Extreme Landing Pro For Pc 18
Lorean Hoefert
I was playing extreme landings pro with failure enabled but i've encountered a bunch of alarm types that does not in the tutorials such as tires etc. What are all alarm types and workarunds in extreme landing. I couldn't find anywhere.
Yesterday around 30-40 knots of crosswind on landing in a TBM north of London. I had a group flight with @Franksaviation515 he has some pictures of it too :). It was a great flight saw all of London airports.
20190607_21323344872804 3.9 MB InfiniteFlight_2019-06-07-20-22-3369124320 2.23 MB
My issue is I'm not sure how to read the map regarding landing as I can never work out the direction of the runway. I can take off, use the auto pilot to set my course but the final approach is never in line with the runways so I usually overshoot turn and try to land then invariably crash.
Human pilots are trained to perform piloting tasks that are required during the different phases of the flight. Performing a complete flight cycle starts with a ground-run on the runway to gain speed, rotate after a certain airspeed is achieved, climb, cruise while navigating between waypoints, descend, prepare for final approach while intercepting the landing runway path line, touchdown, flare, and slowdown to taxi speed [1].
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.
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 Throttle ANN and the Speed Rate of Change ANN with MSE values of 0.0009 and 0.0006 consecutively. Figures 18, 19, and 20 illustrate a comparison between the IAS and the standard autopilot when maintaining three different speeds over time. Since the human pilot used the standard autopilot to maintain speed, the comparison is done between the IAS and the standard autopilot, however, Fig. 21 illustrates a comparison between the IAS and the human pilot when managing the different speeds throughout the complete flight from takeoff to landing. No comparison with the previous version of the IAS is presented since the previous version did not have the ability to maintain a given speed. Tables 12, 13, and 14 show the results of applying TOST to examine the equivalence between the speed hold performance of the IAS and the standard autopilot.
A comparison between the IAS (10 flights represented by the overlapping lines in different blue shades) and the human pilot (1 demonstration represented by the green line) when managing the different speeds over time throughout the complete flight from takeoff to landing (London Heathrow to Birmingham). As can be seen, both the IAS and the human pilot accelerated sharply until the cruise speed of 240 knots was achieved, then, decelerated gradually until the landing speed of 150 knots was achieved before coming to a full stop on the landing runway
One model was generated for the Flaps ANN with an MSE value of 0.006. Figures 22 and 23 show the flaps setting over altitude where Fig. 22 shows the flaps setting during the ground-run, takeoff, level-up, climb, and cruise flight phases, while Fig. 23 shows the flaps setting during the cruise, approach, final approach and landing flight phases. Since the standard autopilot is not capable of controlling the flaps autonomously, the provided comparison is between the IAS and the human pilot. Table 15 shows the corresponding flaps settings given the deflection value. Table 16 shows the mean, minimum, and maximum altitudes that correlate to each flaps setting in addition to the standard deviation.
A comparison between the IAS (10 flights represented by the overlapping lines in different blue shades) and the human pilot (1 demonstration represented by the green line) when managing the different flaps settings over altitude from cruise to landing
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.
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