Intelligent Air Traffic Control Using Neural Networks

IJSTE - International Journal of Science Technology & Engineering | Volume 2 | Issue 08 | February 2016 ISSN (online): 2349-784X

Intelligent Air Traffic Control using Neural Networks Nikhil raj PG Student Department of Electronics and Telecommunication Toc H Institute of Science and Technology Kerala, India

Gnana Sheela K Associate Professor Department of Electronics and Telecommunication Toc H Institute of Science and Technology Kerala, India

Abstract The air traffic control systems used by airports, worldwide, are still depending on systems and algorithms which were developed almost forty years ago. Now the complexity of air traffic control has increased in complexity due to increase in aircrafts and airports. The complex decisions such as take-off, landing etc. are carried out by human traffic controllers. The controllers depending on various parameters such as availability of runway, climate conditions etc. make the decision and such decisions are highly prone to errors. Safety is a major factor limiting the automation of existing air traffic control systems. This project deals with automation of existing air traffic control system using neural networks as a basis. Neural networks come’s under artificial intelligence and has been found to be effective in many fields for making decisions such as in robotics, Medical field etc. Neural networks learn by exposure. The algorithm used is back propagation network for decision making. The network is trained using some predetermined inputs and later the network will be capable of making decisions of its own with minimal or zero error.

MATLAB can be used for training the neural network.

Keywords: Air Traffic Control (ATC), Air traffic parameters, Artificial Neural Networks, Back Propagation Network, Instrument Flight Rules, Visual Flight Rules ________________________________________________________________________________________________________

I. INTRODUCTION Earlier the number of airports and aircrafts were less in number, so it was needed only a little effort by the air traffic controller to control the air traffic, but now it is not the case. The work load on air traffic controller is increasing day by day. Several attempts were made to automate the existing air traffic control system, however not any system offers complete reliability. The proposed work is based on neural networks for the automation of existing air traffic control system. Neural networks can be used for the task of complex decision making based on input parameters available to them.

This project aims at simulation of an international airport Air Traffic Control system for both landing and taking-off aircrafts based on availability of runways, safety criterions, efficient operations management and environmental parameters like wind velocity, weather parameters (rain, fog etc.).

The aircraft parameters selected are that of standard commercial aircrafts. The landing and taking of aircrafts are simulated with pre-defined paths, heights and speeds. Environmental parameters can be initiated through I/O ports. Normal landing and takeoff procedures are incorporated at pre-set aircraft positions. The appropriate traffic control actions will be generated from the pre-taught ANN. The decision optimization is done by using back propagation network. The neural network structure will simplify and streamline the functions of air traffic controller.

The main ATC functions are to provide aerodrome control services, update important information’s such as weather, runway availability etc., deals with airport operations staff (who will be working in autonomous navigation systems, communication and server systems etc.) and dealing with emergency situations such as hurricane, terrorist attacks etc. In many countries, ATC provides services to all private, military, and commercial aircraft operating within its airspace. Depending on the type of flight and the class of airspace, ATC may issue instructions that pilots are required to obey, or advisories (known as flight information in some countries) that pilots may, at their discretion, disregard. The pilot in command is the final authority for the safe operation of the aircraft and may, in an emergency, deviate from ATC instructions to the extent required to maintain safe operation of their aircraft.

II. PROBLEM IDENTIFICATION AND PROJECT OBJECTIVE Problem Identification: Some of the equipments and algorithms used by the air traffic control systems in airports of various nations today are at least 40 years old. Various problems are associated with automation of existing air traffic system. Air traffic systems require highly efficient, reliable equipments as safety of various concerned citizens are at risk. Air traffic control can be dated back to 1930’s by first one being a human being with a colored flag, and then light guns were used then later radio communication equipments

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were started to use there were separate instrument flying machines later. Earlier there were only small number of airports and aircrafts, but as the number of aircrafts and airports increased control of air traffic became a problem as the work load on the controller get increased. So in this project it is proposed highly efficient, reliable neural networks as a basis for the automation of existing human centered air traffic system. Due to heavy congestion problem on ground based transportation that predicted to be worse in future, an alternative transportation solution is a necessity. In the future it will be required to eliminate the location specific (airport based) takeoff and landing. One of the main goals of an air traffic controller is to keep the separation among flights and resolve conflict among them. With increased number of aerial vehicles in a sector, it may become very difficult, if not impossible, for human controllers to perform these operations efficiently. At present, each flight flying under IFR, VFR or SVFR requires to obtain prior clearance for every action they perform.

Project Objective: The solution for all these problems can be developing an artificial intelligence system using artificial neural networks. Several modules can be developed and later integrated together to make complex decisions required in controlling air traffic in a required sector. Since the involvement of human is less this system will be less prone to errors, Fast and reliable when multiple aircrafts are there. Following is a table showing system level metrics and human performance metrics.

ANN is nonlinear model that is easy to use and understand compared to statistical methods. ANN is non-parametric model while most of statistical methods are parametric model that need higher background of statistic. ANN with Back propagation (BP) learning algorithm is widely used in solving various classifications and forecasting problems. Even though BP convergence is slow but it is guaranteed. However, ANN is black box learning approach, cannot interpret relationship between input and output and cannot deal with uncertainties. To overcome this several approaches have been combined with ANN such as feature selection and etc.

