Motion-based Flight Simulator Research Articles

Autorotation Transfer of Training: Effects of Helicopter Dynamics

This paper analyzes the effects of the helicopter dynamics on pilots’ learning process and transfer of learned skills during autorotation training. A quasi-transfer-of-training experiment was performed with 10 experienced helicopter pilots in the SIMONA moving-base flight simulator at Delft University of Technology. Pilots had to control an in-house flight dynamics model setup to simulate two types of helicopter dynamics: (1) a “hard” dynamics characterized by a low autorotative flare index requiring high pilot control compensation and (2) a “easy” dynamics characterized by a high autorotative flare index with low pilot control compensation required. Two groups of pilots tested these types of dynamics in a different training sequence: hard-easy-hard (HEH group) and easy-hard-easy (EHE group). The main conclusion of this study proved that simulator training for autorotation can best start with pilots training in the most resource demanding condition. A more challenging helicopter’s dynamics will require higher pilot agility and more rapid responses to his/her perceptual changes. This will result in pilots developing more robust and adaptable flying skills. Indeed, a clear positive transfer of training effect was observed in the experiment presented in this paper in terms of acquired pilot skills in the HEH group, but not the EHE group. Positive transfer was especially observed in terms of reduced rate of descent at touchdown. The two groups differed in the control strategy applied, with the HEH group having developed a control technique mimicking more closely the one adopted in a real helicopter.

Using problem-based exploratory training to improve pilot understanding of autopilot functions

Previous research indicated a need to improve pilot training with regard to understanding of autopilot logic and behavior, especially in non-routine situations. Therefore, we tested the effect of problem-based exploratory training on pilots’ understanding of autopilot functions. Using a moving-base flight simulator, general aviation pilots (n = 45) were trained to diagnose failures either without foreknowledge and guidance (exploratory group), without foreknowledge but with some guidance (exploratory-guidance group) or with foreknowledge and full guidance (control group). They subsequently performed six test scenarios in which their understanding of the effects of failures was tested by requiring them to deduce the failures and select autopilot modes that were still functioning. Those who received exploratory training with guidance were significantly more likely than the other groups to diagnose failures correctly. The exploratory training group also selected the most appropriate functioning autopilot modes significantly faster than the control group. The results suggest that exploratory training with an appropriate level of guidance is useful for gaining a practical understanding of autopilot logic and behavior. Exploratory training may help to improve transfer of training to operational practice, and prevent automation surprises and accidents.

Effects of Helicopter Dynamics on Autorotation Transfer of Training

This paper analyzes the effects of the helicopter dynamics on pilots’ learning process and transfer of learned skills during autorotation training. A quasi-transfer-of-training experiment was performed with 14 experienced helicopter pilots in a moving-base flight simulator. Two types of helicopter dynamics, characterized by a different autorotative index, were considered: “hard,” with high pilot compensation required, and “easy,” with low compensation required. Two groups of pilots tested the two types of dynamics in a different training sequence: hard-easy-hard (HEH group) and easy-hard-easy (EHE group). Participants of both groups were able to attain adequate performance at touchdown in most of the landings with both types of dynamics. However, a clear positive transfer effect in terms of acquired skills is found in both groups from the hard to the easy dynamics, but not from the easy to the hard dynamics, confirming previous experimental evidence. Positive transfer is especially observed for the rate of descent at touchdown. The two groups differed in the control strategy applied, with the HEH group having developed a more robust control technique. During the last training phase the EHE group aligned its control strategy with that of the HEH group.

Aircraft FDI and human factors analysis of a take-off maneuvre using SIVOR flight simulator

Abstract This paper presents a human factors analysis in aviation within the context of failure detection and identification (FDI) using statistical data analysis and clustering. We used data from experiments in a motion-based flight simulator (SIVOR) with 4 experienced pilots performing a take-off maneuvre under three conditions: normal, under engine failure and under flap failure. We propose two metrics based on statistical data analysis to evaluate and compare human behavior during flight. We also use k-means clustering in order to classify flights according to maneuvre conditions and misclassified flights are further analyzed according to which pilot has performed it. Results show that for the statistical data analysis the behavior of one specific pilot has higher dissimilarity with all other pilots. Moreover, for the k-means clustering, most of the misclassified flights were performed by this same pilot.

