Autonomous flight cycles and extreme landings of airliners beyond the current limits and capabilities using artificial neural networks

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 51; no. 9; pp. 6349 - 6375
• Main Authors: Baomar, Haitham, Bentley, Peter J.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2021; Springer Nature B.V
• Subjects: 30th Anniversary Special Issue; Aerodynamics; Aircraft; Aircraft control; Aircraft landing; Aircraft pilots; Airlines; Airports; Artificial Intelligence; Artificial neural networks; Automatic pilots; Autonomy; Aviation; Computer Science; Crosswinds; Deep learning; Elevators (control surfaces); Flight data recorders; Human performance; Jet aircraft; Learning theory; Machines; Manufacturing; Mechanical Engineering; Neural networks; Pilots; Processes; Rudders; Skills; Training; Weather; Wind shear; Autonomous control; Intelligent autopilot system; Learning from demonstration; Neural networks
• DOI: 10.1007/s10489-021-02202-y
• ISSN: 0924-669X

We describe the Intelligent Autopilot System (IAS), a fully autonomous autopilot capable of piloting large jets such as airliners by learning from experienced human pilots using Artificial Neural Networks. The IAS is capable of autonomously executing the required piloting tasks and handling the different flight phases to fly an aircraft from one airport to another including takeoff, climb, cruise, navigate, descent, approach, and land in simulation. In addition, the IAS is capable of autonomously landing large jets in the presence of extreme weather conditions including severe crosswind, gust, wind shear, and turbulence. The IAS is a potential solution to the limitations and robustness problems of modern autopilots such as the inability to execute complete flights, the inability to handle extreme weather conditions especially during approach and landing where the aircraft’s speed is relatively low, and the uncertainty factor is high, and the pilots shortage problem compared to the increasing aircraft demand. In this paper, we present the work done by collaborating with the aviation industry to provide training data for the IAS to learn from. The training data is used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when executing all the piloting tasks required to pilot an aircraft between two airports. In addition, we introduce new ANNs trained to control the aircraft’s elevators, elevators’ trim, throttle, flaps, and new ailerons and rudder ANNs to counter the effects of extreme weather conditions and land safely. Experiments show that small datasets containing single demonstrations are sufficient to train the IAS and achieve excellent performance by using clearly separable and traceable neural network modules which eliminate the black-box problem of large Artificial Intelligence methods such as Deep Learning. In addition, experiments show that the IAS can handle landing in extreme weather conditions beyond the capabilities of modern autopilots and even experienced human pilots. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots and beyond.