# A NEW ALTERNATIVE AIR DATA COMPUTATION METHOD BASED ON ARTIFICIAL NEURAL NETWORKS

### Abstract

*Air Data Computer (ADC) is an important equipment of the aircraft and the performance of the ADC directly affects the safety and efficiency of the flight. The ADC uses sensors to get small amounts of original messages, such as dynamic pressure, static pressure, and total temperature and computes the air data parameters such as airspeed, pressure altitude, Mach number, static air temperature etc. that have fundamental importance for flight control systems and navigation systems. When ADC failure occurs; there is no alternative way to compute air data parameters in the aircraft. In order to overcome this problem, in this paper, an alternative air data computation method based on artificial neural networks (ANN) is presented. The data set used to train proposed neural model is obtained from the Digital Flight Data Acquisition Unit (DFDAU) of a commercial Boeing 737-400 type aircraft. Simulation results clearly show that the proposed neural method can be used as an alternative air data computation method when ADC failure. The proposed method also provides simple and high accuracy method for computation of the air data parameters instead of traditional nonlinear ADC equations.*

### References

[2]. Scott, M.A., (2008) “Velocity estimate following air data system failure”, MSc., Department of the Air Force Air University, Air Force Institute of Technology, Ohio, USA.

[3]. Erb, R., (2005) “Pitot-statics and the Standard Atmosphere”, Edwards AFB, CA: USAF Test Pilot School Pitot-Statics Textbook.

[4]. Brown, F.S., (2012) “Subsonic Relationships Between Pressure Altitude, Calibrated Airspeed, and Mach Number”, Technical Information Handbook. Air Force Flight Test Center Edwards Air Force Base, California.

[5]. GOODRICH Air Data Handbook.

[6]. McCool, K.M., Haas, D.J., (1996) “A NN based approach to helicopter low airspeed and sideslip angle estimation”, in AIAA Flight Simulation Technologies Conference, San Diego, USA. pp. 91-101.

[7]. Goff, D,A,, Thomas, S.M., Jones ,R.P., Massey, C.P., (2000) “A neural network approach to predicting airspeed in helicopters”, Neural Computing & Applications, 9, pp. 73-82.

[8]. Samlioglu, O., (2002) “A Neural Network Approach for Helicopter Airspeed Prediction”, Storming Media.

[9]. McCool, K., Haas, D.J., (2002) “Neural network system for estimation of aircraft flight data”, US Patent 6466888 B1.

[10]. Elias, F.R., Nathan, V.T., Wesley, P., (2011) “Alternate airspeed computation method when ADC fails”, US Patent 0184592 A1.

[11]. Rajan, P., Kumar A., (2015) “The design and development of analog air data computer based on ARM”, International Journal of Scientific Engineering and Technology Research, 4(4), pp. 738-741.

[12]. Rhudy, M.B., Fravolini M. L., Gu, Y., Napolitano M. R., Gururajan S., Chao H., (2015), “Aircraft model-independent airspeed estimation without pitot tube measurements”, IEEE Transactions on Aerospace And Electronic Systems, 51(3) , pp. 1980-1995.

[13]. Shaqura, M., Claudel, C., (2015), “A hybrid system approach to airspeed, angle of attack and sideslip estimation in unmanned aerial vehicles”, in International Conference on Unmanned Aircraft Systems (ICUAS), Colorado, USA, pp.723-732.

[14]. Haykin, S., (1994) “Neural networks: A Comprehensive Foundation”, New York: Macmillan College Publishing Company.

[15]. Anderson, J.D., (2005) “Introduction to Flight”, Fifth Edition. New York: McGraw-Hill, Incorporated

[16]. Kayton, M., Walter, R.F., (1997) “Avionics Navigation Systems”, 2nd ed. New York: John Wiley & Sons, Incorporated.

[17]. Hagan, M.T., Menjah, M., (1994) “Training feedforward networks with the Marquardt algorithm”, IEEE Transactions on Neural Networks, 5(6), pp. 989-993.

[18]. Levenberg, K., (1944) “A method for the solution of certain nonlinear problems in least squares”, Quarterly of Applied Mathematics; 2, pp. 164-168.

[19]. Marquardt, D.W., (1963) “An algorithm for least-squares estimation of nonlinear parameters”, Journal of Society for Industrial Applied Mathematics, 11, pp. 431-441.

*JAST*, vol. 10, no. 1, pp. 21-29, Sep. 2017.

The manuscript with title and authors is being submitted for publication in Journal of Aeronautics and Space Technologies. This article or a major portion of it was not published, not accepted and not submitted for publication elsewhere. If accepted for publication, I hereby grant the unlimited and all copyright privileges to Journal of Aeronautics and Space Technologies.

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