Reachability Analysis of Landing Sites for Forced Landing of a UAS

This paper details a method to ascertain the reachability of known emergency landing sites for any fixed wing aircraft in a forced landing situation. With a knowledge of the aircraft’s state and parameters, as well as a known wind profile, the area of maximum glide range can be calculated using aircraft equations of motion for gliding flight. A landing descent circuit technique used by human pilots carrying out forced landings called high key low key is employed to account for the extra glide distance required for an approach and landing. By combining maximum glide range analysis with the descent circuit, all the reachable landing sites can be determined. X-Plane flight simulator is used to demonstrate and validate the techniques presented.     

Visual misperception in aviation: glide path performance in a black hole environment

OBJECTIVE: We sought to improve understanding of visual perception in aviation to mitigate mishaps in approaches to landing. BACKGROUND: Research has attempted to identify the most salient visual cues for glide path performance in impoverished visual conditions. Numerous aviation accidents caused by glide path overestimation (GPO) have occurred when a low glide path was induced by a black hole illusion (BHI) in featureless terrain during night approaches. METHOD: Twenty pilots flew simulated approaches under various visual cues of random terrain objects and approach lighting system (ALS) configurations. Performance was assessed relative to the desired 3 degrees glide path in terms of precision, bias, and stability. RESULTS: With the high-ratio (long, narrow) runway, the overall performance between 8.3 and 0.9 km from the runway depicted a concave approach shape found in BHI mishaps. The addition of random terrain objects failed to improve glide path performance, and an ALS commonly used at airports induced GPO and the resulting low glide path. The worst performance, however, resulted from a combination ALS consisting of both side and approach lights. Surprisingly, novice pilots flew more stable approaches than did experienced pilots. CONCLUSIONS: Low, unsafe approaches occur frequently in conditions with limited global and local visual cues. Approach lights lateral of the runway may counter the bias of the BHI. The variability suggested a proactive, cue-seeking behavior among experienced pilots as compared with novice pilots. APPLICATION: Visual spatial disorientation training in flight simulators should be used to demonstrate visual misperceptions in black hole environments and reduce pilots' confidence in their limited visual capabilities.     

Explicit and implicit horizons for simulated landing approaches

In a flight simulator experienced pilots flew landing approaches to a representation of an airport scene in which various sources of information had been distorted or removed. Reasonably accurate approaches could be made to a scene that contained only an aimpoint and a horizon. The addition of a runway outline did not enhance accuracy or stability, which lent credence to the hypothesis that the invariant angle between horizon and aimpoint can support glide slope control. Explicit distortion of this angle by simulation of up-sloping or down-sloping terrain beyond the runway had predictable effects on glide slope control. Implicit specification of a veridical horizon with texture lines parallel to the runway centerline weakened the effect of distortions in the explicit horizon. Thus both explicit and implicit specifications of the horizon contribute to perception of the glide slope angle. Implications of these results for the design of visual scenes for flight simulation are discussed.     

Landing Auto‐Pilots for Aircraft Motion in Longitudinal Plane using Adaptive Control Laws Based on Neural Networks and Dynamic Inversion

This paper presents two new automatic landing systems (ALSs) for aircraft motion in longitudinal plane; the model of the landing geometry determines the flight trajectory and the aircraft calculated altitude; the flight trajectory during landing consists of two parts: the glide slope and the flare. Both designed ALSs have an adaptive system (ACS) for the aircraft output's control; for the first ALS, the output vector consists of the flying altitude and the longitudinal velocity, while, for the second ALS, the output variables are the pitch angle and the longitudinal velocity of aircraft. The second variant of ALS also contains an altitude controller providing the calculated pitch angle. The calculated altitude (for the first ALS), the calculated pitch angle (for the second ALS), and the desired flight velocity are provided to the ACS by means of a block consisting of two reference models. ACS is based on the dynamic inversion concept and contains an adaptive controller which includes a linear dynamic compensator, a state observer, a neural network, and a Pseudo Control Hedging block. The paper is focused both on the design of the two ALSs and on their complex software implementation and validation.     

