Interpolated Linear Modeling of the F16 Benchmark Maarten Schoukens - - PowerPoint PPT Presentation
2017 Workshop on Nonlinear System Identification Benchmarks Interpolated Linear Modeling of the F16 Benchmark Maarten Schoukens April 2017 Overview Global vs Local Nonlinear Behavior Interpolated Linear Modeling F16 Results Best Linear
Maarten Schoukens April 2017
2017 Workshop on Nonlinear System Identification Benchmarks
Global vs Local Nonlinear Behavior Interpolated Linear Modeling F16 Results
ys(t): non-coherent nonlinear contributions ny(t): output noise source
Only valid for a fixed input signal class Input class changes BLA can change
Increasing Amplitude: Shifting Resonances
Shifting BLA ‘Global’ Nonlinear Behavior Model ‘Global’ Nonlinear Behavior using interpolated LTI models
‘Local’ Nonlinear Behavior
Multisine Data – Force to Acceleration Payload Frequency Range: 2-15 Hz 4 Estimation Amplitudes 3 Validation Amplitudes RMS: [12 24 36 61 74 86 98] Nrms
s-domain (continuous time) 12 poles, 12 zeros Start with lowest amplitude, initialize all the other based on lowest amplitude estimate.
Linear interpolation of the numerator and denominator coefficients Other choices possible: Interpolation of the pole & zero locations Interpolation of the pole-residue representation
Interpolated model vs Model estimated on validation data
Interpolated model vs Model estimated on estimation data: lower level
24 Nrms 61 Nrms 86 Nrms Estimated 0.098 Nrms 0.216 Nrms 0.254 Nrms Interpolated 0.103 Nrms 0.338 Nrms 0.263 Nrms Level Lower 0.352 Nrms 0.823 Nrms 0.307 Nrms Level Higher 0.262 Nrms 0.287 Nrms 0.330 Nrms RMS error in excited frequency range
24 Nrms 61 Nrms 86 Nrms Estimated 15,2 % 18,5 % 17,3 % Interpolated 16,0 % 29,0 % 17,9 % Level Lower 55,0 % 70,6 % 20,9 % Level Higher 40,9 % 24,6 % 22,4 % Relative error in excited frequency range
Global Nonlinear Behavior Interpolated LTI Models Good F16-Benchmark Results
Maarten Schoukens April 2017
2017 Workshop on Nonlinear System Identification Benchmarks