in detail by design challenges. To show their disturbance rejection capabilities and practical applicability, two flight simulation scenarios are carried out. For all simulation cases, we only take into account the external disturbances in rotational motions. While we give the attitude trajectory commands to quadrotor attitude control architecture in the first scenario, we issue both way-point and trajectory commands to an outer loop controlling the translational motions in the second one. Presented disturbance observer-based control approaches have successfully completed the given reference commands in the presence of the external disturbances even under the measurement noise. Moreover, simulation experiments have shown that UDEBC approach transmits the external disturbance and measurement noise effects to the actuators directly. As a result, for UDEBC approach, it should be kept in mind that flight accidents may occur due to excessive ESC heating. Baseline attitude controller without disturbance observer-based control approach have failed to follow the given reference commands. The simulation studies have also proved the practical applicability of these methods, which are successful even under measurement noise.

Control of aerial robots is a popular research field as applications with different payloads lead to a variety of flight missions. Quadrotor-type unmanned systems are one such example considered in this paper. The performance in any flight experiment depends strictly on the chosen feedback control scheme, which is the core issue addressed in the paper. A number of approaches have been reported in the literature and this paper presents a survey of these schemes with an in-depth discussion of recent research outcomes. A detailed performance evaluation of the controllers, namely proportional-integral-derivative control, sliding mode control, backstepping control, feedback linearization-based control and fuzzy control schemes, are presented. Due to the popularity of the quadrotor-type aerial vehicles, the contribution of the current work is to provide an in-depth guide to the autopilot designers of quadrotor-type unmanned aerial vehicles.

In a typical control systems course, students are expected to develop the notion of open loop and closed loop first. Under the feedback, traditional control theory offers many elegant solutions to control problems yet teaching those concepts experimentally is an art. This article describes how the notion of feedback control could be developed by utilizing a computer aided experimental setup available at the laboratories of TOBB University of Economics and Technology. A DC motor control system is considered and step by step development of the understanding of feedback control is explained. Didactic aspect is taken care of throughout the manuscript and extensions to research are emphasized. (c) 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 21: 300312, 2013; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.20472

This work considers the fractional order control of a lightweight quadrotor under loss in the battery voltage. Since the outdoor brushless motors are driven via a pulse width modulation (pwm) scheme, handshaking between the dynamic model and controller is established at the pwm level and this constitutes a major contribution of the paper. The attitude control is achieved via fractional order sliding mode control (FSMC) scheme. Necessary stability considerations are presented and it is seen that FSMC is a good alternative for the control of unmanned aerial vehicles.

The applications of Unmanned Aerial Vehicles (UAVs) require robust control schemes that can alleviate disturbances such as model mismatch, wind disturbances, measurement noise, and the effects of changing electrical variables, e. g., the loss in the battery voltage. Proportional Integral and Derivative (PID) type controller with noninteger order derivative and integration is proposed as a remedy. This paper demonstrates that a neural network can be trained to provide the coefficients of a Finite Impulse Response (FIR) type approximator, that approximates to the response of a given analog (PID mu)-D-lambda controller having time varying action coefficients and differintegration orders. The results obtained show that the neural network aided FIR type controller is very successful in driving the vehicle to prescribed trajectories accurately. The response of the proposed scheme is highly similar to the response of the target (PID mu)-D-lambda controller and the computational burden of the proposed scheme is very low.