Intelligent anti-skid brake controller using a neural network

The performance of current anti-skid brake controllers on aircraft becomes degraded due to the uncertain nature of the runway conditions. This paper presents the design of an intelligent anti-skid neural controller to overcome this problem. Their learning ability, nonlinear mapping ability and pattern-recognition capability are the features of neural networks that are ideally suited to the design of intelligent controllers. The controller described here identifies the runway condition from the aircraft-wheel responses, and modulates the brake torque for optimum braking. The proposed controller exhibits robustness under variations in brake characteristics and runway conditions. Simulation results confirm the satisfactory performance of the controller in adapting to changes in runway conditions.     

Planning Stable and Efficient Paths for Reconfigurable Robots On Uneven Terrain

This paper proposes a novel path planning method for improving the feasibility of a forced landing. When an aircraft completely loses its thrust, the only measure it can take is to make a forced landing at an adjacent airport as soon as possible. In such a situation, the flight path to the landing point must be safe and viable. This paper details a method which enables safer and easier landing by transferring the benefits of excess altitude to the final approach length. Moreover, by planning the descent angle of final approach to be in the middle of a non-spoiler and a full-spoiler glide angle, this method enables a change in descent angle to correct any tracking errors, without using thrust. To verify the effectiveness of the proposed method, six degrees-of-freedom nonlinear simulations were performed and the results are compared with comparable methods. From the simulation results, it was confirmed that the proposed method could plan a safe path in a sufficiently short time and the aircraft could reach the landing point safely.     

基于补偿神经网络的模糊飞机刹车控制系统研究

尽管飞机防滑刹车可以在保持可操纵性的同时优化刹车效率,但遇到不同路况时刹车性能却时常下降。为了在防滑的同时获得最大的刹车结合系数,该文提出了新的飞机防滑刹车控制律：基于补偿神经网络的模糊控制。控制器识别飞机和机轮的速度反馈,从而调整刹车力矩实现优化刹车。同时系统又可根据复杂的路况,通过补偿神经网络进行自优化。通过MATLAB、VC仿真得出滑移率跟踪曲线。结果表明刹车系统在适应不同路况时有很好的控制性能。     

Pilot performance, strategy, and workload while executing approaches at steep angles and with lower landing minima

OBJECTIVE: We examined the willingness and ability of general aviation pilots to execute steep approaches in low-visibility conditions into nontowered airports. BACKGROUND: Executing steep approaches in poor weather is required for a proposed Small Aircraft Transportation System (SATS) that consists of small aircraft flying direct routes to a network of regional airports. METHOD: Across two experiments, 17 pilots rated for Instrument Flight Rules at George Mason University or Virginia Tech flew a Cessna 172R simulator into Blacksburg, Virginia. Pilots were familiarized with the simulator and asked to fly approaches with either a 200- or 400-foot ceiling (at approach angles of 3 degrees, 5 degrees, and 7 degrees in the first experiment, 3 degrees and 6 degrees in the second). Pilots rated subjective workload and the simulator recorded flight parameters for each set of approaches. RESULTS: Approaches with a 5 degree approach angle produced safe landings with minimal deviations from normal descent control configurations and were rated as having a moderate level of workload. Approaches with 6 degree and 7 degree approach angles produced safe landings but high workload ratings. Pilots reduced power to control the speed of descent and flew the aircraft slightly above the glide path to gain time to control the landing. CONCLUSION: Although the 6 degree and 7 degree approaches may not be practical for routine approaches, they may be achievable in the event of an emergency. APPLICATION: Further work using other aircraft flying under a wider variety of conditions is needed before implementing SATS-type flights into airports intended to supplant or complement commercial operations in larger airports.     

Fuzzy Logic Based Approach to Design of Autonomous Landing System for Unmanned Aerial Vehicles

This paper is concerned with autonomous flight of UAVs and proposes a fuzzy logic based autonomous flight and landing system controller. Besides three fuzzy logic controllers which are developed for autonomous navigation for UAVs in a previous work as fuzzy logic based autonomous mission control blocks, three more fuzzy logic modules are developed under the main landing system for the control of the horizontal and the vertical positions of the aircraft against the runway under a TACAN (Tactical Air Navigation) approach. The performance of the fuzzy logic based controllers is evaluated using the standard configuration of MATLAB and the Aerosim Aeronautical Simulation Block Set which provides a complete set of tools for rapid development of 6 degree-of-freedom nonlinear generic manned/unmanned aerial vehicle models. Additionally, FlightGear Flight Simulator and GMS aircraft instruments are deployed in order to get visual outputs that aid the designer in evaluating the performance and the potential of the controllers. The simulated test flights on an Aerosonde indicate the capability of the approach in achieving the desired performance despite the simple design procedure.     

