Download Extreme Landings Mod Apk

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The main contribution in this paper is the introduction of an intelligent control approach the uses multiple neural networks working in combination, and sharing the tasks of flying an airliner in simulation, which results in the ability to handle more extreme conditions than conventional PID controllers that are used in modern autopilots, while still being practical for the industry because each component is separable and verifiable - unlike Deep Learning models.

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During final approach, maintaining a desired glideslope ensures safe and soft landings. In [23], controllers that modify the reference model associated with aircraft pitch angle are proposed. The control of the pitch angle and longitudinal velocity is performed by a neural network adaptive control system, based on the dynamic inversion concept [23]. In [24], a network model optimization algorithm based on onboard flight recorder data is suggested.

As section II (Literature Review) suggests, relying on intelligent control approaches tackles the robustness issues of PID controllers which are used in modern autopilots. In addition, it introduces better adaptation capabilities compared to PID controllers which often require back and forth tuning to achieve better results. The proposed Intelligent Autopilot System (IAS) relies fully on an intelligent control approach which utilizes Artificial Neural Networks to provide the necessary set of control components that are required to pilot an aircraft, which as the next section (IV Experiments & Results) shows, provides high level of accuracy and adaptation capabilities compared to conventional control methods used in modern autopilots especially during extreme conditions represented by weather.

The data of interest that was collected and used to train the IAS are the inputs and outputs of the different ANNs discussed in this work and illustrated in Fig. 3. The experiments were conducted on the Elevators ANN to test the ability of maintaining the desired takeoff pitch angle, the new Elevators Trim ANNs to test the ability of maintaining different altitudes, climb rates, and the glideslope during approach and final approach, the new Throttle ANN to test the ability of maintaining different desired speeds, and the modified Flaps ANN to test the ability of extending the flaps correctly. The latter capabilities were not available in the previous versions of the IAS. Furthermore, additional experiments were conducted on the enhanced Ailerons, Rudder, and Roll ANN which replaced the Bearing Adjustment ANN from our previous work [4] to handle runway centreline maintenance during the final approach and landing flight phases in extreme weather conditions beyond the capability of the previous version of the IAS [4] and the capabilities of modern autopilots and even human pilots, as well as the Glideslope Elevators Trim ANN to test its ability to maintain the desired 3 degrees glideslope in the same extreme weather conditions. Our previous work [2,3,4,5] provide detailed explanations of the experiments of autonomous ground-run, navigation, landing procedures after touchdown, and handling emergency situations.

The purpose of this experiment is to assess the behaviour of the IAS compared to the standard autopilot of the model aircraft and the human pilot as well (during the last moments of final approach after disengaging the standard autopilot and taking full control) when maintaining the standard 3 degrees glideslope during the approach and the final approach flight phases in calm weather. In addition, this experiment assesses the behaviour of the IAS compared to the standard autopilot (Autoland) when maintaining the standard 3 degrees glideslope during the approach and the final approach flight phases in extreme weather conditions.

After training the ANNs, the aircraft was reset to the runway in the flight simulator, and the IAS was engaged to test the ability of maintaining the standard 3 degrees glideslope during approach and final approach in calm and extreme weather conditions. After the IAS took the aircraft airborne reached the approach flight phase, the output of the Glideslope Rate of Change ANN and the Glideslope Elevators Trim ANN were used to maintain the desired glideslope. The extreme weather conditions provided strong crosswind, gust, shear, and turbulence. The extreme weather attributes are mentioned in the next section.

The purpose of this experiment is to assess the behaviour of the IAS compared to the standard autopilot of the model aircraft and the human pilot as well (during the last moments of final approach after disengaging the standard autopilot and taking full control) when maintaining the centreline of the runway during the approach, final approach, and landing flight phases in calm weather. In addition, this experiment assesses the behaviour of the IAS compared to the standard autopilot (Autoland) when maintaining the centreline of the runway during the approach, final approach, and landing flight phases in extreme weather conditions. The extreme weather attributes are mentioned in the next section.

