Use of a Task-Pilot-Vehicle (TPV) Model as a Tool for Fight Simulator Math Model Development

Heffley, Robert K.

doi:10.2514/6.2010-7620

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…Whilst flight test data may also be used, it is also possible to use generic task trajectories rather than from piloted flights. These could be obtained through the use of pilot/vehicle task-based modelling techniques, using methods outlined in Refs 37 and 38. The use of generic trajectories rather than specific recorded data would allow the process to be conducted virtually.…”

Section: Discussionmentioning

confidence: 99%

Enhancing motion cueing using an optimisation technique

Jones¹

2018

Aeronaut. j.

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Virtual engineering tools are not currently employed extensively during the certification and commissioning of flight simulator motion systems. Subjective opinion is regarded as sufficient for most applications, as it provides verification that the motion platform does not cause false cueing. However, the results of this practice are systems that may be far from optimal for their specific purpose. This paper presents a new method for tuning motion systems objectively using a novel tuning process and tools which can be applied throughout the simulators life-cycle. The use of the tuning method is shown for a number of simulated test cases.

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Section: Discussionmentioning

confidence: 99%

Enhancing motion cueing using an optimisation technique

Jones¹

2018

Aeronaut. j.

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“…In this subsection, the rotor model is discussed based on basic momentum theory and blade element theory [22]. Considering the airspeed along the X B axis, the configuration of the blades, the airflow through the rotor, and the thrust of the rotor can be expressed as [23]…”

Section: Rotormentioning

confidence: 99%

Safe Reinforcement Learning for Transition Control of Ducted-Fan UAVs

Zhao

Liu

2023

Drones

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Ducted-fan tail-sitter unmanned aerial vehicles (UAVs) provide versatility and unique benefits, attracting significant attention in various applications. This study focuses on developing a safe reinforcement learning method for back-transition control between level flight mode and hover mode for ducted-fan tail-sitter UAVs. Our method enables transition control with a minimal altitude change and transition time while adhering to the velocity constraint. We employ the Trust Region Policy Optimization, Proximal Policy Optimization with Lagrangian, and Constrained Policy Optimization (CPO) algorithms for controller training, showcasing the superiority of the CPO algorithm and the necessity of the velocity constraint. The transition trajectory achieved using the CPO algorithm closely resembles the optimal trajectory obtained via the well-known GPOPS-II software with the SNOPT solver. Meanwhile, the CPO algorithm also exhibits strong robustness under unknown perturbations of UAV model parameters and wind disturbance.

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“…Further extensions of structural models are intended to specify better motion and force feedback [113]. In the aeronautic domain, the Task-Pilot-Vehicle (TPV) model was developed in [114]; this is a simple extension of the structural model that is used for tracking and regulating tasks applied to fast system design. These modifications proved to match the actual data of a human's describing function in [115].…”

Section: Structural Modelmentioning

confidence: 99%

Human Control Model Estimation in Physical Human–Machine Interaction: A Survey

Scibilia

Pedrocchi

Fortuna

2022

Sensors

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The study of human–machine interaction as a unique control system was one of the first research interests in the engineering field, with almost a century having passed since the first works appeared in this area. At the same time, it is a crucial aspect of the most recent technological developments made in application fields such as collaborative robotics and artificial intelligence. Learning the processes and dynamics underlying human control strategies when interacting with controlled elements or objects of a different nature has been the subject of research in neuroscience, aerospace, robotics, and artificial intelligence. The cross-domain nature of this field of study can cause difficulties in finding a guiding line that links motor control theory, modelling approaches in physiological control systems, and identifying human–machine general control models in manipulative tasks. The discussed models have varying levels of complexity, from the first quasi-linear model in the frequency domain to the successive optimal control model. These models include detailed descriptions of physiologic subsystems and biomechanics. The motivation behind this work is to provide a complete view of the linear models that could be easily handled both in the time domain and in the frequency domain by using a well-established methodology in the classical linear systems and control theory.

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Use of a Task-Pilot-Vehicle (TPV) Model as a Tool for Fight Simulator Math Model Development

Cited by 7 publications

References 5 publications

Enhancing motion cueing using an optimisation technique

Enhancing motion cueing using an optimisation technique

Safe Reinforcement Learning for Transition Control of Ducted-Fan UAVs

Human Control Model Estimation in Physical Human–Machine Interaction: A Survey

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