Intelligent automatic landing system using time delay neural network controller

This paper presents an intelligent automatic landing system that uses a time delay neural network controller and a linearized inverse aircraft model to improve the performance of conventional automatic landing systems. The automatic landing system of an airplane is enabled only under limited conditions. If severe wind disturbances are encountered, the pilot must handle the aircraft due to the limits of the automatic landing system. In this study, a learning-through-time process is used in the controller training. Simulation results show that the neural network controller can act as an experienced pilot and guide the aircraft to a safe landing in severe wind disturbance environments without using the gain scheduling technique.     

求解机场终端区飞机着陆调度问题的遗传算法

空中管制员需为到达的飞机安排跑道并计算着陆时间,以飞机空中延误最小为出发点研究了多跑道的飞机着陆调度问题,约束条件为每架飞机的着陆时间应落在规定的时间窗内及相邻两架飞机应满足最小时间间隔。针对该问题设计了一种遗传算法对问题进行求解,其中染色体由飞机排序链表和跑道链表组成,相应的交叉和变异算子也做了改进设计。仿真实验用数据库OR-Library中的实例验证了该算法的有效性。     

无人机滑降着陆控制系统设计

针对滑跑型无人机回收阶段对下滑角跟踪以及触地时姿态角的高要求,设计了一种无人机滑降着陆控制方式。首先,给出了滑降控制系统结构图,在此基础上分别进行了滑降横侧向控制器和滑降纵向控制器的设计,具体进行了直线航迹和圆航迹的控制方法以及下滑段的高度控制量算法的分析。然后,进行了滑降着陆控制模式设计,将滑降过程分解为降高、平飞、下滑以及拉平四个阶段分别进行设计,并在拉平阶段给出了俯偏航距仰角控制量与离地高度的关键技术公式。仿真结果表明,该无人机滑降着陆控制系统平飞段偏航距小于5m,接地时偏航距约为0m;平飞段高度跟踪误差为0m,下滑段高度跟踪误差2m;落地姿态角为0.4度。具有高度控制误差小、偏航距离短、落地姿态角安全性高的优点,能满足滑跑无人机对滑降阶段的控制要求。     

Fuzzy modelling control for aircraft automatic landing system

Aircraft landing control based on fuzzy modelling networks is presented. The proposed scheme uses a fuzzy controller combined with a linearized inverse aircraft model. A multi-layered fuzzy neural network is used as the controller, providing the control signals at each stage of the aircraft-landing phase. The algorithm used to train the network is the Backpropagation Through Time. The linearized inverse aircraft model provides the error signals that will be used to back-propagate through the controller at each stage. The objective of this study is to improve the performance of conventional automatic landing systems. The simulation results are described for the automatic landing system of a commercial aeroplane. Tracking performance and robustness are demonstrated through software simulations. Simulation results show that the fuzzy controller can successfully expand the safety envelope to include more hostile environments such as severe turbulence.     

Nonlinear Fly‐By‐Throttle H∞ Control Using Neural Networks

A neural network approach to gain scheduling H∞ controllers for propulsion controlled aircraft (PCA) systems is introduced. The PCA system is applied to backup control of aircraft experiencing control surface failure. The H∞ technology is applied to the problem of matching the crippled aircraft and the nominal model. Various H∞ controllers at various flight conditions are used to train radial basis function networks (RBFN), which can then be used as the nonlinear controller. Simulation on an L‐1011 under fly‐by‐throttle control demonstrates that the RBFN controller can stabilize the crippled airplane to obtain the desired model and possesses robustness against the engine delay.     

Backstepping dynamic surface control for an anti-skid braking system

The electric aircraft landing system, as one of the important components of more electric aircraft (MEA) and all electric aircraft (AEA), has been a subject of interest in recent years. An anti-skid braking system (ABS), which is the crucial component of the electric aircraft landing system, has the function of regulating the wheel slip ratio such that the braking process operates in a stable state. In this paper, an approach that combines a nonlinear backstepping dynamic surface control (DSC) and an asymmetric barrier Lyapunov function (ABLF) is presented to not only track the reference slip ratio but also to avoid the slip ratio in the unstable region. We demonstrate that the proposed controller can guarantee the boundedness of the output constraints and the stability of the overall system. Using the ABLF allows one to relax the required initial conditions on the starting values of the wheel slip ratio and subsequently make the wheel slip constraints more flexible for various runway surfaces and runway transitions. The DSC is introduced to eliminate repeated differentiation resulting from ABLF synthesis, which can relax the restrictions on the high-order differentiability for stabilizing functions and the high power of wheel slip tracking error transformation. The proposed controller can avoid the negative effects of disturbance produced by repeated differentiation and can construct a simple controller for wheel slip control. The results of simulations with varying runway surfaces have validated the effectiveness of the proposed control scheme, in which the output constraints on the wheel slip ratio are guaranteed not to be violated and self-locking is avoided.     

