Fuzzy Logic-Based Aerodynamic Modeling with Continuous Differentiability

Abstract

This paper presents a modeling method based on a fuzzy-logic algorithm to establish aerodynamic models by using the datasets from flight data recorder (FDR). The fuzzy-logic aerodynamic models are utilized to estimate more accurately the nonlinear unsteady aerodynamics for a transport aircraft, including the effects of atmospheric turbulence. The main objective in this paper is to present the model development and the resulting models with continuous differentiability. The uncertainty and correlation of the data points are estimated and improved by monitoring a multivariable correlation coefficient in the modeling process. The latter is increased by applying a least square method to a set of data points to train a set of modeling coefficients. A commercial transport aircraft encountered severe atmospheric turbulence twice at transonic flight in descending phase is the study case in the present paper. The robustness and nonlinear interpolation capability of the fuzzy-logic algorithm are demonstrated in predicting the degradation in performance and stability characteristics of this transport in severe atmospheric turbulence with sudden plunging motion.

Using Fuzzy Logic System to Optimize Measuring Porosity by Aerodynamics Sensor

“…A fuzzy logic system (FLS) can be defined as the nonlinear mapping of an input data set to a scalar output data (12). A FLS consists of four main parts: fuzzifier, rules, inference engine, and defuzzifier.…”

Aerodynamic Modeling using Neuro-Fuzzy for a scaled model of Cessna 182

“…The Fig. 11 shows the main aircraft stability derivatives for force Z and moments M, L and N. Chang (2013) concludes that the results could provide the understanding of the aerodynamic response of the analyzed aircraft in severe atmospheric turbulence. The author also states that the correlation between the input and output variables was improved by monitoring a multivariable correlation coefficient during the modeling process.…”

“…An interesting unsteady aerodynamic modeling using Neuro-Fuzzy were made by Chang (2013), which used a commercial aircraft equipped with a Flight Data Recorder (FDR) to record the main flight variables, like angle-of-attack, sideslip angle, the Euler angles and its derivatives, applying it to build a fuzzy logic-based aerodynamic model. Also, the methodology adopted by the author considered various triangular membership functions for the input variables.…”

“…The main work which motivates to choose the Neuro-Fuzzy as tool to make the aerodynamic modeling of a Cessna 182 air model were presented by Brandon and Morelli (2012), and similar with Chang (2013) During the flight acquisition data, the authors aimed to achieve the biggest range of flight possibilities with several maneuvers, to include in the training process. Brandon and Morelli (2012) carried out two interesting flight tests to understand better the aircraft dynamics and to acquire affinity with data acquisition system, they applied deflections of the three main command surfaces (δ a , δ e , δ r ) one by one, that means, deflected the elevator alone, then deflected the rudder and later the aileron without moving any other control surface, they called this process as "Doublets", then made all the surfaces deflecting simultaneously and randomly, denominating as "Fuzzy".…”

“…The input variables chosen to correlate with output variables were very similar with those selected by Chang (2013). The determining factor on the choice of the variables is called correlation factor (R 2 ), which express, through a number varying between 1 to 0, the correlation between the input and output variables, if the number is close to 1, means a high correlation, also if the number is close to 0, means a low correlation between the input and output.…”