III. METHODOLOGY Air traffic control requires several parameters these parameters can be classified as aircraft parameters, aerodrome parameters, and weather parameters. Weather parameters are analyzed and monitored by meteorological section of the ATC. All these parameters are duly updated and stored in a central database. To make a final decision all these parameters has to be taken into consideration which is a complex task. If the number of aircrafts is increased the workload on the controller is further increased.

So the first step is to classify the parameters according to its nature. For this project I have classified the parameters as aircraft parameters, aerodrome parameters and weather parameters.

Weather Temperature Wind Rainfall Humidity Fog

Table - 1 Parameters considered Aerodrome Aircraft Runway Availability Fight Level Hangars Flight Heading Departures in Progress Destination Landing in progress Speed Flight plan approval Planes in the Vicinity

IV. NEURAL NETWORK LAYOUTS The System Consists of Mainly Four Modules: weather modules, landing clearance modules, Take off module and mid air traf-fic controller module. Weather module has six input nodes, two hidden layers and two output nodes. Midair traffic module will have five input nodes, two hidden layers and two output nodes. Landing clearance module will have three input nodes, two hidden layers and two output nodes. Similarly Take off module will have three input nodes, two hidden layers and two output nodes. The number of hidden layers can be increased, if needed. The steps followed are first initializing the input parameters of the simulator then de-veloping input and the desired output patterns, which will be fed to the BPN by considering all combinations. Later, Store the input, output patterns in the database. Train BPN by giving stored input output patterns one at a time, after acceptable error stop training the BPN. Store the weights for each pattern in the weight matrix. Develop a database to store random combinations of various pa-rameters of planes viz. Flight level (FL), Direction (ANGLE), Speed (MAC), Destinations. Finally, Design a function which will take random parameters of the plane. Now consider an arrival of any plane in the sector. Get the flight information of the plane.

Change the initialized inputs accordingly. Give input pattern to the BPN. Give the output of the BPN, which is the decision to the plane. Re-peat above procedure for all the incoming planes. One should be noted that random weather conditions can be given and random relations can be computed which will be user defined. Random runways and taxis can be entered which will also change with time. That is as the planes land and take off the flight data will be updated. Maximum traffic that can be controlled by the simulator can be user defined i.e. the maximum number of planes the BPN simulator can control can be varied.

The facility can be extended to mid air traffic control by designing a radar terminal which displays the location of planes entering All rights reserved by www.ijste.org

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the sector after an interval of time. The path of the incoming planes will be predicted by the BPN. The flight level, speed, direction and destination will be entered in the flight record list and using the current flight data suitable decision will be given to the plane about its flight level, speed, direc-tion in order to avoid midair collision.

Fig. 1: Neural Network Layouts

The environment used for neural network simulation is MATLAB. The MATLAB high performance language for technical computing integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. It allows solving many technical computing problems, especially those with matrix and vector formulations, in a fraction of the time it would take to write a program in a scalar non interactive language such as C or FORTRAN. MATLAB features a family of add-on application-specific solutions called toolboxes. Very important to most users of MATLAB, toolboxes allow learning and applying specialized technology. Toolboxes are comprehensive collections of MATLAB functions (M-files) that extend the MATLAB environment to solve particular classes of problems. We can add on toolboxes for signal processing, control systems, neural networks, fuzzy logic, wavelets, simulation, and many other areas. This part of MATLAB is the set of tools and facilities that help you use and become more productive with MATLAB functions and files. Neural Network Toolbox™ provides functions and apps for modeling complex nonlinear systems that are not easily modeled with a closed-form equation. Neural Network Toolbox supports supervised learning with feed forward, radial basis, and dynamic networks. It also supports unsupervised learning with self-organizing maps and competitive layers. With the toolbox you can design, train, visualize, and simulate neural networks.

You can use Neural Network Toolbox for applications such as data fitting, pattern recognition, clustering, time-series prediction, and dynamic system modeling and control. Neural Network Toolbox supports a variety of supervised and unsupervised network architectures. With the toolbox’s modular approach to building networks, you can develop custom network architectures for your specific problem. You can view the network architecture including all inputs, layers, outputs, and interconnections. Some of the features include to speed up training and handle large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. Supervised networks, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and layerrecurrent.Unsupervised networks, including self-organizing maps and competitive layers ,Apps for data-fitting, pattern recognition, and clustering,Parallel computing and GPU support for accelerating training (using Parallel Computing Toolbox),Preprocessing and post processing for improving the efficiency of network training and assessing network performance,Modular network representation for managing and visualizing networks of arbitrary size, Simulink blocks for building and evaluating neural networks.

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V. SIMULATION AND RESULTS Written a MATLAB code for simulating an airport with one runway Planes arrive ready to land or to take off at random times or to proceed through midair. Tool used is MATLAB GUI and MATLAB neural network toolbox. The number of aircrafts can be from one to four. One can check the present weather, aircraft, airport parameters condition and see whether the selected aircraft can land or take off.

Fig. 2: Simulation Results

VI. CONCLUSION The quest for automation of ATC goes back to very long period. Air traffic control has evolved from a single human being control to complex electronic systems control. Artificial intelligence and artificial neural networks has also improved a lot during the past few decades. This project aims at automation of existing ATC by using artificial neural networks. Once trained by using trial and error method artificial neural networks can be used to make complex decisions. The scope of this project not just an air traffic controller on the ground level instead can be extended up to mid air traffic control. The back propagation network is used for decision making. Neural networks are found to be highly efficient in various other fields. By incorporating Neural networks onto the air traffic workload on air traffic can be decreased highly. The proposed system is fast, reliable and highly efficient.

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