Immunity-based accommodation of aircraft subsystem failures

PurposeThis paper aims to describe the design, development and flight-simulation testing of an artificial immune-system-based approach for accommodation of different aircraft sub-system failures/damages.Design/methodology/approachThe approach is based on building an artificial memory, which represents self- (nominal conditions) and non-self (abnormal conditions) within the artificial immune system paradigm. Self- and non-self are structured as a set of memory cells consisting of measurement strings, over pre-defined time windows. Each string is a set of features values at each sample time of the flight. The accommodation algorithm is based on the cell in the memory that is the most similar to the in-coming measurement. Once the best match is found, control commands corresponding to this match are extracted from the memory and used for control purposes.FindingsThe results demonstrate the possibility of extracting pilot compensatory commands from the self/non-self structure and capability of the artificial-immune-system-based scheme to accommodate an actuator malfunction, maintain control and complete the task.Research limitations/implicationsThis paper concentrates on investigation of the possibility of extracting compensatory pilot commands. This is a preliminary step toward a more comprehensive solution to the aircraft abnormal condition accommodation problem.Practical implicationsThe results demonstrate the effectiveness of the proposed approach using a motion-based flight simulator for actuator and sensor failures.Originality/valueThis research effort is focused on investigating the use of the artificial immune system paradigm for control purposes based on a novel methodology.

Comparison of immunity-based schemes for aircraft failure detection and identification

In this paper, two approaches are proposed and compared for the detection and identification of aircraft subsystem failures based on the artificial immune system paradigm combined with the hierarchical multiself strategy. The first approach relies on the heuristic ranking of lower order self/non-self projections and the generation of selective immunity identifiers through structuring of the non-self. The second approach is based on an information processing algorithm inspired by the functionality of the dendritic cells. The artificial dendritic cell is defined as a computational unit that centralizes, fuses, and interprets information from the multiple selves to produce a unique detection and identification outcome. A hierarchical multi-self strategy is used with both approaches considering 2-dimensional self/non-self projections or subselves. A mathematical formulation of the concepts and detailed implementation algorithms are presented. The proposed methodologies are demonstrated and compared using simulation data for a supersonic fighter from a motion-based flight simulator at nominal conditions, under failures of actuators, malfunction of sensors, and wing damage. In all cases considered, both detection and identification schemes achieve excellent detection and identification rates with practically no false alarms.

Simplified Estimation Algorithms for Aircraft Structural Damage Effects Using an Artificial Immune System

The development of simplified models for the estimation of flight envelope reduction under damages to aircraft structural components, within the artificial immune system paradigm, is presented in this paper. The proposed methodology is part of a comprehensive and integrated framework for aircraft abnormal condition detection, identification, evaluation, and accommodation aimed at ensuring aircraft high survivability rates and operation safety. An artificial immune system built through simulation for a fighter aircraft is used in conjunction with a hierarchical multi-self strategy for estimating ranges of flight envelope relevant variables under structural damages affecting the wing, the horizontal tail, and the vertical tail. Flight envelope ranges are predicted when the lift-generating capabilities of the main aerodynamic surfaces are reduced, based on algorithms that are tailored to the nature and characteristics of the failure and 2-dimensional self projections. The performance of the proposed approach is evaluated using ad-hoc metrics and demonstrated successfully through simulation tests in a motion-based flight simulator.

A dendritic cell mechanism for detection, identification, and evaluation of aircraft failures

Successful fault-tolerant control strategies often require vital tools that can accurately detect the failure, identify its root cause, and evaluate its nature and severity. Most of the existing methodologies in the field of failure detection, identification, and evaluation are limited to few subsystems with reduced number of features. Due to the complexity and multidimensionality of the aircraft system, new methodologies that are robust, accurate, and fast enough need to be developed for such systems. The biological immune system is a natural system that possesses vigorous peculiarities in protecting the mammalian body from harmful intruders and, therefore, may represent a rich source of inspiration to solve anomaly problems. This paper presents a novel integrated scheme for aircraft sub-system failure detection, identification, and evaluation based on the functionality of the biological dendritic cells and their interactions with the various components of the immune system. The proposed approach relies on using the self/nonself discrimination principle with the hierarchical multiself strategy to overcome the multidimensionality issues. The information collected by the artificial dendritic cells is fused in a way that convert the identification and evaluation problem into a pattern recognition problem. The proposed scheme was successfully tested for a supersonic fighter aircraft in a motion-based flight simulator with high detection, identification, and evaluation rates and practically zero false alarms.

