Picture of Evaluation of Lateral Maneuvering Motion (Lm²) cueing algorithm
Flight Simulation Technology

Evaluation of Lateral Maneuvering Motion (Lm²) cueing algorithm

  • Status: Completed
  • From May 2010 until January 2011


Ground-based flight simulators are used for a range of applications, from research and development, to engineering and pilot training. For applications that require a pilot-in-the-loop, great care must be taken to match the pilot’s behavior in the simulator with that in the aircraft. An important aspect in this process is the realistic stimulation of the pilot’s senses (perceptual fidelity). One of the senses a pilot uses in controlling the aircraft is his vestibular system, which is sensitive to specific forces and rotational accelerations. Many flight simulators employ a mechanically movable cockpit to mimic the aircraft’s motions and stimulate the pilot’s vestibular system.

Due to the unavoidable limitations of any ground-based motion system, compromises must be made in replicating the aircraft’s motions, in terms of amplitude, phase and frequency content. These compromises must ensure the simulator stays within its physical limits, while still providing the pilot with sufficiently realistic cues to perform his task. In a flight simulator, this process takes the form of a motion filter, also called motion cueing algorithm or motion drive algorithm. In general, a motion cueing algorithm employs several linear (scaling, high- and low-pass filtering) and non-linear (rate and position limiting, coordinate transforms) elements. Examples are the UTIAS classical washout algorithm and the adaptive washout algorithm. Most simulators in the world today employ either one of these algorithms in some form or other.


Although in widespread use, the conventional motion cueing algorithms have some drawbacks that limit the resulting motion fidelity. Specifically, the mechanism to return the cabin to its neutral position after a maneuver (“washout”), which is intended to keep the simulator within its physical boundaries, will in certain conditions lead to false cues. These false cues can distract the pilot, alter his behavior or even lead to nausea and simulator sickness. To address these drawbacks, Acceleration Worx NV has developed the Lateral Maneuvering Motion (Lm²) motion cueing algorithm. Lm² alters the standard motion cueing algorithms in a number of ways, with the goal of realistically presenting the lateral specific forces to the pilot. Inevitably this leads to less realistic cues in other areas, most notably in the rotational motion. However, preliminary tests in a flight simulator seem to indicate that the overall impression is more realistic with Lm² than with conventional cueing. More thorough scientific testing will be necessary to assess if and when this is indeed the case.

The project

In this thesis project, a scientific evaluation of the Lm² motion cueing algorithm will be performed on the Delft University of Technology’s SIMONA Research Simulator (SRS). The main questions to be answered are:

  • What are the underlying principles of the Lm² algorithm and its impact on human perception?
  • Under what conditions Lm² can provide benefits over conventional algorithms?
  • Which other aspects impact the use of Lm² in an operational environment (e.g. required motion space)?

The project can be divided into these preliminary work packages:

  • Implementation of Lm² in SIMONA software environment
  • Development of SIMULINK model of Lm² algorithm
  • Integration with offline SIMONA model
  • Offline comparison with existing implementation
  • Integration in online SIMONA environment
  • Offline validation with human perception and simulator motion base models
  • Selection of appropriate aircraft model(s) and maneuver(s)
  • Integration with existing and, if necessary, modified human perception models
  • Analysis of Lm² impact on perception and motion base performance
  • Online validation on SIMONA Research Simulator
  • Design of experiment, including selection of aircraft model(s) and maneuver(s)
  • Online, human-in-the-loop experiment on SRS
  • Analysis and reporting
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