Real Time Morphing Wing Optimization Validation Using Wind-Tunnel Tests

Попов, А. В.; Grigorie, Teodor Lucian; Botez, Ruxandra Mihaela; Mamou, Mahmoud; Mébarki, Youssef

doi:10.2514/1.47431

Cited by 83 publications

(38 citation statements)

References 12 publications

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“…Nonlinear methods such as fuzzy logic and neural network methods have been applied for aircraft identification and control. 2,3 The nonlinear hybrid fuzzy logic control on a morphing wing was explored in Grigorie et al 4 and Popov et al 5 ). Due to its complexity in the aerospace industry, the determination of the robust FCS is usually carried out using linear methods applied on linear models, and it is further validated using nonlinear models.…”

Section: Introductionmentioning

confidence: 98%

Flight control clearance of the Cessna Citation X using evolutionary algorithms

Boughari¹,

Botez²,

Ghazi³

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this paper, an Aircraft Research Flight Simulator equipped with Flight Dynamics Level D (highest level) was used to collect flight test data and develop new controller methodologies. The changes in the aircraft's mass and center of gravity position are affected by the fuel burn, leading to uncertainties in the aircraft dynamics. A robust controller was designed and optimized using the H 1 method and two different metaheuristic algorithms; in order to ensure acceptable flying qualities within the specified flight envelope despite the presence of uncertainties. The H 1 weighting functions were optimized by using both the genetic algorithm, and the differential evolution algorithm. The differential evolution algorithm revealed high efficiency and gave excellent results in a short time with respect to the genetic algorithm. Good dynamic characteristics for the longitudinal and lateral stability control augmentation systems with a good level of flying qualities were achieved. The optimal controller was used on the Cessna Citation X aircraft linear model for several flight conditions that covered the whole aircraft's flight envelope. The novelty of the new objective function used in this research is that it combined both time-domain performance criteria and frequency-domain robustness criterion, which led to good level aircraft flying qualities specifications. The use of this new objective function helps to reduce considerably the calculation time of both algorithms, and avoided the use of other computationally more complicated methods. The same fitness function was used in both evolutionary algorithms (differential evolution and genetic algorithm), then their results for the validation of the linear model in the flight points were compared. Finally, robustness analysis was performed to the nonlinear model by varying mass and gravity center position. New tools were developed to validate the results obtained for both linear and nonlinear aircraft models. It was concluded that very good performance of the business Cessna Citation X aircraft was achieved in this research.

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Section: Introductionmentioning

confidence: 98%

Flight control clearance of the Cessna Citation X using evolutionary algorithms

Boughari¹,

Botez²,

Ghazi³

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…The horizontal displacement of each actuator is converted into a vertical displacement at a rate 3:1 (results a cam factor c f =1/3). From the optimized airfoils, an approximately 8 mm maximum vertical displacement was obtained for the rods, so, a 24 mm maximum horizontal displacement should be actuated [17][18][19][20][21][22][23][24][25][26][27][28][29][30] .…”

Section: Introductionmentioning

confidence: 99%

Control strategies for an experimental morphing wing model

Grigorie

Попов

Botez

2014

AIAA Atmospheric Flight Mechanics Conference

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The paper presents the control strategies used in an experimental morphing wing model starting from the open loop architecture until a real time optimized closed loop architecture. Three control methods are exposed here, methods designed to obtain and maintain some optimized airfoils during the wind tunnel tests. Also, for all designed architectures the experimental control results are shown. First method uses a database stored in the computer memory, database which contains some optimized airfoils correlated with the airflow cases as combinations of Mach numbers and angles of attack. The method is based on a controller that takes as reference value the necessary displacement of the actuators from the database in order to obtain the morphing wing optimized airfoil shape. The second method uses a similar controller as the first method but the control loop is built around the changes of the Cp values calculated by XFoil software in two fixed positions along the chord of the wing, positions associated to two Kulite sensors linked through aerodynamic interdependence with the actuators positions. The third control method is based on the pressure information received from the sensors and on the transition point position estimation. It includes, as inner loop, the first control method of the actuation lines. The method uses an optimizer code which finds the best actuators configuration in order to maximize the position of the transition, i.e. at the end of optimization sequence the transition should be found nearest possible to the trailing edge. Nomenclature c f = cam factor C D = drag coefficient C L = lift coefficient C p = pressure coefficient dY 1 = vertical deflection of the flexible skin in the first actuation line dY 2 = vertical deflection of the flexible skin in the second actuation line dY 1opt = optimal vertical displacement of actuator 1 dY 2opt = optimal vertical displacement of actuator 2 M = Mach number x tr = transition point position α = angle of attack

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“…A subsequent aeroelastic study proved that the morphing technique would not induce flutter phenomena during wind tunnel testing [26][27] . In addition, many breakthroughs were achieved in active open-loop and closed-loop control using Proportional -Integrate (PI) [28][29] and Fuzzy Logic based controllers in wind tunnel testing [30][31][32] under the auspices of this same project. The research presented in this paper was completed in the frame of the CRIAQ MDO 505 project realized as an international collaboration between Canadian and Italian industries, universities and research centers.…”

Section: Introductionmentioning

confidence: 99%