Evaluation of Aircraft Sensor Failures Effects Using the Artificial Immune System Paradigm

This paper presents the development of simple models for the assessment of flight envelope reduction under aircraft sensor failures within the artificial immune system paradigm. The proposed methodology is expected to facilitate the design of on-board augmentation systems increasing aircraft survivability and improving operation safety. It demonstrates and exploits the capability of the artificial immune system to not only detect and identify aircraft subsystem abnormal conditions but also evaluate their impact and consequences. An artificial immune system built through simulation for a fighter aircraft is used in conjunction with a hierarchical multi-self strategy for estimating ranges of flight envelope relevant variables at post-failure conditions affecting angular rate sensors that are needed within the control augmentation system. Failure-specific algorithms correlated with the characteristics and dimensionality of self projections are developed for roll, pitch, and yaw rate sensors affected by bias. Metrics for the performance evaluation of the proposed approach are defined and used for successful demonstration in a motion-based flight simulator.

Artificial Dendritic Cell Mechanism for Aircraft Immunity-Based Failure Detection and Identification

Biological dendritic cells perform a complex activation/suppression role in the generation, direction, and control of antibodies. Their action relies on balancing information regarding the external antigen type, amount, and virulence, as well as the state and resources of the host organism. In this paper, an information processing algorithm inspired by the functionality of the dendritic cells is proposed to enhance aircraft subsystem abnormal condition detection and identification within the artificial immune system paradigm. A hierarchical multi-self-strategy is used to produce multiple failure detection and identification outcomes at each sample time over a time window. The artificial dendritic cell is defined as a computational unit that centralizes, fuses, and interprets this information to decide upon a unique detection and identification outcome with reduced false alarms and a low number of incorrect identifications. A mathematical formulation of the concept and a detailed implementation algorithm are provided. The proposed methodology is demonstrated using simulation data for a supersonic fighter from a motion-based flight simulator at nominal conditions, under failures of actuators, malfunction of sensors, and wing damage. In all cases considered, the detection and identification scheme achieves excellent detection and identification rates with practically no false alarms.

Predictive Landing Guidance in Synthetic Vision Displays

In order to improve performance during manual control, synthetic vision displays were augmented with predictive guidance. Little is known on how these predictive guidance concepts can be applied to the landing flare maneuver. This paper discusses the investigation into the applicability of 3D predictive guidance in synthetic vision displays during the final phase of the landing. Two types of predictive guidance were examined, the Flight-Path Predictor that indicates the aircraft’s future position a certain time ahead, and the Flight Trajectory Predictor that presents the future trajectory by interpolating a number of sequential predicted positions. A theoretical investigation and an offline simulation were used to optimize the system for the landing. A pilot-in-the-loop experiment, conducted in a moving-base flight simulator, indicated that predictive guidance supports pilots in manual control. Also, the addition of predictive guidance enhances the pilot’s ability to determine the correct flare initiation time in a way that is comparable to providing a more realistic synthetic vision display with textured surfaces. Even though the flare initiation timing was improved by the addition of predictive guidance, the flare control after initiation was not sufficiently supported and there was no noticeable improvement in landing performance.