After training the ANNs, the aircraft was reset to the runway in the flight simulator, and the IAS was engaged to test the ability of maintaining the centreline of the landing runway in calm and extreme weather conditions. After the IAS took the aircraft airborne and reached the approach flight phase, the output of the Roll ANN, the Ailerons ANN, and the Rudder ANNs were used to maintain the centreline of the landing runway. The extreme weather conditions provided strong wind including crosswind, gust, shear, and turbulence.

Two models were generated for the Glideslope Rate of Change ANN and the Glideslope Elevators Trim ANN with MSE values of 0.0006 and 0.0008 consecutively. Figure 24 illustrates a comparison between the IAS, the standard autopilot, and the human pilot (the final moments of final approach after the human pilot disengaged the autopilot and took full control of the aircraft) when attempting to maintain the standard 3 degrees glideslope during final approach in calm weather. Figures 25 and 26 illustrate a comparison between the IAS and the standard autopilot (Autoland) when attempting to maintain the standard 3 degrees glideslope during final approach in extreme weather conditions with the presence of strong wind at a speed of 50 knots with gust up to 70 knots, wind shear direction of 70 degrees (around 360 degrees), and turbulence. Table 17 shows the result of applying the Two One-Sided Test (TOST) to examine the equivalence of the glideslope degrees held by the IAS, the standard autopilot, and the human pilot in calm weather. Table 18 shows the result of applying the Two One-Sided Test (TOST) to examine the equivalence of the glideslope degrees held by the IAS and the standard autopilot (Autoland) in extreme weather.

Figure 24 (F. Final Approach Glideslope Maintenance) shows the identical performance of the IAS, the standard autopilot, and the human pilot when maintaining the standard 3 degrees glideslope angle during final approach and landing in calm weather. Table 17 confirms the equivalence between the performance of the IAS, the standard autopilot, and the human pilot when handling this task. Figures 25 and 26 show the similar performance of the IAS and the standard autopilot (Autoland) while maintaining the standard 3 degrees glideslope angle in extreme weather conditions including 360 degrees wind at a speed of 50 knots with gust up to 70 knots, wind shear direction of 70 degrees, and minor turbulence. Table 18 shows that the means of the glideslope angle maintained by the IAS and the standard autopilot are equivalent, however, the IAS performed better since the glideslope mean is 3.01 which is significantly closer to the desired 3 degrees glideslope compared to the 2.93 mean achieved by the standard autopilot

Overall, the distinct performance of the IAS, which shows a natural and dynamic behaviour when handling the different tasks by manipulating the different control surfaces especially in extreme weather conditions proved its superiority compared to the mechanical-precision-like performance of the standard autopilot, which according to the literature, suffers from robustness issues when facing uncertainty, which hinders the reaction time, and the ability to cope with such extreme and sometimes sudden conditions. Figure 33 shows the moment of touchdown on the landing runway in the presence of extreme crosswind conditions where the wind speed is 64 knots, and the wind direction is around 270 degrees as the weather values show on the IAS interface (near bottom left corner).Footnote 1

To involve the aviation industry in evaluating the performance of the IAS, and in addition to providing training data for the IAS, the experienced pilot provided his feedback after being presented with complete (airport to airport) flight demonstrations of the IAS, and landings in calm and extreme weather conditions as the experiments above show. We asked him the following questions, and he answered as follows:

Proving the ability of Artificial Neural Networks and the learning from demonstration concept to not only learn how to fly an aircraft, but to learn how to fly and execute the necessary piloting tasks like experienced human pilots of airliners, and to handle landings in extreme weather conditions that are beyond the current limits and abilities of modern autopilots and experienced human teachers as well.

NASA is exploring a research activity to identify the technologies that will enable an Extreme Short Take-Off and Landing (ESTOL) aircraft. ESTOL aircraft have the potential to offer a viable solution to airport congestion, delay, capacity, and community noise concerns. This can be achieved by efficiently operating in the underutilized or unused airport ground and airspace infrastructure, while operating simultaneously, but not interfering with, conventional air traffic takeoffs and landings. Concurrently, the Air Force is exploring ESTOL vehicle solutions in the same general performance class as the NASA ESTOL vehicle to meet a number of Advanced Air Mobility missions. The capability goals of both the military and civil vehicles suggests synergistic technology development benefits. This paper presents a summary of the activities being supported by the NASA ESTOL Vehicle Sector. 2351a5e196