Time Domain Pilot Model Identification Using Maximum Likelihood Estimation

Abstract

Currently, multi-channel pilot models parameter estimation is done using a two-step frequency domain technique—identifying a non-parametric frequency response and fitting a parametric model to it. A time domain identification method would only require one step— directly fitting a parametric pilot model to the time domain data. Time domain identification has additional advantages in that the forcing functions used do not have specific limitations and multi-channel identification can be accomplished with only one forcing function. This paper displays the results of single- and multi-channel pilot model identifications done using the MATLAB MMLE3 toolbox. Identification was performed first on Simulink simulations and then on actual data collected using the SIMONA Research Simulator at the Delft University of Technology. 6 . A time domain identification method only requires one step—directly fitting a parametric pilot model to the time domain data. Such an identification method may have several advantages over frequency domain methods. The forcing functions used do not need to be sums of sine waves, as the Fourier Coefficient method requires. For example, the transient response to a step input may be used to perform the identification in the time domain. Furthermore, it may be possible to identify a multi-channel pilot model with just a target signal or just a disturbance signal, but not both, as frequency domain methods require. This study is concerned with testing a method for performing multi-channel pilot model identification in the time domain. The method looked at was the Maximum Likelihood Estimation method. This method was originally developed to identify aerodynamic coefficients of aircraft, but can be applied to a pilot model. The pilot model that is used is the Van der Vaart model, which uses central visual and motion perception paths with constant gains and time delays, along with neuromuscular dynamics, to represent the pilot control behavior. A brief explanation of pilot model identification is given in Section II, followed by a discussion about the MLE method in Section III. The method was initially used to identify a pilot model simulation done in MATLAB Simulink. Information about the simulation setup is presented in Section IV. The identification results are presented in Section V including the method's sensitivity to initial parameter guesses, as well as robustness to remnant noise gain. In addition to the simulations, experimental data collected using the SIMONA Research Simulator (SRS) were also analyzed. Here the method was used to make both single- and multi-channel identifications. A discussion of the results is presented in Section IV.