Artificial-Immune-System-Based Detection Scheme for Aircraft Engine Failures

A detection scheme based on the artificial immune system paradigm was developed for specific classes of aircraft jet engine actuator and sensor failures, including throttle, burner fuel-flow valve, variable nozzle-area actuator, variablemixer-area actuator, low-pressure spool-speed sensor, low-pressure turbine exit static-pressure sensor, and mixer pressure-ratio sensor. The NASA Modular Aero-Propulsion System Simulation model was linearized and interfaced with a supersonic fighter aircraft model and a motion-based flight simulator, providing the adequate framework for development and testing. Several engine actuator and sensor failures weremodeled and implemented into this simulation environment. A five-dimensional hyperspace was determined to build the self within the artificial immune system paradigm for detection purposes. The artificial immune system interactive design environment based on evolutionary algorithms developed at West Virginia University was used for data processing, detector generation, and optimization. Flight-simulation data for system development and testing were acquired through experiments in a motion-based flight simulator over extended areas of the flight envelope. The performance of the artificial-immune-system-based detection scheme was evaluated in terms of detection rates and false alarms. Results show that the artificial-immune-system-based approach has excellent potential for the detection of all of the classes of engine failures considered.

Artificial-Immune-System-Based Aircraft Failure Evaluation over Extended Flight Envelope

This paper describes the design, development, and flight-simulation testing of an artificial-immune-system-based approach for the evaluation of different aircraft subsystem failures/damages. The evaluation consists of the estimation of the magnitude/severity of the failure and the prediction of the achievable states, leading to an overall assessment of the effects of the failure on reducing the flight envelope. A supersonic fighter model is used, which includes model-following adaptive control laws based on nonlinear dynamic inversion and artificial neural network augmentation. Data collected from a motion-based flight simulator were used to define the self for a wide area of the flight envelope and to test and validate the proposed approach. Example results are presented for failure-magnitude evaluation and flight-envelope-reduction prediction for abnormal conditions affecting sensors, actuators, engine, and wing structure. Successful failure detection and identification are assumed before evaluation. The results show the capabilities of the artificial-immune-system-based scheme to evaluate the severity of the failure and to predict the reduction of the flight envelope in a general manner.

Identification of Multimodal Pilot Models Using Ramp Target and Multisine Disturbance Signals

This paper investigates the extent to which target forcing functions consisting of multiple ramplike or steplike changes in target attitude can be used for identification of multimodal pilot models. The main question addressed is whether such signals provide enough excitation of the pilot-vehicle system to allow for reliable estimation of pilot model parameters from measured pilot control data. The compensatory control task from a previous human-in-the-loop experiment performed in a moving base flight simulator is used to define a typical multimodal pilot model identification problem. Using simulations of this control task, where different multisine and ramp target forcing function signals are used to excite the pilot-vehicle system (in addition to a multisine disturbance signal), the accuracy and consistency of pilot model estimation results are evaluated. It is found that both the bias and variance of the pilot model parameter estimates decrease with increasing ramp signal steepness. If the steepnesses of the ramps in the target signals are higher than 3 deg /s, estimation results are found to be more accurate than those obtained using a quasi-random target forcing function. This indicates that reliable pilot model identification results can be obtained using such ramp target forcing function signals.

Aircraft failure detection and identification over an extended flight envelope using an artificial immune system

AbstractAn integrated artificial immune system-based scheme that can operate over extended areas of the flight envelope is proposed in this paper for the detection and identification of a variety of aircraft sensor, actuator, propulsion, and structural failures/damages. A hierarchical multi-self strategy has been developed in which different self configurations are selected for detection and identification of specific abnormal conditions. Data collected using a motion-based flight simulator were used to define the self for a wide area of the flight envelope and to test and validate the scheme. The aircraft model represents a supersonic fighter, including model-following direct adaptive control laws based on non-linear dynamic inversion and artificial neural network augmentation. The proposed detection scheme achieves low false alarm rates and high detection and identification rates for all the categories of failures considered.