Online safety calculations for glide-slope recapture

As unmanned aerial vehicles (UAVs) increase in popularity and usage, an appropriate increase in confidence in their behavior is expected. This research addresses a particular portion of the flight of an aircraft (whether autonomous, unmanned, or manned): specifically, the recapture of the glide slope after a wave-off maneuver during landing. While this situation is rare in commercial aircraft, its applicability toward unmanned aircraft has been limited due to the complexity of the calculations of safety of the maneuvers. In this paper, we present several control laws for this glide-slope recapture, and inferences into their convergence to the glide slope, as well as reachability calculations which show their guaranteed safety. We also present a methodology which theoretically allows us to apply these offline-computed safety data to all kinds of unmanned fixed-wing aerial vehicles while online, permitting the use of the controllers to reduce wait times during landing. Finally, we detail the live aircraft application demonstration which was done to show feasibility of the controller, and give the results of offline simulations which show the correctness of online decisions at that demonstration.This work was sponsored by the Large National Science Foundation Grant for Information Technology Research (NSF ITR) ‘‘Foundations of Hybrid and Embedded Software Systems’’, Award #0225610, and by Defense Advanced Research Projects Administration (DARPA) ‘‘Software Enabled Control’’ (SEC) Program, under contract #F33615-98-C-3614.     

Development of a bespoke human factors taxonomy for gliding accident analysis and its revelations about highly inexperienced UK glider pilots

Low-hours solo glider pilots have a high risk of accidents compared to more experienced pilots. Numerous taxonomies for causal accident analysis have been produced for powered aviation but none of these is suitable for gliding, so a new taxonomy was required. A human factors taxonomy specifically for glider operations was developed and used to analyse all UK gliding accidents from 2002 to 2006 for their overall causes as well as factors specific to low hours pilots. Fifty-nine categories of pilot-related accident causation emerged, which were formed into progressively larger categories until four overall human factors groups were arrived at: ‘judgement’; ‘handling’; ‘strategy’; ‘attention’. ‘Handling’ accounted for a significantly higher proportion of injuries than other categories. Inexperienced pilots had considerably more accidents in all categories except ‘strategy’. Approach control (path judgement, airbrake and speed handling) as well as landing flare misjudgement were chiefly responsible for the high accident rate in early solo glider pilots.     

基于神经网络的飞机静液刹车系统设计

针对无人机的滑跑安全问题,为有效缩短刹车距离,提高刹车效率,设计了一种新型的静液刹车系统;根据新型刹车系统的特点,并综合考虑飞机机体、机轮、跑道状况的特性,以及刹车系统的非线性和不确定性,难以精确控制的特点,设计了神经网络控制器(NNC);并将神经网络控制器,新型刹车系统和飞机滑跑模型应用于仿真环境,建立了整体的仿真模型;仿真结果表明,采用神经网络的刹车系统鲁棒性增强,刹车效率提高,明显优于采用传统控制律的刹车系统。     

飞翼无人机着陆过程中的抗侧风控制研究

飞翼布局无人机的无尾布局给横侧向的稳定与控制带来困难,尤其是下降阶段有侧风时的航迹控制.带侧滑保持航向和带偏流保持航向是常用的两种抗侧风策略,为解决稳定性控制,研究了两种策略的优缺点,并针对两种控制策略分别设计了横航向抗侧风控制器.结合飞翼布局无人机的特点,在着陆过程中的不同阶段选择不同的抗侧风策略,在进场和下滑初始阶段采用带偏流保持航向方式,在下滑到一定高度时转为带侧滑保持航向的控制策略.仿真结果表明,选择的控制策略以及设计的控制器合理、可行,满足飞翼无人机着陆阶段的抗侧风要求.     

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

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.