Intelligent Flight Control Systems Evaluation for Loss-of-Control Recovery and Prevention

Smaïli, M.H.; Breeman, J.H.; Lombaerts, Thomas; Mulder, J.A.; Chu, Qi; Stroosma, Olaf

doi:10.2514/1.g001756

Cited by 16 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Joint points of the head and foot are extracted by horizontal line scanning algorithm, joint points of the knee, hip, neck, and chest are extracted by length proportion constraint, and joint points of the hand, elbow, and shoulder are extracted by vertical scanning algorithm [18,19] e internal structure of physical training includes body shape, body function, sports quality, and health level [20].…”

Section: Human Pose Estimation Based On Contour Edgementioning

confidence: 99%

Assistant Training System of Teenagers’ Physical Ability Based on Artificial Intelligence

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The rapid development of artificial intelligence technology makes it widely used in various fields. In order to more scientifically assist teenagers in physical training, this paper develops a set of teenagers’ physical training system based on artificial intelligence technology. Firstly, the experimental platform is built, and the sensor nodes are connected with the test host through the serial port to collect data to the experimental platform. The system consists of target detection module, data analysis module, and human posture estimation module. The background modeling method based on vibe model is used to form the target detection module, and the canny edge detection algorithm is used to form the data analysis module. Finally, the posture auxiliary index is established to estimate the human posture. This paper makes a systematic application test on a youth sports team. The experimental group was trained with artificial intelligence-based physical training system, while the control group was trained with traditional training methods. Before the experiment, the physical fitness of the two groups of subjects were evaluated, including standing long jump, 50 meters sprint, 30 s single swing rope skipping, pull-up, and squat 1RM. After 3 and 6 weeks of training, the physical fitness was evaluated again. The experimental results show that the intelligent assistant system established in this paper can accurately show that the physiological load of the athlete is in line with the law of physiological function change. After six weeks of training, the standing long jump of the experimental group has been improved by 20.97 cm, the 50 meters dash has been accelerated by 1.21 s, the 30 second single swing rope has been increased by 13.76, the pull-up has been increased by 1.41, and the squat 1RM has been increased by 15.16. This shows that the auxiliary training system based on artificial intelligence can help young athletes improve their physical quality and enhance their sports skills.

show abstract

Section: Human Pose Estimation Based On Contour Edgementioning

confidence: 99%

Assistant Training System of Teenagers’ Physical Ability Based on Artificial Intelligence

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Upset Prevention and Recovery Training (UPRT) is one example of such scenario [14], where adequate simulation training can help reduce loss of control in-flight (LOC-I), a significant contributor to fatal accidents [15,16]. Various efforts have been undertaken to reduce LOC-I, such as predictive cueing [17], haptic feedback [18] and adaptive flight control systems [19,20]. In the European research project 'Simulation of Upset Recovery in Aviation' (SUPRA), centrifuge-based simulation has shown to significantly improve pilot's ability to recover from adverse flight attitudes [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Mitigating Coriolis Effects in Centrifuge Simulators Through Allowing Small, Unperceived G-Vector Misalignments

Mkhoyan¹,

Wentink²,

Graaf³

et al. 2022

Preprint

View full text Add to dashboard Cite

When coupled with additional degrees of freedom, centrifuge-based motion platforms can combine the agility of hexapod-based platforms with the ability to sustain higher G-levels and an extended motion space, required for simulating extreme maneuvers. However, the false and often nauseating sensations of rotation, by Coriolis effects induced by the centrifuge rotation in combination with rotations of the centrifuge cabin or the pilot's head, are a major disadvantage. This paper discusses the development of a motion filter, the Coherent Alignment Method (COHAM), which aims at reducing Coriolis effects by allowing small mismatches in the G-vector alignment, reducing cabin rotations. Simulations show that as long as these mismatches remain within a region where humans perceive the G-vector as 'coherent', the Coherent Alignment Zone (CAZ), the cabin angular accelerations can indeed be reduced. COHAM was tested in a high G-maneuver task with a fixed CAZ threshold obtained in a previous study. It was experimentally compared to an existing motion filter, using metrics such as sickness, comfort and false cues. Results show that sickness, dizziness and discomfort are reduced, making the centrifuge sessions more bearable. It is recommended to further improve the filter design and tuning, and test it with more fighter pilots.

show abstract

“…In the research of the aircraft system, many papers on fault-tolerant and adaptive control methods are developed and demonstrated for LOC hazards mitigation [16], [17]. For example, reference [18] utilized a new intelligent flight control system to prevent the LOC and validated the effectiveness by conducting the pilotin-the-loop experiments. Reference [19] proposed a stable adaptive approach which is used in the simultaneous sensor, actuator, and slow-engine gain failure accommodation of the flight control.…”

Section: Introductionmentioning

confidence: 99%

Mechanism Analysis of Smart Cue on Aircraft for Loss of Control Mitigation

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

This paper analyzes the mechanism of the smart inceptor on the aircraft, as a means to mitigate human-vehicle system loss-of-control. We divide the smart inceptor cue into three modes: the smart cue on the human pilot, the smart cue on the flight control system and the smart cue on both of them. The control mechanism of these three modes is developed and analyzed in depth. To evaluate the effect of the three modes, we utilize an intelligent human pilot model to establish the human-vehicle system with the smart inceptor and a scalogram-based pilot induced oscillation metric to predict the handling qualities of the three modes. This paper presents details of the cueing modes and the results of the prediction focused on effectiveness of these modes in preventing the pilots from entering a loss-of-control event. The simulation results indicate that the smart cue on both of them was the most effective method to mitigate the impact of the pilot-aircraft system oscillations for the given failure scenarios. It embodies the function of pilot-aircraft cooperation. INDEX TERMS Flight simulation, human-vehicle system, human pilot model, interface, manual control.

show abstract

Intelligent Flight Control Systems Evaluation for Loss-of-Control Recovery and Prevention

Abstract: The contents of this report may be cited on condition that full credit is given to NLR and the author(s).

Cited by 16 publications

References 16 publications

Assistant Training System of Teenagers’ Physical Ability Based on Artificial Intelligence

Assistant Training System of Teenagers’ Physical Ability Based on Artificial Intelligence

Mitigating Coriolis Effects in Centrifuge Simulators Through Allowing Small, Unperceived G-Vector Misalignments

Mechanism Analysis of Smart Cue on Aircraft for Loss of Control Mitigation

Contact Info

Product

Resources

About