Integrated Framework for Artificial Immunity-Based Aircraft Failure Detection, Identification, and Evaluation

This paper presents a novel conceptual framework for an integrated set of methodologies for the detection, identification, and evaluation of a wide variety of failures of aircraft subsystems based on the artificial immune system paradigm. The detection represents the capability to declare that a failure within any of the aircraft subsystems has occurred. The identification process determines which element has failed. The evaluation of the failure addresses three aspects: the type of the failure, its magnitude, and the reassessment of the generalized flight envelope. Failure detection, identification, and evaluation schemes are included using the bioimmune system metaphor combined with other artificial intelligence techniques. The immunity-based fault detection operates in a similar manner as does the immune system when it distinguishes between entities that belong to the organism and entities that do not. The proposed approach directly addresses the complexity and multidimensionality of aircraft dynamic response in the context of abnormal conditions and provides the adequate tools to solve the failure detection problem in an integrated and comprehensive manner. A multiself failure detection and identification scheme is presented for actuator, sensor, engine, and structural failures/damages, which was developed and tested using a motion-based flight simulator. The scheme achieves excellent detection rates and a low number of false alarms and demonstrates the effectiveness of the proposed framework.

Aircraft Failure Detection and Identification Using anImmunological Hierarchical Multiself Strategy

This paper presents the development and application of an integrated artificial-immune-system-based scheme for the detection and identification of a variety of aircraft sensor, actuator, propulsion, and structural failures/damages. The proposed approach is based on a hierarchical multiself strategy in which different self configurations are selected for detection and identification of specific abnormal conditions. Data collected using a motion-based flight simulator were used to define the self for a subregion of the flight envelope. The aircraft model represents a supersonic fighter, including model-followinig adaptive control laws based on nonlinear dynamic inversion and artificial neural network augmentation. The proposed detection scheme achieves low false alarm rates and high detection and identification rates for all four categories of failures considered.

Evaluation of a Fault-Tolerant Scheme in a Six-Degree-of-Freedom Motion Flight Simulator

This paper discusses the evaluation of a neurally augmented fault-tolerant flight control scheme for a high-performance military aircraft featuring an adaptive actuator and sensor failure detection, isolation, and identification algorithm in a motion-based flight simulator. The design of the fault-tolerant control scheme is based on a nonlinear dynamic inversion schemewithaneuralnetwork-basedaugmentationforreducingthedynamicinversionerrors associated with the occurrence of an actuator failure while a set of online learning neural observers is used for dealing with specific sensor failures. The failure detection, isolation, and identification scheme is based on an adaptive threshold technique for estimating failure bounds associated with both actuator and sensor failures. Also, an ‘ad-hoc’ parameter is proposed here for the novel task of evaluating the pilot workload in compensating for both actuatorandsensorfailuresonboardtheaircraft.Ageneraloutcomeoftheeffortisademonstration of the importance of realistic motion-based simulation environments for evaluation of this specific class of flight control laws. The study also demonstrated the importance of the neural augmentation for failure accommodation purposes and the effectiveness of the proposed adaptive threshold technique for failure identification purposes.

2101 モーション・キューが人間パイロットの操縦特性に及ぼす影響(OS6-1 シミュレータ・ソフトウエアの開発,OS6 ヒューマンファクタ,オーガナイズド・セッション)

This study carries out the fundamental experiment to understand the effect of motion cues on human pilot's control characteristics on a motion based flight simulator. The experiment provides pilot with flight vertical motion cues which changed its magnitude and direction. As a result, these motion cues influence the control characteristics. That makes his workload increase or decrease. Therefore it is necessary to consider the effect for thinking about the handling quality.

ENHANCING THE FLIGHT SIMULATOR FEELING BY A MINIMISING BACKLASH-EFFECTS PROCESS

In this paper we addressed the problem of improving the control of AC motors used for the specific application of 3 degrees of freedom moving base flight simulator. Indeed the presence of backlash in DC motors gearboxes induces shocks and naturally limits the flight feeling. A comparison is hence set up between two techniques aiming to deal with this problem: Adaptive Fuzzy Controller and Neural Controller. Dynamic inversion with Fuzzy Logic is used to design an adaptive backlash compensator. The classification property of fuzzy logic techniques makes them a natural candidate for the rejection of errors induced by the backlash. A tuning algorithm is given for the fuzzy logic parameters, so that the output backlash compensation scheme becomes adaptive. The compensator uses the Neural Networks techniques demonstrate that artificial neural networks can be used to compensate hysteresis caused by gear backlash in precision position-controlled mechanisms. A major contribution of this research is that physical analysis of the system nonlinearities and optimal control are used to design the neural network structure.