This paper proposes a method to predict nonlinear Pilot-Induced Oscillation(PIO)using an intelligent human pilot model.This method is based on a scalogram-based PIO metric,which uses wavelet transforms to analyze the ...This paper proposes a method to predict nonlinear Pilot-Induced Oscillation(PIO)using an intelligent human pilot model.This method is based on a scalogram-based PIO metric,which uses wavelet transforms to analyze the nonlinear characteristics of a time-varying system.The intelligent human pilot model includes three modules:perception module,decision and adaptive module,and execution module.Intelligent and adaptive features,including a neural network receptor,fuzzy decision and adaptation,are also introduced into the human pilot model to describe the behavior of the human pilot accommodating the nonlinear events.Furthermore,an algorithm is proposed to describe the procedure of the PIO prediction method with nonlinear evaluation cases.The prediction results obtained by numerical simulation are compared with the assessments of flight test data to validate the utility of the method.The flight test data were generated in the evaluation of the Smart-Cue/Smart-Gain,which is capable of reducing the PIO tendencies considerably.The results show that the method can be applied to predict the nonlinear PIO events by human pilot model simulation.展开更多
A systematic methodology including a computational pilot model and a pattern recognition method is presented to identify the boundary of the flight performance margin for quantifying the human factors. The pilot model...A systematic methodology including a computational pilot model and a pattern recognition method is presented to identify the boundary of the flight performance margin for quantifying the human factors. The pilot model is proposed to correlate a set of quantitative human factors which represent the attributes and characteristics of a group of pilots. Three information processing components which are influenced by human factors are modeled： information perception, decision making, and action execution. By treating the human factors as stochastic variables that follow appropriate probability density functions, the effects of human factors on flight performance can be investigated through Monte Carlo（MC） simulation. Kernel density estimation algorithm is selected to find and rank the influential human factors. Subsequently, human factors are quantified through identifying the boundary of the flight performance margin by the k-nearest neighbor（k-NN） classifier. Simulation-based analysis shows that flight performance can be dramatically improved with the quantitative human factors.展开更多
Modeling human pilot control behavior aims to understand and describe how humans control aircrafts and devices,to provide a foundation for the study of the dynamic characteristics of the human-vehicle system.In the pr...Modeling human pilot control behavior aims to understand and describe how humans control aircrafts and devices,to provide a foundation for the study of the dynamic characteristics of the human-vehicle system.In the presence of aircraft failures,the human pilot has a control process of the refractory period,which may cause adverse aircraft-pilot couplings,and even lead to loss-of-control events.This refractory period will make the pilot emerge with time-varying and adaptive features.This paper investigates how pilot control behavior changes to adapt to the aircraft failure situation and develops a time-varying pilot model during the refractory period.Six aviation pilots performed a human-in-the-loop simulation experiment on a ground flight simulator to simulate the failure situations for a pitch-tracking task.To characterize the pilot’s time-varying response mechanism,a time-frequency-spectrum method was used to analyze the pilot control signal.Main innovations in the proposed model can be embodied in the description of the fuzziness,time-varying,and adaptation of the pilot for the failures in the refractory period.Based on fuzzy logic theory,the pilot’s judgment and identification of failures are described.The adaptation of manual control behavior to time-varying aircraft dynamics is depicted by adaptive model theory.Time-domain and time-frequency-spectrum analysis show that the simulation results of the pilot model are consistent with the human-in-the-loop experimental results.The model simulation evaluations are within the range of the experimental evaluation,which shows the rationality of the timevarying behavior model of the human pilot in a failure refractory period.The model has practical values for guiding the pilot to deal with abnormal conditions and predicting nonlinear aircraft-pilot couplings.展开更多
基金co-supported by the National Natural Science Foundation of China (No. 11502008)the Aeronautical Science Foundation of China (No. 2017ZA51002)
文摘This paper proposes a method to predict nonlinear Pilot-Induced Oscillation(PIO)using an intelligent human pilot model.This method is based on a scalogram-based PIO metric,which uses wavelet transforms to analyze the nonlinear characteristics of a time-varying system.The intelligent human pilot model includes three modules:perception module,decision and adaptive module,and execution module.Intelligent and adaptive features,including a neural network receptor,fuzzy decision and adaptation,are also introduced into the human pilot model to describe the behavior of the human pilot accommodating the nonlinear events.Furthermore,an algorithm is proposed to describe the procedure of the PIO prediction method with nonlinear evaluation cases.The prediction results obtained by numerical simulation are compared with the assessments of flight test data to validate the utility of the method.The flight test data were generated in the evaluation of the Smart-Cue/Smart-Gain,which is capable of reducing the PIO tendencies considerably.The results show that the method can be applied to predict the nonlinear PIO events by human pilot model simulation.
基金supported by the National Basic Research Program of China(No.2010CB734103)
文摘A systematic methodology including a computational pilot model and a pattern recognition method is presented to identify the boundary of the flight performance margin for quantifying the human factors. The pilot model is proposed to correlate a set of quantitative human factors which represent the attributes and characteristics of a group of pilots. Three information processing components which are influenced by human factors are modeled： information perception, decision making, and action execution. By treating the human factors as stochastic variables that follow appropriate probability density functions, the effects of human factors on flight performance can be investigated through Monte Carlo（MC） simulation. Kernel density estimation algorithm is selected to find and rank the influential human factors. Subsequently, human factors are quantified through identifying the boundary of the flight performance margin by the k-nearest neighbor（k-NN） classifier. Simulation-based analysis shows that flight performance can be dramatically improved with the quantitative human factors.
基金supported by the China Postdoctoral Science Foundation(Grant No.2021M690288)the Aeronautical Science Foundation of China (Grant No.20185702003)。
文摘Modeling human pilot control behavior aims to understand and describe how humans control aircrafts and devices,to provide a foundation for the study of the dynamic characteristics of the human-vehicle system.In the presence of aircraft failures,the human pilot has a control process of the refractory period,which may cause adverse aircraft-pilot couplings,and even lead to loss-of-control events.This refractory period will make the pilot emerge with time-varying and adaptive features.This paper investigates how pilot control behavior changes to adapt to the aircraft failure situation and develops a time-varying pilot model during the refractory period.Six aviation pilots performed a human-in-the-loop simulation experiment on a ground flight simulator to simulate the failure situations for a pitch-tracking task.To characterize the pilot’s time-varying response mechanism,a time-frequency-spectrum method was used to analyze the pilot control signal.Main innovations in the proposed model can be embodied in the description of the fuzziness,time-varying,and adaptation of the pilot for the failures in the refractory period.Based on fuzzy logic theory,the pilot’s judgment and identification of failures are described.The adaptation of manual control behavior to time-varying aircraft dynamics is depicted by adaptive model theory.Time-domain and time-frequency-spectrum analysis show that the simulation results of the pilot model are consistent with the human-in-the-loop experimental results.The model simulation evaluations are within the range of the experimental evaluation,which shows the rationality of the timevarying behavior model of the human pilot in a failure refractory period.The model has practical values for guiding the pilot to deal with abnormal conditions and predicting nonlinear aircraft-pilot couplings.
