Comparison of Robust Control Techniques for Use in Flight Simulator Motion Bases

Author(s):  
Mauricio Becerra-Vargas
2011 ◽  
Vol 34 (5) ◽  
pp. 1519-1528 ◽  
Author(s):  
Mauricio Becerra-Vargas ◽  
Eduardo M. Belo

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 580
Author(s):  
Michał Gołębiewski ◽  
Marta Galant-Gołębiewska ◽  
Remigiusz Jasiński

Protection of the natural environment is a key activity driving development in the transport discipline today. The use of simulators to train civil aviation pilots provides an excellent opportunity to maintain the balance between efficiency and limit the negative impact of transport on the environment. Therefore, we decided to determine the impact of selected simulations of air operations on energy consumption. The aim of the research was to determine the energy consumption of the flight simulator depending on the type of flight operation and configuration used. We also decided to compare the obtained result with the energy consumption of an aircraft of a similar class, performing a similar aviation operation and other means of transport. In order to obtain the results, a research plan was proposed consisting of 12 scenarios differing in the simulated aircraft model, weather conditions and the use of the simulator motion platform. In each of the scenarios, energy consumption was measured, taking into account the individual components of the simulator. The research showed that the use of a flight simulator has a much smaller negative impact on the natural environment than flying in a traditional plane. Use of a motion platform indicated a change in energy consumption of approximately 40% (in general, flight simulator configuration can change energy consumption by up to 50%). The deterioration of weather conditions during the simulation caused an increase in energy consumption of 14% when motion was disabled and 18% when motion was enabled. Energy consumption in the initial stages of pilot training can be reduced by 97% by using flight simulators compared to aircraft training.


1988 ◽  
Vol 25 (7) ◽  
pp. 639-646 ◽  
Author(s):  
Lloyd D. Reid ◽  
Meyer A. Nahon

Author(s):  
PAUL W. CARO

Flight simulator motion has been demonstrated to affect performance in the simulator, but recent transfer of training studies have failed to demonstrate an effect upon in-flight performance. However, these transfer studies examined the effects of motion in experimental designs that did not permit a dependency relationship to be established between the characteristics of the motion simulated and the training objectives or the performance measured. Another investigator has suggested that motion cues which occur in flight can be dichotomized as maneuver and disturbance cues, i.e., as resulting from pilot control action or from external forces. This paper examines each type cue and relates it analytically to training requirements. The need to establish such relationships in simulator design is emphasized. Future transfer studies should examine specific training objectives that can be expected to be effected by motion.


Author(s):  
Curt A. Laubscher ◽  
Jerzy T. Sawicki

Abstract Linear robust control techniques such as μ-synthesis can be used to design controllers for linear systems to guarantee specified performance criteria in the presence of modeling uncertainties, disturbances, and sensor noise. However, these techniques are rather uncommon in robotics due to the nonlinear nature of the plant where direct application would require large model uncertainties and therefore may only create a satisfactory controller if using lenient performance criteria. The inclusion of feedback linearization can rectify this by effectively converting the plant from a nonlinear system to a linear one, resulting in smaller model uncertainties. This paper proposes the use of feedback linearization to enable the use of linear robust control techniques on nonlinear systems. This approach is applied to a provisional version of a powered pediatric lower-limb orthosis. Sine sweep experiments are conducted to determine frequency response data for the system with and without feedback linearization. Models are identified to match the recorded data using optimization for both cases. Uncertainties are manually applied such that they encapsulate the observed measurements. The amount of uncertainties in the two models are quantified and a comparison shows that the uncertainties in the feedback-linearized system are smaller than in the system without feedback linearization.


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