This paper adds data to help the development of simulator motion cueing guidelines for stall recovery training by identifying time-varying manual control behavior in a stall recovery task under different simulator motion conditions. A study was conducted with seventeen general aviation pilots in the NASA Ames Vertical Motion Simulator. Pilots had to follow a flight director through four stages of a high-altitude stall task. A time-varying identification method was used to quantify how pilot manual control parameters change throughout different stages of the task in both roll and pitch. Four motion configurations were used: no motion, generic hexapod motion, enhanced hexapod motion and full motion. Pilot performance was highest for the enhanced hexapod and full motion configurations in both roll and pitch, and the lowest without motion. In the roll axis, the pilot position gain did not significantly change throughout the stall task, but was the lowest for the condition with no motion. The pilot roll velocity gain was significantly different between motion conditions, the largest difference being found close to the stall point. The enhanced hexapod motion condition had the highest pilot roll velocity gain. In the pitch axis, the pilot position gain was significantly different between time segments but not between motion conditions. The pilot pitch velocity gain was highest for the full motion condition and increased close to the stall point, but did not change significantly for the other motion conditions. Overall, pilot control behavior under enhanced hexapod motion was most similar to that under full aircraft motion. This indicates that motion cueing for stall recovery training on hexapod simulators might be improved by using the principles behind the enhanced hexapod motion configuration. configurations similar to those in a previous experiment. 20 The generic hexapod motion condition ( GH ) had motion similar to what current hexapod training simulators provide. The enhanced hexapod motion condition ( EH ) eliminated translational c.g. accelerations to allow for increased fidelity of the translational accelerations as a result from rotations about the c.g. and the rotational accelerations themselves; that is, EH had a higher fidelity of the motion cues most important for aircraft control during the stall task compared to GH . For tracking tasks with controlled elements requiring lead equalization, such as the aircraft dynamics used in this study, motion feedback is used by human controllers to reduce the amount of visually generated lead, allowing for better disturbance-rejection performance. 24 The extent to which motion feedback is used is affected by the fidelity of motion stimuli important to the task. Attenuation of these motion cues, either by scaling or high-pass filtering, results in human manual control with lower gains and increased reliance on visual lead, which typically results in worse disturbance-rejection performance. As the stability of the a
{"title":"Time-Varying Manual Control Identification in a Stall Recovery Task under Different Simulator Motion Conditions","authors":"Alexandru Popovici, P. Zaal, Marc A. Pieters","doi":"10.2514/6.2018-2936","DOIUrl":"https://doi.org/10.2514/6.2018-2936","url":null,"abstract":"This paper adds data to help the development of simulator motion cueing guidelines for stall recovery training by identifying time-varying manual control behavior in a stall recovery task under different simulator motion conditions. A study was conducted with seventeen general aviation pilots in the NASA Ames Vertical Motion Simulator. Pilots had to follow a flight director through four stages of a high-altitude stall task. A time-varying identification method was used to quantify how pilot manual control parameters change throughout different stages of the task in both roll and pitch. Four motion configurations were used: no motion, generic hexapod motion, enhanced hexapod motion and full motion. Pilot performance was highest for the enhanced hexapod and full motion configurations in both roll and pitch, and the lowest without motion. In the roll axis, the pilot position gain did not significantly change throughout the stall task, but was the lowest for the condition with no motion. The pilot roll velocity gain was significantly different between motion conditions, the largest difference being found close to the stall point. The enhanced hexapod motion condition had the highest pilot roll velocity gain. In the pitch axis, the pilot position gain was significantly different between time segments but not between motion conditions. The pilot pitch velocity gain was highest for the full motion condition and increased close to the stall point, but did not change significantly for the other motion conditions. Overall, pilot control behavior under enhanced hexapod motion was most similar to that under full aircraft motion. This indicates that motion cueing for stall recovery training on hexapod simulators might be improved by using the principles behind the enhanced hexapod motion configuration. configurations similar to those in a previous experiment. 20 The generic hexapod motion condition ( GH ) had motion similar to what current hexapod training simulators provide. The enhanced hexapod motion condition ( EH ) eliminated translational c.g. accelerations to allow for increased fidelity of the translational accelerations as a result from rotations about the c.g. and the rotational accelerations themselves; that is, EH had a higher fidelity of the motion cues most important for aircraft control during the stall task compared to GH . For tracking tasks with controlled elements requiring lead equalization, such as the aircraft dynamics used in this study, motion feedback is used by human controllers to reduce the amount of visually generated lead, allowing for better disturbance-rejection performance. 24 The extent to which motion feedback is used is affected by the fidelity of motion stimuli important to the task. Attenuation of these motion cues, either by scaling or high-pass filtering, results in human manual control with lower gains and increased reliance on visual lead, which typically results in worse disturbance-rejection performance. As the stability of the a","PeriodicalId":326346,"journal":{"name":"2018 Modeling and Simulation Technologies Conference","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122193626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A helicopter height velocity (HV) diagram was analytically constructed using optimal control techniques. A three degree of freedom, point-mass, dynamic model was developed and validated with flight test data. An induced velocity calculation was incorporated which addressed the affects of vortex ring state. The problem was posed as an open final time, constrained initial state, constrained final state problem, with the objective function as a weighted sum of the initial altitude and quadratic controls. This formulation was solved using direct pseudo-spectral collocation and adaptive mesh refinement as implemented by the GPOPS-II® software suite. Proper adjustment of path constraints was crucial in achieving solutions which were comparable with flight test data. Results compare favorably with flight test data and previous analytical HV diagrams.
{"title":"Analytical Determination of a Helicopter Height-Velocity Curve","authors":"M. J. Harris","doi":"10.2514/6.2018-3258","DOIUrl":"https://doi.org/10.2514/6.2018-3258","url":null,"abstract":"A helicopter height velocity (HV) diagram was analytically constructed using optimal control techniques. A three degree of freedom, point-mass, dynamic model was developed and validated with flight test data. An induced velocity calculation was incorporated which addressed the affects of vortex ring state. The problem was posed as an open final time, constrained initial state, constrained final state problem, with the objective function as a weighted sum of the initial altitude and quadratic controls. This formulation was solved using direct pseudo-spectral collocation and adaptive mesh refinement as implemented by the GPOPS-II® software suite. Proper adjustment of path constraints was crucial in achieving solutions which were comparable with flight test data. Results compare favorably with flight test data and previous analytical HV diagrams.","PeriodicalId":326346,"journal":{"name":"2018 Modeling and Simulation Technologies Conference","volume":"353 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122850266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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