Modeling, Analysis and Validation of a Small Airplane Flight Dynamics

Kamal, Ashraf; Aly, Amgad M.; Elshabka, Ahmed

doi:10.2514/6.2015-1138

Cited by 14 publications

(6 citation statements)

References 2 publications

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“…A lot of work is conducted by the authors in the flight testing discipline to collect the required data for the purpose of validating our model. On the other hand, a 6-DoF nonlinear simulation model of the UAV that developed and validated using flight testing in [8] is used. The core of this simulation model is the dynamic model, which include the aircraft equations of motion, mass and inertia properties.…”

Section: Validation Of the Propulsion Model Using Flight Testingmentioning

confidence: 99%

Modeling and Simulation of Propeller Propulsion Model Using Wind Tunnel

Kamal

Aly

Elshabka

2015

AIAA Modeling and Simulation Technologies Conference

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Accurate investigation of UAV propulsion model plays a very important role in developing a high fidelity complete nonlinear simulation model for UAVs. The present paper focuses on the investigation of an accurate propulsion model used in small propeller-driven UAV. First, both propeller static and dynamic characteristics are measured. Then, the engine model is identified based on experimental measurements of the engine input and output signals. Finally, the complete propulsion model is developed. Using a low speed wind tunnel, a test rig is designed and built to measure static and dynamic performance characteristics of the propeller used in the UAV. Also, it is used to measure engine input and output signals. A sting balance measures the thrust and torque. A magnetic rpm sensor measures propeller rpm and the flight data recorder measures engine throttle position. The test results are then used to find thrust, torque and power coefficients in addition to propeller efficiency. During the tests, dynamic characteristics are obtained by changing either throttle position or wind tunnel airspeed while keeping the other one constant. Both ways are used to allow covering the whole range of advance ratio until reaching the braking and windmill modes. The static characteristics are obtained over a large range of propeller rotational speeds (rpm). The results show good agreement with published data. Engine and servomotor dynamic model is then identified and the complete propulsion model is developed and validated with a very good agreement between simulation and measured data. In order to test and validate the resultant propulsion model, it is integrated into a complete six-DoFUAV simulation model. The simulation results are compared with flight test results for the UAV. This check insists on the quality of the developed propulsion simulation model. Nomenclature C P = Propeller power coefficient. C q = Propeller torque coefficient. C T = Propeller thrust coefficient. D = Diameter of propeller in meter. J = Advance ratio. n = Propeller rotational speed measured in revolutions per second. P = Propeller power. s P = Static pressure. T P = Total pressure. Q = Propeller torque moment. R = Specific gas constant. T = Propeller thrust force.  = Density of air.  = Propeller efficiency.

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Section: Validation Of the Propulsion Model Using Flight Testingmentioning

confidence: 99%

Modeling and Simulation of Propeller Propulsion Model Using Wind Tunnel

Kamal

Aly

Elshabka

2015

AIAA Modeling and Simulation Technologies Conference

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“…The aerodynamic model introduces the total aerodynamic forces and moments, the forces and moments can be written as follows: ⃗ ⃗⃗⃗ (4.5) These aerodynamic forces and moments can be classified into longitudinal and lateral forces and moments [5]. The longitudinal forces and moments are affected by the angle of attack and its derivative ̇, pitch deflection angle and pitch rate .…”

Section: Aerodynamic Modelmentioning

confidence: 99%

Aerodynamic Design Optimization of Range Extension Kit of a Subsonic Flying Body

Elsherbiny¹,

Bayoumy

Elshabka³

et al. 2017

International Conference on Aerospace Sciences and Aviation Tec

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This paper aims to convert the aerodynamic shape of a conventional aerial subsonic flying body into a glide one by providing a range extension kit and fins. It describes the selection of configuration and airfoils depending on the tactical requirements and flight regimes. The wing and fins sizing is obtained using three different methods subjected to geometric constraints. The first method is an iterative optimization method using linear aerodynamic data. The second method is a genetic algorithm multi-objective function aims to maximize stability, controllability and lift-drag ratio within certain weights using linear aerodynamic data. The third method is a genetic algorithm optimization function integrated with MISSILE DATCOM aims to maximize lift-drag ratio. Then perform a direct uncontrolled six degree of freedom simulation for the three optimized designs and the conventional flying body. Comparing the results of ranges for these bodies reveals that the third method has the best range over the other designs including the conventional flying body.

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“…In 2015, Kamal [8] presented the process of compiling a detailed nonlinear 6-DOF model DOF for a small Tiger-Trainer RC aircraft. The propulsion model with propeller characteristics was investigated based on experimental results of wind tunnel tests, the dynamics of the engine and its servomotor were identified as a first-order model.…”

Section: Introductionmentioning

confidence: 99%

“…The system's response to the required change is shortened and unwanted oscillations are also eliminated. According to [8], in the process of estimating the optimal state of the system, it is also necessary to consider random vibrations caused either by the UAV engine or by external influences, among which the crosswind can be included.…”

Section: Introductionmentioning

confidence: 99%

“…The output variable will be the angular velocity of the longitudinal inclination of the aircraft. The creation of an aircraft simulation model is described in the literature [5,8,10]. During the flight, several different types of forces act on the aircraft, the interaction of which affects the movement and position of the aircraft in space: the lift, thrust, aerodynamic drag and weight.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Control and Estimation of the Condition of a Small Unmanned Aircraft Using a Kalman Filter

2021

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The article was motivated by the design of adaptive control algorithms for the control of a a fixed wing unmanned aerial vehicle (UAV). An adaptive system is a system that, with its structure or parameters, adapts to changes in the behavior of the object and based on the knowledge of variable properties, maintains the quality of its regulatory transition. The knowledge gained on this small UAV can be applied to larger aircraft. The creation of the proposed adaptive control into the UAV consisted of the creation of a simulation model of the aircraft based on known physical laws, the properties of the aircraft and a mathematical description. An adaptive PID controller for stabilization with changing coefficients based on the airspeed of the aircraft was designed and simulated. A validated control of the mathematical model of an unmanned aircraft was designed and simulated using the methods of estimation and identification of the UAV model parameter based on measured data from flight tests. Identifying dynamic parameters is a challenging task due to several factors, such as random vibration noise, interference, and sensor measurement uncertainty. The designed adaptive UAV control provides very promising results in improving the controllability of the aircraft while reducing the effect of speed changes on the stability and controllability of the system compared to the conventional PID controller. The comparison was performed on three selected types of PID controllers. The first type had fixed coefficients for the entire range of speeds calculated using the Control toolbox in MatLab. The second type also had constant coefficients over the entire range of speeds calculated using the Naslin method. The third adaptive type of PID controller had variable coefficients based on approximate polynomials dependent on the change in flight speed. The reason for the comparison was to show an increase in margin of stability using the method of variable coefficients of the PID controller based on the change of flight speeds. The obtained results show that the proposed adaptive control algorithm is robust enough to control the movement of the aircraft in the longitudinal plane and due to the introduced process and measurement errors, while the used Kalman filter effectively eliminates these errors.

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Modeling, Analysis and Validation of a Small Airplane Flight Dynamics

Cited by 14 publications

References 2 publications

Modeling and Simulation of Propeller Propulsion Model Using Wind Tunnel

Modeling and Simulation of Propeller Propulsion Model Using Wind Tunnel

Aerodynamic Design Optimization of Range Extension Kit of a Subsonic Flying Body

Adaptive Control and Estimation of the Condition of a Small Unmanned Aircraft Using a Kalman Filter

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