Computation of the pressure altitude using adaptive neuro fuzzy inference system for Air Data Computer in Aircrafts

For a safe flight and accurate Air navigation, the key Data are calculated using Air Data Computer (ADC). Altitude information is one of the important parameter computed by the ADC. According to Aircraft type, accuracy of the altitude corrected using tables or charts showing the actual corrections in altitude information special to the Aircraft. This correction is important in any high performance and jet Aircraft. In this paper, adaptive neuro fuzzy system (ANFIS) are used to calculate altitude information more easily and accurately using Data obtained from tables formed under standard atmosphere conditions. The pressure altitude values obtained using proposed ANFIS model don’t need to correction.

Uçaklardaki Hava Veri Bilgisayarinda Basinç İrtifasinin Bulanik Mantik Sistemine Dayali Uyarlanir Ağ İle Hesaplanmasi Computation of the Pressure Altitude Using Adaptive Neuro Fuzzy Inference System for Air Data Computer in Aircrafts

For a safe flight and accurate Air navigation, the key Data are calculated using Air Data Computer (ADC). Altitude information is one of the important parameter computed by the ADC. According to Aircraft type, accuracy of the altitude corrected using tables or charts showing the actual corrections in altitude information special to the Aircraft. This correction is important in any high performance and jet Aircraft. In this paper, adaptive neuro fuzzy system (ANFIS) are used to calculate altitude information more easily and accurately using Data obtained from tables formed under standard atmosphere conditions. The pressure altitude values obtained using proposed ANFIS model don’t need to correction. 1. GIRIŞ

ISSPIT – The pressure altitude computation using artificial neural network for Air Data Computer

Air Data Computer (ADC) calculates the key Data necessary for performing a safe flight and accurate Air navigation. Altitude information is one of the important parameter computed by the ADC. Accuracy of the altitude corrected using tables or charts showing the actual corrections in altitude information special to Aircraft type. This correction is important in any high performance and jet Aircraft. In order to correct these imperfections, certain calibrations need to be made within the ADC. In this paper, artificial neural networks (ANNs) are used to calculate altitude information more easily and accurately using Data obtained from tables formed under standard atmosphere conditions.

An alternative neural Airspeed computation method for Aircrafts

Purpose

This paper aims to present an alternative Airspeed computation method based on artificial neural networks (ANN) without requiring pitot-static system measurements.

Design/methodology/approach

The Data set used to train proposed neural model is obtained from the Digital Flight Data Acquisition Unit records of a Boeing 737 type commercial Aircraft for real flight routes. The proposed method uses the flight parameters as inputs of the ANN. The Levenberg–Marquardt training algorithm was used to train the neural model.

Findings

The predicted Airspeed values obtained with ANN are in good agreement with the measured Airspeed values. The proposed neural model can be used as an alternative Airspeed computation method.

Practical implications

The proposed alternative Airspeed computation method can be used when the Air Data Computer or pitot-static system has failed.

Originality/value

The proposed method uses flight parameters as inputs for the ANN. As such, Airspeed is calculated using flight parameters instead of the pitot-static system measurements.

SIU – Airspeed computation with ANN using FDR records

Airspeed is one of the most important parameters of the Aircrafts. It is measured by the “pitot static system”. It is very difficult to measure true Airspeed because of the Air pressure varies with altitude and temperature. While TAS can be calculated using Air Data Computer in new generation Aircrafts, the computation of the TAS requires utilizing some tables and diagrams using outer Air temperature and pressure in other Aircrafts. This state causes additional task for pilots. In this study, in order to overcome this problem, an artificial neural network (ANN) model is proposed to calculate TAS using F800 type FDR records of CN-235 CASA Aircrafts. The proposed model is also an alternative method that could be used to calculate TAS for new generation Aircrafts.

Computation of the pressure altitude using adaptive neuro fuzzy inference system for Air Data Computer in Aircrafts

For a safe flight and accurate Air navigation, the key Data are calculated using Air Data Computer (ADC). Altitude information is one of the important parameter computed by the ADC. According to Aircraft type, accuracy of the altitude corrected using tables or charts showing the actual corrections in altitude information special to the Aircraft. This correction is important in any high performance and jet Aircraft. In this paper, adaptive neuro fuzzy system (ANFIS) are used to calculate altitude information more easily and accurately using Data obtained from tables formed under standard atmosphere conditions. The pressure altitude values obtained using proposed ANFIS model don’t need to correction.

Flight test results from fiber optic control system integration (FOCSI) fiber optic total pressure transducer

We have previously reported on the development of a temperature compensated, self- referenced, microbend fiber optic pressure transducer for total pressure measurement. This transducer outputs the sum of altitude-dependent background pressure and dynamic pressure proportional to Air speed. During flight tests at NASA Dryden, this fiber optic pressure transducer was installed in the same pitot pressure line as the existing electronic pressure transducer in the Air Data Computer. Pressure Data were simultaneously recorded from the transducers during flight tests. Presented in this paper is the comparison and analysis of the Data from both transducers and a summary of the results.